nmodel (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.model_selection import KFold,cross_val_score,train_test_split,RandomizedSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"train_LZdllcl.csv\")\n",
    "test=pd.read_csv(\"test_2umaH9m.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>65141</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>58896</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>20379</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_20</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>16290</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_34</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>73202</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_20</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>28911</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_1</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id         department     region         education gender  \\\n",
       "0        65438  Sales & Marketing   region_7  Master's & above      f   \n",
       "1        65141         Operations  region_22        Bachelor's      m   \n",
       "2         7513  Sales & Marketing  region_19        Bachelor's      m   \n",
       "3         2542  Sales & Marketing  region_23        Bachelor's      m   \n",
       "4        48945         Technology  region_26        Bachelor's      m   \n",
       "5        58896          Analytics   region_2        Bachelor's      m   \n",
       "6        20379         Operations  region_20        Bachelor's      f   \n",
       "7        16290         Operations  region_34  Master's & above      m   \n",
       "8        73202          Analytics  region_20        Bachelor's      m   \n",
       "9        28911  Sales & Marketing   region_1  Master's & above      m   \n",
       "\n",
       "  recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0            sourcing                1   35                   5.0   \n",
       "1               other                1   30                   5.0   \n",
       "2            sourcing                1   34                   3.0   \n",
       "3               other                2   39                   1.0   \n",
       "4               other                1   45                   3.0   \n",
       "5            sourcing                2   31                   3.0   \n",
       "6               other                1   31                   3.0   \n",
       "7            sourcing                1   33                   3.0   \n",
       "8               other                1   28                   4.0   \n",
       "9            sourcing                1   32                   5.0   \n",
       "\n",
       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                  8              1            0                  49   \n",
       "1                  4              0            0                  60   \n",
       "2                  7              0            0                  50   \n",
       "3                 10              0            0                  50   \n",
       "4                  2              0            0                  73   \n",
       "5                  7              0            0                  85   \n",
       "6                  5              0            0                  59   \n",
       "7                  6              0            0                  63   \n",
       "8                  5              0            0                  83   \n",
       "9                  5              1            0                  54   \n",
       "\n",
       "   is_promoted  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "5            0  \n",
       "6            0  \n",
       "7            0  \n",
       "8            0  \n",
       "9            0  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bachelor's</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>38562</td>\n",
       "      <td>Procurement</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
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       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
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       "      <td>4</td>\n",
       "      <td>64486</td>\n",
       "      <td>Finance</td>\n",
       "      <td>region_29</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>46232</td>\n",
       "      <td>Procurement</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>54542</td>\n",
       "      <td>Finance</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>57</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>67269</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>66174</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>76303</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   employee_id         department     region         education gender  \\\n",
       "0         8724         Technology  region_26        Bachelor's      m   \n",
       "1        74430                 HR   region_4        Bachelor's      f   \n",
       "2        72255  Sales & Marketing  region_13        Bachelor's      m   \n",
       "3        38562        Procurement   region_2        Bachelor's      f   \n",
       "4        64486            Finance  region_29        Bachelor's      m   \n",
       "5        46232        Procurement   region_7        Bachelor's      m   \n",
       "6        54542            Finance   region_2        Bachelor's      m   \n",
       "7        67269          Analytics  region_22        Bachelor's      m   \n",
       "8        66174         Technology   region_7  Master's & above      m   \n",
       "9        76303         Technology  region_22        Bachelor's      m   \n",
       "\n",
       "  recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "0            sourcing                1   24                   NaN   \n",
       "1               other                1   31                   3.0   \n",
       "2               other                1   31                   1.0   \n",
       "3               other                3   31                   2.0   \n",
       "4            sourcing                1   30                   4.0   \n",
       "5            sourcing                1   36                   3.0   \n",
       "6               other                1   33                   5.0   \n",
       "7            sourcing                2   36                   3.0   \n",
       "8               other                1   51                   4.0   \n",
       "9            sourcing                1   29                   5.0   \n",
       "\n",
       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \n",
       "0                  1              1            0                  77  \n",
       "1                  5              0            0                  51  \n",
       "2                  4              0            0                  47  \n",
       "3                  9              0            0                  65  \n",
       "4                  7              0            0                  61  \n",
       "5                  2              0            0                  68  \n",
       "6                  3              1            0                  57  \n",
       "7                  3              0            0                  85  \n",
       "8                 11              0            0                  75  \n",
       "9                  2              1            0                  76  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((54808, 14), (23490, 13))"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['employee_id',\n",
       " 'department',\n",
       " 'region',\n",
       " 'education',\n",
       " 'gender',\n",
       " 'recruitment_channel',\n",
       " 'no_of_trainings',\n",
       " 'age',\n",
       " 'previous_year_rating',\n",
       " 'length_of_service',\n",
       " 'KPIs_met >80%',\n",
       " 'awards_won?',\n",
       " 'avg_training_score',\n",
       " 'is_promoted']"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['employee_id',\n",
       " 'department',\n",
       " 'region',\n",
       " 'education',\n",
       " 'gender',\n",
       " 'recruitment_channel',\n",
       " 'no_of_trainings',\n",
       " 'age',\n",
       " 'previous_year_rating',\n",
       " 'length_of_service',\n",
       " 'KPIs_met >80%',\n",
       " 'awards_won?',\n",
       " 'avg_training_score']"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((54808, 14), (23490, 13))"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54808 entries, 0 to 54807\n",
      "Data columns (total 14 columns):\n",
      "employee_id             54808 non-null int64\n",
      "department              54808 non-null object\n",
      "region                  54808 non-null object\n",
      "education               52399 non-null object\n",
      "gender                  54808 non-null object\n",
      "recruitment_channel     54808 non-null object\n",
      "no_of_trainings         54808 non-null int64\n",
      "age                     54808 non-null int64\n",
      "previous_year_rating    50684 non-null float64\n",
      "length_of_service       54808 non-null int64\n",
      "KPIs_met >80%           54808 non-null int64\n",
      "awards_won?             54808 non-null int64\n",
      "avg_training_score      54808 non-null int64\n",
      "is_promoted             54808 non-null int64\n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"999.2308058823368\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b9da8c01c8>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#percentage of people who got promoted from each dept\n",
    "plt.rcParams['figure.figsize'] = [10, 5]\n",
    "ct = pd.crosstab(train.department,train.is_promoted,normalize='index')\n",
    "ct.plot.bar(stacked=True)\n",
    "plt.legend(title='is_promoted',bbox_to_anchor=(1,0.5))\n",
    "##Clearly the Technology department had the highest number of promotions while\n",
    "##Legal dept had the least"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b9d84a4cc8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#percentage of promotions across regions\n",
    "reg = pd.crosstab(train.region,train.is_promoted,normalize='index')\n",
    "reg.plot.bar(stacked=True)\n",
    "plt.legend(title='is_promoted',bbox_to_anchor=(1,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"499.2307875424454\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b9f3a08148>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#promotion distribution among people with diffn education backgrounds\n",
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "edu = pd.crosstab(train.education,train.is_promoted,normalize='index')\n",
    "edu.plot.bar(stacked=True)\n",
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "plt.legend(title='is_promoted',bbox_to_anchor=(1,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_promoted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>other</td>\n",
       "      <td>0.916048</td>\n",
       "      <td>0.083952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>referred</td>\n",
       "      <td>0.879159</td>\n",
       "      <td>0.120841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sourcing</td>\n",
       "      <td>0.914987</td>\n",
       "      <td>0.085013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_promoted                 0         1\n",
       "recruitment_channel                    \n",
       "other                0.916048  0.083952\n",
       "referred             0.879159  0.120841\n",
       "sourcing             0.914987  0.085013"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train.recruitment_channel,train.is_promoted,normalize='index')\n",
    "##clearly percentage of promotions is higher among the employees who recruited through referrals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>is_promoted</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.960413</td>\n",
       "      <td>0.039587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.830906</td>\n",
       "      <td>0.169094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "is_promoted           0         1\n",
       "KPIs_met >80%                    \n",
       "0              0.960413  0.039587\n",
       "1              0.830906  0.169094"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train['KPIs_met >80%'],train.is_promoted,normalize='index')\n",
    "##people who satisfied KPI condition were promoted in far higher numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b9f3d79b48>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating = pd.crosstab(train.previous_year_rating,train.is_promoted,normalize='index')\n",
    "rating.plot.bar(stacked=True)\n",
    "plt.legend(title='is_promoted',loc='upper left',bbox_to_anchor=(1, 0.5))\n",
    "##Obviously, people who got higher prev yr ratings were easily promoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50-60     16020\n",
       "40-50     11996\n",
       "60-70      9973\n",
       "80-90      8739\n",
       "70-80      7494\n",
       "90-100      579\n",
       "30-40         7\n",
       "Name: score_groups, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distribution of avg training score\n",
    "groups = [30,40,50,60,70,80,90,100]\n",
    "labels = ['30-40','40-50','50-60','60-70','70-80','80-90','90-100']\n",
    "train['score_groups'] = pd.cut(train['avg_training_score'], bins=groups, labels=labels)\n",
    "train['score_groups'].value_counts()\n",
    "##most of the employees have score in the range of 50-60.\n",
    "##Extreme ends have low number of people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b9d8c7a7c8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [10, 5]\n",
    "score_groups = pd.crosstab(train.score_groups,train.is_promoted,normalize='index')\n",
    "score_groups.plot.bar(stacked=True)\n",
    "plt.legend(title='is_promoted',loc='upper left',bbox_to_anchor=(1, 0.5))\n",
    "##promotions ratio increases with the score\n",
    "##Extremely high ratio of promotions is there in 90-100\n",
    "##Clearly getting promoted is highly dependent on the avg score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"499.2307875424454\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b9d8c14fc8>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "age_groups = [20,30,40,50,60]\n",
    "age_labels = ['20-30','30-40','40-50','50-60']\n",
    "train['age_groups'] = pd.cut(train['age'], bins=groups, labels=labels)\n",
    "train['age_groups'].value_counts()\n",
    "age_groups = pd.crosstab(train.age_groups,train.is_promoted,normalize='index')\n",
    "age_groups.plot.bar(stacked=True)\n",
    "plt.rcParams['figure.figsize'] = [5, 5]\n",
    "plt.legend(title='is_promoted',loc='upper left',bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "##clerly age is not affecting promotions much"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9f47bd208>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [14, 5]\n",
    "sns.scatterplot(x='age',y='avg_training_score',hue='is_promoted',data=train)\n",
    "\n",
    "##This avg training score vs age scatterplot clearly shows that avg training score is a more important parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=int(train['previous_year_rating'].mean())\n",
    "train['previous_year_rating'].fillna(value=k,inplace=True)\n",
    "m=int(test['previous_year_rating'].mean())\n",
    "test['previous_year_rating'].fillna(value=m,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"education\"].fillna(value=\"Not Available\",inplace=True)\n",
    "test[\"education\"].fillna(value=\"Not Available\",inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "      <th>score_groups</th>\n",
       "      <th>age_groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808</td>\n",
       "      <td>54808</td>\n",
       "      <td>54808</td>\n",
       "      <td>54808</td>\n",
       "      <td>54808</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>50684.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808</td>\n",
       "      <td>36690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50-60</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16840</td>\n",
       "      <td>12343</td>\n",
       "      <td>36669</td>\n",
       "      <td>38496</td>\n",
       "      <td>30446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16020</td>\n",
       "      <td>26028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>39195.830627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.253011</td>\n",
       "      <td>34.803915</td>\n",
       "      <td>3.329256</td>\n",
       "      <td>5.865512</td>\n",
       "      <td>0.351974</td>\n",
       "      <td>0.023172</td>\n",
       "      <td>63.386750</td>\n",
       "      <td>0.085170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>22586.581449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.609264</td>\n",
       "      <td>7.660169</td>\n",
       "      <td>1.259993</td>\n",
       "      <td>4.265094</td>\n",
       "      <td>0.477590</td>\n",
       "      <td>0.150450</td>\n",
       "      <td>13.371559</td>\n",
       "      <td>0.279137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>19669.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>39225.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>58730.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>78298.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         employee_id         department    region   education gender  \\\n",
       "count   54808.000000              54808     54808       54808  54808   \n",
       "unique           NaN                  9        34           4      2   \n",
       "top              NaN  Sales & Marketing  region_2  Bachelor's      m   \n",
       "freq             NaN              16840     12343       36669  38496   \n",
       "mean    39195.830627                NaN       NaN         NaN    NaN   \n",
       "std     22586.581449                NaN       NaN         NaN    NaN   \n",
       "min         1.000000                NaN       NaN         NaN    NaN   \n",
       "25%     19669.750000                NaN       NaN         NaN    NaN   \n",
       "50%     39225.500000                NaN       NaN         NaN    NaN   \n",
       "75%     58730.500000                NaN       NaN         NaN    NaN   \n",
       "max     78298.000000                NaN       NaN         NaN    NaN   \n",
       "\n",
       "       recruitment_channel  no_of_trainings           age  \\\n",
       "count                54808     54808.000000  54808.000000   \n",
       "unique                   3              NaN           NaN   \n",
       "top                  other              NaN           NaN   \n",
       "freq                 30446              NaN           NaN   \n",
       "mean                   NaN         1.253011     34.803915   \n",
       "std                    NaN         0.609264      7.660169   \n",
       "min                    NaN         1.000000     20.000000   \n",
       "25%                    NaN         1.000000     29.000000   \n",
       "50%                    NaN         1.000000     33.000000   \n",
       "75%                    NaN         1.000000     39.000000   \n",
       "max                    NaN        10.000000     60.000000   \n",
       "\n",
       "        previous_year_rating  length_of_service  KPIs_met >80%   awards_won?  \\\n",
       "count           50684.000000       54808.000000   54808.000000  54808.000000   \n",
       "unique                   NaN                NaN            NaN           NaN   \n",
       "top                      NaN                NaN            NaN           NaN   \n",
       "freq                     NaN                NaN            NaN           NaN   \n",
       "mean                3.329256           5.865512       0.351974      0.023172   \n",
       "std                 1.259993           4.265094       0.477590      0.150450   \n",
       "min                 1.000000           1.000000       0.000000      0.000000   \n",
       "25%                 3.000000           3.000000       0.000000      0.000000   \n",
       "50%                 3.000000           5.000000       0.000000      0.000000   \n",
       "75%                 4.000000           7.000000       1.000000      0.000000   \n",
       "max                 5.000000          37.000000       1.000000      1.000000   \n",
       "\n",
       "        avg_training_score   is_promoted score_groups age_groups  \n",
       "count         54808.000000  54808.000000        54808      36690  \n",
       "unique                 NaN           NaN            7          3  \n",
       "top                    NaN           NaN        50-60      30-40  \n",
       "freq                   NaN           NaN        16020      26028  \n",
       "mean             63.386750      0.085170          NaN        NaN  \n",
       "std              13.371559      0.279137          NaN        NaN  \n",
       "min              39.000000      0.000000          NaN        NaN  \n",
       "25%              51.000000      0.000000          NaN        NaN  \n",
       "50%              60.000000      0.000000          NaN        NaN  \n",
       "75%              76.000000      0.000000          NaN        NaN  \n",
       "max              99.000000      1.000000          NaN        NaN  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>department</th>\n",
       "      <th>region</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>23490.000000</td>\n",
       "      <td>23490</td>\n",
       "      <td>23490</td>\n",
       "      <td>23490</td>\n",
       "      <td>23490</td>\n",
       "      <td>23490</td>\n",
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       "      <td>23490.000000</td>\n",
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       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7315</td>\n",
       "      <td>5299</td>\n",
       "      <td>15578</td>\n",
       "      <td>16596</td>\n",
       "      <td>13078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>39041.399149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.254236</td>\n",
       "      <td>34.782929</td>\n",
       "      <td>3.339146</td>\n",
       "      <td>5.810387</td>\n",
       "      <td>0.358834</td>\n",
       "      <td>0.022776</td>\n",
       "      <td>63.263133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>22640.809201</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.600910</td>\n",
       "      <td>7.679492</td>\n",
       "      <td>1.263294</td>\n",
       "      <td>4.207917</td>\n",
       "      <td>0.479668</td>\n",
       "      <td>0.149191</td>\n",
       "      <td>13.411750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>19370.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>38963.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>58690.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
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       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>78295.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         employee_id         department    region   education gender  \\\n",
       "count   23490.000000              23490     23490       23490  23490   \n",
       "unique           NaN                  9        34           4      2   \n",
       "top              NaN  Sales & Marketing  region_2  Bachelor's      m   \n",
       "freq             NaN               7315      5299       15578  16596   \n",
       "mean    39041.399149                NaN       NaN         NaN    NaN   \n",
       "std     22640.809201                NaN       NaN         NaN    NaN   \n",
       "min         3.000000                NaN       NaN         NaN    NaN   \n",
       "25%     19370.250000                NaN       NaN         NaN    NaN   \n",
       "50%     38963.500000                NaN       NaN         NaN    NaN   \n",
       "75%     58690.000000                NaN       NaN         NaN    NaN   \n",
       "max     78295.000000                NaN       NaN         NaN    NaN   \n",
       "\n",
       "       recruitment_channel  no_of_trainings           age  \\\n",
       "count                23490     23490.000000  23490.000000   \n",
       "unique                   3              NaN           NaN   \n",
       "top                  other              NaN           NaN   \n",
       "freq                 13078              NaN           NaN   \n",
       "mean                   NaN         1.254236     34.782929   \n",
       "std                    NaN         0.600910      7.679492   \n",
       "min                    NaN         1.000000     20.000000   \n",
       "25%                    NaN         1.000000     29.000000   \n",
       "50%                    NaN         1.000000     33.000000   \n",
       "75%                    NaN         1.000000     39.000000   \n",
       "max                    NaN         9.000000     60.000000   \n",
       "\n",
       "        previous_year_rating  length_of_service  KPIs_met >80%   awards_won?  \\\n",
       "count           21678.000000       23490.000000   23490.000000  23490.000000   \n",
       "unique                   NaN                NaN            NaN           NaN   \n",
       "top                      NaN                NaN            NaN           NaN   \n",
       "freq                     NaN                NaN            NaN           NaN   \n",
       "mean                3.339146           5.810387       0.358834      0.022776   \n",
       "std                 1.263294           4.207917       0.479668      0.149191   \n",
       "min                 1.000000           1.000000       0.000000      0.000000   \n",
       "25%                 3.000000           3.000000       0.000000      0.000000   \n",
       "50%                 3.000000           5.000000       0.000000      0.000000   \n",
       "75%                 4.000000           7.000000       1.000000      0.000000   \n",
       "max                 5.000000          34.000000       1.000000      1.000000   \n",
       "\n",
       "        avg_training_score  \n",
       "count         23490.000000  \n",
       "unique                 NaN  \n",
       "top                    NaN  \n",
       "freq                   NaN  \n",
       "mean             63.263133  \n",
       "std              13.411750  \n",
       "min              39.000000  \n",
       "25%              51.000000  \n",
       "50%              60.000000  \n",
       "75%              76.000000  \n",
       "max              99.000000  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.set_index(\"employee_id\",inplace=True)\n",
    "test.set_index(\"employee_id\",inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department</th>\n",
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       "      <th>age_groups</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employee_id</th>\n",
       "      <th></th>\n",
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       "      <td>65438</td>\n",
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       "      <td>49</td>\n",
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       "      <td>40-50</td>\n",
       "      <td>30-40</td>\n",
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       "    <tr>\n",
       "      <td>65141</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>50-60</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>40-50</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>40-50</td>\n",
       "      <td>30-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48945</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_26</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    department     region         education gender  \\\n",
       "employee_id                                                          \n",
       "65438        Sales & Marketing   region_7  Master's & above      f   \n",
       "65141               Operations  region_22        Bachelor's      m   \n",
       "7513         Sales & Marketing  region_19        Bachelor's      m   \n",
       "2542         Sales & Marketing  region_23        Bachelor's      m   \n",
       "48945               Technology  region_26        Bachelor's      m   \n",
       "\n",
       "            recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "employee_id                                                                   \n",
       "65438                  sourcing                1   35                   5.0   \n",
       "65141                     other                1   30                   5.0   \n",
       "7513                   sourcing                1   34                   3.0   \n",
       "2542                      other                2   39                   1.0   \n",
       "48945                     other                1   45                   3.0   \n",
       "\n",
       "             length_of_service  KPIs_met >80%  awards_won?  \\\n",
       "employee_id                                                  \n",
       "65438                        8              1            0   \n",
       "65141                        4              0            0   \n",
       "7513                         7              0            0   \n",
       "2542                        10              0            0   \n",
       "48945                        2              0            0   \n",
       "\n",
       "             avg_training_score  is_promoted score_groups age_groups  \n",
       "employee_id                                                           \n",
       "65438                        49            0        40-50      30-40  \n",
       "65141                        60            0        50-60        NaN  \n",
       "7513                         50            0        40-50      30-40  \n",
       "2542                         50            0        40-50      30-40  \n",
       "48945                        73            0        70-80      40-50  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8724</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
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       "    <tr>\n",
       "      <td>72255</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_13</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
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       "    <tr>\n",
       "      <td>38562</td>\n",
       "      <td>Procurement</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Bachelor's</td>\n",
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       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64486</td>\n",
       "      <td>Finance</td>\n",
       "      <td>region_29</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    department     region   education gender  \\\n",
       "employee_id                                                    \n",
       "8724                Technology  region_26  Bachelor's      m   \n",
       "74430                       HR   region_4  Bachelor's      f   \n",
       "72255        Sales & Marketing  region_13  Bachelor's      m   \n",
       "38562              Procurement   region_2  Bachelor's      f   \n",
       "64486                  Finance  region_29  Bachelor's      m   \n",
       "\n",
       "            recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "employee_id                                                                   \n",
       "8724                   sourcing                1   24                   NaN   \n",
       "74430                     other                1   31                   3.0   \n",
       "72255                     other                1   31                   1.0   \n",
       "38562                     other                3   31                   2.0   \n",
       "64486                  sourcing                1   30                   4.0   \n",
       "\n",
       "             length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \n",
       "employee_id                                                                     \n",
       "8724                         1              1            0                  77  \n",
       "74430                        5              0            0                  51  \n",
       "72255                        4              0            0                  47  \n",
       "38562                        9              0            0                  65  \n",
       "64486                        7              0            0                  61  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>department</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Analytics</td>\n",
       "      <td>5352.0</td>\n",
       "      <td>1.337818</td>\n",
       "      <td>0.587229</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>1.304022</td>\n",
       "      <td>0.605888</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>2418.0</td>\n",
       "      <td>1.053763</td>\n",
       "      <td>0.232817</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>1.089509</td>\n",
       "      <td>0.383518</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>11348.0</td>\n",
       "      <td>1.160821</td>\n",
       "      <td>0.427888</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>1.322779</td>\n",
       "      <td>0.793726</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1.402402</td>\n",
       "      <td>0.700869</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>16840.0</td>\n",
       "      <td>1.277435</td>\n",
       "      <td>0.660753</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>1.260857</td>\n",
       "      <td>0.596130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count      mean       std  min  25%  50%  75%   max\n",
       "department                                                              \n",
       "Analytics           5352.0  1.337818  0.587229  1.0  1.0  1.0  2.0   4.0\n",
       "Finance             2536.0  1.304022  0.605888  1.0  1.0  1.0  1.0   5.0\n",
       "HR                  2418.0  1.053763  0.232817  1.0  1.0  1.0  1.0   3.0\n",
       "Legal               1039.0  1.089509  0.383518  1.0  1.0  1.0  1.0   3.0\n",
       "Operations         11348.0  1.160821  0.427888  1.0  1.0  1.0  1.0   5.0\n",
       "Procurement         7138.0  1.322779  0.793726  1.0  1.0  1.0  1.0  10.0\n",
       "R&D                  999.0  1.402402  0.700869  1.0  1.0  1.0  2.0   5.0\n",
       "Sales & Marketing  16840.0  1.277435  0.660753  1.0  1.0  1.0  1.0  10.0\n",
       "Technology          7138.0  1.260857  0.596130  1.0  1.0  1.0  1.0   7.0"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('department')[\"no_of_trainings\"].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Analytics</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>1.348426</td>\n",
       "      <td>0.589845</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>1.313474</td>\n",
       "      <td>0.628667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>1.065438</td>\n",
       "      <td>0.272263</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>445.0</td>\n",
       "      <td>1.076404</td>\n",
       "      <td>0.353242</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>4764.0</td>\n",
       "      <td>1.174643</td>\n",
       "      <td>0.442991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>1.340397</td>\n",
       "      <td>0.808630</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>440.0</td>\n",
       "      <td>1.352273</td>\n",
       "      <td>0.682348</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>7315.0</td>\n",
       "      <td>1.268216</td>\n",
       "      <td>0.629656</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>3011.0</td>\n",
       "      <td>1.245766</td>\n",
       "      <td>0.564784</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "department                                                            \n",
       "Analytics          2319.0  1.348426  0.589845  1.0  1.0  1.0  2.0  5.0\n",
       "Finance            1091.0  1.313474  0.628667  1.0  1.0  1.0  1.0  5.0\n",
       "HR                 1085.0  1.065438  0.272263  1.0  1.0  1.0  1.0  3.0\n",
       "Legal               445.0  1.076404  0.353242  1.0  1.0  1.0  1.0  3.0\n",
       "Operations         4764.0  1.174643  0.442991  1.0  1.0  1.0  1.0  5.0\n",
       "Procurement        3020.0  1.340397  0.808630  1.0  1.0  1.0  1.0  9.0\n",
       "R&D                 440.0  1.352273  0.682348  1.0  1.0  1.0  2.0  5.0\n",
       "Sales & Marketing  7315.0  1.268216  0.629656  1.0  1.0  1.0  1.0  7.0\n",
       "Technology         3011.0  1.245766  0.564784  1.0  1.0  1.0  1.0  6.0"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('department')[\"no_of_trainings\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>department</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Analytics</td>\n",
       "      <td>5352.0</td>\n",
       "      <td>32.431241</td>\n",
       "      <td>5.994745</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>32.058754</td>\n",
       "      <td>6.521766</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>2418.0</td>\n",
       "      <td>34.385856</td>\n",
       "      <td>8.608311</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>33.832531</td>\n",
       "      <td>7.398306</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>11348.0</td>\n",
       "      <td>36.073669</td>\n",
       "      <td>7.496754</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>35.899972</td>\n",
       "      <td>7.998411</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>999.0</td>\n",
       "      <td>32.842843</td>\n",
       "      <td>4.544627</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>16840.0</td>\n",
       "      <td>34.860629</td>\n",
       "      <td>7.769406</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>34.867190</td>\n",
       "      <td>8.090095</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>60.0</td>\n",
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      ],
      "text/plain": [
       "                     count       mean       std   min   25%   50%   75%   max\n",
       "department                                                                   \n",
       "Analytics           5352.0  32.431241  5.994745  20.0  28.0  31.0  35.0  60.0\n",
       "Finance             2536.0  32.058754  6.521766  20.0  28.0  31.0  35.0  60.0\n",
       "HR                  2418.0  34.385856  8.608311  20.0  28.0  32.0  38.0  60.0\n",
       "Legal               1039.0  33.832531  7.398306  23.0  29.0  32.0  37.0  60.0\n",
       "Operations         11348.0  36.073669  7.496754  20.0  31.0  35.0  40.0  60.0\n",
       "Procurement         7138.0  35.899972  7.998411  20.0  30.0  34.0  40.0  60.0\n",
       "R&D                  999.0  32.842843  4.544627  24.0  30.0  32.0  36.0  50.0\n",
       "Sales & Marketing  16840.0  34.860629  7.769406  20.0  29.0  33.0  39.0  60.0\n",
       "Technology          7138.0  34.867190  8.090095  20.0  29.0  33.0  39.0  60.0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('department')['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Analytics</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>32.354463</td>\n",
       "      <td>5.928559</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>59.0</td>\n",
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       "      <td>Finance</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>32.471127</td>\n",
       "      <td>6.977542</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>34.290323</td>\n",
       "      <td>8.608090</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>445.0</td>\n",
       "      <td>33.786517</td>\n",
       "      <td>7.385266</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>4764.0</td>\n",
       "      <td>36.110202</td>\n",
       "      <td>7.551699</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>36.130795</td>\n",
       "      <td>8.175277</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>R&amp;D</td>\n",
       "      <td>440.0</td>\n",
       "      <td>32.820455</td>\n",
       "      <td>4.427296</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>7315.0</td>\n",
       "      <td>34.665755</td>\n",
       "      <td>7.612539</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>3011.0</td>\n",
       "      <td>34.935237</td>\n",
       "      <td>8.239770</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count       mean       std   min   25%   50%   75%   max\n",
       "department                                                                  \n",
       "Analytics          2319.0  32.354463  5.928559  20.0  28.0  31.0  35.0  59.0\n",
       "Finance            1091.0  32.471127  6.977542  22.0  28.0  31.0  35.0  60.0\n",
       "HR                 1085.0  34.290323  8.608090  20.0  28.0  32.0  38.0  60.0\n",
       "Legal               445.0  33.786517  7.385266  24.0  29.0  32.0  37.0  60.0\n",
       "Operations         4764.0  36.110202  7.551699  20.0  31.0  35.0  40.0  60.0\n",
       "Procurement        3020.0  36.130795  8.175277  20.0  30.0  34.0  40.0  60.0\n",
       "R&D                 440.0  32.820455  4.427296  24.0  30.0  32.0  35.0  49.0\n",
       "Sales & Marketing  7315.0  34.665755  7.612539  20.0  29.0  33.0  39.0  60.0\n",
       "Technology         3011.0  34.935237  8.239770  20.0  29.0  33.0  39.0  60.0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('department')['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
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       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Analytics</td>\n",
       "      <td>5352.0</td>\n",
       "      <td>4.932735</td>\n",
       "      <td>3.354521</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>4.819401</td>\n",
       "      <td>3.613654</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>2418.0</td>\n",
       "      <td>5.683623</td>\n",
       "      <td>4.408166</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>4.599615</td>\n",
       "      <td>3.850961</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>11348.0</td>\n",
       "      <td>6.416020</td>\n",
       "      <td>4.359909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>6.331045</td>\n",
       "      <td>4.532367</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>999.0</td>\n",
       "      <td>4.554555</td>\n",
       "      <td>2.749162</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>16840.0</td>\n",
       "      <td>5.912292</td>\n",
       "      <td>4.292995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>5.914822</td>\n",
       "      <td>4.510480</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count      mean       std  min  25%  50%  75%   max\n",
       "department                                                              \n",
       "Analytics           5352.0  4.932735  3.354521  1.0  3.0  4.0  7.0  34.0\n",
       "Finance             2536.0  4.819401  3.613654  1.0  2.0  4.0  6.0  33.0\n",
       "HR                  2418.0  5.683623  4.408166  1.0  3.0  5.0  7.0  33.0\n",
       "Legal               1039.0  4.599615  3.850961  1.0  2.0  4.0  6.0  32.0\n",
       "Operations         11348.0  6.416020  4.359909  1.0  3.0  5.0  8.0  34.0\n",
       "Procurement         7138.0  6.331045  4.532367  1.0  3.0  5.0  8.0  32.0\n",
       "R&D                  999.0  4.554555  2.749162  1.0  3.0  4.0  6.0  20.0\n",
       "Sales & Marketing  16840.0  5.912292  4.292995  1.0  3.0  5.0  8.0  37.0\n",
       "Technology          7138.0  5.914822  4.510480  1.0  3.0  5.0  7.0  34.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('department')['length_of_service'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
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       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Analytics</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>4.956878</td>\n",
       "      <td>3.373480</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>5.005500</td>\n",
       "      <td>3.541622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>5.626728</td>\n",
       "      <td>4.303717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>445.0</td>\n",
       "      <td>4.602247</td>\n",
       "      <td>3.477274</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>4764.0</td>\n",
       "      <td>6.414777</td>\n",
       "      <td>4.447572</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>6.238742</td>\n",
       "      <td>4.418669</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>440.0</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>2.984585</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>7315.0</td>\n",
       "      <td>5.778947</td>\n",
       "      <td>4.200042</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>3011.0</td>\n",
       "      <td>5.849552</td>\n",
       "      <td>4.406083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%   max\n",
       "department                                                             \n",
       "Analytics          2319.0  4.956878  3.373480  1.0  3.0  4.0  7.0  28.0\n",
       "Finance            1091.0  5.005500  3.541622  1.0  2.0  4.0  7.0  31.0\n",
       "HR                 1085.0  5.626728  4.303717  1.0  3.0  5.0  7.0  30.0\n",
       "Legal               445.0  4.602247  3.477274  1.0  2.0  4.0  6.0  21.0\n",
       "Operations         4764.0  6.414777  4.447572  1.0  3.0  5.0  8.0  34.0\n",
       "Procurement        3020.0  6.238742  4.418669  1.0  3.0  5.0  8.0  31.0\n",
       "R&D                 440.0  4.750000  2.984585  1.0  2.0  4.0  6.0  18.0\n",
       "Sales & Marketing  7315.0  5.778947  4.200042  1.0  3.0  5.0  7.0  32.0\n",
       "Technology         3011.0  5.849552  4.406083  1.0  3.0  5.0  7.0  34.0"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('department')['length_of_service'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Analytics</td>\n",
       "      <td>4886.0</td>\n",
       "      <td>3.557307</td>\n",
       "      <td>1.199749</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>Finance</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>3.506436</td>\n",
       "      <td>1.145529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>2172.0</td>\n",
       "      <td>3.482965</td>\n",
       "      <td>1.185349</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>871.0</td>\n",
       "      <td>3.405281</td>\n",
       "      <td>1.223306</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>10760.0</td>\n",
       "      <td>3.632156</td>\n",
       "      <td>1.158748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>6709.0</td>\n",
       "      <td>3.298852</td>\n",
       "      <td>1.182876</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>912.0</td>\n",
       "      <td>3.563596</td>\n",
       "      <td>1.191035</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>15529.0</td>\n",
       "      <td>3.067937</td>\n",
       "      <td>1.371335</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>6592.0</td>\n",
       "      <td>3.158677</td>\n",
       "      <td>1.176440</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count      mean       std  min  25%  50%  75%  max\n",
       "department                                                             \n",
       "Analytics           4886.0  3.557307  1.199749  1.0  3.0  3.0  5.0  5.0\n",
       "Finance             2253.0  3.506436  1.145529  1.0  3.0  3.0  4.0  5.0\n",
       "HR                  2172.0  3.482965  1.185349  1.0  3.0  3.0  5.0  5.0\n",
       "Legal                871.0  3.405281  1.223306  1.0  3.0  3.0  4.0  5.0\n",
       "Operations         10760.0  3.632156  1.158748  1.0  3.0  4.0  5.0  5.0\n",
       "Procurement         6709.0  3.298852  1.182876  1.0  3.0  3.0  4.0  5.0\n",
       "R&D                  912.0  3.563596  1.191035  1.0  3.0  3.0  5.0  5.0\n",
       "Sales & Marketing  15529.0  3.067937  1.371335  1.0  2.0  3.0  4.0  5.0\n",
       "Technology          6592.0  3.158677  1.176440  1.0  3.0  3.0  4.0  5.0"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('department')['previous_year_rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Analytics</td>\n",
       "      <td>2084.0</td>\n",
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       "      <td>1.187676</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>988.0</td>\n",
       "      <td>3.536437</td>\n",
       "      <td>1.183168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>983.0</td>\n",
       "      <td>3.526958</td>\n",
       "      <td>1.210542</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>385.0</td>\n",
       "      <td>3.454545</td>\n",
       "      <td>1.292461</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>4521.0</td>\n",
       "      <td>3.657155</td>\n",
       "      <td>1.137124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>2841.0</td>\n",
       "      <td>3.263640</td>\n",
       "      <td>1.190859</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>399.0</td>\n",
       "      <td>3.661654</td>\n",
       "      <td>1.140091</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>6711.0</td>\n",
       "      <td>3.090449</td>\n",
       "      <td>1.384182</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>2766.0</td>\n",
       "      <td>3.151844</td>\n",
       "      <td>1.166473</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "department                                                            \n",
       "Analytics          2084.0  3.536468  1.187676  1.0  3.0  3.0  5.0  5.0\n",
       "Finance             988.0  3.536437  1.183168  1.0  3.0  3.0  5.0  5.0\n",
       "HR                  983.0  3.526958  1.210542  1.0  3.0  3.0  5.0  5.0\n",
       "Legal               385.0  3.454545  1.292461  1.0  3.0  3.0  5.0  5.0\n",
       "Operations         4521.0  3.657155  1.137124  1.0  3.0  4.0  5.0  5.0\n",
       "Procurement        2841.0  3.263640  1.190859  1.0  3.0  3.0  4.0  5.0\n",
       "R&D                 399.0  3.661654  1.140091  1.0  3.0  4.0  5.0  5.0\n",
       "Sales & Marketing  6711.0  3.090449  1.384182  1.0  2.0  3.0  4.0  5.0\n",
       "Technology         2766.0  3.151844  1.166473  1.0  3.0  3.0  4.0  5.0"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('department')['previous_year_rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
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       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Analytics</td>\n",
       "      <td>5352.0</td>\n",
       "      <td>84.602952</td>\n",
       "      <td>2.855495</td>\n",
       "      <td>75.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>2536.0</td>\n",
       "      <td>60.222003</td>\n",
       "      <td>4.929564</td>\n",
       "      <td>50.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>2418.0</td>\n",
       "      <td>50.018197</td>\n",
       "      <td>4.680137</td>\n",
       "      <td>40.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>59.868142</td>\n",
       "      <td>4.103776</td>\n",
       "      <td>52.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>11348.0</td>\n",
       "      <td>60.226648</td>\n",
       "      <td>4.818071</td>\n",
       "      <td>50.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>70.122443</td>\n",
       "      <td>4.305110</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>999.0</td>\n",
       "      <td>84.596597</td>\n",
       "      <td>2.921215</td>\n",
       "      <td>73.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>16840.0</td>\n",
       "      <td>50.261698</td>\n",
       "      <td>5.551414</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>79.928692</td>\n",
       "      <td>3.355511</td>\n",
       "      <td>68.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count       mean       std   min   25%   50%   75%   max\n",
       "department                                                                   \n",
       "Analytics           5352.0  84.602952  2.855495  75.0  83.0  85.0  86.0  99.0\n",
       "Finance             2536.0  60.222003  4.929564  50.0  58.0  60.0  62.0  98.0\n",
       "HR                  2418.0  50.018197  4.680137  40.0  48.0  50.0  51.0  97.0\n",
       "Legal               1039.0  59.868142  4.103776  52.0  58.0  60.0  61.0  97.0\n",
       "Operations         11348.0  60.226648  4.818071  50.0  58.0  60.0  62.0  99.0\n",
       "Procurement         7138.0  70.122443  4.305110  60.0  68.0  70.0  72.0  99.0\n",
       "R&D                  999.0  84.596597  2.921215  73.0  83.0  85.0  86.0  98.0\n",
       "Sales & Marketing  16840.0  50.261698  5.551414  39.0  48.0  50.0  52.0  99.0\n",
       "Technology          7138.0  79.928692  3.355511  68.0  78.0  80.0  82.0  99.0"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('department')['avg_training_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>Analytics</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>84.541182</td>\n",
       "      <td>2.888182</td>\n",
       "      <td>72.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>99.0</td>\n",
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       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>60.322640</td>\n",
       "      <td>4.982394</td>\n",
       "      <td>50.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>50.305991</td>\n",
       "      <td>5.482647</td>\n",
       "      <td>41.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>445.0</td>\n",
       "      <td>59.575281</td>\n",
       "      <td>3.512759</td>\n",
       "      <td>51.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>4764.0</td>\n",
       "      <td>60.300798</td>\n",
       "      <td>5.038003</td>\n",
       "      <td>49.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>99.0</td>\n",
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       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>70.146026</td>\n",
       "      <td>4.229214</td>\n",
       "      <td>61.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>99.0</td>\n",
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       "      <td>R&amp;D</td>\n",
       "      <td>440.0</td>\n",
       "      <td>84.413636</td>\n",
       "      <td>2.956294</td>\n",
       "      <td>76.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>7315.0</td>\n",
       "      <td>50.085988</td>\n",
       "      <td>5.211089</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>3011.0</td>\n",
       "      <td>79.860511</td>\n",
       "      <td>3.380097</td>\n",
       "      <td>69.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>99.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                    count       mean       std   min   25%   50%   75%   max\n",
       "department                                                                  \n",
       "Analytics          2319.0  84.541182  2.888182  72.0  83.0  84.0  86.0  99.0\n",
       "Finance            1091.0  60.322640  4.982394  50.0  58.0  60.0  62.0  97.0\n",
       "HR                 1085.0  50.305991  5.482647  41.0  48.0  50.0  52.0  98.0\n",
       "Legal               445.0  59.575281  3.512759  51.0  57.0  59.0  62.0  91.0\n",
       "Operations         4764.0  60.300798  5.038003  49.0  58.0  60.0  62.0  99.0\n",
       "Procurement        3020.0  70.146026  4.229214  61.0  68.0  70.0  72.0  99.0\n",
       "R&D                 440.0  84.413636  2.956294  76.0  82.0  84.0  86.0  96.0\n",
       "Sales & Marketing  7315.0  50.085988  5.211089  39.0  48.0  50.0  51.0  99.0\n",
       "Technology         3011.0  79.860511  3.380097  69.0  78.0  80.0  82.0  99.0"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('department')['avg_training_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2418.0</td>\n",
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       "      <td>0.471794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>0.333975</td>\n",
       "      <td>0.471858</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>11348.0</td>\n",
       "      <td>0.430737</td>\n",
       "      <td>0.495201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>0.377557</td>\n",
       "      <td>0.484810</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0.416416</td>\n",
       "      <td>0.493211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>16840.0</td>\n",
       "      <td>0.265083</td>\n",
       "      <td>0.441391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>7138.0</td>\n",
       "      <td>0.344354</td>\n",
       "      <td>0.475190</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count      mean       std  min  25%  50%  75%  max\n",
       "department                                                             \n",
       "Analytics           5352.0  0.404148  0.490772  0.0  0.0  0.0  1.0  1.0\n",
       "Finance             2536.0  0.414826  0.492789  0.0  0.0  0.0  1.0  1.0\n",
       "HR                  2418.0  0.334160  0.471794  0.0  0.0  0.0  1.0  1.0\n",
       "Legal               1039.0  0.333975  0.471858  0.0  0.0  0.0  1.0  1.0\n",
       "Operations         11348.0  0.430737  0.495201  0.0  0.0  0.0  1.0  1.0\n",
       "Procurement         7138.0  0.377557  0.484810  0.0  0.0  0.0  1.0  1.0\n",
       "R&D                  999.0  0.416416  0.493211  0.0  0.0  0.0  1.0  1.0\n",
       "Sales & Marketing  16840.0  0.265083  0.441391  0.0  0.0  0.0  1.0  1.0\n",
       "Technology          7138.0  0.344354  0.475190  0.0  0.0  0.0  1.0  1.0"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('department')['KPIs_met >80%'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Analytics</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>0.404053</td>\n",
       "      <td>0.490814</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Finance</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>0.396884</td>\n",
       "      <td>0.489476</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>0.349309</td>\n",
       "      <td>0.476972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Legal</td>\n",
       "      <td>445.0</td>\n",
       "      <td>0.341573</td>\n",
       "      <td>0.474771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Operations</td>\n",
       "      <td>4764.0</td>\n",
       "      <td>0.444165</td>\n",
       "      <td>0.496925</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Procurement</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>0.377483</td>\n",
       "      <td>0.484838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>440.0</td>\n",
       "      <td>0.440909</td>\n",
       "      <td>0.497061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>7315.0</td>\n",
       "      <td>0.276418</td>\n",
       "      <td>0.447257</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Technology</td>\n",
       "      <td>3011.0</td>\n",
       "      <td>0.350714</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "department                                                            \n",
       "Analytics          2319.0  0.404053  0.490814  0.0  0.0  0.0  1.0  1.0\n",
       "Finance            1091.0  0.396884  0.489476  0.0  0.0  0.0  1.0  1.0\n",
       "HR                 1085.0  0.349309  0.476972  0.0  0.0  0.0  1.0  1.0\n",
       "Legal               445.0  0.341573  0.474771  0.0  0.0  0.0  1.0  1.0\n",
       "Operations         4764.0  0.444165  0.496925  0.0  0.0  0.0  1.0  1.0\n",
       "Procurement        3020.0  0.377483  0.484838  0.0  0.0  0.0  1.0  1.0\n",
       "R&D                 440.0  0.440909  0.497061  0.0  0.0  0.0  1.0  1.0\n",
       "Sales & Marketing  7315.0  0.276418  0.447257  0.0  0.0  0.0  1.0  1.0\n",
       "Technology         3011.0  0.350714  0.477273  0.0  0.0  0.0  1.0  1.0"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('department')['KPIs_met >80%'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>education</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>36669.0</td>\n",
       "      <td>1.270883</td>\n",
       "      <td>0.633341</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>805.0</td>\n",
       "      <td>1.275776</td>\n",
       "      <td>0.621723</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>14925.0</td>\n",
       "      <td>1.219095</td>\n",
       "      <td>0.559485</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>2409.0</td>\n",
       "      <td>1.183479</td>\n",
       "      <td>0.504507</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%   max\n",
       "education                                                              \n",
       "Bachelor's        36669.0  1.270883  0.633341  1.0  1.0  1.0  1.0  10.0\n",
       "Below Secondary     805.0  1.275776  0.621723  1.0  1.0  1.0  1.0   4.0\n",
       "Master's & above  14925.0  1.219095  0.559485  1.0  1.0  1.0  1.0  10.0\n",
       "Not Available      2409.0  1.183479  0.504507  1.0  1.0  1.0  1.0   6.0"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('education')[\"no_of_trainings\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>education</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>15578.0</td>\n",
       "      <td>1.271087</td>\n",
       "      <td>0.619917</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>374.0</td>\n",
       "      <td>1.294118</td>\n",
       "      <td>0.763874</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>6504.0</td>\n",
       "      <td>1.222478</td>\n",
       "      <td>0.555552</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1.185687</td>\n",
       "      <td>0.496208</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "education                                                             \n",
       "Bachelor's        15578.0  1.271087  0.619917  1.0  1.0  1.0  1.0  9.0\n",
       "Below Secondary     374.0  1.294118  0.763874  1.0  1.0  1.0  1.0  8.0\n",
       "Master's & above   6504.0  1.222478  0.555552  1.0  1.0  1.0  1.0  8.0\n",
       "Not Available      1034.0  1.185687  0.496208  1.0  1.0  1.0  1.0  6.0"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('education')[\"no_of_trainings\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bachelor's</td>\n",
       "      <td>36669.0</td>\n",
       "      <td>33.445226</td>\n",
       "      <td>7.047381</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>Below Secondary</td>\n",
       "      <td>805.0</td>\n",
       "      <td>23.836025</td>\n",
       "      <td>1.829266</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>14925.0</td>\n",
       "      <td>39.352630</td>\n",
       "      <td>7.070637</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>2409.0</td>\n",
       "      <td>30.968867</td>\n",
       "      <td>7.550183</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count       mean       std   min   25%   50%   75%   max\n",
       "education                                                                   \n",
       "Bachelor's        36669.0  33.445226  7.047381  20.0  29.0  32.0  36.0  60.0\n",
       "Below Secondary     805.0  23.836025  1.829266  20.0  23.0  24.0  25.0  31.0\n",
       "Master's & above  14925.0  39.352630  7.070637  20.0  34.0  38.0  43.0  60.0\n",
       "Not Available      2409.0  30.968867  7.550183  20.0  26.0  29.0  34.0  60.0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('education')[\"age\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>15578.0</td>\n",
       "      <td>33.406086</td>\n",
       "      <td>7.052300</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <td>Below Secondary</td>\n",
       "      <td>374.0</td>\n",
       "      <td>23.951872</td>\n",
       "      <td>1.761316</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>6504.0</td>\n",
       "      <td>39.340867</td>\n",
       "      <td>7.134473</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
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       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>30.773694</td>\n",
       "      <td>7.177938</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count       mean       std   min   25%   50%   75%   max\n",
       "education                                                                   \n",
       "Bachelor's        15578.0  33.406086  7.052300  20.0  29.0  32.0  36.0  60.0\n",
       "Below Secondary     374.0  23.951872  1.761316  20.0  23.0  24.0  25.0  30.0\n",
       "Master's & above   6504.0  39.340867  7.134473  20.0  34.0  38.0  43.0  60.0\n",
       "Not Available      1034.0  30.773694  7.177938  20.0  26.0  29.0  34.0  60.0"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('education')[\"age\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Bachelor's</td>\n",
       "      <td>36669.0</td>\n",
       "      <td>5.344542</td>\n",
       "      <td>3.892569</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37.0</td>\n",
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       "      <td>Below Secondary</td>\n",
       "      <td>805.0</td>\n",
       "      <td>1.704348</td>\n",
       "      <td>0.816218</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>14925.0</td>\n",
       "      <td>7.605293</td>\n",
       "      <td>4.725710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
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       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>2409.0</td>\n",
       "      <td>4.407223</td>\n",
       "      <td>3.723495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%   75%   max\n",
       "education                                                               \n",
       "Bachelor's        36669.0  5.344542  3.892569  1.0  3.0  4.0   7.0  37.0\n",
       "Below Secondary     805.0  1.704348  0.816218  1.0  1.0  2.0   2.0   5.0\n",
       "Master's & above  14925.0  7.605293  4.725710  1.0  4.0  7.0  10.0  34.0\n",
       "Not Available      2409.0  4.407223  3.723495  1.0  2.0  3.0   6.0  32.0"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('education')[\"length_of_service\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bachelor's</td>\n",
       "      <td>15578.0</td>\n",
       "      <td>5.324689</td>\n",
       "      <td>3.899684</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Below Secondary</td>\n",
       "      <td>374.0</td>\n",
       "      <td>1.754011</td>\n",
       "      <td>0.886855</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>6504.0</td>\n",
       "      <td>7.440959</td>\n",
       "      <td>4.573041</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>Not Available</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>4.338491</td>\n",
       "      <td>3.597828</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
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      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%   max\n",
       "education                                                              \n",
       "Bachelor's        15578.0  5.324689  3.899684  1.0  3.0  4.0  7.0  34.0\n",
       "Below Secondary     374.0  1.754011  0.886855  1.0  1.0  2.0  2.0   5.0\n",
       "Master's & above   6504.0  7.440959  4.573041  1.0  4.0  7.0  9.0  31.0\n",
       "Not Available      1034.0  4.338491  3.597828  1.0  2.0  3.0  6.0  25.0"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('education')['length_of_service'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>Bachelor's</td>\n",
       "      <td>33404.0</td>\n",
       "      <td>3.324302</td>\n",
       "      <td>1.266106</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>Below Secondary</td>\n",
       "      <td>458.0</td>\n",
       "      <td>3.493450</td>\n",
       "      <td>1.105467</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>14798.0</td>\n",
       "      <td>3.362549</td>\n",
       "      <td>1.243104</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>2024.0</td>\n",
       "      <td>3.130435</td>\n",
       "      <td>1.293270</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "education                                                             \n",
       "Bachelor's        33404.0  3.324302  1.266106  1.0  3.0  3.0  4.0  5.0\n",
       "Below Secondary     458.0  3.493450  1.105467  1.0  3.0  3.0  4.0  5.0\n",
       "Master's & above  14798.0  3.362549  1.243104  1.0  3.0  3.0  4.0  5.0\n",
       "Not Available      2024.0  3.130435  1.293270  1.0  2.0  3.0  4.0  5.0"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('education')[\"previous_year_rating\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bachelor's</td>\n",
       "      <td>14167.0</td>\n",
       "      <td>3.335074</td>\n",
       "      <td>1.270224</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Below Secondary</td>\n",
       "      <td>221.0</td>\n",
       "      <td>3.561086</td>\n",
       "      <td>1.092173</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>Master's &amp; above</td>\n",
       "      <td>6431.0</td>\n",
       "      <td>3.370704</td>\n",
       "      <td>1.249214</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>859.0</td>\n",
       "      <td>3.112922</td>\n",
       "      <td>1.268245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "education                                                             \n",
       "Bachelor's        14167.0  3.335074  1.270224  1.0  3.0  3.0  4.0  5.0\n",
       "Below Secondary     221.0  3.561086  1.092173  1.0  3.0  3.0  5.0  5.0\n",
       "Master's & above   6431.0  3.370704  1.249214  1.0  3.0  3.0  4.0  5.0\n",
       "Not Available       859.0  3.112922  1.268245  1.0  3.0  3.0  4.0  5.0"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('education')[\"previous_year_rating\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bachelor's</td>\n",
       "      <td>36669.0</td>\n",
       "      <td>63.422046</td>\n",
       "      <td>13.365371</td>\n",
       "      <td>39.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>99.0</td>\n",
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       "      <td>Below Secondary</td>\n",
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       "      <td>64.925466</td>\n",
       "      <td>11.249960</td>\n",
       "      <td>43.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>97.0</td>\n",
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       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>14925.0</td>\n",
       "      <td>64.061240</td>\n",
       "      <td>13.219803</td>\n",
       "      <td>40.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>99.0</td>\n",
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       "    <tr>\n",
       "      <td>Not Available</td>\n",
       "      <td>2409.0</td>\n",
       "      <td>58.156496</td>\n",
       "      <td>13.892265</td>\n",
       "      <td>40.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>98.0</td>\n",
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      ],
      "text/plain": [
       "                    count       mean        std   min   25%   50%   75%   max\n",
       "education                                                                    \n",
       "Bachelor's        36669.0  63.422046  13.365371  39.0  51.0  60.0  76.0  99.0\n",
       "Below Secondary     805.0  64.925466  11.249960  43.0  57.0  62.0  75.0  97.0\n",
       "Master's & above  14925.0  64.061240  13.219803  40.0  52.0  61.0  76.0  99.0\n",
       "Not Available      2409.0  58.156496  13.892265  40.0  49.0  52.0  62.0  98.0"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('education')[\"avg_training_score\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bachelor's</td>\n",
       "      <td>15578.0</td>\n",
       "      <td>63.172936</td>\n",
       "      <td>13.440785</td>\n",
       "      <td>40.0</td>\n",
       "      <td>51.0</td>\n",
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       "      <td>76.00</td>\n",
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       "      <td>65.422460</td>\n",
       "      <td>11.048182</td>\n",
       "      <td>43.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>74.75</td>\n",
       "      <td>93.0</td>\n",
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       "    <tr>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>6504.0</td>\n",
       "      <td>64.114391</td>\n",
       "      <td>13.199512</td>\n",
       "      <td>39.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>62.0</td>\n",
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       "      <td>1034.0</td>\n",
       "      <td>58.486460</td>\n",
       "      <td>13.994962</td>\n",
       "      <td>41.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>64.75</td>\n",
       "      <td>96.0</td>\n",
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      "text/plain": [
       "                    count       mean        std   min   25%   50%    75%   max\n",
       "education                                                                     \n",
       "Bachelor's        15578.0  63.172936  13.440785  40.0  51.0  60.0  76.00  99.0\n",
       "Below Secondary     374.0  65.422460  11.048182  43.0  58.0  62.0  74.75  93.0\n",
       "Master's & above   6504.0  64.114391  13.199512  39.0  52.0  62.0  76.00  99.0\n",
       "Not Available      1034.0  58.486460  13.994962  41.0  49.0  51.0  64.75  96.0"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test.groupby('education')[\"avg_training_score\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Bachelor's</td>\n",
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       "      <td>0.478367</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.490203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.431351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "education                                                             \n",
       "Bachelor's        36669.0  0.354496  0.478367  0.0  0.0  0.0  1.0  1.0\n",
       "Below Secondary     805.0  0.400000  0.490203  0.0  0.0  0.0  1.0  1.0\n",
       "Master's & above  14925.0  0.360134  0.480055  0.0  0.0  0.0  1.0  1.0\n",
       "Not Available      2409.0  0.246990  0.431351  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('education')[\"KPIs_met >80%\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Bachelor's</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>6504.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.444084</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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      ],
      "text/plain": [
       "                    count      mean       std  min  25%  50%  75%  max\n",
       "education                                                             \n",
       "Bachelor's        15578.0  0.360316  0.480107  0.0  0.0  0.0  1.0  1.0\n",
       "Below Secondary     374.0  0.438503  0.496868  0.0  0.0  0.0  1.0  1.0\n",
       "Master's & above   6504.0  0.364852  0.481426  0.0  0.0  0.0  1.0  1.0\n",
       "Not Available      1034.0  0.269826  0.444084  0.0  0.0  0.0  1.0  1.0"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.groupby('education')[\"KPIs_met >80%\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>0.079126</td>\n",
       "      <td>0.270001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>0.092324</td>\n",
       "      <td>0.289534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>3147.0</td>\n",
       "      <td>0.097871</td>\n",
       "      <td>0.297187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>3405.0</td>\n",
       "      <td>0.086344</td>\n",
       "      <td>0.280912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>3665.0</td>\n",
       "      <td>0.088404</td>\n",
       "      <td>0.283920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.282926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>0.084041</td>\n",
       "      <td>0.277488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>3210.0</td>\n",
       "      <td>0.099377</td>\n",
       "      <td>0.299214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>3076.0</td>\n",
       "      <td>0.092978</td>\n",
       "      <td>0.290449</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2711.0</td>\n",
       "      <td>0.104390</td>\n",
       "      <td>0.305822</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2517.0</td>\n",
       "      <td>0.082638</td>\n",
       "      <td>0.275389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>2165.0</td>\n",
       "      <td>0.084988</td>\n",
       "      <td>0.278929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>1923.0</td>\n",
       "      <td>0.095684</td>\n",
       "      <td>0.294234</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>1695.0</td>\n",
       "      <td>0.094985</td>\n",
       "      <td>0.293281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1663.0</td>\n",
       "      <td>0.076368</td>\n",
       "      <td>0.265666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>1289.0</td>\n",
       "      <td>0.080683</td>\n",
       "      <td>0.272453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>1149.0</td>\n",
       "      <td>0.079199</td>\n",
       "      <td>0.270167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>992.0</td>\n",
       "      <td>0.077621</td>\n",
       "      <td>0.267709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>847.0</td>\n",
       "      <td>0.082645</td>\n",
       "      <td>0.275507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>760.0</td>\n",
       "      <td>0.063158</td>\n",
       "      <td>0.243407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>697.0</td>\n",
       "      <td>0.061693</td>\n",
       "      <td>0.240770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>557.0</td>\n",
       "      <td>0.048474</td>\n",
       "      <td>0.214959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>557.0</td>\n",
       "      <td>0.078995</td>\n",
       "      <td>0.269973</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>441.0</td>\n",
       "      <td>0.074830</td>\n",
       "      <td>0.263416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>521.0</td>\n",
       "      <td>0.059501</td>\n",
       "      <td>0.236787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>389.0</td>\n",
       "      <td>0.087404</td>\n",
       "      <td>0.282789</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>351.0</td>\n",
       "      <td>0.079772</td>\n",
       "      <td>0.271327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>364.0</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.192572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>313.0</td>\n",
       "      <td>0.076677</td>\n",
       "      <td>0.266505</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>294.0</td>\n",
       "      <td>0.061224</td>\n",
       "      <td>0.240150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.079545</td>\n",
       "      <td>0.271102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>238.0</td>\n",
       "      <td>0.021008</td>\n",
       "      <td>0.143714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>58</td>\n",
       "      <td>213.0</td>\n",
       "      <td>0.075117</td>\n",
       "      <td>0.264202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>209.0</td>\n",
       "      <td>0.081340</td>\n",
       "      <td>0.274012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.246238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count      mean       std  min  25%  50%  75%  max\n",
       "age                                                     \n",
       "20    113.0  0.035398  0.185607  0.0  0.0  0.0  0.0  1.0\n",
       "21     98.0  0.051020  0.221171  0.0  0.0  0.0  0.0  1.0\n",
       "22    231.0  0.077922  0.268631  0.0  0.0  0.0  0.0  1.0\n",
       "23    428.0  0.079439  0.270740  0.0  0.0  0.0  0.0  1.0\n",
       "24    845.0  0.082840  0.275804  0.0  0.0  0.0  0.0  1.0\n",
       "25   1299.0  0.053118  0.224355  0.0  0.0  0.0  0.0  1.0\n",
       "26   2060.0  0.079126  0.270001  0.0  0.0  0.0  0.0  1.0\n",
       "27   2827.0  0.092324  0.289534  0.0  0.0  0.0  0.0  1.0\n",
       "28   3147.0  0.097871  0.297187  0.0  0.0  0.0  0.0  1.0\n",
       "29   3405.0  0.086344  0.280912  0.0  0.0  0.0  0.0  1.0\n",
       "30   3665.0  0.088404  0.283920  0.0  0.0  0.0  0.0  1.0\n",
       "31   3534.0  0.087719  0.282926  0.0  0.0  0.0  0.0  1.0\n",
       "32   3534.0  0.084041  0.277488  0.0  0.0  0.0  0.0  1.0\n",
       "33   3210.0  0.099377  0.299214  0.0  0.0  0.0  0.0  1.0\n",
       "34   3076.0  0.092978  0.290449  0.0  0.0  0.0  0.0  1.0\n",
       "35   2711.0  0.104390  0.305822  0.0  0.0  0.0  0.0  1.0\n",
       "36   2517.0  0.082638  0.275389  0.0  0.0  0.0  0.0  1.0\n",
       "37   2165.0  0.084988  0.278929  0.0  0.0  0.0  0.0  1.0\n",
       "38   1923.0  0.095684  0.294234  0.0  0.0  0.0  0.0  1.0\n",
       "39   1695.0  0.094985  0.293281  0.0  0.0  0.0  0.0  1.0\n",
       "40   1663.0  0.076368  0.265666  0.0  0.0  0.0  0.0  1.0\n",
       "41   1289.0  0.080683  0.272453  0.0  0.0  0.0  0.0  1.0\n",
       "42   1149.0  0.079199  0.270167  0.0  0.0  0.0  0.0  1.0\n",
       "43    992.0  0.077621  0.267709  0.0  0.0  0.0  0.0  1.0\n",
       "44    847.0  0.082645  0.275507  0.0  0.0  0.0  0.0  1.0\n",
       "45    760.0  0.063158  0.243407  0.0  0.0  0.0  0.0  1.0\n",
       "46    697.0  0.061693  0.240770  0.0  0.0  0.0  0.0  1.0\n",
       "47    557.0  0.048474  0.214959  0.0  0.0  0.0  0.0  1.0\n",
       "48    557.0  0.078995  0.269973  0.0  0.0  0.0  0.0  1.0\n",
       "49    441.0  0.074830  0.263416  0.0  0.0  0.0  0.0  1.0\n",
       "50    521.0  0.059501  0.236787  0.0  0.0  0.0  0.0  1.0\n",
       "51    389.0  0.087404  0.282789  0.0  0.0  0.0  0.0  1.0\n",
       "52    351.0  0.079772  0.271327  0.0  0.0  0.0  0.0  1.0\n",
       "53    364.0  0.038462  0.192572  0.0  0.0  0.0  0.0  1.0\n",
       "54    313.0  0.076677  0.266505  0.0  0.0  0.0  0.0  1.0\n",
       "55    294.0  0.061224  0.240150  0.0  0.0  0.0  0.0  1.0\n",
       "56    264.0  0.079545  0.271102  0.0  0.0  0.0  0.0  1.0\n",
       "57    238.0  0.021008  0.143714  0.0  0.0  0.0  0.0  1.0\n",
       "58    213.0  0.075117  0.264202  0.0  0.0  0.0  0.0  1.0\n",
       "59    209.0  0.081340  0.274012  0.0  0.0  0.0  0.0  1.0\n",
       "60    217.0  0.064516  0.246238  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('age')['is_promoted'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                 54808\n",
       "unique                    9\n",
       "top       Sales & Marketing\n",
       "freq                  16840\n",
       "Name: department, dtype: object"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['department'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                 54808\n",
       "unique                    9\n",
       "top       Sales & Marketing\n",
       "freq                  16840\n",
       "Name: department, dtype: object"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['department'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          54808\n",
       "unique             4\n",
       "top       Bachelor's\n",
       "freq           36669\n",
       "Name: education, dtype: object"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"education\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          23490\n",
       "unique             4\n",
       "top       Bachelor's\n",
       "freq           15578\n",
       "Name: education, dtype: object"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"education\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    54808.000000\n",
       "mean        34.803915\n",
       "std          7.660169\n",
       "min         20.000000\n",
       "25%         29.000000\n",
       "50%         33.000000\n",
       "75%         39.000000\n",
       "max         60.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23490.000000\n",
       "mean        34.782929\n",
       "std          7.679492\n",
       "min         20.000000\n",
       "25%         29.000000\n",
       "50%         33.000000\n",
       "75%         39.000000\n",
       "max         60.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    54808.000000\n",
       "mean         1.253011\n",
       "std          0.609264\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max         10.000000\n",
       "Name: no_of_trainings, dtype: float64"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['no_of_trainings'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23490.000000\n",
       "mean         1.254236\n",
       "std          0.600910\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max          9.000000\n",
       "Name: no_of_trainings, dtype: float64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['no_of_trainings'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    54808.000000\n",
       "mean         5.865512\n",
       "std          4.265094\n",
       "min          1.000000\n",
       "25%          3.000000\n",
       "50%          5.000000\n",
       "75%          7.000000\n",
       "max         37.000000\n",
       "Name: length_of_service, dtype: float64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['length_of_service'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23490.000000\n",
       "mean         5.810387\n",
       "std          4.207917\n",
       "min          1.000000\n",
       "25%          3.000000\n",
       "50%          5.000000\n",
       "75%          7.000000\n",
       "max         34.000000\n",
       "Name: length_of_service, dtype: float64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['length_of_service'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    50684.000000\n",
       "mean         3.329256\n",
       "std          1.259993\n",
       "min          1.000000\n",
       "25%          3.000000\n",
       "50%          3.000000\n",
       "75%          4.000000\n",
       "max          5.000000\n",
       "Name: previous_year_rating, dtype: float64"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['previous_year_rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    21678.000000\n",
       "mean         3.339146\n",
       "std          1.263294\n",
       "min          1.000000\n",
       "25%          3.000000\n",
       "50%          3.000000\n",
       "75%          4.000000\n",
       "max          5.000000\n",
       "Name: previous_year_rating, dtype: float64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['previous_year_rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    54808.000000\n",
       "mean        63.386750\n",
       "std         13.371559\n",
       "min         39.000000\n",
       "25%         51.000000\n",
       "50%         60.000000\n",
       "75%         76.000000\n",
       "max         99.000000\n",
       "Name: avg_training_score, dtype: float64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['avg_training_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23490.000000\n",
       "mean        63.263133\n",
       "std         13.411750\n",
       "min         39.000000\n",
       "25%         51.000000\n",
       "50%         60.000000\n",
       "75%         76.000000\n",
       "max         99.000000\n",
       "Name: avg_training_score, dtype: float64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['avg_training_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    54808.000000\n",
       "mean         0.351974\n",
       "std          0.477590\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: KPIs_met >80%, dtype: float64"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['KPIs_met >80%'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23490.000000\n",
       "mean         0.358834\n",
       "std          0.479668\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          1.000000\n",
       "max          1.000000\n",
       "Name: KPIs_met >80%, dtype: float64"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['KPIs_met >80%'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findBand(age):\n",
    "    if age <=29:\n",
    "        return 1\n",
    "    elif age>29 and age<=33:\n",
    "        return 2\n",
    "    elif age>33 and age<=39:\n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "train['band'] = train['age'].apply(findBand)\n",
    "test['band'] = test['age'].apply(findBand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>65438</td>\n",
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       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
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       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
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       "      <td>7513</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_19</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2542</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_23</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Technology</td>\n",
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       "      <td>Bachelor's</td>\n",
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       "      <td>4</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                    department     region         education gender  \\\n",
       "employee_id                                                          \n",
       "65438        Sales & Marketing   region_7  Master's & above      f   \n",
       "65141               Operations  region_22        Bachelor's      m   \n",
       "7513         Sales & Marketing  region_19        Bachelor's      m   \n",
       "2542         Sales & Marketing  region_23        Bachelor's      m   \n",
       "48945               Technology  region_26        Bachelor's      m   \n",
       "\n",
       "            recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "employee_id                                                                   \n",
       "65438                  sourcing                1   35                   5.0   \n",
       "65141                     other                1   30                   5.0   \n",
       "7513                   sourcing                1   34                   3.0   \n",
       "2542                      other                2   39                   1.0   \n",
       "48945                     other                1   45                   3.0   \n",
       "\n",
       "             length_of_service  KPIs_met >80%  awards_won?  \\\n",
       "employee_id                                                  \n",
       "65438                        8              1            0   \n",
       "65141                        4              0            0   \n",
       "7513                         7              0            0   \n",
       "2542                        10              0            0   \n",
       "48945                        2              0            0   \n",
       "\n",
       "             avg_training_score  is_promoted score_groups age_groups  band  \n",
       "employee_id                                                                 \n",
       "65438                        49            0        40-50      30-40     3  \n",
       "65141                        60            0        50-60        NaN     2  \n",
       "7513                         50            0        40-50      30-40     3  \n",
       "2542                         50            0        40-50      30-40     3  \n",
       "48945                        73            0        70-80      40-50     4  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <td>74430</td>\n",
       "      <td>HR</td>\n",
       "      <td>region_4</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>72255</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_13</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
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       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38562</td>\n",
       "      <td>Procurement</td>\n",
       "      <td>region_2</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64486</td>\n",
       "      <td>Finance</td>\n",
       "      <td>region_29</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    department     region   education gender  \\\n",
       "employee_id                                                    \n",
       "8724                Technology  region_26  Bachelor's      m   \n",
       "74430                       HR   region_4  Bachelor's      f   \n",
       "72255        Sales & Marketing  region_13  Bachelor's      m   \n",
       "38562              Procurement   region_2  Bachelor's      f   \n",
       "64486                  Finance  region_29  Bachelor's      m   \n",
       "\n",
       "            recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
       "employee_id                                                                   \n",
       "8724                   sourcing                1   24                   NaN   \n",
       "74430                     other                1   31                   3.0   \n",
       "72255                     other                1   31                   1.0   \n",
       "38562                     other                3   31                   2.0   \n",
       "64486                  sourcing                1   30                   4.0   \n",
       "\n",
       "             length_of_service  KPIs_met >80%  awards_won?  \\\n",
       "employee_id                                                  \n",
       "8724                         1              1            0   \n",
       "74430                        5              0            0   \n",
       "72255                        4              0            0   \n",
       "38562                        9              0            0   \n",
       "64486                        7              0            0   \n",
       "\n",
       "             avg_training_score  band  \n",
       "employee_id                            \n",
       "8724                         77     1  \n",
       "74430                        51     2  \n",
       "72255                        47     2  \n",
       "38562                        65     2  \n",
       "64486                        61     2  "
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"999.2308058823368\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b9f5366648>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = train.corr()\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(corr, vmax=1, square=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>employee_id</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005121</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.004209</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>-0.002501</td>\n",
       "      <td>0.008420</td>\n",
       "      <td>-0.000586</td>\n",
       "      <td>0.001206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>no_of_trainings</td>\n",
       "      <td>-0.005121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.081278</td>\n",
       "      <td>-0.061564</td>\n",
       "      <td>-0.057275</td>\n",
       "      <td>-0.045576</td>\n",
       "      <td>-0.007628</td>\n",
       "      <td>0.042517</td>\n",
       "      <td>-0.024896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>-0.081278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026810</td>\n",
       "      <td>0.657111</td>\n",
       "      <td>-0.025592</td>\n",
       "      <td>-0.008169</td>\n",
       "      <td>-0.048380</td>\n",
       "      <td>-0.017166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous_year_rating</td>\n",
       "      <td>0.004209</td>\n",
       "      <td>-0.061564</td>\n",
       "      <td>0.026810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023504</td>\n",
       "      <td>0.337367</td>\n",
       "      <td>0.026587</td>\n",
       "      <td>0.071926</td>\n",
       "      <td>0.153230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>length_of_service</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>-0.057275</td>\n",
       "      <td>0.657111</td>\n",
       "      <td>0.023504</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077693</td>\n",
       "      <td>-0.039927</td>\n",
       "      <td>-0.038122</td>\n",
       "      <td>-0.010670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>KPIs_met &gt;80%</td>\n",
       "      <td>-0.002501</td>\n",
       "      <td>-0.045576</td>\n",
       "      <td>-0.025592</td>\n",
       "      <td>0.337367</td>\n",
       "      <td>-0.077693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097000</td>\n",
       "      <td>0.078391</td>\n",
       "      <td>0.221582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>awards_won?</td>\n",
       "      <td>0.008420</td>\n",
       "      <td>-0.007628</td>\n",
       "      <td>-0.008169</td>\n",
       "      <td>0.026587</td>\n",
       "      <td>-0.039927</td>\n",
       "      <td>0.097000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.072138</td>\n",
       "      <td>0.195871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>avg_training_score</td>\n",
       "      <td>-0.000586</td>\n",
       "      <td>0.042517</td>\n",
       "      <td>-0.048380</td>\n",
       "      <td>0.071926</td>\n",
       "      <td>-0.038122</td>\n",
       "      <td>0.078391</td>\n",
       "      <td>0.072138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.181147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>is_promoted</td>\n",
       "      <td>0.001206</td>\n",
       "      <td>-0.024896</td>\n",
       "      <td>-0.017166</td>\n",
       "      <td>0.153230</td>\n",
       "      <td>-0.010670</td>\n",
       "      <td>0.221582</td>\n",
       "      <td>0.195871</td>\n",
       "      <td>0.181147</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      employee_id  no_of_trainings       age  \\\n",
       "employee_id              1.000000        -0.005121  0.000437   \n",
       "no_of_trainings         -0.005121         1.000000 -0.081278   \n",
       "age                      0.000437        -0.081278  1.000000   \n",
       "previous_year_rating     0.004209        -0.061564  0.026810   \n",
       "length_of_service        0.001274        -0.057275  0.657111   \n",
       "KPIs_met >80%           -0.002501        -0.045576 -0.025592   \n",
       "awards_won?              0.008420        -0.007628 -0.008169   \n",
       "avg_training_score      -0.000586         0.042517 -0.048380   \n",
       "is_promoted              0.001206        -0.024896 -0.017166   \n",
       "\n",
       "                      previous_year_rating  length_of_service  KPIs_met >80%  \\\n",
       "employee_id                       0.004209           0.001274      -0.002501   \n",
       "no_of_trainings                  -0.061564          -0.057275      -0.045576   \n",
       "age                               0.026810           0.657111      -0.025592   \n",
       "previous_year_rating              1.000000           0.023504       0.337367   \n",
       "length_of_service                 0.023504           1.000000      -0.077693   \n",
       "KPIs_met >80%                     0.337367          -0.077693       1.000000   \n",
       "awards_won?                       0.026587          -0.039927       0.097000   \n",
       "avg_training_score                0.071926          -0.038122       0.078391   \n",
       "is_promoted                       0.153230          -0.010670       0.221582   \n",
       "\n",
       "                      awards_won?  avg_training_score  is_promoted  \n",
       "employee_id              0.008420           -0.000586     0.001206  \n",
       "no_of_trainings         -0.007628            0.042517    -0.024896  \n",
       "age                     -0.008169           -0.048380    -0.017166  \n",
       "previous_year_rating     0.026587            0.071926     0.153230  \n",
       "length_of_service       -0.039927           -0.038122    -0.010670  \n",
       "KPIs_met >80%            0.097000            0.078391     0.221582  \n",
       "awards_won?              1.000000            0.072138     0.195871  \n",
       "avg_training_score       0.072138            1.000000     0.181147  \n",
       "is_promoted              0.195871            0.181147     1.000000  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "##we can clearly see that is_promoted is most affected by prev year rating, KPIs_met, awards won, and avg_training_score since they have a significant positive relation\n",
    "##while factors like no_of_trainings, age, length_of_service is having very little effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# scatterplot of different columns with is_promoted\\nimport seaborn as sns\\nsns.set(rc={\"figure.figsize\":(12,12)})\\nplt.subplot(3,3,1)\\nsns.scatterplot(train[\"is_promoted\"],train[\"no_of_trainings\"])\\nplt.subplot(3,3,2)\\nsns.scatterplot(train[\"is_promoted\"],train[\"age\"],color=\\'g\\')\\nplt.subplot(3,3,3)\\nsns.scatterplot(train[\"is_promoted\"],train[\"previous_year_rating\"],color=\\'red\\')\\nplt.subplot(3,3,4)\\nsns.scatterplot(train[\"is_promoted\"],train[\"length_of_service\"],color=\"blue\")\\nplt.subplot(3,3,5)\\nsns.scatterplot(train[\"is_promoted\"],train[\"KPIs_met >80%\"],color=\\'violet\\')\\nplt.subplot(3,3,6)\\nsns.scatterplot(train[\"is_promoted\"],train[\"awards_won?\"],color=\\'c\\')\\nplt.subplot(3,3,7)\\nsns.scatterplot(train[\"is_promoted\"],train[\"avg_training_score\"],color=\\'orange\\')\\n'"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "# scatterplot of different columns with is_promoted\n",
    "import seaborn as sns\n",
    "sns.set(rc={\"figure.figsize\":(12,12)})\n",
    "plt.subplot(3,3,1)\n",
    "sns.scatterplot(train[\"is_promoted\"],train[\"no_of_trainings\"])\n",
    "plt.subplot(3,3,2)\n",
    "sns.scatterplot(train[\"is_promoted\"],train[\"age\"],color='g')\n",
    "plt.subplot(3,3,3)\n",
    "sns.scatterplot(train[\"is_promoted\"],train[\"previous_year_rating\"],color='red')\n",
    "plt.subplot(3,3,4)\n",
    "sns.scatterplot(train[\"is_promoted\"],train[\"length_of_service\"],color=\"blue\")\n",
    "plt.subplot(3,3,5)\n",
    "sns.scatterplot(train[\"is_promoted\"],train[\"KPIs_met >80%\"],color='violet')\n",
    "plt.subplot(3,3,6)\n",
    "sns.scatterplot(train[\"is_promoted\"],train[\"awards_won?\"],color='c')\n",
    "plt.subplot(3,3,7)\n",
    "sns.scatterplot(train[\"is_promoted\"],train[\"avg_training_score\"],color='orange')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'C:\\Users\\Ayush' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install xgboost  ##This will directly install xgboost directly within the Jupyter notebook\n",
    "#from xgboost import XGBClassifier\n",
    "%matplotlib notebook\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn import preprocessing,linear_model, naive_bayes\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "##First we will do validation over various ML models and then determine the best out of them\n",
    "##And then we will finally apply it on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train[['no_of_trainings','age','previous_year_rating','length_of_service','KPIs_met >80%',\"awards_won?\",\"avg_training_score\"]]\n",
    "Y=train['is_promoted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y,test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##DO ANALYSIS OF EACH MODEL LATER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ayush Agrawal\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n",
      "28686    0\n",
      "16191    0\n",
      "12951    0\n",
      "7890     1\n",
      "29387    0\n",
      "        ..\n",
      "10771    0\n",
      "35658    0\n",
      "35453    1\n",
      "6155     0\n",
      "29736    0\n",
      "Name: is_promoted, Length: 10962, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_predict=model.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9158190028736943"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.918354314906039"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12852969814995133"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "f1_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.918354314906039"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_predict)\n",
    "accuracy\n",
    "##What fraction of the positive predictions are correct?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07166123778501629"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall = metrics.recall_score(y_test,y_predict)\n",
    "recall\n",
    "##hat fraction of all positive instances does the classifier correctly identify as positive?\n",
    "\n",
    "##Clearly poor recall is there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.residplot(x=y_predict, y=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "##RANDOM FORESTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "clas = RandomForestClassifier(n_estimators=30,random_state=10)\n",
    "##tried taking n_estimators=10,20,30,40,50: 30 gave the best result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=30,\n",
       "                       n_jobs=None, oob_score=False, random_state=10, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clas.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9757560552844045"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clas.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9120598430943259"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clas.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 1 0 0]\n",
      "28686    0\n",
      "16191    0\n",
      "12951    0\n",
      "7890     1\n",
      "29387    0\n",
      "        ..\n",
      "10771    0\n",
      "35658    0\n",
      "35453    1\n",
      "6155     0\n",
      "29736    0\n",
      "Name: is_promoted, Length: 10962, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_predict=clas.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30043541364296084"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9120598430943259"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_predict)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2247557003257329"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall = metrics.recall_score(y_test,y_predict)\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "##SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ayush Agrawal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
      "    kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
      "    shrinking=True, tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "cla= SVC().fit(X_train,y_train)\n",
    "print(cla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n",
      "28686    0\n",
      "16191    0\n",
      "12951    0\n",
      "7890     1\n",
      "29387    0\n",
      "        ..\n",
      "10771    0\n",
      "35658    0\n",
      "35453    1\n",
      "6155     0\n",
      "29736    0\n",
      "Name: is_promoted, Length: 10962, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_predict=cla.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19980970504281637"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9232804232804233"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_predict)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11400651465798045"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall = metrics.recall_score(y_test,y_predict)\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##These low scores in SVM are because in SVM efficacy decreases as dataset is too large\n",
    "##i.e. over 50000 samples. Therefore don't use SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "##DECISION TREE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=30,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier=DecisionTreeClassifier(max_depth=30, random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 1 0 1]\n",
      "28686    0\n",
      "16191    0\n",
      "12951    0\n",
      "7890     1\n",
      "29387    0\n",
      "        ..\n",
      "10771    0\n",
      "35658    0\n",
      "35453    1\n",
      "6155     0\n",
      "29736    0\n",
      "Name: is_promoted, Length: 10962, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_predict=classifier.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2760629004076879"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8866082831600073"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_predict)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25732899022801303"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall = metrics.recall_score(y_test,y_predict)\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "##KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 1 0 0]\n",
      "28686    0\n",
      "16191    0\n",
      "12951    0\n",
      "7890     1\n",
      "29387    0\n",
      "        ..\n",
      "10771    0\n",
      "35658    0\n",
      "35453    1\n",
      "6155     0\n",
      "29736    0\n",
      "Name: is_promoted, Length: 10962, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "y_predict=knn.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9189928845101258"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2210526315789474"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9189928845101258"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = metrics.accuracy_score(y_test, y_predict)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13680781758957655"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall = metrics.recall_score(y_test,y_predict)\n",
    "recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Random forests clearly come out as the best algo\n",
    "##Therefore we will employ the Random Forests on our test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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