15. Diversity in the Workplace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import lightgbm as lgb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>boss_id</th>\n",
       "      <th>dept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46456</td>\n",
       "      <td>175361.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>104708</td>\n",
       "      <td>29733.0</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120853</td>\n",
       "      <td>41991.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>142630</td>\n",
       "      <td>171266.0</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72711</td>\n",
       "      <td>198240.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id   boss_id   dept\n",
       "0        46456  175361.0  sales\n",
       "1       104708   29733.0     HR\n",
       "2       120853   41991.0  sales\n",
       "3       142630  171266.0     HR\n",
       "4        72711  198240.0  sales"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company = pd.read_csv('./data/HR/company_hierarchy.csv')\n",
    "company.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      "employee_id    10000 non-null int64\n",
      "boss_id        9999 non-null float64\n",
      "dept           10000 non-null object\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 234.5+ KB\n"
     ]
    }
   ],
   "source": [
    "company.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>degree_level</th>\n",
       "      <th>sex</th>\n",
       "      <th>yrs_experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>138719</td>\n",
       "      <td>0</td>\n",
       "      <td>273000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3192</td>\n",
       "      <td>0</td>\n",
       "      <td>301000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114657</td>\n",
       "      <td>0</td>\n",
       "      <td>261000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29039</td>\n",
       "      <td>0</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>High_School</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118607</td>\n",
       "      <td>0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  signing_bonus    salary degree_level sex  yrs_experience\n",
       "0       138719              0  273000.0       Master   M               2\n",
       "1         3192              0  301000.0     Bachelor   F               1\n",
       "2       114657              0  261000.0       Master   F               2\n",
       "3        29039              0   86000.0  High_School   F               4\n",
       "4       118607              0  126000.0     Bachelor   F               3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee = pd.read_csv('./data/HR/employee.csv')\n",
    "employee.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 6 columns):\n",
      "employee_id       10000 non-null int64\n",
      "signing_bonus     10000 non-null int64\n",
      "salary            10000 non-null float64\n",
      "degree_level      10000 non-null object\n",
      "sex               10000 non-null object\n",
      "yrs_experience    10000 non-null int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 468.8+ KB\n"
     ]
    }
   ],
   "source": [
    "employee.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>yrs_experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100001.855800</td>\n",
       "      <td>0.301400</td>\n",
       "      <td>189111.800000</td>\n",
       "      <td>3.875200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57500.488641</td>\n",
       "      <td>0.458889</td>\n",
       "      <td>88973.796898</td>\n",
       "      <td>3.107409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50574.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110000.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99244.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>182000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>149748.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>255000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>199956.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>700000.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         employee_id  signing_bonus         salary  yrs_experience\n",
       "count   10000.000000   10000.000000   10000.000000    10000.000000\n",
       "mean   100001.855800       0.301400  189111.800000        3.875200\n",
       "std     57500.488641       0.458889   88973.796898        3.107409\n",
       "min        40.000000       0.000000   60000.000000        1.000000\n",
       "25%     50574.500000       0.000000  110000.000000        2.000000\n",
       "50%     99244.500000       0.000000  182000.000000        3.000000\n",
       "75%    149748.500000       1.000000  255000.000000        5.000000\n",
       "max    199956.000000       1.000000  700000.000000       34.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employee.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>employee_id</th>\n",
       "      <th>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>degree_level</th>\n",
       "      <th>sex</th>\n",
       "      <th>yrs_experience</th>\n",
       "      <th>boss_id</th>\n",
       "      <th>dept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>138719</td>\n",
       "      <td>0</td>\n",
       "      <td>273000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>43602.0</td>\n",
       "      <td>engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3192</td>\n",
       "      <td>0</td>\n",
       "      <td>301000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>87847.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114657</td>\n",
       "      <td>0</td>\n",
       "      <td>261000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>180854.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29039</td>\n",
       "      <td>0</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>High_School</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>88370.0</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118607</td>\n",
       "      <td>0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>23565.0</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  signing_bonus    salary degree_level sex  yrs_experience  \\\n",
       "0       138719              0  273000.0       Master   M               2   \n",
       "1         3192              0  301000.0     Bachelor   F               1   \n",
       "2       114657              0  261000.0       Master   F               2   \n",
       "3        29039              0   86000.0  High_School   F               4   \n",
       "4       118607              0  126000.0     Bachelor   F               3   \n",
       "\n",
       "    boss_id         dept  \n",
       "0   43602.0  engineering  \n",
       "1   87847.0        sales  \n",
       "2  180854.0        sales  \n",
       "3   88370.0           HR  \n",
       "4   23565.0        sales  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge employee and company information\n",
    "data = pd.merge(left=employee, right=company, on='employee_id')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1\n",
    "In the company there are 6 levels (described below). Identify, for each employee, her corresponding level.\n",
    "* Individual Contributors (IC) - they don't manage anyone\n",
    "* Middle Managers (MM) - they are the direct bosses of IC\n",
    "* Directors (D) - they are the direct bosses of MM\n",
    "* VP - D direct bosses\n",
    "* Executives (E) - VP direct bosses\n",
    "* CEO - The direct boss of E."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels = ['CEO', 'E', 'VP', 'D', 'MM', 'IC']\n",
    "data.loc[data['dept'] == 'CEO', 'level'] = 'CEO'\n",
    "\n",
    "for i in range(len(levels) - 1):\n",
    "    boss_level = levels[i]\n",
    "    empl_level = levels[i + 1]\n",
    "    boss_id = data[data['level'] == boss_level]['employee_id']\n",
    "    data.loc[data['boss_id'].isin(boss_id), 'level'] = empl_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>142588</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>60314</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5054</th>\n",
       "      <td>125605</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3404</th>\n",
       "      <td>145140</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>179489</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>33343</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9875</th>\n",
       "      <td>8473</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>8665</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4205</th>\n",
       "      <td>139032</td>\n",
       "      <td>VP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7327</th>\n",
       "      <td>148358</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      employee_id level\n",
       "654        142588    IC\n",
       "2934        60314    IC\n",
       "5054       125605    IC\n",
       "3404       145140    IC\n",
       "860        179489    IC\n",
       "2797        33343    IC\n",
       "9875         8473     D\n",
       "2158         8665     D\n",
       "4205       139032    VP\n",
       "7327       148358    IC"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['employee_id', 'level']].sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IC     9000\n",
       "MM      800\n",
       "D       160\n",
       "VP       35\n",
       "E         4\n",
       "CEO       1\n",
       "Name: level, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts for each group\n",
    "data['level'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 2\n",
    "How many people each employee manages? You should have a table with employee ids and the number of people managed. Consider that if John directly manages 2 people and these two people manage 5 people each, then we conclude that John manages 12 people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the managed employees for each employee\n",
    "data['counts'] = 0\n",
    "\n",
    "for level in ['IC', 'MM', 'D', 'VP', 'E']:\n",
    "    boss_ids = data[data['level'] == level]['boss_id']\n",
    "    employee_ids = data[data['level'] == level]['employee_id']\n",
    "    for b_id, e_id in zip(boss_ids, employee_ids):\n",
    "        e_counts = int(1 + data.loc[data['employee_id'] == e_id, 'counts'])\n",
    "        b_counts = int(data.loc[data['employee_id'] == b_id, 'counts'])\n",
    "        data.loc[data['employee_id'] == b_id, 'counts'] = e_counts + b_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>level</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7521</th>\n",
       "      <td>108648</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8260</th>\n",
       "      <td>2111</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5424</th>\n",
       "      <td>106915</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>133826</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>20965</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>26830</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3897</th>\n",
       "      <td>127586</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2625</th>\n",
       "      <td>141211</td>\n",
       "      <td>MM</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>88303</td>\n",
       "      <td>MM</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118607</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      employee_id level  counts\n",
       "7521       108648    IC       0\n",
       "8260         2111    IC       0\n",
       "5424       106915    IC       0\n",
       "1874       133826    IC       0\n",
       "2815        20965    IC       0\n",
       "2250        26830    IC       0\n",
       "3897       127586    IC       0\n",
       "2625       141211    MM       7\n",
       "2033        88303    MM      11\n",
       "4          118607    IC       0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['employee_id', 'level', 'counts']].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 3\n",
    "Build a model to predict the salary of each employee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>degree_level</th>\n",
       "      <th>sex</th>\n",
       "      <th>yrs_experience</th>\n",
       "      <th>boss_id</th>\n",
       "      <th>dept</th>\n",
       "      <th>level</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>138719</td>\n",
       "      <td>0</td>\n",
       "      <td>273000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>43602.0</td>\n",
       "      <td>engineering</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3192</td>\n",
       "      <td>0</td>\n",
       "      <td>301000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>87847.0</td>\n",
       "      <td>sales</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114657</td>\n",
       "      <td>0</td>\n",
       "      <td>261000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>180854.0</td>\n",
       "      <td>sales</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29039</td>\n",
       "      <td>0</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>High_School</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>88370.0</td>\n",
       "      <td>HR</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118607</td>\n",
       "      <td>0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>23565.0</td>\n",
       "      <td>sales</td>\n",
       "      <td>IC</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  signing_bonus    salary degree_level sex  yrs_experience  \\\n",
       "0       138719              0  273000.0       Master   M               2   \n",
       "1         3192              0  301000.0     Bachelor   F               1   \n",
       "2       114657              0  261000.0       Master   F               2   \n",
       "3        29039              0   86000.0  High_School   F               4   \n",
       "4       118607              0  126000.0     Bachelor   F               3   \n",
       "\n",
       "    boss_id         dept level  counts  \n",
       "0   43602.0  engineering    IC       0  \n",
       "1   87847.0        sales    IC       0  \n",
       "2  180854.0        sales    IC       0  \n",
       "3   88370.0           HR    IC       0  \n",
       "4   23565.0        sales    IC       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of salary\n",
    "warnings.filterwarnings('ignore')\n",
    "hist_kws={'histtype': 'bar', 'edgecolor':'black', 'alpha': 0.2}\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 5))\n",
    "sns.distplot(data['salary'], ax=ax[0], hist_kws=hist_kws)\n",
    "sns.distplot(np.log(data['salary']), ax=ax[1], hist_kws=hist_kws)\n",
    "ax[1].set_xlabel('log(salary)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of 'signing_bonus'\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "sns.countplot(x='signing_bonus', data=data, ax=ax[0])\n",
    "ax[0].set_xlabel('signing_bonus', fontsize=12)\n",
    "ax[0].set_ylabel('Count', fontsize=12)\n",
    "ax[0].set_title('Count Plot of signing_bonus', fontsize=16)\n",
    "\n",
    "sns.barplot(x='signing_bonus', y='salary', data=data, ax=ax[1])\n",
    "ax[1].set_xlabel('signing_bonus', fontsize=12)\n",
    "ax[1].set_ylabel('Salary', fontsize=12)\n",
    "ax[1].set_title('Salary vs. signing_bonus', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of 'degree_level'\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "sns.countplot(x='degree_level', data=data, ax=ax[0])\n",
    "ax[0].set_xlabel('degree_level', fontsize=12)\n",
    "ax[0].set_ylabel('Count', fontsize=12)\n",
    "ax[0].set_title('Count Plot of degree_level', fontsize=16)\n",
    "\n",
    "sns.barplot(x='degree_level', y='salary', data=data, ax=ax[1])\n",
    "ax[1].set_xlabel('degree_level', fontsize=12)\n",
    "ax[1].set_ylabel('Salary', fontsize=12)\n",
    "ax[1].set_title('Salary vs. degree_level', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of 'sex'\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "sns.countplot(x='sex', data=data, ax=ax[0])\n",
    "ax[0].set_xlabel('sex', fontsize=12)\n",
    "ax[0].set_ylabel('Count', fontsize=12)\n",
    "ax[0].set_title('Count Plot of sex', fontsize=16)\n",
    "\n",
    "sns.barplot(x='sex', y='salary', data=data, ax=ax[1])\n",
    "ax[1].set_xlabel('sex', fontsize=12)\n",
    "ax[1].set_ylabel('Salary', fontsize=12)\n",
    "ax[1].set_title('Salary vs. sex', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of 'yrs_experience'\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "sns.countplot(x='yrs_experience', data=data, ax=ax[0])\n",
    "ax[0].set_xlabel('yrs_experience', fontsize=12)\n",
    "ax[0].set_ylabel('Count', fontsize=12)\n",
    "ax[0].set_title('Count Plot of yrs_experience', fontsize=16)\n",
    "\n",
    "sns.barplot(x='yrs_experience', y='salary', data=data, ax=ax[1])\n",
    "ax[1].set_xlabel('yrs_experience', fontsize=12)\n",
    "ax[1].set_ylabel('Salary', fontsize=12)\n",
    "ax[1].set_title('Salary vs. yrs_experience', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of 'dept'\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "sns.countplot(x='dept', data=data, ax=ax[0])\n",
    "ax[0].set_xlabel('dept', fontsize=12)\n",
    "ax[0].set_ylabel('Count', fontsize=12)\n",
    "ax[0].set_title('Count Plot of dept', fontsize=16)\n",
    "\n",
    "sns.barplot(x='dept', y='salary', data=data, ax=ax[1])\n",
    "ax[1].set_xlabel('dept', fontsize=12)\n",
    "ax[1].set_ylabel('Salary', fontsize=12)\n",
    "ax[1].set_title('Salary vs. dept', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization of 'signing_bonus'\n",
    "orders = ['IC', 'MM', 'D', 'VP', 'E', 'CEO']\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
    "sns.countplot(x='level', data=data, ax=ax[0], order=orders)\n",
    "ax[0].set_xlabel('level', fontsize=12)\n",
    "ax[0].set_ylabel('Count', fontsize=12)\n",
    "ax[0].set_title('Count Plot of level', fontsize=16)\n",
    "\n",
    "sns.barplot(x='level', y='salary', data=data, ax=ax[1], order=orders)\n",
    "ax[1].set_xlabel('level', fontsize=12)\n",
    "ax[1].set_ylabel('Salary', fontsize=12)\n",
    "ax[1].set_title('Salary vs. level', fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W/hh4NA/gBOB94OS02imkRlpuCs9dQ92AnwJbSJ2iUJo2zw2kTlnYDExKq5cC68I8c/j76QwZ19HcI1cHt5SUlMTah/Zs1KhRjU4pcE8Nbsk0sCYJ4v6bl09Mg1viHtzi7vtDCKXX3gcuzdDWgSlNLOcB4IEM9VXAZzLUM65DJN2TTz4ZdxdEJAa6O0MO69WrF+vWrYu7G4lz/fXXc/3118fdDRGJiC5Z1gJvZqSjSHuiv3VJCgVfMzp16sT777+vLwRp99xT9+Pr1KlT3F0RiZwOdTajuLiYioqK+uHlIu1Z3R3YRdo7BV8z8vPzdTdqEZF2Roc6RUQkURR8IiKSKAo+ERFJFAWfiIgkioJPREQSRcEnIiKJouATEZFEUfCJiEiiKPhERCRRdOUWidyS1ZW6uaaI5AwFn0RqyepKpi9eS3VNLQCVVdVMX7wWQOEnIrHQoU6J1KxlG+tDr051TS2zlm2MqUciknQKPonUtqrqI6qLiERNwSeR6lFYcER1EZGoKfgkUlPL+lOQn3dYrSA/j6ll/WPqkYgknQa3SKTqBrBoVKeI5AoFn0RuzOAiBZ2I5Awd6hQRkURR8ImISKIo+EREJFEUfCIikigKPhERSRQFn4iIJIqCT0REEkXBJyIiiaLgExGRRFHwiYhIoij4REQkURR8IiKSKAo+ERFJFAWfiIgkSqTBZ2aFZrbIzDaY2Ztm9jkz62pmy81sU3juEtqamc02s81m9rqZDUlbzsTQfpOZTUyrDzWztWGe2WZmoZ5xHSIiIlHv8f0f4Gl3PxsYCLwJTANWuHs/YEV4DXAF0C88JgNzIRViwAzgfOA8YEZakM0NbevmKw/1ptYhIiIJF1nwmVln4CLgFwDuftDdq4DRwLzQbB4wJkyPBuZ7ygtAoZl1B8qA5e6+290/AJYD5eG9zu7+vLs7ML/BsjKtQ0REEi7KPb4+wE7gQTNbbWb3m9mJwOnuvh0gPJ8W2hcBW9Pmrwi15uoVGeo0s47DmNlkM1tlZqt27tx59FsqItIG6DsvJcrg6wAMAea6+2BgH80fcrQMNT+Kequ5+33uXurupd26dTuSWUVE2hx956VEGXwVQIW7vxheLyIVhO+Fw5SE5x1p7XumzV8MbGuhXpyhTjPrEBGRhIss+Nz9r8BWM+sfSpcC64EngLqRmROBpWH6CeC6MLpzGLAnHKZcBlxuZl3CoJbLgWXhvb1mNiyM5ryuwbIyrUNERBKuQ8TL/ybwsJl1BN4CJpEK28fM7EbgXeCq0PYp4IvAZmB/aIu77zazHwAvh3Z3uPvuMH0T8BBQAPw2PABmNrEOERFJOEsNiJTS0lJftWpV3N0QEfkkMo19yKidfue1avt15RYREUkUBZ+IiCSKgk9ERBJFwSciIomi4BMRkURR8ImISKIo+EREJFEUfCIikigKPhERSRQFn4iIJIqCT0REEkXBJyIiiaLgExGRRIn6tkTSRi1ZXcmsZRvZVlVNj8ICppb1Z8zgori71aS21l8RiY+CTxpZsrqS6YvXUl1TC0BlVTXTF68FyMkwaWv9FZF46VCnNDJr2cb6EKlTXVPLrGUbY+pR89paf0UkXgo+aWRbVfUR1ePW1vorIvFS8EkjPQoLjqget7bWXxGJl4JPGpla1p+C/LzDagX5eUwt6x9Tj5rX1vorIvHS4BZppG5ASFsZJdnW+isi8VLwSUZjBhe1qeBoa/0VkfjoUKeIiCSKgk9ERBJFwSciIomi4BMRkURR8ImISKIo+EREJFEUfCIikigKPhERSRQFn4iIJIqCT0REEkXBJyIiiaLgExGRRFHwiYhIoij4REQkUSINPjN7x8zWmtkaM1sVal3NbLmZbQrPXULdzGy2mW02s9fNbEjaciaG9pvMbGJafWhY/uYwrzW3DklZsrqS4TNX0nvabxg+cyVLVlfG3SURkazJxh7fJe4+yN1Lw+tpwAp37wesCK8BrgD6hcdkYC6kQgyYAZwPnAfMSAuyuaFt3XzlLawj8ZasrmT64rVUVlXjQGVVNdMXr1X4iUhixHGoczQwL0zPA8ak1ed7ygtAoZl1B8qA5e6+290/AJYD5eG9zu7+vLs7ML/BsjKtI/FmLdtIdU3tYbXqmlpmLdsYU49ERLIr6uBz4Hdm9oqZTQ610919O0B4Pi3Ui4CtafNWhFpz9YoM9ebWcRgzm2xmq8xs1c6dO49yE9uWbVXVR1QXkfYjid95mUQdfMPdfQipw5hTzOyiZtpahpofRb3V3P0+dy9199Ju3bodyaxtVo/CgiOqi0j7kcTvvEwiDT533xaedwC/JvUb3XvhMCXheUdoXgH0TJu9GNjWQr04Q51m1pF4U8v6U5Cfd1itID+PqWX9Y+qRiEh2RRZ8ZnaimZ1UNw1cDqwDngDqRmZOBJaG6SeA68LozmHAnnCYchlwuZl1CYNaLgeWhff2mtmwMJrzugbLyrSOxBszuIi7xg6gqLAAA4oKC7hr7ADGDC5qcV4RkfagQ4TLPh34dTjDoAPwiLs/bWYvA4+Z2Y3Au8BVof1TwBeBzcB+YBKAu+82sx8AL4d2d7j77jB9E/AQUAD8NjwAZjaxDiEVfgo6EUkqSw2IlNLSUl+1alXc3RAR+SQyjX3IqJ1+57Vq+3XlFhERSRQFn4iIJIqCT0REEkXBJyIiiaLgExGRRFHwiYhIoij4REQkURR8IiKSKAo+ERFJFAWfiIgkioJPREQSRcEnIiKJouATEZFEUfCJiEiiKPhERCRRFHwiIpIoCj4REUkUBZ+IiCSKgk9ERBJFwSciIomi4BMRkURR8ImISKIo+EREJFEUfCIikigKPhERSRQFn4iIJIqCT0REEkXBJyIiiaLgExGRRFHwiYhIoij4REQkURR8IiKSKAo+ERFJFAWfiIgkSquCz8zyou6IiIhINrR2j2+zmc0ys3OOdAVmlmdmq83syfC6t5m9aGabzGyhmXUM9ePD683h/V5py5ge6hvNrCytXh5qm81sWlo94zpERCRl976DPPLiuzzy4rtxdyXrOrSy3bnAeOB+MzsOeABY4O4ftmLefwXeBDqH13cDP3H3BWZ2L3AjMDc8f+Dufc1sfGh3TQjb8UAJ0AP4vZn9Y1jWT4HLgArgZTN7wt3XN7OOY6bXtN80qr0zc9SxXIWIiESgVXt87r7X3X/u7hcA3wZmANvNbJ6Z9W1qPjMrBkYB94fXBowEFoUm84AxYXp0eE14/9LQfjSpkD3g7m8Dm4HzwmOzu7/l7geBBcDoFtZxTGQKvbr68JkrWbK6kiWrKxk+cyW902oiIhK/Vu3xhd/4RgGTgF7Aj4GHgQuBp4B/bGLWe0gF5Unh9SlAlbsfCq8rgKIwXQRsBXD3Q2a2J7QvAl5IW2b6PFsb1M9vYR0Nt2syMBngzDPPbGITjkxlVTVTF70GDjUfe31t+uK1AIwZnLErIiKRS//OO/WM5H4XtfY3vk2k9rxmuftgd/8Pd3/P3RcBT2eawcy+BOxw91fSyxmaegvvHat646L7fe5e6u6l3bp1y9TkqNTUen3o1amuqWXWso3HbB0iIkcq/TvvpMKucXcnNi3u8YW9vYfc/Y5M77v7zU3MOhy40sy+CHQi9RvfPUChmXUIe2TFwLbQvgLoCVSYWQfgZGB3Wr1O+jyZ6ruaWUestlVVx90FEZHEa3GPz91rgUuOdMHuPt3di929F6nBKSvdfQLwDDAuNJsILA3TT4TXhPdXuruH+vgw6rM30A94CXgZ6BdGcHYM63gizNPUOmLVo7Ag7i6IiDRSN7ozKaM8Wzuq889mNgdYCOyrK7r7q0exztuABWb2Q2A18ItQ/wXwSzPbTGpPb3xYxxtm9hiwHjgETAlhjJl9A1gG5AEPuPsbLawja44DPk57XZCfx9Sy/tnuhoiINNDa4LsgPKcf7nRSoydb5O7PAs+G6bdIjchs2OYj4Kom5r8TuDND/SlSg2sa1jOuI5tOPiGfEzp2YFtVNT0KC5ha1l8DW0REckBrT2e4JMOjVaGXVFX7a/jTtJH85JpBAHxr4Rqd1iAikgNau8eHmY0idRJ5p7paUwNeJPV73pLVlUxfvJbqmlpApzWIiOSC1l6r817gGuCbpE4XuAo4K8J+tWkGTC3rz6xlG+tDr45OaxARiVdrz+O7wN2vI3VJse8Dn+PwUwkkjZPao2vq9AWd1iAiEp/WBl/dN/V+M+sB1AC9o+lS21cUTlto6vQFndYgIhKf1v7G96SZFQKzgFdJ7dTcH1mv2jADLjm7G8NnrqSyqhrj8MvG6LQGEZF4tSr43P0HYfJX4fZCndx9T3Tdarsu+FRXfvVKZf1ve3XXUHNSe4I6rUFEJF7NBp+ZjW3mPdx98bHvUtv2zvvVjQa01IXen6bpDBARkbi1tMf35Wbec0DB10ClBrSIiOS0ZoPP3SdlqyPt3ckF+Y1qS1ZXMmvZRl3dRURySvr1Or96/rG5ZVsu0QnsWWINbpakk9tFROKhE9izpGp/zWGvdXK7iEg8dAJ7ljQ8d08nt4uIxKO1wfdReK47gf0QOoG91TKdu6eT20VE4tHa4Pt/DU5gfxt4NLJetQNdTsjHSJ3GcNfYAY1+t5ta1p+C/LzDajq5XUQkeq0d3LIBqHX3X5nZOcAQYEl03WrbupyQz+rvXV7/esnqSobPXJlx9KZGdYqIZFdrg++77v64mY0ALgN+DMwFzo+sZ23YjC+X1E+3NHpTQScikl2tPdRZN/xwFHCvuy8FOkbTpfZFozdFRHJLa4Ov0sx+BlwNPGVmxx/BvImTHmoavSkikltaG15XA8uAcnevAroCUyPrVRuXHmoavSkikltaFXzuvt/dF7v7pvB6u7v/LtqutV3plyfT6E0Rkdyiw5UR2H/wUP30mMFF3DV2AEWFBc2e3iAikovSr9vZXrT6Wp3SegdrXRegFhHJUQq+iDR3CoNCUUQkPgq+iGQ6heGWhWv41mNrcP97XXdlEBHJLv3Gl2XpoVdH5/WJiGSPgi9H6Lw+EZHs0KHOHKHz+kQkV7W3O7Jrjy8iXU7Ip6iVYabz+kREskfBF5EZXy7hT9NGcs81gxqdwJ6usCBf5/WJiGSRDnVGpC7I0m8/VFlVTZ4Zte4U6TQGEZFYKPgi0PAQp24/JCKSO3SoMwL6vU5EJHdpjy8Cj696V3t4ItIutYcRntrji8CftuyOuwsiItKEyILPzDqZ2Utm9pqZvWFm3w/13mb2opltMrOFZtYx1I8PrzeH93ulLWt6qG80s7K0enmobTazaWn1jOsQERGJco/vADDS3QcCg4ByMxsG3A38xN37AR8AN4b2NwIfuHtf4CehHWZ2DjAeKAHKgf9rZnlmlgf8FLgCOAe4NrSlmXWIiEjCRRZ8nvK38DI/PBwYCSwK9XnAmDA9OrwmvH+pmVmoL3D3A+7+NrAZOC88Nrv7W+5+EFgAjA7zNLUOERFJuEh/4wt7ZmuAHcByYAtQ5e51d2qtAOpGgRQBWwHC+3uAU9LrDeZpqn5KM+to2L/JZrbKzFbt3Lnzk2yqiEjOS//O21v1yccitNWb1EYafO5e6+6DgGJSe2ifztQsPFsT7x2reqb+3efupe5e2q1bt0xNRETajfTvvJMKu8bdndhkZVSnu1cBzwLDgEIzqzuNohjYFqYrgJ4A4f2Tgd3p9QbzNFXf1cw6sub2JWuzvUoREWmFKEd1djOzwjBdAHwBeBN4BhgXmk0ElobpJ8Jrwvsr3d1DfXwY9dkb6Ae8BLwM9AsjODuSGgDzRJinqXVkzaMvbm25kYiIZF2Ue3zdgWfM7HVSIbXc3Z8EbgNuNbPNpH6P+0Vo/wvglFC/FZgG4O5vAI8B64GngSnhEOoh4BvAMlKB+lhoSzPryJpad5asrsz2akVEpAWRXbnF3V8HBmeov0Xq976G9Y+Aq5pY1p3AnRnqTwFPtXYd2fathWtY9T+7+eGYAfW1JasrmbVsI9uqqumhC1WLiGSdLlkWIQcefuFdSs/qypjBRSxZXcn0xWuprqkFoLKqmumLU78FKvxEpC1qi5cw0yXLIuakbklEeK4LvTrVNbX174uISPQUfFlQWVXNktWVbKuqzvh+U3URETn2FHxZMn3xWgpDKfMeAAAPoUlEQVRPyM/4Xo8G9+8TEZHoKPiypLqmFncoyM87rF6Qn6f794mIZJGCL4v2VNdw19gBFBUWYKTu1H7X2AEa2CIikkUa1ZlFPQoLGDO4SEEnIu1S3QjPXB/dqT2+LNEhTRGR3KDgy4I8Mx3SFBHJEQq+LPjx1QMVeiIiOULBlwUKPRGR3KHgi1ieZbo9oIiIxEXBF7Frz+/ZciMREckaBV+Ehn+q62F3ZhARkfgp+CL06rt7dE8+EZEco+CLkO68ICKSexR8EdOdF0REcouCL2K684KISG5R8EWssqqa4TNX6rc+EUmM9Luy5yIFXxZUVlUzffFahZ+ISA5Q8GWJBrqIiOQGBV8WaaCLiEj8FHxZpIEuIiLxU/Blie7HJyKSG3QH9iwoKixgall/3aVBRBKj4cjOXLoru4IvYkWFBfxp2si4uyEiIoEOdUZIhzdFRHKPgi9Cd40doMObIiI5RsEXoVnLNuqkdRGRHKPgi5Cu2CIiknsUfBHTFVtERFKjPHPlGp4KvizQFVtERHKHgi8LdMUWEZHcoeCLmE5pEBHJLTqBPUK6YouISO7RHl9EjjMUeiIiOSiy4DOznmb2jJm9aWZvmNm/hnpXM1tuZpvCc5dQNzObbWabzex1MxuStqyJof0mM5uYVh9qZmvDPLPNzJpbRzZ97DB10WsM+v7v6D3tN7oLu4gkXq5crzPKPb5DwL+5+6eBYcAUMzsHmAascPd+wIrwGuAKoF94TAbmQirEgBnA+cB5wIy0IJsb2tbNVx7qTa0jq2pqnarqGhyd0ycikisiCz533+7ur4bpvcCbQBEwGpgXms0DxoTp0cB8T3kBKDSz7kAZsNzdd7v7B8ByoDy819ndn3d3B+Y3WFamdcSquqaWf3vsNe0BiojEKCu/8ZlZL2Aw8CJwurtvh1Q4AqeFZkXA1rTZKkKtuXpFhjrNrKNhvyab2SozW7Vz586j3bwjUuuuPUARiUX6d97eqt1xdyc2kQefmf0D8CvgFnf/sLmmGWp+FPVWc/f73L3U3Uu7det2JLMeE7qqi4hkU/p33kmFXePuTmwiDT4zyycVeg+7++JQfi8cpiQ87wj1CqBn2uzFwLYW6sUZ6s2tI+foqi4iItkV5ahOA34BvOnu/5H21hNA3cjMicDStPp1YXTnMGBPOEy5DLjczLqEQS2XA8vCe3vNbFhY13UNlpVpHbHJs0w7qLqqi4gkR65crzPKPb7hwNeAkWa2Jjy+CMwELjOzTcBl4TXAU8BbwGbg58C/ALj7buAHwMvhcUeoAdwE3B/m2QL8NtSbWkdsfnz1QAry8w6r6aouIiLZF9mVW9z9OTL/DgdwaYb2DkxpYlkPAA9kqK8CPpOh/n6mdcTpWwvXcHJBPp3yj6Nqfw09dFUXEZFY6JJlWeJAVXUNBfl5/OSaQQo8EZGY6JJlWaaRnCIi8VLwxUAjOUVE4qNDnTHQSE4RSZpcuU4naI8v6ww0klNEJEYKviwyYMKwMzWwRUQkRjrUmSWFBfn8+5UlCj0RkZgp+CJSVFjAtqpqna8nIpJjFHwR+dO0kXF3QUQkJ+TSwBbQb3yRKCzIj7sLIiLSBAVfBP79ypK4uyAiIk1Q8ImISKIo+CLwnV+vjbsLIiLSBAVfBPYdrI27CyIi0gQFn4iIRCpXbkBbR8EXgaZuQigiIvFT8EXggk91jbsLIiLSBAVfBN55X7cdEhHJVQq+CFTqfnsiIjlLwReBPNOvfCIiuUrX6oxArXvcXRARyRm6VmcCFOTrn1VEJFfpGzoC1TUfs2R1ZdzdEBGRDBR8EZm1bGPcXRARkQwUfBHZppGdIiI5ScEXkcITdE8+EZFcpOCLiAZ2iojk3ohOUPBFZk91TdxdEBGRDBR8EelRWBB3F0REJAMFX0SmlvWPuwsiIpKBgi8is5Zt1Ll8IiI5SMEXkcqqaqYvXqvwExHJMbpWZ4Sqa2qZtWwjYwYXxd0VEZFYpN95PVdGeGqPL2I6kV1EJLco+CJ2nJkOd4qI5JDIgs/MHjCzHWa2Lq3W1cyWm9mm8Nwl1M3MZpvZZjN73cyGpM0zMbTfZGYT0+pDzWxtmGe2WeomeE2tIy617vqtT0Qkh0S5x/cQUN6gNg1Y4e79gBXhNcAVQL/wmAzMhVSIATOA84HzgBlpQTY3tK2br7yFdcSm7rc+ERGJX2TB5+5/BHY3KI8G5oXpecCYtPp8T3kBKDSz7kAZsNzdd7v7B8ByoDy819ndn3d3B+Y3WFamdcRKv/WJiOSGbI/qPN3dtwO4+3YzOy3Ui4Ctae0qQq25ekWGenPraMTMJpPaa+TMM6MdbaQruYhI3NK/8049I/ujzdNHeDaUzRGfuTK4xTLU/CjqR8Td73P3Uncv7dat25HO3moF+Xm6kouIxC79O++kwq5xdyc22Q6+98JhSsLzjlCvAHqmtSsGtrVQL85Qb24dsSgsyOeusQN0Lp+ISI7IdvA9AdSNzJwILE2rXxdGdw4D9oTDlcuAy82sSxjUcjmwLLy318yGhdGc1zVYVqZ1ZFWeGfdcM4g1My5X6ImI5JDIfuMzs0eBi4FTzayC1OjMmcBjZnYj8C5wVWj+FPBFYDOwH5gE4O67zewHwMuh3R3uXjdg5iZSI0cLgN+GB82sI6t+fPVABZ6ISA6KLPjc/dom3ro0Q1sHpjSxnAeABzLUVwGfyVB/P9M6sk2hJyKSm3StThERiV02R3zmyqjOdkdXahERyU0KvojoSi0iIrlJwRcRXalFRCQ3Kfgioiu1iIjkJgVfBAx0pRYRkRylUZ0RcHQ6g4jIsXKsR3xqjy8iGtUpIpKbFHwR0ahOEZHcpOCLiEZ1iojkJgVfRDSqU0QkNyn4IpCfZxrVKSKSozSqMwKzxunODCKS27qe2DGrdz3PJdrjO8YKC/IVeiIiOUzBd4z9+5UlcXdBRESaoeA7Cu/MHJWxfs81g7S3JyKS4/Qb31FqKvxERCS3aY9PREQSRcEnIiKJouATEZFEUfCJiEiiKPhERCRRFHwiIpIoCj4REUkUBZ+IiCSKgk9ERBLF3D3uPuQEM9sJ/M9RzHoqsOsYdyeXtOfta8/bBtq+tu5otm+Xu5e3pqGZPd3atu2Ngu8TMrNV7l4adz+i0p63rz1vG2j72rr2vn1x0qFOERFJFAWfiIgkioLvk7sv7g5ErD1vX3veNtD2tXXtfftio9/4REQkUbTHJyIiiaLgExGRRFHwHSUzKzezjWa22cymxd2f1jKznmb2jJm9aWZvmNm/hnpXM1tuZpvCc5dQNzObHbbzdTMbkrasiaH9JjObGNc2NWRmeWa22syeDK97m9mLoZ8LzaxjqB8fXm8O7/dKW8b0UN9oZmXxbEljZlZoZovMbEP4DD/Xzj67b4W/y3Vm9qiZdWrLn5+ZPWBmO8xsXVrtmH1eZjbUzNaGeWabmWV3C9sod9fjCB9AHrAF6AN0BF4Dzom7X63se3dgSJg+CfgLcA7wI2BaqE8D7g7TXwR+CxgwDHgx1LsCb4XnLmG6S9zbF/p2K/AI8GR4/RgwPkzfC9wUpv8FuDdMjwcWhulzwmd6PNA7fNZ5cW9X6Ns84J/DdEegsL18dkAR8DZQkPa5Xd+WPz/gImAIsC6tdsw+L+Al4HNhnt8CV8T9ObaFh/b4js55wGZ3f8vdDwILgNEx96lV3H27u78apvcCb5L6whlN6kuV8DwmTI8G5nvKC0ChmXUHyoDl7r7b3T8AlgOxXwXCzIqBUcD94bUBI4FFoUnDbavb5kXApaH9aGCBux9w97eBzaQ+81iZWWdSX6S/AHD3g+5eRTv57IIOQIGZdQBOALbThj8/d/8jsLtB+Zh8XuG9zu7+vKdScH7asqQZCr6jUwRsTXtdEWptSjg0NBh4ETjd3bdDKhyB00KzprY1V/8N7gG+DXwcXp8CVLn7ofA6vZ/12xDe3xPa5+q29QF2Ag+GQ7n3m9mJtJPPzt0rgf8NvEsq8PYAr9B+Pr86x+rzKgrTDevSAgXf0cl0HL1NnRdiZv8A/Aq4xd0/bK5phpo3U4+NmX0J2OHur6SXMzT1Ft7LuW0LOpA6bDbX3QcD+0gdKmtKm9q+8FvXaFKHJ3sAJwJXZGjaVj+/lhzp9rTV7Yydgu/oVAA9014XA9ti6ssRM7N8UqH3sLsvDuX3wqETwvOOUG9qW3Px32A4cKWZvUPq8PNIUnuAheHQGRzez/ptCO+fTOqwVC5uG6T6VeHuL4bXi0gFYXv47AC+ALzt7jvdvQZYDFxA+/n86hyrz6siTDesSwsUfEfnZaBfGG3WkdQP60/E3KdWCb+B/AJ4093/I+2tJ4C60WITgaVp9evCiLNhwJ5weGYZcLmZdQn/p355qMXG3ae7e7G79yL1max09wnAM8C40KzhttVt87jQ3kN9fBg12BvoR2oQQazc/a/AVjPrH0qXAutpB59d8C4wzMxOCH+nddvXLj6/NMfk8wrv7TWzYeHf67q0ZUlz4h5d01YfpEZg/YXUiLHvxN2fI+j3CFKHQ14H1oTHF0n9NrIC2BSeu4b2Bvw0bOdaoDRtWTeQGjiwGZgU97Y12M6L+fuozj6kvvg2A48Dx4d6p/B6c3i/T9r83wnbvJEcGikHDAJWhc9vCalRfu3mswO+D2wA1gG/JDUys81+fsCjpH6vrCG1h3bjsfy8gNLwb7UFmEO4GpcezT90yTIREUkUHeoUEZFEUfCJiEiiKPhERCRRFHwiIpIoCj4REUkUBZ9IjjCzW8zshLj7IdLe6XQGkRwRrjhT6u674u6LSHumPT6RI2Bm14V7pb1mZr80s7PMbEWorTCzM0O7h8xsXNp8fwvPF5vZs/b3e+o9HK7UcTOp61M+Y6n7JeaFZawL91v7VjxbLNL+dGi5iYgAmFkJqSuCDHf3XWbWldRtZea7+zwzuwGYTcu3hhkMlJC6ruKfwvJmm9mtwCVh2UOBInf/TFh3YUSbJZI42uMTab2RwKK6Q5HuvpvUTUAfCe//ktQl4VrykrtXuPvHpC4Z1ytDm7eAPmb2n2ZWDjR3Bw0ROQIKPpHWM1q+7Uvd+4cI/32FCwh3TGtzIG26lgxHXjx1w9GBwLPAFMKNdUXkk1PwibTeCuBqMzsFIBzq/DOpO0EATACeC9PvAEPD9GggvxXL3wucFJZ9KnCcu/8K+C6p2w+JyDGg3/hEWsnd3zCzO4E/mFktsBq4GXjAzKaSujv6pND858BSM3uJVGDua8Uq7gN+a2bbgVtI3Wm97n9Opx/DTRFJNJ3OICIiiaJDnSIikigKPhERSRQFn4iIJIqCT0REEkXBJyIiiaLgExGRRFHwiYhIovx/kDSqDOHB/w4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='counts', y='salary', data=data, kind='scatter', size=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "subset = data[['signing_bonus', 'yrs_experience', 'counts', 'salary']]\n",
    "sns.heatmap(subset.corr(), ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Machine Learning Models with LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>signing_bonus</th>\n",
       "      <th>salary</th>\n",
       "      <th>degree_level</th>\n",
       "      <th>sex</th>\n",
       "      <th>yrs_experience</th>\n",
       "      <th>dept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>273000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>301000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>261000.0</td>\n",
       "      <td>Master</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>86000.0</td>\n",
       "      <td>High_School</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>HR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>sales</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   signing_bonus    salary degree_level sex  yrs_experience         dept\n",
       "0              0  273000.0       Master   M               2  engineering\n",
       "1              0  301000.0     Bachelor   F               1        sales\n",
       "2              0  261000.0       Master   F               2        sales\n",
       "3              0   86000.0  High_School   F               4           HR\n",
       "4              0  126000.0     Bachelor   F               3        sales"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop CEO department\n",
    "data = data[data['dept'] != 'CEO']\n",
    "\n",
    "# drop useless features\n",
    "data = data.drop(labels=['employee_id', 'boss_id', 'level', 'counts'], axis=1)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding categorical data\n",
    "data['sex'] = (data['sex'] == 'M').astype(int)\n",
    "\n",
    "degree_encoder = LabelEncoder()\n",
    "data['degree_level'] = degree_encoder.fit_transform(data['degree_level'])\n",
    "\n",
    "dept_encoder = LabelEncoder()\n",
    "data['dept'] = dept_encoder.fit_transform(data['dept'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9999 entries, 0 to 9999\n",
      "Data columns (total 6 columns):\n",
      "signing_bonus     9999 non-null category\n",
      "salary            9999 non-null float64\n",
      "degree_level      9999 non-null category\n",
      "sex               9999 non-null category\n",
      "yrs_experience    9999 non-null int64\n",
      "dept              9999 non-null category\n",
      "dtypes: category(4), float64(1), int64(1)\n",
      "memory usage: 274.0 KB\n"
     ]
    }
   ],
   "source": [
    "# define categorical features\n",
    "categorical_feature = ['signing_bonus', 'degree_level', 'sex', 'dept']\n",
    "data[categorical_feature] = data[categorical_feature].astype('category')\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and test data set\n",
    "X = data.drop('salary', axis=1)\n",
    "y = data['salary']\n",
    "\n",
    "X_train, X_test, y_train, y_test \\\n",
    "    = train_test_split(X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# transform into log scale\n",
    "y_train = np.log(y_train)\n",
    "y_test = np.log(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create LightGBM dataset\n",
    "d_train = lgb.Dataset(data=X_train, label=y_train, free_raw_data=False, \n",
    "                      categorical_feature=categorical_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best rounds:\t 901\n"
     ]
    }
   ],
   "source": [
    "# Cross validation\n",
    "params = {'learning_rate': 0.01, \n",
    "          'boosting_type': 'gbdt', \n",
    "          'objective': 'regression', \n",
    "          'metric': 'rmse', \n",
    "          'sub_feature':0.5, \n",
    "          'num_leaves': 31, \n",
    "          'min_data': 50, \n",
    "          'max_depth': 30}\n",
    "\n",
    "history = lgb.cv(params, train_set=d_train, num_boost_round=1000, nfold=5, \n",
    "                 early_stopping_rounds=20, seed=42, stratified=False)\n",
    "\n",
    "best_round = len(history['rmse-mean'])\n",
    "print('Best rounds:\\t', best_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rZ0P3Bi04FhERqSEFnCMU8SOVNTbXv34Rbz13Nlv2ZCtF/8yMaBClPlZPrpgjX8xPZXdFRERmFAWcIxTxI5V6OE3JkPe+ch4wxmJjwMPTgmMREZEaUsA5Qg2xhkrAAYiGHk3JYPRiYwCDQrEw+r6IiIhMCgWcIxTxI0S8SNXamnkNEX74xz3c+4dOhi/e1tENIiIitaWAcxRCP6zaCv5X57SyrDXGZ9Zs5ZEN6cp93/PJFo/dI+dFRI5Hx+Mu4mPdRP6ZKOAchWgQrZqmeu3pTfyvK04EYEPH/iqRGsEREZlewjAcVc1Xpl4mkyEMwwl5lgLOUUgECQqlApn8/n9JUrGAVMxn+7DFxr7nky/l9f8WRESmidmzZ7Nt2zYGBwf13+ZpwDnH4OAg27ZtY/bs2RPyTFUyPgp10ToW2AK2p7dX3Z/XEDng0Q2B6bdcRGSq1dfXA+UTt/N5lfGYDsIwZM6cOZU/m6Olv22PUuAFo9L//MYIGzuHqhsaFEoFAk+/5SIi00F9ff2E/WUq04+mqI5S4AUYVnVvXmOUHb25UcFHZ1OJiIjUhgLOUfLNx/d8hgr7R2zmN0bIFRx7+vcvQDasakGyiIiITB4FnKNkZsypm1MVXuY3RgB4dFO6MoozMgSJiIjI5KlZwDGz1Wb2nJmtM7OPjPH6O82sw8z+uPfn3cNee4eZvbD35x216vN4hV71lrZls+MEnvGpezdz9R0v8J/P9WiruIiISA3VZMWrmfnATcAFQDvwqJnd65x7ekTT/+ucu3bEe5uAjwMrAQc8tve93TXo+rj4nl/u2V5z6iPc8/en8Ytne/jiA+1s6NjCj/7b6RrBERERqZFajeCsAtY55zY453LAncAbx/neC4GfOOe69oaanwCrJ6mfR2TfOpz+bP/+AzjrQi5b2cq1fz6f9FCRgWyJUqlUdbSDiIiITI5aBZwFwNZh1+177410mZk9bmbfM7OFh/NeM3uvma01s7UdHR0T1e9xMTMWNiykOdE8apRmQWMUgG3dORxOC41FRERqoFYBx8a4N7J05A+BJc65FwE/BW47jPfinPuGc26lc25la2vrUXX2SET8CMlIctTW8LamcsBp785iZtoqLiIiUgO1CjjtwMJh121AVflf59we59y+Eyn/DXjJeN87XYxVE2ffjqpt3eWvphEcERGRyVergPMocKKZLTWzCPAW4N7hDcxs3rDLi4Fn9v76AeC1ZjbLzGYBr917b9rxzQeoOpsqHvFpTgZs687im0+2oFPFRUREJltNdlE55wpmdi3lYOID33LOPWVmnwTWOufuBd5vZhcDBaALeOfe93aZ2acohySATzrnumrR78NlZrQ1tNGd6WYgN0AykgRgwawo7d05fM8nW1TAERERmWw1OxjJObcGWDPi3g3Dfn09cP0B3vst4FuT2sEJkggTeOYx0DNQudfWFOXRjWmN4IiIiNSIKhlPgpEHai5ojNKRzlMoGgVXGLUQWURERCaWAs4k8M2v2vu1YFZ5ofH2niylUonOwU6FHBERkUmkgDMJzIzACypbwhfM2rdVPEddtI7OwU4d2yAiIjKJFHAmSdSPUnTlgNM2a1+xvyyeefier+3iIiIik0gBZ5LEglhlBKc+7lMX9Su1cAzTCI6IiMgkUsCZJBE/QqaQoVgqYmYsmBWpBJzAC+gc7KRzoFOVjUVERCaBAs4kSYQJ5tXNI1vIks6mK7VwAKJBlGgQZdfALo3kiIiITAIFnEniez7NiWaWzlpK1I8yrzFgR2+WQqm8eyrwAkIv1FocERGRSaCAM8l8zycZSTK3IaBYgt19uarXVPhPRERk4ing1EDUjzK3oXxO1b51OFAexeka6qI/2z9VXRMRETkuKeDUQOAHzGsMAWjv2j+CE/ohUT9K52DnVHVNRETkuKSAUwOBF9BSFxAJjK3dQ6Ney5fyqmwsIiIygRRwaiDwAjwz2mZFae+qXnNjZjjnKkUBRURE5Ogp4NSAZx6e59E2K8qGjiEGc6PDjHZTiYiITBwFnBqJ+lFWLk2wvSfHe299ftSUlAKOiIjIxFHAqZFYEOOiFzfwnvPnsaFjiJ7B/YHG8zxtFxcREZlACjg1su/wzZPmxQHYOmwtjm8+vdle+nP9lFxpqrooIiJy3FDAqZHAD3DOsbCpfLL4V36yjWd3DALloxucc2zq2cRQYehgjxEREZFxUMCpkcALAJjXGOWkuXGe2THIVbc9T7ZQHrGJh3GifpR8MT+V3RQRETkuKODUSOAFGEbgGd/+u5O55jXzyRUd24dVNvY9XyM4IiIiE0ABp0Y88wi8oLLG5uzFKQC2dFUf3aCAIyIicvQUcGooGkQr28H3rcX5/mOddA+Up6V888kVc6pqLCIicpQUcGoo6kcplspF/upiPu971Tx+vznNHb/bBaiqsYiIyERRwKmhaLA/4AC84+VzWd4aZ2PH/mkph6NjoKOqnYiIiBweBZwaCrwAR/X00+KWGFv27F+Hk4wk6cp0kSlkat09ERGR44YCTg3t2yo+3OLmKDt7c2Tz5cXHnnnEgpgqG4uIiBwFBZwaCrwAM6u6t6g5hmNEZWPPZzA/qMXGIiIiR0gBp4bMjNALq0ZnFjeXd1Nt3rN/HU7ohaSzaU1TiYiIHCEFnBqbWzeXkitVKhYvbIoBcPtvdnH7b3finMPMiAQRTVOJiIgcIQWcGouHcRqiDZV6OLHQ44qXzWEwX+TmX+xge08OKNfEUdE/ERGRI1OzgGNmq83sOTNbZ2YfOUi7N5uZM7OVe69DM7vNzJ4ws2fM7Ppa9XmyjNwuftWr53PDxYsB2NRZDjWhH9Iz1MOO9A6dTyUiInKYahJwzMwHbgJeB5wKvNXMTh2jXQp4P/DIsNt/BUSdc2cALwHeZ2ZLJrvPkyn0QwqlAulsunJ0w+Lm8lTVvrU4nnmkoim6h7oZzA9OWV9FRESORbUawVkFrHPObXDO5YA7gTeO0e5TwOeA4XMzDkiaWQDEgRzQN8n9nVRRP8rypuUkwkRlnU19PGBWMqiM4MD+LeOaqhIRETk8tQo4C4Ctw67b996rMLOzgIXOuftGvPd7wACwA9gCfME51zWJfZ10ZkY0iFIfra86lmFJc4xNndULiwMvYCA/UBnpERERkUOrVcCxMe5ViryYmQd8GfjQGO1WAUVgPrAU+JCZLRv1AWbvNbO1Zra2o6NjYno9yUI/pFTaH1yWtMTYvGeIfHH/vcALGMoP0Z/tn4ouioiIHJNqFXDagYXDrtuA7cOuU8DpwENmtgk4D7h370LjtwE/ds7lnXO7gd8AK0d+gHPuG865lc65la2trZP0NSZW1I8SDaL058rhZVlrjPRQkdf/85P0ZQr72wVRhoqaphIRERmvWgWcR4ETzWypmUWAtwD37nvROdfrnGtxzi1xzi0BHgYuds6tpTwt9RorS1IOP8/WqN+Tyvd8FjUsqlQsfv2Lm3nP+fNIDxVZt3t/kb/QD+nP9Ve2louIiMjB1STgOOcKwLXAA8AzwF3OuafM7JNmdvEh3n4TUAc8STkofds59/ikdriGfM/H93xKrkQ09Hj9i5sAqk4Y980nX8yzqXuTjm8QEREZh9GnP04S59waYM2IezccoO2rhv26n/JW8eNWzI9RKBWI+BFaUiHJqMf//a8OLj6rmdD3MDNS0RTpbJpCqUDoh1PdZRERkWlNlYyngXgYrxT+MzMue0kr7d1Znt1RXf/GzMiXVPRPRETkUBRwpoGRlY3fcGYzUD1NBeWpql39uzRNJSIicggKONNA4AXkS3lyxfI5VPMaI8RCjwee7OaXz/VUAk08jJMv5qtq54iIiMhoCjjTQNSPMj81n2KpSCafwTPjohc18Yct/Vz/vY08v2v/jiqH09lUIiIih6CAMw2YGbPis5idnF2ZqvrQ6oXc/u6TAVg/bMu4Zx47+newPb2dvqE+VTgWEREZgwLONDJyd9SS1hgR33hs0/4qxslIksALyBaybO3bSnemu9bdFBERmfYUcKaRwAtw+0+wIPCMU+Yn+NETXZVTxve1iwZRkpEkmXxmrEeJiIjMaAo400jgBXjmVe2S+vu/KJ9J+vS2gVHtQy/UEQ4iIiJjUMCZZmJBrKrWzYq5CSK+8fvN/WRy1bunzIxiqagjHEREREZQwJlmUtEU2UKWbCELlKepTpgT5/7Hu/jEDzaPam+YAo6IiMgICjjTTGOskfmp+VVbwT/z5mW8dHk9z2wfPU0F0J/tH/O+iIjITKWAMw2N3E3Vkgp5yZI6OvsL7OmvroETC2N0DHaoNo6IiMgwCjjTUOiFmFmlsjHA6QuSAHz5wfaqtoEX4Hu+pqlERESGUcCZhnzPZ07dHHKF/QHnRQvrWLUsxeNbR09HGUY6lyZbyOqcKhERERRwpq2IHxl179xl9XT2F+joy1Xdj4dxeod62dSziQ3dG6oO7hQREZmJgqnugIxt3zRVOpsmHsYJvICT58YBeOPXnmJOfcirT2nkqlfNJxJ4JCPlKax0Nk2umCPuxaey+yIiIlNKIzjTlO/5LGlcQkuihaF8uZjfmYvq+PJbl/PeV85j+ew4dz7Swc+erj6qYeTaHRERkZlIAWcaC/2wMjID5fBy7rJ63vlnc/nsXy0jGljVSeNQXnSs4xtERGSmU8CZ5kIvxOFG7ZLyPWP57DjrdmdGtc8UFHBERGRmU8CZ5vbtqNo3TTXcCbPjvLArU7Vzyvd8csVcpRKyiIjITKSAcwyIBTGw0fdPnBOnL1OkI11d5M8zj87Bzhr1TkREZPpRwDkGhF4IDgZy1Uc1rNi7q+pj399UdT8exjVNJSIiM5oCzjHA93wWNy7GsKoaN6fOT7K0JcbzOwcpFPdPU3nm6ZRxERGZ0RRwjhHRIEoykqwKLb5nvOPlc8gV3ajFxoDOpxIRkRlLAecYkggTo7aAnzq/vI38Pd9+jqtue567Ht0NqB6OiIjMbAo4x5D6aD2xMFY1TdXWFOWbV67gb182h8fbB7jpZ9spFB2hFzKYH5zC3oqIiEydQwYcM/vqiOu/G3H9HxPdKRmbmZEMk+RL1VNPp85P8r5XzefGS5aQLzo2dmYI/VAF/0REZMYazwjOO0dcf37E9QUT0xUZj0SYIFvI0p/tZyA3QMmVKq+dtHdX1XM7MnjmkSvmFHJERGRGGk/AGVmBZYyKLFIrqWiKE5pOYFHjIhpjjVVbx9uaoiQiHs/tLE9NhX5Ix2DHVHVVRERkyown4LhDXEuNhX5ILIjRGGvEbH/e9Mw4cU6cnz7dzVC+RCyIkS1kqyodi4iIzATBeNqY2avZP3Iz8tqflJ7JIQVegGE45ypB5yWLU/xp6wDff6yDt503h5IrkS/lifiRKe6tiIhI7YxnBGc38C3glr0/e0Zc7x7PB5nZajN7zszWmdlHDtLuzWbmzGzlsHsvMrPfmdlTZvaEmcXG85nHOzMjFsSqauP83flzaUwEPL19sNJG9XBERGSmOeQIjnNuydF+iJn5wE2UFyS3A4+a2b3OuadHtEsB7wceGXYvAL4DvN059yczawb0N/ZeiTBBz1APoR8C5UBz1qI6nt1RDji++WTyGZKR5FR2U0REpKaOqA6OmZ1kZpea2eJxvmUVsM45t8E5lwPuBN44RrtPAZ8Dhh+d/VrgcefcnwCcc3ucc8Ux3jsjJcIEQ4Whqt1Up8xPsL0nR+9ggcALGMgPHOQJIiIix5/x1MH5opn97bDrK4CngG8Az5rZ68bxOQuArcOu2/feG/45ZwELnXP3jXjvCsCZ2QNm9nszu24cnzdjxMM4TfGmqqrFJ89LAPDMjkFCPyRbzFYFIBERkePdeEZwLgH+c9j1/wTe75xrBa4CPj6OZ4y1tbyytcfMPODLwIfGaBcAfwb8zd7/vdTM/nzUB5i918zWmtnajo6ZtTV6X22cfSFmXz2cfdNUzjmtwxERkRllPAGn1Tm3BcDMTgeaKS8uhvLamBXjeEY7sHDYdRuwfdh1CjgdeMjMNgHnAffuXWjcDvzSOdfpnBsE1gBnj/wA59w3nHMrnXMrW1tbx9Gl40cykmRWbBaZfIa+bB/JqMeipmgl4BhGtpCd4l6KiIjUzngCTq+Zzdn761cAa516nzmKAAAgAElEQVRz+/62DBlf4b9HgRPNbKmZRYC3APfue9E51+uca3HOLdm7qPlh4GLn3FrgAeBFZpbYu+D4lcDToz9i5gq8gLmpuZzQdAL1kXpyxRwnz0vwn8/38uS2AQI/oGeoR9NUIiIyY4wn4NwF3Glm7wc+Avz7sNfOBdYf6gHOuQJwLeWw8gxwl3PuKTP7pJldfIj3dgNfohyS/gj83jl3/zj6PeOYGXWROgrFApetLI9i/ejxLmJBjP5cP0OFoUM8QURE5PgwnkJ/HwE+SnmL9zeAfx322pl77x2Sc24N5eml4fduOEDbV424/g7l6TA5hEgQAYMz2pKcszTFk9sGKvcz+QyJMDHFPRQREZl846mDkwduPMBrX5nwHslRifiRyvLt0xckue03O8nkioR+yEB+gGaap7aDIiIiNXDIgLN3W/hBOedun5juyNHyzCMWxBjMDXLaggQlV95NddbiFP25fkquhGdHVP5IRETkmDGeKapbgXXATg683VsBZxqZUzeHzT2bOW1+uXrxk9vKAadYKtI50MnsutlT3EMREZHJNZ6A81XgzUCacpC5Z9guKpmGokEUz/NIRDwWNkV5au86nFQ0Rc9QD63J1qpTyEVERI43h5yrcM59EFgMfB14E7DJzP7NzP5ssjsnRy4RJsgX85y2IMFT2wZwzuGZh8PRleliIDdQVRxQRETkeDKuxRjOuaJz7n7n3F8DJwHdlIvyvXpSeydHLBkmyZfynDY/yZ6BArv6ypWM42GcrkwX7X3tbOrZxJbeLVPcUxERkYk37tWmZtZgZu8DfgxcSvlgzD9OVsfk6OzbTXX6gvI6nDWP7wHKRQGTkSSpaIpUNEWumKNQKkxlV0VERCbceA7bfL2ZfZdygb6zgA875050zt24twifTEMRP4LDsXx2nEhgfOtXOxnIjj6E3TlH52DnFPRQRERk8oxnBOdeyudE/W9gN3Dh3grElZ9J7aEcEd/zifgR8qUhPvHGJZQclaJ/w9VF6ugd6qVYGh1+REREjlXj2UV1O+Wt4C0HeN0d4L5Msbl1c9nau5VzlqbwDJ5oH+DcZfVVbfbtpurKdNEUb8L3/KnoqoiIyIQaTyXjdx7oNTN7MfCxieyQTJx4GMfzPGKBsXx2nCfaR4/gQHnH1e6B3QRewKz4rBr3UkREZOKNZw1Owsw+ZWY/NLMvmVm9mS0zs7uBX1OetpJpKhkmyRVznNGW5NGNaX75bM+oNr7nk4wkGciNHYBERESONeNZg3MT8AbgaeAvgP8Afgk8BSx1zl0zed2To5UMkxSKBS49uzzD+MM/7RmzXeiFDBYGVRdHRESOC+NZg3MhcKZzbreZfQ3YArzSOferye2aTIRoEK3spnrDmc387OluvvKTdtpmRVncEuOMBUmioVdZizOYHyQRJnRelYiIHNPGE3DqnHO7AZxz7WbWr3Bz7Aj9EDPDOcfrzmjiifYBfvCHPQzlyyM1bzizmesvWgSUa+Rs79uO7/ksm7VMxzmIiMgxazwBJ9hbsbjyt93Ia+fczyehbzIBPPOIB3HypTxnLqrj3993Srn2TX+B/+++zTyyvq/SNhbEIID+bD+5Yo5oEJ3CnouIiBy58QSc3cC3hl3vGXHtgGUT2SmZWHWROjoHO8vVjSlvDW9NhbzshHr+a0Oam36+jWtes6DqPdlCVgFHRESOWeM5bHOJc27pQX4Ubqa5WBAjX8yPWkB80YuaaU2F/Oq53qr7oR/Sl+3DOZU4EhGRY5NWks4AsSBGc6KZocJQ1f1k1OevV7WypSvLtd95gf6hcjXjiB+hN9vLuq51bOjawOaezexI7yCTz0xF90VERA6bAs4MYGakoqkxj2NYfUYTrzujid9v7ue/NvZV2s+KzyIRJogEEcyMdC5N95COHhMRkWODAs4MsW/9zUhNyZDrL1pEIuLx2Kb+qtfMDM+88gnkYZK+oT4Gc4O16K6IiMhRUcCZIQIvIPRDsoXs6Nd848UL6/j95vQB329mBH5AV6ZrMrspIiIyIRRwZpCWeMuodTj7nL24js17snz8nk08PGzr+HDxIE46l2ZHegddmS5VPRYRkWlLAWcGqY/VEw2iDBWGRu2Q+vNTZ3HO0hQPr+/jaz/dNub7zYxEmCBTyLAzvXPM0SAREZHpQAFnhmlJtOCbT6aQoT/bT3+2vO5mbkOEr7ztBK542Rw2dg7Rmc6P+f7QD4kFMSJBhExBu6pERGR6UsCZYeqj9SxuXMwJTSewvGk5vudXTTWtXJICYO2mA6/HgfKi5b6hsaeyREREptp4KhnLccr3fFLRFF2ZLuqj9QCcODdOfdzn1t/s5LFNaRoSAS11IXMaIrzixAYCv3xCR+AF9OZ6ae9rZ17dPHzPn8qvIiIiUkUBZ4ZrjjeTzqYpuRKeeXhmvO282TzwZDePbkzTkymQK5TX6/yPNyzidS9qrrw3FU2RzqaZFZtFMpKcqq8gIiIyigLODLdvFKdzoJO6aB2BF3DFy+ZyxcvmAuCcIz1U5G3/+gz/6xfbeeXJjSQi5dEazzwifoR0Nq2AIyIi04rW4AjN8WbmpuaOuYXczKiPB7xiRQOd/QXu+9OeqtejQZT+fD/5Yl5nV4mIyLShgCPlUZxIqnwu/AFc97qFzKkPufuxTv7lZ9t46NkeoDyKU3Il1netZ2f/zhr1WERE5OBqFnDMbLWZPWdm68zsIwdp92Yzc2a2csT9RWbWb2b/ffJ7O/OEfkjgBWOeVwXlkZy/OqeV3kyR7z7awSd+sIlsvrz7qi5SV16Pk0szmB9UfRwREZlyNQk4ZuYDNwGvA04F3mpmp47RLgW8H3hkjMd8GfjRZPZzpquP1pdDygHOm3rbeXNY8w9n8Nm/Wkau4KqOdjAzfPNp721nffd6BvM6s0pERKZOrUZwVgHrnHMbnHM54E7gjWO0+xTwOaBqMYiZXQJsAJ6a7I7OZE2JJpY2LsU8o1AqHLDdWYvriAbGwxuqa+XEwzh10TqifpTuTPcBj4UQERGZbLUKOAuArcOu2/feqzCzs4CFzrn7RtxPAv8vcONkd3Km88wjHsZpjDaSK+YO2C4aeJy9OMU9v+/kurvWj1pcHAtiDOQH2Na3TQuPRURkStQq4NgY9yp/85mZR3kK6kNjtLsR+LJzrv+gH2D2XjNba2ZrOzo6jqqzM10iTBxwLc4+73nlPF6yuI5fv9DH5j3Va27MjLpIHYVSge3p7aSzB6+KLCIiMtFqFXDagYXDrtuA7cOuU8DpwENmtgk4D7h370Ljc4HP7b3/QeCjZnbtyA9wzn3DObfSObeytbV1cr7FDBENogReQH+2n3Q2XfnJF/efT3XyvAQf/stFAPx2Xe+Yz0lGkmSLWXb079DJ4yIiUlO1KvT3KHCimS0FtgFvAd6270XnXC/Qsu/azB4C/rtzbi3wimH3PwH0O+f+pTbdnpk881jSuISiK1JyJUquRK6YY/fAbkI/rLSb1xBhWWuMB57spqUu5OUnNpCM+lXPiQUx0tk067vWUxepY15q3lR8JRERmWFqMoLjnCsA1wIPAM8AdznnnjKzT5rZxbXogxwe3/OJ+BFiQYxEmKAh2oCHRyZffYL4BafN4oVdGT7xg83c+uux6+CkoikSYYK+bJ8WHouISE3Y8bgIdOXKlW7t2rVT3Y3jzmB+kK29W0lFU5V7zjm6Bwt8/J5NrN89xMtOqGdRc5S3v3QOZtVLrzL5DM45ljctH/WaiIjIeJjZY865lYdqp0rGMm6JMEHUj5LOpunJ9FAsFTEzmpIhb1k1m1mJgN+t7+PmX+xgY+fokZp4GKfoihrFERGRSaeAI4dlceNiTmw+kdZkK9ni/t1TLz+xgf/9vlO49e9OBuDa76zjx090jXp/4AX05w66IU5EROSoKeDIYTEzPPNIRVNjbiVvTYV8ePVCYqHH/3lkN5v3DFEs7Z8GjQZRerO9qo8jIiKTSgFHjkjUj2IYA7mBUWHl0pe0cNlLWnhhV4a33vwMX/3ptsprnnkUS0W6M9217rKIiMwgtdomLscZM2Nhw0I6BzsZKgwRD+NVr1++qpWT5ia47bc7efDJLupjPq86uZHls+PEwzi7BnZRF60j9EItOBYRkQmnERw5YvEwTnOiecxzq0LfY+XSFH/70jnkCo5bfrWTLz7QDpTX4YReyMbujWzq2aQigCIiMuEUcOSoxIIYAIVSYcx1Necuq+dn172Yd71iLn/c0s+//XIHAIlIglQ0Ra6Y064qERGZcAo4clQ882hJtJAv5Q965tSbXtJCQ9zn7t93UijuD0KBF9A31FeLroqIyAyigCNHrTnRzLLGZYR+SH+uv1LQb7imZMj1Fy2iZ7DA2k37g1AsiJHOpQ95uKeIiMjhUMCRCWFmLKhfwJzkHGJBjMH84Kg25y2vpy7q89Onu6veV3IlMoXMqPYiIiJHSgFHJkwsiNEQa6Al0ULJlRjIDVS9Hgk8zj+pgTWPd/FE+/7XIn6ErsEuhgpDWnAsIiITQgFHJlw0iLK8aTmeeWQL2arpqjec2QzAZ9dsobT3fjSIMlQcYkPXBjoHO6ekzyIicnxRwJFJEXgBrclWPPPoHeqt3H/xwjo++NoFbOgY4s/+5x/59A83A1AXqaMh1kDnQOdBFyuLiIiMhwKOTJr6aD2LGxdTH62nd6iXTL68zuaNZ7bw93+xgFXLUjzwZBfdA3mgvB4nGUnSMdChoxxEROSoKODIpJubmsuihkVAuV5ONPR467mz+fs/X0CxBA8+tX/RceiH5Ev5qoM8RUREDpcCjky6wAtIRpK0JFpIZ9OV0Znls+OcPDfOjx7vGtW+J9MzFV0VEZHjhAKO1ExDrIHmeHNV5eLXvaiZ53dlWL97/zbxWBCja6iLLT1bVB9HRESOiAKO1FR9rL7q7KoLTptF4Blf+9k27nxkN0P5EmZGQ7SBTCFDx0AHvUO9WpMjIiKHRQFHaioexPHNr9S7aUwErD5jFv+1Ic1Xf7qNO367C9i/4HggP0B7Xzt9WR3nICIi46eAIzVlZjTGGqtGZT76+sX86vozOXV+gm//eie/er68rdwzj3gYJxVNsa1vGwO5AY3kiIjIuCjgSM01J5rLISe7f+u47xmfuGQJgWd8b21HVfvAC4iHcdr72lnXtY5sQTusRETk4BRwpObMjNnJ2SxILQCDfLFcB6dtVpQrXzGXRzemae+qDjHRIEoqmioXDsz2jvVYERGRCgUcmRKhH9IQa6A13lpV8+b1L27GN7jlVztGhRzYu8NqsIvdA7vZM7iHPYN76M/217LrIiJyDFDAkSmVjCTBUVlb05oKedXJjTzwZDfvve150kOFqnU3ZkY8jJPOpukZ6qF7qJtt6W2ks2lNXYmISIUCjkwp3/Opj9ZX1cb52MWLueHixfQMFrjwi0/wzm8+R7G0P+SEfkg8jBMP4yTCBKEfsqN/B1t6t5DJZ8gWsuSKOS1IFhGZwYKp7oBIQ6yB7qFuAi8g8AKigceFp88C4In2Ae7+fSf/7c71/PNbl2Nmo94fC2IAZPIZtvZtxTlHyZWYWzeXWfFZNf0uIiIyPSjgyJSLBTGa4k1kChn6c/2koinMjNVnNPEXp81iQ0eGRzem+fg9mzhtQZK/XjV7zOfEw3jl18VSkT2De2iINeCZBipFRGYa/ZdfppyZMaduDosaFhF4AYO5wcprgWd8+a0n8OKFSdZu6ucrP9nGczsHD/K0Mt/zcTg29WyqFBUUEZGZw47HdQorV650a9eunepuyBHIF/Ns7N5IMpIcNR2VHipw6dee4uzFdbzpJa0EnuH7xinzEsTCsbN6f64fw4j6Udoa2jSaIyJyjDOzx5xzKw/VTlNUMq2EfkhjvJF0Nl015QSQigVcfFYzdz7Swa9f2H90w2tPn8U1r55PcyrEGxGK6iJ1OOdI59L0Z/upj9XX5HuIiMjU0giOTDvZQpYN3RtIRpIEXnUGzxdLrNs1RKHkKJYcP/zjHn70RBcAl57dwodWt40KOQCFUoHB3CCxIEZLooW6aF1NvouIiEysaTeCY2arga8APvBN59xnDtDuzcB3gXOcc2vN7ALgM0AEyAEfds79vEbdlikQDaK0JlrpyfZQLBWJBtHKa6Hvccr8ROX6xDlxzlma4hfP9nD37zu5+/edGNCQCPjmlSuY31h+b+AF1EXrKJQK7BrYRSKS0HSViMhxrCYBx8x84CbgAqAdeNTM7nXOPT2iXQp4P/DIsNudwBucc9vN7HTgAWBBLfotU6cl2UIykqS9r51MJkMykiT0w1HtklGf1Wc08dLl9fywbQ/ZfIl80fF/HtnNd367i+v+clGlrWceET/CQHGArb1baatvw/f8Wn4tERGpkVqN4KwC1jnnNgCY2Z3AG4GnR7T7FPA54L/vu+Gc+8Ow158CYmYWdc6pbO1xLh7GObH5RNLZNFt7t1IXrRs1ZbVPQyLgb186p3KdHipy9+87aU1FuPIVc6vaJiNJ0tk0G7s30pJsoTHWOKnfQ0REaq9WY/QLgK3DrtsZMQpjZmcBC51z9x3kOZcBfxgr3JjZe81srZmt7ejoGOOtcqxKRVPMT82nP9tPf7afYql4yPdc+WdzmdcQ4du/3smVtzzL9x6t/mciFU0RCSJs79vOuq51bOzeWDn0U0REjn21CjijV31CZXWzmXnAl4EPHfABZqcBnwXeN9brzrlvOOdWOudWtra2HmV3ZbppiDWwrGkZzYlmBvOHroPTkgq5+YoTeeXJDRRKjq/8tJ3tPdW5OPACGuONxIM4hVKBrkzXZHVfRERqrFYBpx1YOOy6Ddg+7DoFnA48ZGabgPOAe81sJYCZtQF3A1c459bXpMcyrZgZsSBGY6wRM2MgN0AmnyGTz5Ar5sZ8T2t9hE9dupQvveUEfM/4f25/gb//3y/Q3p0d9exkJEl3prvqTCwRETl21SrgPAqcaGZLzSwCvAW4d9+Lzrle51yLc26Jc24J8DBw8d5dVI3A/cD1zrnf1Ki/Mk35ns+C1AJaEuW1M42xRnLFHJl85oBTTK2pkH98/WJOmpvg6W2D3PSzbWO2iwZRdvXvYiA3wGB+UId1iogcw2oScJxzBeBayjugngHucs49ZWafNLOLD/H2a4ETgP9hZn/c+zP2YUQyIyQjSWbFZ9GcaKY50cz8uvnUReoYKgwxVBga8zTxC06bxecuX8bfvmwOv3yul0/eu2lUgIkGUQqlAtv6trGpZxO92d5afzUREZkgKvQnx43+bD/pXJqSK1EsFRkqDlEXqS7ol82X+B93b+TXL/TRmAi44eLFnNGWJBZ6+N7+pWIlV2IwN8jSWUvH3J4uIiJTY7yF/hRw5LhUciU2dm8k8IJRAaVQdPzHYx3c9V8d7Ogtr985aW6cm9+xgmiwf1Azk88QD+PMT82vad9FROTApl0lY5Fa8sxjTnIO7X3tlFyJwAsqRf0C3/jrVbM5f0UDv3yul57BArf/dhd3PrKbd7x8f82ceBind6iXRJA4YKFBERGZnhRw5LhVF62jrb6NPZk9DOQGSEQSVYUC5zVGecu55eVcW7qy3PabXaw+o4k59ZH9z4jUsWtgF96gR0uihdALdY6ViMgxQAFHjmt10TrqonWks2na+9qJBTFCPxx1DtXf/8UCfreul+u/t5FT5iVYPjvGpWe34Hs+qWiKXDHHnswe8sU8yUiSWBCjNdGKjXGwp4iITD2dNigzQl2kjpZEC4EXMJAbGPX6vIYIH7igjY6+HD99upsv/LidXzzbU3k94kdIhAlS0RTOOfYM7qEv21fLryAiIodBIzgyI5gZrclyheutvVtJZ9OkoqmqNpec3cIlZ7dQKDne8+3n+NID7axckqI+vv9fE888PN8j5aXY2b+TkisxKz6rpt9FREQOTSM4MuPMrZtLIkzQk+kZswpy4BnXX7SI3sECX/3p2EUBPfOIh3F29u+kO9NNtpAlW8hScqXJ7r6IiIyDRnBkxgn9kPmp+QzEBtjZv7NSFDAZSVbW5qyYm+BvXjqH23+7i2y+xFvPnc2pC5JVzwm8gFQ0xe6B3ZgZJVeiIdrA3Lq5WpsjIjLFFHBkRvI9n/poPbEgRrFUZCA/QFemq6ow4JWvmMuGjiEe2ZDmV8/3kor5/NWqVl5xYgOLmmP4nuGZVzXV1Zftoz/XT1O8iUSYIOJHKtvTRUSkdlToT4RyYcBNPZvwzR9V76a9K8tdj3bw/M5BHm8vL1B+6fJ6Pv/Xy/DGGKkpuRIDuQEcjngQZ2HDwlG7tkRE5Mio0J/IYfDMY27dXDb3bCbmYkD5bCqAtqYo/+3CNvLFEo+sT/PktgFu/+0u/v3h3fztS+eM+ax9ozoDuQF2pnfSkmwh4kdGtRURkcmhgCOyVyJMMLduLkOFIfLFPAO5AZKR/etuQt/jz1Y08PIT69nYOcTXf76d9bsz3HDx4gOuuUlGkgzkBkh3pysBx/d8muPNRINoVeFBERGZOPqvq8gw+7Z8F0tFNvVsIl/Mj5qyMjNuuHgxX36wnTWPd7GrL08i4lEX87n2NQtoSVW3T0aSlFypcnp5vpSnva+dwAtY1LBIR0CIiEwCBRyRMfiez7zUPLb0bhkzgCSjPv/4+kXUx33+sLmfoVyRxzal2daV5bKVrfz5qY2E/v51N555YPufHQtiDBWG2Nq7lZZEC4lIAsO0IFlEZIJokbHIQezq30VXpotoECUWxA7a9mdPd/OJH2yiWIKmZMCi5hgfvGABK+YmDvieXDHHUGEI25t+WpOtNEQb8MzTVnMRkTGMd5GxAo7IQRRLRfpz/XRmOikUCxhGMpI8YPgYyBZZ83gXj2zo49kdgwSe8c0rTxo1bTUW5xzpXBoPj9APWVC/QAuTRURGUMBRwJEJVCwVKZQK9Az10JftI1/MUx+rP+j27+d3DnLV7S+QzZd423mzmd8YpeQcy1pjnLU4dcD3AZWFzs3xZmbFZ2nqSkRkLwUcBRyZRL1DvexI76A+Vn/Qdk+0D/C5H21h/e6hqvvzGyNc9er5/MWpBz7HyjlHf64fzzza6tuIh/EJ6buIyLFMdXBEJlF9tJ7B/CDdQ93URw88knNGW5Lb3n0ye9J5PM/IFx3fW9vBoxvS3PiDTeQLJS48o2nMgoFmRiqaIl/Ms7l3M4kwQUO0gbpInUZ0REQOQSM4Ikehd6iX7ent+OaDQeiFlQKBB9M/VOSaO17ghd0ZlrbEWNwcZU5DhKteNZ9oODoslVyJQqlAtpDFzCqBKhVJEQtiRPwIsSCmhckictzTFJUCjtRIJp/B4XDOsSO9AwyifvSQoywD2SJ3PrKbXzzbgwM2dQxx8rwES1tjvO6MJl6yZOx1Ovv+nXU4hgpDOOcouiLxIE5LolwxWbV1ROR4pYCjgCNTYKgwRDqbpjvTDZTr3yQiB94mPtz9f9rDbb/ZRW+mQCZX4rKVLbz3lfOIR8Y3HbVvYbJhNMWbiAUx6qJ1h36jiMgxRAFHAUemUKFUoFgqsrN/J4VS4bAWCPdlCnzs+xtZu6mfhU1R3veqebzmlAMvRh6pWCqSK+bIFXM0xhpJhAk88ypHQ+jgTxE5lingKODINFAoFdjSu4VsIVs1bRQP4odcL/PLZ3v44gPtdPbnefvL5rBqaYqzF9cd1jqbTD5D0RUr01qBF9CSaCEexvHM01lYInLMUcBRwJFpwjlHOpum6IoA5It5uofKU1h1kYMHlnyxxD/dv4UfP1Fuv2BWhAtPayIW8YgFHq88uZG6qDfuaaxCqUAmn6lcR/wInpULCzbFm4j6US1UFpFpTQFHAUemsWKpSFemiz2De0hGkgddkOycY/OeLL95oZe7f9/J9p5c1esG/397dx4kyVneefz7ZNbVd/ccmhnNaCSNLhghEKwMaAF7Qes1xmARsbItjG3AGK0j0Ppa1gFeOwx2aA/sNQvG6wgW5AUbhEEovDI4TNjcBCCQkGGRRojxSINmWnN19/RVXUdmPv7jfbMqq7p6prrVZ/XzCSa68qistzqp6p/ek5dcM8wdP3YpV+9Z3lw5URKhqkRJRJREFMICo6XRxrIUadOWMcZsFhZwLOCYTU5VmShPMFmZBP8xLOQKF1yeIVGlWk8A+MHpBR4ZL/PPZxb4/JHzJImyf6zI6158CS+5epixgeWPpKrHdWpxrTEqTFFKYYmR0gj9+X5ExJaPMMZsKAs4FnDMFpHWnsRJzPjsOIkmbvSVDxTdmJir8xdfPcWR8TJHni4DcMvhUd7+qoMMFJ/ZpID1uE41qoK4UJYuDFoICwwVhyiEBfryfeSCHNnvE2vqMsasBQs4FnDMFlSP60RJxEx1hsmFSfryfcuqMYkT5YuPnef7p8rc840zAASBsHsozwsuH+QnnrP0/DorKWs9qZMkCYoLPuqrogRhuDjMcGkYwdX62OzLxpjVYAHHAo7ZwlSVcr3MqblTxBozkF96BfOlPHJynq88Pk2cKA8dn+PkVJXZSszVl5S44cAgo/05bjgwQBjAdXv7Ge5bvRFVqm4SwiiJXLkVBgoDjZFbg4XBxpB1RQkltBofY0xXbC0qY7YwEWGgMMDlo5czXZnmzPwZCmGBYq7Y9Tw21+8f4Pr9A43tSj3ho18/zecfO8/nj0wxvRA3jo30hdxyeIzbX3QJ+0cLzzhsiEjL3D+qSi12naMTTZhcmGyp8Umb5AYLg+TDPPmg2X8oDEKbu8cYs2zrVoMjIq8E3guEwAdV9b8vcd5twCeBH1HVB/2+dwBvBmLg11T1sxd6LavBMb1mtjrLfG2e6eo0QOMPfj7Mr3jyvvHzVc7O1lmoJdzzwBkePj5HlCgHdxTZPewCxrP29XPrjbs4sGNtR1KlAaie1BcdE4Rirkhfro/+fD+5INfynq3mx5jtZVM1UYlICDwO/DhwAvgW8DpVfbTtvCHgM0ABuFNVHxSRw8A9wFzJpm4AABY/SURBVAuBS4F/BK5V1ZglWMAxvSpKIuZr843tqYUpYo2pJ3Vykut6WYhOTs/U+Mx3Jnj4h3NEsRIlyqMnyyhw0xWD7B8rsm+0wN6RAqEI1+8fYM9wfs0DRnYYe5REjf0i0gg/Q4Uh8mEewS1EGgahzdpsTI/abE1ULwSOquoxABH5OHAr8GjbeX8IvBt4W2bfrcDHVbUKPCEiR/31vr7mpTZmk8kFOUZKI43t9HE1qjK5MMlMdQagsTzDcsLHnuECv/yyfS37jp1d4EuPTfPp70xw9EyF8+Wo7Tl5Du4s8VPP20ExDNLR7owN5HjugeX3G+pERFyzVYcFRNPwc658rjGsXUTcaC8RimGRYlgkCFqDTrrqezYABRIQSmidoY3pEesVcPYDT2W2TwAvyp4gIs8HLlPVT4vI29qe+4225+5vfwERuQO4A+DgwYOrVGxjtoZirsjewb3sHtjdWOyzltQo5UrPaN6aQ7v7OLS7jze9bC8AT0/XqEUJ0+WIh47PcezMAt95ao53/s3xRc+9dk8fhy/tZ6gvx6ueu4PLd5ZWXI6ldBN+ylGZ9prqRJNF+8Ct0J4PWmulirkioYQUwgK5IIeItCxxkT5Oa4+sycyYzWG9Ak6nT3zj20VEAuA9wBuX+9zGDtUPAB8A10S1olIas4WJCDnJMdY3xljfGJWowvjsOHPVOQASEnJBjv78ypux9o34sLQTnnuZW6m8Wk84PlHxZXB/6B87VeaT3zzLlx+fZmYh5i+/dprhPlcz8qx9/dx81TAHfb+eYj7g2j39DJZWt+akEX5Y3oSHcdLa+l2NqijKbG22EYzSuYAQmt9GfldOci0hJ+0zlP4DWmqO0tCUBq606U1EGqPOrKnNmOVbr4BzArgss30AGM9sDwHPAb7ovxj2AveLyE938VxjTAelXIkrR69EURJNqEZV5mpzTFWmGn9A80GeQIJGv5WVKOYDrt3bGpqu3tPHq5+3E4DJuTqf/u4E52brRLHy1R9M881js4uuc+PBQZ69r59r9/Yx4oes5wLh8P5++rtca2s1tP8elvt7ydYOKer6SEX1RhMa0FJ7pCjuf9oYUp+dV0gQgiAgIGiUJxt80ma1dDv9lw1KnWqrssfTwGa1T6aXrFcn4xyuk/EtwElcJ+OfV9VHljj/i8DbfCfj64GP0exk/DngGutkbMzK1OJao1ZipjKD4kYwRUlEMVdc86UYElVOT9c4Nxch4gLQo+NlvnZ0mmNnKyRtX0n5UDi4s8h1e/u58bJBrthd4uCOImEg9Be2R5NQGo6yPxNNWvYlmrQ9icbs09AhvGSCVPss1WmtUnsfpXQ7+1rt56Sy52RrobI/2/elZVxqRuy0b1X2PXU6V9oq/rPXyB5r/51kg143s3K3v06356ZBd72s5O/8UmVM70E31uq7ZFN1MlbVSETuBD6LGyZ+t6o+IiJ/ADyoqvdf4LmPiMgncB2SI+CtFwo3xpgLK4TN9a6Gi8OA+2M0X5tnojzBbLVZuyIilHKllj4nz1Qgwr7RIvtGm0PPf/S6UX715ZdSjRKePFehFrkv1/lqzLeemOXx02W+8vg0f/fdyZZrjfSFPHtfPzceHCQXCnuGCwyVQg7sKLJv5JnP57NZNGpZ1untpLVQ2RqnWGNi/9Wb/rFOa6jSc9rLnO6vxtXO/aDaZsBub+5raQ6ERec3arnS+9z2/MwTm8/PlCv7PrJlztaotZehpSwd/v/V6fxF52bD5RrJXn+psl7w+dr8HS913Ys5tOPQqn53LJfNZGyMaVBV6olbLkIQ6nGds+Wzbhi6/6JKJ+Fb7wn46nHCickqT05UGZ+qEqtyYrLKP/1wnhNT1UXn7xspcNUlfewczPn35vbnQ+G6ff0cGCvSX3AjvwJxo8hWczZnY7azuercmgWcTVWDY4zZGtLVwtManr58H8OlYRbqC1SiCnESsxAtADS201FF2dmHYfl9Vy4mHwZcubuPK3f3LTpWrsXEiXJqusZ0Oeb4RIVvPjHLU5NVjow35w0SgYV6wqceOtfxNfYM5zm0u8/XWMBAIeDgzhIDxZBdQ3lK+YD9owVG+91X59hAjnxoHYCN2Yws4BhjLqov39ey9AI0Zx+ONWayPNkyC3G6llb6X29rvdhm2gl5qORe76Yrh/j3N+3ueG6iylOTVU5MVoliRQTqsXJ6psaj42XGp6qu/0kAT5+v8eXHpxf1C0oVQmFsIEcxH1AMA0YHcuwdLlDMBxRyQi4Qhkohu4fy7BzMEwjkwoChUkgpH1DKu8dh0BtNacZsJhZwjDErIuJmEQboH1k89HyhvsBcba7RmTnWuNGPpBgWGx1Z11sgwuU7S8ual2e2EjG9EFOuxhyfqFKuxajCyakqU+WIaj2hGiVMzEV8/ew01UipRQn1WJcMRykBF4ZCIR8G5AIhHwqlQsClIwX2jxXZP1ZktD9HKR/QX3ChaLQ/x2h/jkLOapCM6cQCjjFmTWRrfXb176ISublyojhiqjLFfH2+tfMnQiFXWDTR3mYwVMo1aofah8RfzHw15uxsnYm5OgrUooS5Skw1UhbqMTPlmKoPQ3W/REYUK/PVmPHzVb59fI6FerLk9fOhMNIXsmswz66hArsGcwz15SiELigV8y40hYH4nxD67XQk2kAxJPC1Vv2FgJG+HIMlt8+YrcoCjjFmzaWrhQOQh+HScMsw4npcp1wvM1ubpVxvnXlY0ca8L4Vw642MGiiGDBRDrti1spmcVZWp+YiZSkylnjBfjZmtxEwvRJwvR8xXY6bLMWdna5yarvK9k/PMViLipTNR1/I+JOVC8YEpIAyEQCAIBBEIxdXmpT+DwNWSBUIjNBXCgGLOha20aa6Yc/9yYTN05TLBKxvE0hY8N5rMD5ISCP31XRkDV95A/DUzP6U11IUB5EL3elvt/0+mexZwjDEbIjsCq5grUswVGesbawxJTkVJRKVeYb4+72p9aJ3bJZRw0bpSvURE2DGYZ8fg8mZkTlSpR0o1SogTJU4gStQ/dv+iRJmvJpRrMUkCcaKUazEzlZjZhZhqnLiFV2OlFiv1OCFRSBJFFWL/OFH3em6f/+n31yNlvhpRjRKq9YRK5t/Fmu/WQ9okmPNhLn0cBkIoi2u8Ah+W0pAXSvax+xlkA1XbNYKgObw9O4hZfGATaQ2I6ewAIpmAJ82Q5yZrbJ7jriVt2+lSIq3b2WzXuGbm+TReI3NOtgwdXj89nmiVQztW9VYtmwUcY8ymki45kcoFOUq5EqN9oySaNFYVTzQhTmIqUYWZ6kxzsU2k8cWfriPV7cRtvSQQoZh3tSabVZQosW+WawYvF5Ji31SX+LAEbqh/OmdNojSCVJS4MFePE2pxM7zFPrS1XDtpvl7dh7e6D2+NZsJYfRl8UEufr83r1RWSJGkEucSPvEsDX/v7STKvn2qZGsdfI71WknRYk2gLGe0Puf2mGza0DBZwjDFbRiBByzB2gBFGuGTgksZsvmnwKdfLzNXmGottZlcUTzRpzPSblS6qmQ1AvVoztBnkfLNU8eKnblsuuKfhzm/7x2T30ZzrKQ2Crdutz21cw5+QPm65Ds2NxnMy127u00XHK/WFtfuldMkCjjFmy0tn+m2EkdB1ct7Zv7Pj+WktUHZhzTiJqcZVZquzJCQEBCQkjTCUrR1qLC2ANEaD2WriZi00mouybUZbwFx1FTqBPUMWcIwx2052Ze92uwda58+JkxhFG81iiSbU47qrKdKYhfpCo99Qeg6waOFMkebCltk1k9qHy2ePG2NWzgKOMcZcQDpBYTdTzqu6IJRdMylO3BpOceICENAShCpRpdGpOq01ipO4ud6P0OhHBM3H2cUqjTGLWcAxxphVIiLkw7bRTiuYwDntMB0lEfW4TiWqNEJR2rRW01pzqH1m5fBAAsIgbNYadVjFOm1OsyY108ss4BhjzCaTBpO0Q/VAYaDjeemq32kNUfqzElUazWnZ+YayoiQi1th3PG2dd2hR6PEBqn0V6WxT24Vkw1R67lou3WEMWMAxxpgtK5DANWERttQUjTDS9TXS2iJoBqb2cJQ2tSWaEEjQEprSx+n5abNZ+jPWuNFnKe23pKpUokozWAkdR7Vl+zBla5/SENaYDyn7RN8JfKkw1t6sl+3z1F52s7VZwDHGmG0sW6uynn/Ys8EqfZytSQIaQ/+Xen5Lh26aAS3bxyndv1SNVrpPVYk0ciPnksw5afhq3/ZNgunvrlHz1XZudlLKC9V0ZWvNVLXj+d3UmHU6Jw2DSz1/qWbK9Lyt2oxpAccYY8y6a/kDvon/fqbhq1GblQljmkkzaZBp35ee2ymoZWu8Oh1PR/Cl101fY6nQlx7L1qplw036/OxM4e2hEiChLQQmS7xehxq3tKyhhBdtulxrFnCMMcaYJSyaY8k0dKpxS/enI/02kgUcY4wxxixbe9PVZquRs0hqjDHGmJ5jAccYY4wxPccCjjHGGGN6jgUcY4wxxvQcCzjGGGOM6TkWcIwxxhjTcyzgGGOMMabnWMAxxhhjTM+xgGOMMcaYnmMBxxhjjDE9xwKOMcYYY3qOdFpJdKsTkbPA8TW6/C7g3Bpd26yc3ZfNye7L5mT3ZXOy+9Kdy1V198VO6smAs5ZE5EFVvWmjy2Fa2X3ZnOy+bE52XzYnuy+ry5qojDHGGNNzLOAYY4wxpudYwFm+D2x0AUxHdl82J7svm5Pdl83J7ssqsj44xhhjjOk5VoNjjDHGmJ5jAccYY4wxPccCTpdE5JUi8n0ROSoib9/o8mwnInKZiHxBRI6IyCMi8ut+/w4R+QcR+YH/Oeb3i4i8z9+r74rICzb2HfQ2EQlF5GER+bTfvlJEHvD35a9FpOD3F/32UX/8io0sdy8TkVERuVdEHvOfm5vt87LxROQ3/XfY90TkHhEp2edl7VjA6YKIhMCfAT8JHAZeJyKHN7ZU20oE/CdVfTbwYuCt/vf/duBzqnoN8Dm/De4+XeP/3QH8+foXeVv5deBIZvt/AO/x92UKeLPf/2ZgSlWvBt7jzzNr473A36vqs4Dn4e6PfV42kIjsB34NuElVnwOEwO3Y52XNWMDpzguBo6p6TFVrwMeBWze4TNuGqj6tqt/2j2dxX9b7cffgw/60DwOv9Y9vBT6izjeAURHZt87F3hZE5ADwU8AH/bYArwDu9ae035f0ft0L3OLPN6tIRIaBHwU+BKCqNVU9j31eNoMc0CciOaAfeBr7vKwZCzjd2Q88ldk+4feZdearaZ8PPADsUdWnwYUg4BJ/mt2v9fO/gN8GEr+9EzivqpHfzv7uG/fFH5/255vVdQg4C/yFbzr8oIgMYJ+XDaWqJ4E/Bn6ICzbTwEPY52XNWMDpTqfUbOPr15mIDAKfAn5DVWcudGqHfXa/VpmIvBo4o6oPZXd3OFW7OGZWTw54AfDnqvp8YJ5mc1Qndl/Wge/zdCtwJXApMIBrHmxnn5dVYgGnOyeAyzLbB4DxDSrLtiQieVy4+aiq3ud3n06r0v3PM36/3a/18RLgp0XkSVyz7StwNTqjvgoeWn/3jfvij48Ak+tZ4G3iBHBCVR/w2/fiAo99XjbWvwWeUNWzqloH7gP+NfZ5WTMWcLrzLeAa39u9gOsYdv8Gl2nb8O3OHwKOqOqfZA7dD7zBP34D8P8y+3/Jjw55MTCdVs2b1aOq71DVA6p6Be4z8XlVfT3wBeA2f1r7fUnv123+fPsv0lWmqqeAp0TkOr/rFuBR7POy0X4IvFhE+v13Wnpf7POyRmwm4y6JyKtw/3UaAner6l0bXKRtQ0ReCnwF+P80+3r8Dq4fzieAg7gvj59R1Un/5fF+4JVAGXiTqj647gXfRkTk3wBvU9VXi8ghXI3ODuBh4BdUtSoiJeAvcX2oJoHbVfXYRpW5l4nIjbiO3wXgGPAm3H/Q2udlA4nIu4Cfw40MfRj4FVxfG/u8rAELOMYYY4zpOdZEZYwxxpieYwHHGGOMMT3HAo4xxhhjeo4FHGOMMcb0HAs4xhhjjOk5FnCMMZuKiMz5oeY9S0TeKSJ/tdHlMKaXWcAxxgAgIk+KyIIPGFMi8hkRueziz3xGr/lFEfmV7D5VHVyL+T58qKj793deRL4mIjev9usYYzYHCzjGmKzXqOogsA84DfzpBpdntf21f3+7cDPIfnKDy2OMWSMWcIwxi6hqBbeG0eF0n4iMiMhHROSsiBwXkd8VkcAfC/z2cRE5488b8cdKIvJXIjLha06+JSJ7ROQu4GXA+32tyvv9+SoiV/vH/1dE/szXJs2KyAMiclWmTP9ORL4vItMi8r9F5EvtNUJLvL8I+CiwX0R2Z673FhE5KiKTInK/iFzq91/hy5XLnNuofRKRN4rIV0Xkj33t1xMi8pOZc6/0ZZsVkX/ABSxjzBqygGOMWURE+nFTyn8js/tPcQv+HQJ+DPgl3BIAAG/0/17ujw/ipv8Ht57OCG7hwJ3ArwILqvpfcEtw3Ombpe5cojivA94FjAFHgbt8GXfhQtg7/HW/j1u8sJv3V/DlnwCm/L5XAP8N+FlcDdZx3BT63XqRL8Mu4N3Ah/wyCAAfAx7yx/6Q5hpDxpg1YgHHGJP1NyJyHpgBfhz4IwARCXGB5x2qOquqTwL/E/hF/7zXA3+iqsdUdQ4XOm73NR51XAC5WlVjVX1IVWeWUab7VPWbmVqXG/3+VwGPqOp9/tj7gFMXudbP+ve3ALwFuM0/N30Pd6vqt1W16t/DzSJyRZflPK6q/0dVY+DDuJC0R0QOAj8C/J6qVlX1y8DfdnlNY8wKWcAxxmS9VlVHgSJwJ/AlEdmLq3ko4Go1UsdxCwUCXNrhWA7Yg1sw8LPAx0VkXETeLSL5ZZQpG1rKuNqh9DWfSg/4lZZPXORan/Dvbw/wPeBfZY61vAcf1CZovseuy6mqZf9w0F93SlXnM+dmf1fGmDVgAccYs4ivabkPiIGXAudwNTGXZ047CJz0j8c7HIuA06paV9V3qephXBPSq3HNQwDPZLXfp4ED6YZvDjqw9OlNqnoO+A/AO0VkX6f3ICIDuJqnk0AaTvozl9m7jHKO+eulDnb5XGPMClnAMcYsIs6tuH4vR3yzyyeAu0RkSEQuB34LSOdyuQf4Td+ZdhD4r7gRS5GIvFxEbvDNXDO4oBT7553G9dlZic8AN4jIa31T2FvpPnSgqo/hapZ+2+/6GPAmEblRRIr+PTygqk+q6llc0PkFEQlF5JeBqzpeePHrHAceBN4lIgUReSnwmm7LaYxZGQs4xpisvxWROVwQuQt4g6o+4o/9R1xNxjHgq7hAcLc/djeuKerLwBNAxZ8PLnTc6695BPgSzWD0XuA2P/LofcspqK+F+Rlch94J3IivB4HqMi7zR8AdInKJqn4O+D3gU7hal6uA2zPnvgX4z/61rge+tozX+XlcJ+RJ4PeBjyzjucaYFRDXbG2MMVubH7J+Ani9qn5ho8tjjNlYVoNjjNmyROQnRGTUNyn9DiC0Dm03xmxTFnCMMVvZzcA/4zpBvwY3CmxhY4tkjNkMrInKGGOMMT3HanCMMcYY03Ms4BhjjDGm51jAMcYYY0zPsYBjjDHGmJ5jAccYY4wxPedfAM2IfcN7qPpKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "rmse = np.array(history['rmse-mean'])\n",
    "std = np.array(history['rmse-stdv'])\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "ax.plot(range(1, best_round + 1), rmse, label='RMSE')\n",
    "ax.fill_between(range(1, best_round + 1), rmse + std, rmse - std, \n",
    "                alpha=0.1, color='g', label='Std')\n",
    "ax.set_xlabel('Boosting Round', fontsize=12)\n",
    "ax.set_ylabel('RMSE', fontsize=12)\n",
    "ax.legend(fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-train the model and make predictions\n",
    "clf = lgb.train(params, train_set=d_train, num_boost_round=best_round)\n",
    "pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE:\t 0.39172018134681813\n",
      "Test R2:\t 0.3992060247540986\n"
     ]
    }
   ],
   "source": [
    "print('Test RMSE:\\t', np.sqrt(mean_squared_error(y_test, pred)))\n",
    "print('Test R2:\\t', r2_score(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 4\n",
    "Describe the main factors impacting employee salaries. Do you think the company has been treating its employees fairly? Do you see any bias? What are the next steps you would suggest to the Head of HR?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# feature importance\n",
    "features = clf.feature_name()\n",
    "importance = clf.feature_importance()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "lgb.plot_importance(clf, ax=ax, height=0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above figure, we can see that `years experience` is the most influencial factor for employee's salary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For bias, you can refer to [Link](https://github.com/stasi009/TakeHomeDataChallenges/blob/master/15.WorkPlace/diverse_work_place.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}