BAIN_KP_RF_ET.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from xgboost import XGBRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor,BaggingRegressor,AdaBoostRegressor,ExtraTreesRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import StackingRegressor\n",
    "from ngboost import NGBRegressor\n",
    "from sklearn.metrics import mean_squared_log_error\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import KFold,StratifiedKFold, GroupKFold\n",
    "import datetime\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from fastai.tabular import *\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((512087, 11), (36000, 9), (548087, 11), (36000, 2))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"train.csv\")\n",
    "test = pd.read_csv(\"test_QkPvNLx.csv\")\n",
    "submission = pd.read_csv(\"sample_submission_pn2DrMq.csv\")\n",
    "data = train.append(test, ignore_index=True,sort=False)\n",
    "train.shape,test.shape,data.shape,submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def day_to_date(x):\n",
    "    return datetime.datetime(2017, 1, 1) + datetime.timedelta(x - 1)\n",
    "data['date']=data['Day_No'].apply(day_to_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2017-01-01 00:00:00'), Timestamp('2019-07-31 00:00:00'))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['date'].min(),data['date'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11004</td>\n",
       "      <td>0.007</td>\n",
       "      <td>81</td>\n",
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      "text/plain": [
       "   ID  Day_No  Course_ID Course_Domain Course_Type  Short_Promotion  \\\n",
       "0   1       1          1   Development      Course                0   \n",
       "\n",
       "   Public_Holiday  Long_Promotion  User_Traffic  Competition_Metric  Sales  \n",
       "0               1               1         11004               0.007     81  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['date'] = pd.to_datetime(data.date,format='%Y-%m-%d')\n",
    "data['dayofmonth'] = data.date.dt.day\n",
    "data['dayofyear'] = data.date.dt.dayofyear\n",
    "data['dayofweek'] = data.date.dt.dayofweek\n",
    "data['month'] = data.date.dt.month\n",
    "data['year'] = data.date.dt.year\n",
    "data['weekofyear'] = data.date.dt.weekofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>weekofyear</th>\n",
       "      <th>weekday_cos</th>\n",
       "      <th>weekday_sin</th>\n",
       "      <th>day_month_cos</th>\n",
       "      <th>day_month_sin</th>\n",
       "      <th>month_year_cos</th>\n",
       "      <th>month_year_sin</th>\n",
       "      <th>day_year_cos</th>\n",
       "      <th>day_year_sin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11004.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>52</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13650.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.979530</td>\n",
       "      <td>0.201299</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.999852</td>\n",
       "      <td>0.017213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11655.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>0.918958</td>\n",
       "      <td>0.394356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.999407</td>\n",
       "      <td>0.034422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12054.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>0.820763</td>\n",
       "      <td>0.571268</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.998667</td>\n",
       "      <td>0.051620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6804.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>0.688967</td>\n",
       "      <td>0.724793</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.997630</td>\n",
       "      <td>0.068802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10395.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>0.528964</td>\n",
       "      <td>0.848644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.996298</td>\n",
       "      <td>0.085965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16023.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>0.347305</td>\n",
       "      <td>0.937752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.994671</td>\n",
       "      <td>0.103102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14385.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.151428</td>\n",
       "      <td>0.988468</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.992749</td>\n",
       "      <td>0.120208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16485.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.050649</td>\n",
       "      <td>0.998717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.990532</td>\n",
       "      <td>0.137279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13377.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>-0.250653</td>\n",
       "      <td>0.968077</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.988023</td>\n",
       "      <td>0.154309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11319.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.440394</td>\n",
       "      <td>0.897805</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.985220</td>\n",
       "      <td>0.171293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7077.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.612106</td>\n",
       "      <td>0.790776</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.982126</td>\n",
       "      <td>0.188227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11802.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>-0.758758</td>\n",
       "      <td>0.651372</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.978740</td>\n",
       "      <td>0.205104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11130.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.874347</td>\n",
       "      <td>0.485302</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.975065</td>\n",
       "      <td>0.221922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10773.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>-0.954139</td>\n",
       "      <td>0.299363</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.971100</td>\n",
       "      <td>0.238673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12411.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.994869</td>\n",
       "      <td>0.101168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.966848</td>\n",
       "      <td>0.255353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8463.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>-0.994869</td>\n",
       "      <td>-0.101168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.962309</td>\n",
       "      <td>0.271958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9114.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.954139</td>\n",
       "      <td>-0.299363</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.957485</td>\n",
       "      <td>0.288482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6363.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.874347</td>\n",
       "      <td>-0.485302</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.952378</td>\n",
       "      <td>0.304921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8547.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>-0.758758</td>\n",
       "      <td>-0.651372</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.946988</td>\n",
       "      <td>0.321270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13902.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.612106</td>\n",
       "      <td>-0.790776</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.941317</td>\n",
       "      <td>0.337523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13776.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>-0.440394</td>\n",
       "      <td>-0.897805</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.935368</td>\n",
       "      <td>0.353676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14490.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.250653</td>\n",
       "      <td>-0.968077</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.929141</td>\n",
       "      <td>0.369725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12222.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>-0.050649</td>\n",
       "      <td>-0.998717</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.922640</td>\n",
       "      <td>0.385663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10962.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>0.151428</td>\n",
       "      <td>-0.988468</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.915864</td>\n",
       "      <td>0.401488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6363.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>0.347305</td>\n",
       "      <td>-0.937752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.908818</td>\n",
       "      <td>0.417194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11256.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>0.528964</td>\n",
       "      <td>-0.848644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.901502</td>\n",
       "      <td>0.432776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10248.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>0.688967</td>\n",
       "      <td>-0.724793</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.893919</td>\n",
       "      <td>0.448229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9996.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.820763</td>\n",
       "      <td>-0.571268</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.886071</td>\n",
       "      <td>0.463550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12768.0</td>\n",
       "      <td>0.007</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.918958</td>\n",
       "      <td>-0.394356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.877960</td>\n",
       "      <td>0.478734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548057</th>\n",
       "      <td>548058</td>\n",
       "      <td>913</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>0.979530</td>\n",
       "      <td>0.201299</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.999963</td>\n",
       "      <td>0.008607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548058</th>\n",
       "      <td>548059</td>\n",
       "      <td>914</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>0.918958</td>\n",
       "      <td>0.394356</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.999963</td>\n",
       "      <td>-0.008607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548059</th>\n",
       "      <td>548060</td>\n",
       "      <td>915</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>0.820763</td>\n",
       "      <td>0.571268</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.999667</td>\n",
       "      <td>-0.025818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548060</th>\n",
       "      <td>548061</td>\n",
       "      <td>916</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>0.688967</td>\n",
       "      <td>0.724793</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.999074</td>\n",
       "      <td>-0.043022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548061</th>\n",
       "      <td>548062</td>\n",
       "      <td>917</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>0.528964</td>\n",
       "      <td>0.848644</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.998186</td>\n",
       "      <td>-0.060213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548062</th>\n",
       "      <td>548063</td>\n",
       "      <td>918</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>27</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.347305</td>\n",
       "      <td>0.937752</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.997001</td>\n",
       "      <td>-0.077386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548063</th>\n",
       "      <td>548064</td>\n",
       "      <td>919</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.151428</td>\n",
       "      <td>0.988468</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.995521</td>\n",
       "      <td>-0.094537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548064</th>\n",
       "      <td>548065</td>\n",
       "      <td>920</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>-0.050649</td>\n",
       "      <td>0.998717</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.993747</td>\n",
       "      <td>-0.111659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548065</th>\n",
       "      <td>548066</td>\n",
       "      <td>921</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.250653</td>\n",
       "      <td>0.968077</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.991677</td>\n",
       "      <td>-0.128748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548066</th>\n",
       "      <td>548067</td>\n",
       "      <td>922</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.440394</td>\n",
       "      <td>0.897805</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.989314</td>\n",
       "      <td>-0.145799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548067</th>\n",
       "      <td>548068</td>\n",
       "      <td>923</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>-0.612106</td>\n",
       "      <td>0.790776</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.986658</td>\n",
       "      <td>-0.162807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548068</th>\n",
       "      <td>548069</td>\n",
       "      <td>924</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.758758</td>\n",
       "      <td>0.651372</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.983709</td>\n",
       "      <td>-0.179767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548069</th>\n",
       "      <td>548070</td>\n",
       "      <td>925</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>28</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>-0.874347</td>\n",
       "      <td>0.485302</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.980469</td>\n",
       "      <td>-0.196673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548070</th>\n",
       "      <td>548071</td>\n",
       "      <td>926</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.954139</td>\n",
       "      <td>0.299363</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.976938</td>\n",
       "      <td>-0.213521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548071</th>\n",
       "      <td>548072</td>\n",
       "      <td>927</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>-0.994869</td>\n",
       "      <td>0.101168</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.973118</td>\n",
       "      <td>-0.230306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548072</th>\n",
       "      <td>548073</td>\n",
       "      <td>928</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.994869</td>\n",
       "      <td>-0.101168</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.969010</td>\n",
       "      <td>-0.247022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548073</th>\n",
       "      <td>548074</td>\n",
       "      <td>929</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>-0.954139</td>\n",
       "      <td>-0.299363</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.964614</td>\n",
       "      <td>-0.263665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548074</th>\n",
       "      <td>548075</td>\n",
       "      <td>930</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>-0.874347</td>\n",
       "      <td>-0.485302</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.959933</td>\n",
       "      <td>-0.280231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548075</th>\n",
       "      <td>548076</td>\n",
       "      <td>931</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.758758</td>\n",
       "      <td>-0.651372</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.954967</td>\n",
       "      <td>-0.296713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548076</th>\n",
       "      <td>548077</td>\n",
       "      <td>932</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>29</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>-0.612106</td>\n",
       "      <td>-0.790776</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.949718</td>\n",
       "      <td>-0.313107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548077</th>\n",
       "      <td>548078</td>\n",
       "      <td>933</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.440394</td>\n",
       "      <td>-0.897805</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.944188</td>\n",
       "      <td>-0.329408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548078</th>\n",
       "      <td>548079</td>\n",
       "      <td>934</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>-0.250653</td>\n",
       "      <td>-0.968077</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.938377</td>\n",
       "      <td>-0.345612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548079</th>\n",
       "      <td>548080</td>\n",
       "      <td>935</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.050649</td>\n",
       "      <td>-0.998717</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.932289</td>\n",
       "      <td>-0.361714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548080</th>\n",
       "      <td>548081</td>\n",
       "      <td>936</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>0.433884</td>\n",
       "      <td>0.151428</td>\n",
       "      <td>-0.988468</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.925925</td>\n",
       "      <td>-0.377708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548081</th>\n",
       "      <td>548082</td>\n",
       "      <td>937</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>0.347305</td>\n",
       "      <td>-0.937752</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.919286</td>\n",
       "      <td>-0.393590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548082</th>\n",
       "      <td>548083</td>\n",
       "      <td>938</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>0.528964</td>\n",
       "      <td>-0.848644</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.912375</td>\n",
       "      <td>-0.409356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548083</th>\n",
       "      <td>548084</td>\n",
       "      <td>939</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>30</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.688967</td>\n",
       "      <td>-0.724793</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.905193</td>\n",
       "      <td>-0.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548084</th>\n",
       "      <td>548085</td>\n",
       "      <td>940</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>31</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.820763</td>\n",
       "      <td>-0.571268</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.897743</td>\n",
       "      <td>-0.440519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548085</th>\n",
       "      <td>548086</td>\n",
       "      <td>941</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>31</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>0.781831</td>\n",
       "      <td>0.918958</td>\n",
       "      <td>-0.394356</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.890028</td>\n",
       "      <td>-0.455907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548086</th>\n",
       "      <td>548087</td>\n",
       "      <td>942</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>31</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>0.979530</td>\n",
       "      <td>-0.201299</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>-0.882048</td>\n",
       "      <td>-0.471160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>548087 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Day_No  Course_ID       Course_Domain Course_Type  \\\n",
       "0            1       1          1         Development      Course   \n",
       "1            2       2          1         Development      Course   \n",
       "2            3       3          1         Development      Course   \n",
       "3            4       4          1         Development      Course   \n",
       "4            5       5          1         Development      Course   \n",
       "5            6       6          1         Development      Course   \n",
       "6            7       7          1         Development      Course   \n",
       "7            8       8          1         Development      Course   \n",
       "8            9       9          1         Development      Course   \n",
       "9           10      10          1         Development      Course   \n",
       "10          11      11          1         Development      Course   \n",
       "11          12      12          1         Development      Course   \n",
       "12          13      13          1         Development      Course   \n",
       "13          14      14          1         Development      Course   \n",
       "14          15      15          1         Development      Course   \n",
       "15          16      16          1         Development      Course   \n",
       "16          17      17          1         Development      Course   \n",
       "17          18      18          1         Development      Course   \n",
       "18          19      19          1         Development      Course   \n",
       "19          20      20          1         Development      Course   \n",
       "20          21      21          1         Development      Course   \n",
       "21          22      22          1         Development      Course   \n",
       "22          23      23          1         Development      Course   \n",
       "23          24      24          1         Development      Course   \n",
       "24          25      25          1         Development      Course   \n",
       "25          26      26          1         Development      Course   \n",
       "26          27      27          1         Development      Course   \n",
       "27          28      28          1         Development      Course   \n",
       "28          29      29          1         Development      Course   \n",
       "29          30      30          1         Development      Course   \n",
       "...        ...     ...        ...                 ...         ...   \n",
       "548057  548058     913        600  Software Marketing     Program   \n",
       "548058  548059     914        600  Software Marketing     Program   \n",
       "548059  548060     915        600  Software Marketing     Program   \n",
       "548060  548061     916        600  Software Marketing     Program   \n",
       "548061  548062     917        600  Software Marketing     Program   \n",
       "548062  548063     918        600  Software Marketing     Program   \n",
       "548063  548064     919        600  Software Marketing     Program   \n",
       "548064  548065     920        600  Software Marketing     Program   \n",
       "548065  548066     921        600  Software Marketing     Program   \n",
       "548066  548067     922        600  Software Marketing     Program   \n",
       "548067  548068     923        600  Software Marketing     Program   \n",
       "548068  548069     924        600  Software Marketing     Program   \n",
       "548069  548070     925        600  Software Marketing     Program   \n",
       "548070  548071     926        600  Software Marketing     Program   \n",
       "548071  548072     927        600  Software Marketing     Program   \n",
       "548072  548073     928        600  Software Marketing     Program   \n",
       "548073  548074     929        600  Software Marketing     Program   \n",
       "548074  548075     930        600  Software Marketing     Program   \n",
       "548075  548076     931        600  Software Marketing     Program   \n",
       "548076  548077     932        600  Software Marketing     Program   \n",
       "548077  548078     933        600  Software Marketing     Program   \n",
       "548078  548079     934        600  Software Marketing     Program   \n",
       "548079  548080     935        600  Software Marketing     Program   \n",
       "548080  548081     936        600  Software Marketing     Program   \n",
       "548081  548082     937        600  Software Marketing     Program   \n",
       "548082  548083     938        600  Software Marketing     Program   \n",
       "548083  548084     939        600  Software Marketing     Program   \n",
       "548084  548085     940        600  Software Marketing     Program   \n",
       "548085  548086     941        600  Software Marketing     Program   \n",
       "548086  548087     942        600  Software Marketing     Program   \n",
       "\n",
       "        Short_Promotion  Public_Holiday  Long_Promotion  User_Traffic  \\\n",
       "0                     0               1               1       11004.0   \n",
       "1                     0               0               1       13650.0   \n",
       "2                     0               0               1       11655.0   \n",
       "3                     0               0               1       12054.0   \n",
       "4                     0               0               1        6804.0   \n",
       "5                     0               0               1       10395.0   \n",
       "6                     1               0               1       16023.0   \n",
       "7                     1               0               1       14385.0   \n",
       "8                     1               0               1       16485.0   \n",
       "9                     1               0               1       13377.0   \n",
       "10                    1               0               1       11319.0   \n",
       "11                    0               0               1        7077.0   \n",
       "12                    0               0               1       11802.0   \n",
       "13                    0               0               1       11130.0   \n",
       "14                    0               0               1       10773.0   \n",
       "15                    0               0               1       12411.0   \n",
       "16                    0               0               1        8463.0   \n",
       "17                    0               0               1        9114.0   \n",
       "18                    0               0               1        6363.0   \n",
       "19                    0               0               1        8547.0   \n",
       "20                    1               0               1       13902.0   \n",
       "21                    1               0               1       13776.0   \n",
       "22                    1               0               1       14490.0   \n",
       "23                    1               0               1       12222.0   \n",
       "24                    1               0               1       10962.0   \n",
       "25                    0               0               1        6363.0   \n",
       "26                    0               0               1       11256.0   \n",
       "27                    0               0               1       10248.0   \n",
       "28                    0               0               1        9996.0   \n",
       "29                    0               0               1       12768.0   \n",
       "...                 ...             ...             ...           ...   \n",
       "548057                1               0               1           NaN   \n",
       "548058                1               0               1           NaN   \n",
       "548059                0               0               1           NaN   \n",
       "548060                0               0               1           NaN   \n",
       "548061                0               0               1           NaN   \n",
       "548062                0               0               1           NaN   \n",
       "548063                0               0               1           NaN   \n",
       "548064                0               0               1           NaN   \n",
       "548065                0               0               1           NaN   \n",
       "548066                0               0               1           NaN   \n",
       "548067                0               0               1           NaN   \n",
       "548068                1               0               1           NaN   \n",
       "548069                1               0               1           NaN   \n",
       "548070                1               0               1           NaN   \n",
       "548071                1               0               1           NaN   \n",
       "548072                1               0               1           NaN   \n",
       "548073                0               0               1           NaN   \n",
       "548074                0               0               1           NaN   \n",
       "548075                0               0               1           NaN   \n",
       "548076                0               0               1           NaN   \n",
       "548077                0               0               1           NaN   \n",
       "548078                0               0               1           NaN   \n",
       "548079                0               0               1           NaN   \n",
       "548080                0               0               1           NaN   \n",
       "548081                0               0               1           NaN   \n",
       "548082                1               0               1           NaN   \n",
       "548083                1               0               1           NaN   \n",
       "548084                1               0               1           NaN   \n",
       "548085                1               0               1           NaN   \n",
       "548086                1               0               1           NaN   \n",
       "\n",
       "        Competition_Metric  ...  year  weekofyear  weekday_cos  weekday_sin  \\\n",
       "0                    0.007  ...  2017          52     0.623490    -0.781831   \n",
       "1                    0.007  ...  2017           1     1.000000     0.000000   \n",
       "2                    0.007  ...  2017           1     0.623490     0.781831   \n",
       "3                    0.007  ...  2017           1    -0.222521     0.974928   \n",
       "4                    0.007  ...  2017           1    -0.900969     0.433884   \n",
       "5                    0.007  ...  2017           1    -0.900969    -0.433884   \n",
       "6                    0.007  ...  2017           1    -0.222521    -0.974928   \n",
       "7                    0.007  ...  2017           1     0.623490    -0.781831   \n",
       "8                    0.007  ...  2017           2     1.000000     0.000000   \n",
       "9                    0.007  ...  2017           2     0.623490     0.781831   \n",
       "10                   0.007  ...  2017           2    -0.222521     0.974928   \n",
       "11                   0.007  ...  2017           2    -0.900969     0.433884   \n",
       "12                   0.007  ...  2017           2    -0.900969    -0.433884   \n",
       "13                   0.007  ...  2017           2    -0.222521    -0.974928   \n",
       "14                   0.007  ...  2017           2     0.623490    -0.781831   \n",
       "15                   0.007  ...  2017           3     1.000000     0.000000   \n",
       "16                   0.007  ...  2017           3     0.623490     0.781831   \n",
       "17                   0.007  ...  2017           3    -0.222521     0.974928   \n",
       "18                   0.007  ...  2017           3    -0.900969     0.433884   \n",
       "19                   0.007  ...  2017           3    -0.900969    -0.433884   \n",
       "20                   0.007  ...  2017           3    -0.222521    -0.974928   \n",
       "21                   0.007  ...  2017           3     0.623490    -0.781831   \n",
       "22                   0.007  ...  2017           4     1.000000     0.000000   \n",
       "23                   0.007  ...  2017           4     0.623490     0.781831   \n",
       "24                   0.007  ...  2017           4    -0.222521     0.974928   \n",
       "25                   0.007  ...  2017           4    -0.900969     0.433884   \n",
       "26                   0.007  ...  2017           4    -0.900969    -0.433884   \n",
       "27                   0.007  ...  2017           4    -0.222521    -0.974928   \n",
       "28                   0.007  ...  2017           4     0.623490    -0.781831   \n",
       "29                   0.007  ...  2017           5     1.000000     0.000000   \n",
       "...                    ...  ...   ...         ...          ...          ...   \n",
       "548057               0.070  ...  2019          27     0.623490     0.781831   \n",
       "548058               0.070  ...  2019          27    -0.222521     0.974928   \n",
       "548059               0.070  ...  2019          27    -0.900969     0.433884   \n",
       "548060               0.070  ...  2019          27    -0.900969    -0.433884   \n",
       "548061               0.070  ...  2019          27    -0.222521    -0.974928   \n",
       "548062               0.070  ...  2019          27     0.623490    -0.781831   \n",
       "548063               0.070  ...  2019          28     1.000000     0.000000   \n",
       "548064               0.070  ...  2019          28     0.623490     0.781831   \n",
       "548065               0.070  ...  2019          28    -0.222521     0.974928   \n",
       "548066               0.070  ...  2019          28    -0.900969     0.433884   \n",
       "548067               0.070  ...  2019          28    -0.900969    -0.433884   \n",
       "548068               0.070  ...  2019          28    -0.222521    -0.974928   \n",
       "548069               0.070  ...  2019          28     0.623490    -0.781831   \n",
       "548070               0.070  ...  2019          29     1.000000     0.000000   \n",
       "548071               0.070  ...  2019          29     0.623490     0.781831   \n",
       "548072               0.070  ...  2019          29    -0.222521     0.974928   \n",
       "548073               0.070  ...  2019          29    -0.900969     0.433884   \n",
       "548074               0.070  ...  2019          29    -0.900969    -0.433884   \n",
       "548075               0.070  ...  2019          29    -0.222521    -0.974928   \n",
       "548076               0.070  ...  2019          29     0.623490    -0.781831   \n",
       "548077               0.070  ...  2019          30     1.000000     0.000000   \n",
       "548078               0.070  ...  2019          30     0.623490     0.781831   \n",
       "548079               0.070  ...  2019          30    -0.222521     0.974928   \n",
       "548080               0.070  ...  2019          30    -0.900969     0.433884   \n",
       "548081               0.070  ...  2019          30    -0.900969    -0.433884   \n",
       "548082               0.070  ...  2019          30    -0.222521    -0.974928   \n",
       "548083               0.070  ...  2019          30     0.623490    -0.781831   \n",
       "548084               0.070  ...  2019          31     1.000000     0.000000   \n",
       "548085               0.070  ...  2019          31     0.623490     0.781831   \n",
       "548086               0.070  ...  2019          31    -0.222521     0.974928   \n",
       "\n",
       "        day_month_cos  day_month_sin  month_year_cos  month_year_sin  \\\n",
       "0            1.000000       0.000000             1.0    0.000000e+00   \n",
       "1            0.979530       0.201299             1.0    0.000000e+00   \n",
       "2            0.918958       0.394356             1.0    0.000000e+00   \n",
       "3            0.820763       0.571268             1.0    0.000000e+00   \n",
       "4            0.688967       0.724793             1.0    0.000000e+00   \n",
       "5            0.528964       0.848644             1.0    0.000000e+00   \n",
       "6            0.347305       0.937752             1.0    0.000000e+00   \n",
       "7            0.151428       0.988468             1.0    0.000000e+00   \n",
       "8           -0.050649       0.998717             1.0    0.000000e+00   \n",
       "9           -0.250653       0.968077             1.0    0.000000e+00   \n",
       "10          -0.440394       0.897805             1.0    0.000000e+00   \n",
       "11          -0.612106       0.790776             1.0    0.000000e+00   \n",
       "12          -0.758758       0.651372             1.0    0.000000e+00   \n",
       "13          -0.874347       0.485302             1.0    0.000000e+00   \n",
       "14          -0.954139       0.299363             1.0    0.000000e+00   \n",
       "15          -0.994869       0.101168             1.0    0.000000e+00   \n",
       "16          -0.994869      -0.101168             1.0    0.000000e+00   \n",
       "17          -0.954139      -0.299363             1.0    0.000000e+00   \n",
       "18          -0.874347      -0.485302             1.0    0.000000e+00   \n",
       "19          -0.758758      -0.651372             1.0    0.000000e+00   \n",
       "20          -0.612106      -0.790776             1.0    0.000000e+00   \n",
       "21          -0.440394      -0.897805             1.0    0.000000e+00   \n",
       "22          -0.250653      -0.968077             1.0    0.000000e+00   \n",
       "23          -0.050649      -0.998717             1.0    0.000000e+00   \n",
       "24           0.151428      -0.988468             1.0    0.000000e+00   \n",
       "25           0.347305      -0.937752             1.0    0.000000e+00   \n",
       "26           0.528964      -0.848644             1.0    0.000000e+00   \n",
       "27           0.688967      -0.724793             1.0    0.000000e+00   \n",
       "28           0.820763      -0.571268             1.0    0.000000e+00   \n",
       "29           0.918958      -0.394356             1.0    0.000000e+00   \n",
       "...               ...            ...             ...             ...   \n",
       "548057       0.979530       0.201299            -1.0    1.224647e-16   \n",
       "548058       0.918958       0.394356            -1.0    1.224647e-16   \n",
       "548059       0.820763       0.571268            -1.0    1.224647e-16   \n",
       "548060       0.688967       0.724793            -1.0    1.224647e-16   \n",
       "548061       0.528964       0.848644            -1.0    1.224647e-16   \n",
       "548062       0.347305       0.937752            -1.0    1.224647e-16   \n",
       "548063       0.151428       0.988468            -1.0    1.224647e-16   \n",
       "548064      -0.050649       0.998717            -1.0    1.224647e-16   \n",
       "548065      -0.250653       0.968077            -1.0    1.224647e-16   \n",
       "548066      -0.440394       0.897805            -1.0    1.224647e-16   \n",
       "548067      -0.612106       0.790776            -1.0    1.224647e-16   \n",
       "548068      -0.758758       0.651372            -1.0    1.224647e-16   \n",
       "548069      -0.874347       0.485302            -1.0    1.224647e-16   \n",
       "548070      -0.954139       0.299363            -1.0    1.224647e-16   \n",
       "548071      -0.994869       0.101168            -1.0    1.224647e-16   \n",
       "548072      -0.994869      -0.101168            -1.0    1.224647e-16   \n",
       "548073      -0.954139      -0.299363            -1.0    1.224647e-16   \n",
       "548074      -0.874347      -0.485302            -1.0    1.224647e-16   \n",
       "548075      -0.758758      -0.651372            -1.0    1.224647e-16   \n",
       "548076      -0.612106      -0.790776            -1.0    1.224647e-16   \n",
       "548077      -0.440394      -0.897805            -1.0    1.224647e-16   \n",
       "548078      -0.250653      -0.968077            -1.0    1.224647e-16   \n",
       "548079      -0.050649      -0.998717            -1.0    1.224647e-16   \n",
       "548080       0.151428      -0.988468            -1.0    1.224647e-16   \n",
       "548081       0.347305      -0.937752            -1.0    1.224647e-16   \n",
       "548082       0.528964      -0.848644            -1.0    1.224647e-16   \n",
       "548083       0.688967      -0.724793            -1.0    1.224647e-16   \n",
       "548084       0.820763      -0.571268            -1.0    1.224647e-16   \n",
       "548085       0.918958      -0.394356            -1.0    1.224647e-16   \n",
       "548086       0.979530      -0.201299            -1.0    1.224647e-16   \n",
       "\n",
       "        day_year_cos  day_year_sin  \n",
       "0           1.000000      0.000000  \n",
       "1           0.999852      0.017213  \n",
       "2           0.999407      0.034422  \n",
       "3           0.998667      0.051620  \n",
       "4           0.997630      0.068802  \n",
       "5           0.996298      0.085965  \n",
       "6           0.994671      0.103102  \n",
       "7           0.992749      0.120208  \n",
       "8           0.990532      0.137279  \n",
       "9           0.988023      0.154309  \n",
       "10          0.985220      0.171293  \n",
       "11          0.982126      0.188227  \n",
       "12          0.978740      0.205104  \n",
       "13          0.975065      0.221922  \n",
       "14          0.971100      0.238673  \n",
       "15          0.966848      0.255353  \n",
       "16          0.962309      0.271958  \n",
       "17          0.957485      0.288482  \n",
       "18          0.952378      0.304921  \n",
       "19          0.946988      0.321270  \n",
       "20          0.941317      0.337523  \n",
       "21          0.935368      0.353676  \n",
       "22          0.929141      0.369725  \n",
       "23          0.922640      0.385663  \n",
       "24          0.915864      0.401488  \n",
       "25          0.908818      0.417194  \n",
       "26          0.901502      0.432776  \n",
       "27          0.893919      0.448229  \n",
       "28          0.886071      0.463550  \n",
       "29          0.877960      0.478734  \n",
       "...              ...           ...  \n",
       "548057     -0.999963      0.008607  \n",
       "548058     -0.999963     -0.008607  \n",
       "548059     -0.999667     -0.025818  \n",
       "548060     -0.999074     -0.043022  \n",
       "548061     -0.998186     -0.060213  \n",
       "548062     -0.997001     -0.077386  \n",
       "548063     -0.995521     -0.094537  \n",
       "548064     -0.993747     -0.111659  \n",
       "548065     -0.991677     -0.128748  \n",
       "548066     -0.989314     -0.145799  \n",
       "548067     -0.986658     -0.162807  \n",
       "548068     -0.983709     -0.179767  \n",
       "548069     -0.980469     -0.196673  \n",
       "548070     -0.976938     -0.213521  \n",
       "548071     -0.973118     -0.230306  \n",
       "548072     -0.969010     -0.247022  \n",
       "548073     -0.964614     -0.263665  \n",
       "548074     -0.959933     -0.280231  \n",
       "548075     -0.954967     -0.296713  \n",
       "548076     -0.949718     -0.313107  \n",
       "548077     -0.944188     -0.329408  \n",
       "548078     -0.938377     -0.345612  \n",
       "548079     -0.932289     -0.361714  \n",
       "548080     -0.925925     -0.377708  \n",
       "548081     -0.919286     -0.393590  \n",
       "548082     -0.912375     -0.409356  \n",
       "548083     -0.905193     -0.425000  \n",
       "548084     -0.897743     -0.440519  \n",
       "548085     -0.890028     -0.455907  \n",
       "548086     -0.882048     -0.471160  \n",
       "\n",
       "[548087 rows x 25 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_cyclic_datepart(data, 'date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lag_feats(df, gpby_cols, target_col, lags):\n",
    "    gpby = df.groupby(gpby_cols)\n",
    "    for i in lags:\n",
    "        df['_'.join([target_col, 'lag', str(i)])] = gpby[target_col].shift(i).values\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = create_lag_feats(data, gpby_cols=['Course_ID'], target_col='Sales', \n",
    "                                    lags=[60,61,68,75,82,89,90,91,98,105,112,119,126,182,364,365,366,546])\n",
    "\n",
    "data = create_lag_feats(data, gpby_cols=['Course_ID'], target_col='User_Traffic', \n",
    "                                    lags=[60,61,68,75,82,89,90,91,98,105,112,119,126,182,364,365,366,546])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(548087, 61)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training and Validation\n",
    "\n",
    "### Last 2 months from train set taken as validation for each COURSE_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# pred=[]\n",
    "# test_y1=[]\n",
    "# grouped = data.groupby('Course_ID')\n",
    "# for g in grouped.groups:\n",
    "#     group = grouped.get_group(g)\n",
    "#     train_x=group.iloc[:-120,:]\n",
    "#     test_x=group.iloc[-120:-60,:]\n",
    "#     train_y=train_x['Sales']\n",
    "#     test_y=test_x['Sales']\n",
    "#     train_x.fillna(-999,inplace=True)\n",
    "#     test_x.fillna(-999,inplace=True)\n",
    "#     train_x=train_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "#     test_x=test_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "#     m = RandomForestRegressor(random_state=18121995,n_jobs=-1)\n",
    "#     m.fit(train_x,train_y)\n",
    "#     predx=m.predict(test_x)\n",
    "#     pred+=list(predx)\n",
    "#     test_y1+=list(test_y)\n",
    "#     print(g,end=' ')\n",
    "# print(\"\\nRMSLE: \",(1000*(np.sqrt(mean_squared_log_error(test_y1,pred)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training and Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 40min 48s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pred=[]\n",
    "grouped = data.groupby('Course_ID')\n",
    "for g in grouped.groups:\n",
    "    group = grouped.get_group(g)\n",
    "    train_x=group.iloc[:-60,:]\n",
    "    test_x=group.iloc[-60:,:]\n",
    "    train_y=train_x['Sales']\n",
    "    train_x.fillna(-999,inplace=True)\n",
    "    test_x.fillna(-999,inplace=True)\n",
    "    train_x=train_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "    test_x=test_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "    m3 = RandomForestRegressor(n_estimators=500,random_state=18121995,bootstrap=True,n_jobs=-1)\n",
    "    m3.fit(train_x,train_y)\n",
    "    predx=m3.predict(test_x)\n",
    "    pred+=list(predx)\n",
    "    print(g,end=' ')\n",
    "final=pd.DataFrame()\n",
    "final['ID'] = submission['ID']\n",
    "final['Sales'] = pred\n",
    "final['Sales'] = np.clip(final['Sales'], 0, final['Sales'].max())\n",
    "final.to_csv('rf_V18.csv', index=False)\n",
    "feature_imp = pd.DataFrame(sorted(zip(m3.feature_importances_, train_x.columns), reverse=True)[:], columns=['Value','Feature'])\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
    "plt.title('Random Forest Features')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ExtraTrees Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 17min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "pred=[]\n",
    "grouped = data.groupby('Course_ID')\n",
    "for g in grouped.groups:\n",
    "    group = grouped.get_group(g)\n",
    "    train_x=group.iloc[:-60,:]\n",
    "    test_x=group.iloc[-60:,:]\n",
    "    train_y=train_x['Sales']\n",
    "    train_x.fillna(-999,inplace=True)\n",
    "    test_x.fillna(-999,inplace=True)\n",
    "    train_x=train_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "    test_x=test_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "    m4 = ExtraTreesRegressor(n_estimators=500,random_state=18121995, bootstrap=True,n_jobs=-1)\n",
    "    m4.fit(train_x,train_y)\n",
    "    predx=m4.predict(test_x)\n",
    "    pred+=list(predx)\n",
    "    print(g,end=' ')\n",
    "final=pd.DataFrame()\n",
    "final['ID'] = submission['ID']\n",
    "final['Sales'] = pred\n",
    "final['Sales'] = np.clip(final['Sales'], 0, final['Sales'].max())\n",
    "final.to_csv('et_V18.csv', index=False)\n",
    "feature_imp = pd.DataFrame(sorted(zip(m4.feature_importances_, train_x.columns), reverse=True)[:], columns=['Value','Feature'])\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False))\n",
    "plt.title('Extra Trees Features')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# pred=[]\n",
    "# grouped = data.groupby('Course_ID')\n",
    "# for g in grouped.groups:\n",
    "#     group = grouped.get_group(g)\n",
    "#     train_x=group.iloc[:-60,:]\n",
    "#     test_x=group.iloc[-60:,:]\n",
    "#     train_y=train_x['Sales']\n",
    "#     train_x.fillna(-999,inplace=True)\n",
    "#     test_x.fillna(-999,inplace=True)\n",
    "#     train_x=train_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "#     test_x=test_x.drop(['ID','Sales','Day_No','Course_ID','User_Traffic','Course_Domain','Course_Type','Competition_Metric'],axis=1)\n",
    "#     estimators = [('xgb', XGBRegressor(random_state=18121995,objective='reg:squarederror')),('gb', GradientBoostingRegressor(random_state=18121995)),('rf', RandomForestRegressor(random_state=18121995,bootstrap=True,n_jobs=-1)),('et', ExtraTreesRegressor(random_state=18121995, bootstrap=True,n_jobs=-1))]\n",
    "#     m4 = StackingRegressor(estimators=estimators,final_estimator=LinearRegression(n_jobs=-1),n_jobs=-1)\n",
    "#     m4.fit(train_x,train_y)\n",
    "#     predx=m4.predict(test_x)\n",
    "#     pred+=list(predx)\n",
    "#     print(g,end=' ')\n",
    "# final=pd.DataFrame()\n",
    "# final['ID'] = submission['ID']\n",
    "# final['Sales'] = pred\n",
    "# final['Sales'] = np.clip(final['Sales'], 0, final['Sales'].max())\n",
    "# final.to_csv('stack_all_lag_V18.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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