Machine Learning for Covid-19 Part A.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Definition and Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This Notebook is for Non-Normalised Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Dictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Field          | Description                                                                           |\n",
    "|----------------|---------------------------------------------------------------------------------------|\n",
    "|Date |Date of incident\t|\n",
    "|Province/State |If there is a breakdown in states\t|\n",
    "|Lat |Latitude\t|\n",
    "|Lon |Longitude\t|\n",
    "|Confirmed |Confirmed cases\t|\n",
    "|Recovered |Recovered cases\t|\n",
    "|Deaths |Death cases\t|\n",
    "|Active |Active cases\t|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model performance indicator will be Root Mean Square Error (RMSE) to be as accurate as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm      | MSE | RMSE | R2 |\n",
    "|----------------|-----|------|----|\n",
    "|Linear Regression | 2.038  | 1.428 |  0.319 |\n",
    "|Extra Trees Regressor | 2.282 | 1.511 | 0.238 |\n",
    "|XGBoost Regressor |1.656  | 1.287 | 0.447  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import shap\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBRegressor\n",
    "from xgboost import plot_importance\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style('dark')\n",
    "sns.set(font_scale=1.2)\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.feature_selection import RFE, RFECV, SelectKBest, f_classif, f_regression, chi2\n",
    "\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, OneHotEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix, classification_report, mean_absolute_error, mean_squared_error,r2_score\n",
    "from sklearn.metrics import plot_confusion_matrix, plot_precision_recall_curve, plot_roc_curve, accuracy_score\n",
    "from sklearn.metrics import auc, f1_score, precision_score, recall_score, roc_auc_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pickle\n",
    "from pickle import dump, load\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "from pycaret.regression import *\n",
    "\n",
    "pd.set_option('display.max_columns',50)\n",
    "#pd.set_option('display.max_rows',100)\n",
    "pd.set_option('display.width', 1000)\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"malaysiacovid2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Active</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>ActiveDiff</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Confirmed  Recovered  Deaths  Active  ConfirmDiff  DeathsDiff  RecoverDiff  ActiveDiff  Month  Day\n",
       "0  2020-01-22          0          0       0       0          0.0         0.0          0.0         0.0      1   22\n",
       "1  2020-01-23          0          0       0       0          0.0         0.0          0.0         0.0      1   23\n",
       "2  2020-01-24          0          0       0       0          0.0         0.0          0.0         0.0      1   24\n",
       "3  2020-01-25          3          0       0       3          3.0         0.0          0.0         3.0      1   25\n",
       "4  2020-01-26          4          0       0       4          1.0         0.0          0.0         1.0      1   26"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Date         309 non-null    object \n",
      " 1   Confirmed    309 non-null    int64  \n",
      " 2   Recovered    309 non-null    int64  \n",
      " 3   Deaths       309 non-null    int64  \n",
      " 4   Active       309 non-null    int64  \n",
      " 5   ConfirmDiff  309 non-null    float64\n",
      " 6   DeathsDiff   309 non-null    float64\n",
      " 7   RecoverDiff  309 non-null    float64\n",
      " 8   ActiveDiff   309 non-null    float64\n",
      " 9   Month        309 non-null    int64  \n",
      " 10  Day          309 non-null    int64  \n",
      "dtypes: float64(4), int64(6), object(1)\n",
      "memory usage: 26.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Active</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>ActiveDiff</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>309</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2020-11-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10535.511327</td>\n",
       "      <td>8236.019417</td>\n",
       "      <td>111.669903</td>\n",
       "      <td>2187.822006</td>\n",
       "      <td>193.582524</td>\n",
       "      <td>1.116505</td>\n",
       "      <td>150.488673</td>\n",
       "      <td>41.977346</td>\n",
       "      <td>6.265372</td>\n",
       "      <td>15.857605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12082.498984</td>\n",
       "      <td>8826.720903</td>\n",
       "      <td>81.039279</td>\n",
       "      <td>3547.802649</td>\n",
       "      <td>359.375952</td>\n",
       "      <td>1.988445</td>\n",
       "      <td>306.753450</td>\n",
       "      <td>172.885226</td>\n",
       "      <td>2.943317</td>\n",
       "      <td>8.814048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1382.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4119.000000</td>\n",
       "      <td>1487.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-12.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8596.000000</td>\n",
       "      <td>8231.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>751.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9583.000000</td>\n",
       "      <td>9143.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59817.000000</td>\n",
       "      <td>46501.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>14353.000000</td>\n",
       "      <td>2188.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2348.000000</td>\n",
       "      <td>1027.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date     Confirmed     Recovered      Deaths        Active  ConfirmDiff  DeathsDiff  RecoverDiff   ActiveDiff       Month         Day\n",
       "count          309    309.000000    309.000000  309.000000    309.000000   309.000000  309.000000   309.000000   309.000000  309.000000  309.000000\n",
       "unique         309           NaN           NaN         NaN           NaN          NaN         NaN          NaN          NaN         NaN         NaN\n",
       "top     2020-11-19           NaN           NaN         NaN           NaN          NaN         NaN          NaN          NaN         NaN         NaN\n",
       "freq             1           NaN           NaN         NaN           NaN          NaN         NaN          NaN          NaN         NaN         NaN\n",
       "mean           NaN  10535.511327   8236.019417  111.669903   2187.822006   193.582524    1.116505   150.488673    41.977346    6.265372   15.857605\n",
       "std            NaN  12082.498984   8826.720903   81.039279   3547.802649   359.375952    1.988445   306.753450   172.885226    2.943317    8.814048\n",
       "min            NaN      0.000000      0.000000    0.000000      0.000000     0.000000    0.000000     0.000000 -1382.000000    1.000000    1.000000\n",
       "25%            NaN   4119.000000   1487.000000   65.000000    153.000000     7.000000    0.000000     6.000000   -12.000000    4.000000    8.000000\n",
       "50%            NaN   8596.000000   8231.000000  121.000000    751.000000    33.000000    0.000000    28.000000     3.000000    6.000000   16.000000\n",
       "75%            NaN   9583.000000   9143.000000  128.000000   1973.000000   150.000000    1.000000    96.000000    51.000000    9.000000   23.000000\n",
       "max            NaN  59817.000000  46501.000000  345.000000  14353.000000  2188.000000   12.000000  2348.000000  1027.000000   11.000000   31.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Confirmed', 'Recovered', 'Deaths', 'Active', 'ConfirmDiff', 'DeathsDiff', 'RecoverDiff', 'ActiveDiff', 'Month', 'Day'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop unwanted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Date', 'Confirmed', 'Recovered', 'Deaths', 'Active','ActiveDiff'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0      1   22          0.0          0.0         0.0\n",
       "1      1   23          0.0          0.0         0.0\n",
       "2      1   24          0.0          0.0         0.0\n",
       "3      1   25          3.0          0.0         0.0\n",
       "4      1   26          1.0          0.0         0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ConfirmDiff', 'DeathsDiff', 'RecoverDiff', 'Month', 'Day'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['Month', 'Day','ConfirmDiff','RecoverDiff', 'DeathsDiff']]"
   ]
  },
  {
   "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>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0      1   22          0.0          0.0         0.0\n",
       "1      1   23          0.0          0.0         0.0\n",
       "2      1   24          0.0          0.0         0.0\n",
       "3      1   25          3.0          0.0         0.0\n",
       "4      1   26          1.0          0.0         0.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50, figsize=(20,15))\n",
    "plt.suptitle('Feature Distribution', x=0.5, y=1.02, ha='center', fontsize=20)\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 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(figsize=(20,10))\n",
    "plt.suptitle('BoxPlot of Features', x=0.5, y=1.02, ha='center', fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098535</td>\n",
       "      <td>0.604269</td>\n",
       "      <td>0.545945</td>\n",
       "      <td>0.341420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.098535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>0.035592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <td>0.604269</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875959</td>\n",
       "      <td>0.641523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecoverDiff</th>\n",
       "      <td>0.545945</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>0.875959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.561145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeathsDiff</th>\n",
       "      <td>0.341420</td>\n",
       "      <td>0.035592</td>\n",
       "      <td>0.641523</td>\n",
       "      <td>0.561145</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Month       Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "Month        1.000000 -0.098535     0.604269     0.545945    0.341420\n",
       "Day         -0.098535  1.000000     0.002108     0.027499    0.035592\n",
       "ConfirmDiff  0.604269  0.002108     1.000000     0.875959    0.641523\n",
       "RecoverDiff  0.545945  0.027499     0.875959     1.000000    0.561145\n",
       "DeathsDiff   0.341420  0.035592     0.641523     0.561145    1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.heatmap(df.corr(),cmap=\"coolwarm\",annot=True,fmt='.2f',linewidths=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)\n",
    "plt.suptitle('Pairplots of features', x=0.5, y=1.02, ha='center', fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treat Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated(keep='first').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treat Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.265372</td>\n",
       "      <td>15.857605</td>\n",
       "      <td>193.582524</td>\n",
       "      <td>150.488673</td>\n",
       "      <td>1.116505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.943317</td>\n",
       "      <td>8.814048</td>\n",
       "      <td>359.375952</td>\n",
       "      <td>306.753450</td>\n",
       "      <td>1.988445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2188.000000</td>\n",
       "      <td>2348.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Month         Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "count  309.000000  309.000000   309.000000   309.000000  309.000000\n",
       "mean     6.265372   15.857605   193.582524   150.488673    1.116505\n",
       "std      2.943317    8.814048   359.375952   306.753450    1.988445\n",
       "min      1.000000    1.000000     0.000000     0.000000    0.000000\n",
       "25%      4.000000    8.000000     7.000000     6.000000    0.000000\n",
       "50%      6.000000   16.000000    33.000000    28.000000    0.000000\n",
       "75%      9.000000   23.000000   150.000000    96.000000    1.000000\n",
       "max     11.000000   31.000000  2188.000000  2348.000000   12.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No treatment of outliers needed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treat Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Month        309 non-null    int64  \n",
      " 1   Day          309 non-null    int64  \n",
      " 2   ConfirmDiff  309 non-null    float64\n",
      " 3   RecoverDiff  309 non-null    float64\n",
      " 4   DeathsDiff   309 non-null    float64\n",
      "dtypes: float64(3), int64(2)\n",
      "memory usage: 12.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Month\"] = df[\"Month\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Day\"] = df[\"Day\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   Month        309 non-null    category\n",
      " 1   Day          309 non-null    category\n",
      " 2   ConfirmDiff  309 non-null    float64 \n",
      " 3   RecoverDiff  309 non-null    float64 \n",
      " 4   DeathsDiff   309 non-null    float64 \n",
      "dtypes: category(2), float64(3)\n",
      "memory usage: 9.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and save processed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv(\"traincovid.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    31\n",
       "8     31\n",
       "7     31\n",
       "5     31\n",
       "3     31\n",
       "9     30\n",
       "6     30\n",
       "4     30\n",
       "2     29\n",
       "11    25\n",
       "1     10\n",
       "Name: Month, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Month\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23    11\n",
       "25    11\n",
       "24    11\n",
       "22    11\n",
       "6     10\n",
       "11    10\n",
       "10    10\n",
       "9     10\n",
       "8     10\n",
       "7     10\n",
       "16    10\n",
       "13    10\n",
       "5     10\n",
       "4     10\n",
       "3     10\n",
       "2     10\n",
       "12    10\n",
       "1     10\n",
       "14    10\n",
       "15    10\n",
       "17    10\n",
       "18    10\n",
       "19    10\n",
       "20    10\n",
       "21    10\n",
       "26    10\n",
       "27    10\n",
       "28    10\n",
       "29    10\n",
       "30     9\n",
       "31     6\n",
       "Name: Day, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Day\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.get_dummies(data=df, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "      <th>Month_2</th>\n",
       "      <th>Month_3</th>\n",
       "      <th>Month_4</th>\n",
       "      <th>Month_5</th>\n",
       "      <th>Month_6</th>\n",
       "      <th>Month_7</th>\n",
       "      <th>Month_8</th>\n",
       "      <th>Month_9</th>\n",
       "      <th>Month_10</th>\n",
       "      <th>Month_11</th>\n",
       "      <th>Day_2</th>\n",
       "      <th>Day_3</th>\n",
       "      <th>Day_4</th>\n",
       "      <th>Day_5</th>\n",
       "      <th>Day_6</th>\n",
       "      <th>Day_7</th>\n",
       "      <th>Day_8</th>\n",
       "      <th>Day_9</th>\n",
       "      <th>Day_10</th>\n",
       "      <th>Day_11</th>\n",
       "      <th>Day_12</th>\n",
       "      <th>Day_13</th>\n",
       "      <th>Day_14</th>\n",
       "      <th>Day_15</th>\n",
       "      <th>Day_16</th>\n",
       "      <th>Day_17</th>\n",
       "      <th>Day_18</th>\n",
       "      <th>Day_19</th>\n",
       "      <th>Day_20</th>\n",
       "      <th>Day_21</th>\n",
       "      <th>Day_22</th>\n",
       "      <th>Day_23</th>\n",
       "      <th>Day_24</th>\n",
       "      <th>Day_25</th>\n",
       "      <th>Day_26</th>\n",
       "      <th>Day_27</th>\n",
       "      <th>Day_28</th>\n",
       "      <th>Day_29</th>\n",
       "      <th>Day_30</th>\n",
       "      <th>Day_31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ConfirmDiff  RecoverDiff  DeathsDiff  Month_2  Month_3  Month_4  Month_5  Month_6  Month_7  Month_8  Month_9  Month_10  Month_11  Day_2  Day_3  Day_4  Day_5  Day_6  Day_7  Day_8  Day_9  Day_10  Day_11  Day_12  Day_13  Day_14  Day_15  Day_16  Day_17  Day_18  Day_19  Day_20  Day_21  Day_22  Day_23  Day_24  Day_25  Day_26  Day_27  Day_28  Day_29  Day_30  Day_31\n",
       "0          0.0          0.0         0.0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0       0\n",
       "1          0.0          0.0         0.0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0\n",
       "2          0.0          0.0         0.0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0\n",
       "3          3.0          0.0         0.0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0\n",
       "4          1.0          0.0         0.0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ConfirmDiff', 'RecoverDiff', 'DeathsDiff', 'Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6', 'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11', 'Day_2', 'Day_3', 'Day_4', 'Day_5', 'Day_6', 'Day_7', 'Day_8', 'Day_9', 'Day_10', 'Day_11', 'Day_12', 'Day_13', 'Day_14', 'Day_15', 'Day_16', 'Day_17', 'Day_18', 'Day_19', 'Day_20', 'Day_21', 'Day_22', 'Day_23', 'Day_24', 'Day_25', 'Day_26', 'Day_27', 'Day_28', 'Day_29', 'Day_30', 'Day_31'], dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2[['Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6', 'Month_7', 'Month_8', 'Month_9', \n",
    "           'Month_10', 'Month_11', 'Day_2', 'Day_3', 'Day_4', 'Day_5', 'Day_6', 'Day_7', 'Day_8', 'Day_9', \n",
    "           'Day_10', 'Day_11', 'Day_12', 'Day_13', 'Day_14', 'Day_15', 'Day_16', 'Day_17', 'Day_18', \n",
    "           'Day_19', 'Day_20', 'Day_21', 'Day_22', 'Day_23', 'Day_24', 'Day_25', 'Day_26', 'Day_27', \n",
    "           'Day_28', 'Day_29', 'Day_30', 'Day_31', 'ConfirmDiff', 'RecoverDiff', 'DeathsDiff']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6', 'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11', 'Day_2', 'Day_3', 'Day_4', 'Day_5', 'Day_6', 'Day_7', 'Day_8', 'Day_9', 'Day_10', 'Day_11', 'Day_12', 'Day_13', 'Day_14', 'Day_15', 'Day_16', 'Day_17', 'Day_18', 'Day_19', 'Day_20', 'Day_21', 'Day_22', 'Day_23', 'Day_24', 'Day_25', 'Day_26', 'Day_27', 'Day_28', 'Day_29', 'Day_30', 'Day_31', 'ConfirmDiff', 'RecoverDiff', 'DeathsDiff'], dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Month_2</th>\n",
       "      <th>Month_3</th>\n",
       "      <th>Month_4</th>\n",
       "      <th>Month_5</th>\n",
       "      <th>Month_6</th>\n",
       "      <th>Month_7</th>\n",
       "      <th>Month_8</th>\n",
       "      <th>Month_9</th>\n",
       "      <th>Month_10</th>\n",
       "      <th>Month_11</th>\n",
       "      <th>Day_2</th>\n",
       "      <th>Day_3</th>\n",
       "      <th>Day_4</th>\n",
       "      <th>Day_5</th>\n",
       "      <th>Day_6</th>\n",
       "      <th>Day_7</th>\n",
       "      <th>Day_8</th>\n",
       "      <th>Day_9</th>\n",
       "      <th>Day_10</th>\n",
       "      <th>Day_11</th>\n",
       "      <th>Day_12</th>\n",
       "      <th>Day_13</th>\n",
       "      <th>Day_14</th>\n",
       "      <th>Day_15</th>\n",
       "      <th>Day_16</th>\n",
       "      <th>Day_17</th>\n",
       "      <th>Day_18</th>\n",
       "      <th>Day_19</th>\n",
       "      <th>Day_20</th>\n",
       "      <th>Day_21</th>\n",
       "      <th>Day_22</th>\n",
       "      <th>Day_23</th>\n",
       "      <th>Day_24</th>\n",
       "      <th>Day_25</th>\n",
       "      <th>Day_26</th>\n",
       "      <th>Day_27</th>\n",
       "      <th>Day_28</th>\n",
       "      <th>Day_29</th>\n",
       "      <th>Day_30</th>\n",
       "      <th>Day_31</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month_2  Month_3  Month_4  Month_5  Month_6  Month_7  Month_8  Month_9  Month_10  Month_11  Day_2  Day_3  Day_4  Day_5  Day_6  Day_7  Day_8  Day_9  Day_10  Day_11  Day_12  Day_13  Day_14  Day_15  Day_16  Day_17  Day_18  Day_19  Day_20  Day_21  Day_22  Day_23  Day_24  Day_25  Day_26  Day_27  Day_28  Day_29  Day_30  Day_31  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "1        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "2        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "3        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0          3.0          0.0         0.0\n",
       "4        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0          1.0          0.0         0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and save processed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2.to_csv(\"traincovid2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 43)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.iloc[:,0:42]\n",
    "y = df2.iloc[:,42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        [   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        [   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        ...,\n",
       "        [   0.,    0.,    0., ...,    0., 1884.,  883.],\n",
       "        [   0.,    0.,    0., ...,    0., 2188., 1673.],\n",
       "        [   0.,    0.,    0., ...,    0.,  970., 2348.]]),\n",
       " array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  2.,  0.,  0.,  1.,  1.,  6.,  4.,  2.,  4.,  3.,\n",
       "         3.,  1.,  8.,  2.,  6.,  2.,  5.,  3.,  4.,  4.,  1.,  1.,  2.,\n",
       "         2.,  3.,  3.,  3.,  1.,  5.,  1.,  1.,  2.,  2.,  1.,  0.,  3.,\n",
       "         1.,  2.,  1.,  2.,  0.,  1.,  1.,  0.,  2.,  1.,  0.,  2.,  0.,\n",
       "         1.,  1.,  0.,  0.,  1.,  0.,  1.,  0.,  2.,  1.,  0.,  1.,  0.,\n",
       "         0.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,\n",
       "         1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  3.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  2.,  0.,  0.,  1.,  0.,  0.,  4.,  0.,\n",
       "         5.,  6.,  3.,  2.,  2.,  4.,  4.,  3.,  6.,  4.,  7.,  3.,  3.,\n",
       "         6.,  5., 10.,  7.,  8.,  7.,  2.,  8.,  0.,  3.,  0.,  0.,  2.,\n",
       "        12.,  8.,  6.,  2.,  3.,  4.,  8.,  6.,  2.,  1.,  1.,  2.,  3.,\n",
       "         4.,  5.,  4.,  4.,  3.,  3.,  3.,  2.,  4.,  4.]))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.values, y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((231, 42), (78, 42), (231,), (78,))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#minmax = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train_scaled = minmax.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_test_scaled = minmax.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using PyCaret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "pycaret = pd.read_csv(\"traincovid.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row1_col1\" class=\"data row1 col1\" >DeathsDiff</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row2_col0\" class=\"data row2 col0\" >Original Data</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row2_col1\" class=\"data row2 col1\" >(309, 5)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row3_col0\" class=\"data row3 col0\" >Missing Values</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row3_col1\" class=\"data row3 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row4_col0\" class=\"data row4 col0\" >Numeric Features</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row4_col1\" class=\"data row4 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row5_col0\" class=\"data row5 col0\" >Categorical Features</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row5_col1\" class=\"data row5 col1\" >2</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row6_col0\" class=\"data row6 col0\" >Ordinal Features</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row7_col0\" class=\"data row7 col0\" >High Cardinality Features</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row8_col0\" class=\"data row8 col0\" >High Cardinality Method</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row8_col1\" class=\"data row8 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row9_col0\" class=\"data row9 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row9_col1\" class=\"data row9 col1\" >(231, 44)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row10_col0\" class=\"data row10 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row10_col1\" class=\"data row10 col1\" >(78, 44)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row11_col0\" class=\"data row11 col0\" >Shuffle Train-Test</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row12_col0\" class=\"data row12 col0\" >Stratify Train-Test</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row13_col0\" class=\"data row13 col0\" >Fold Generator</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row13_col1\" class=\"data row13 col1\" >KFold</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row14_col0\" class=\"data row14 col0\" >Fold Number</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row14_col1\" class=\"data row14 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row15_col0\" class=\"data row15 col0\" >CPU Jobs</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row15_col1\" class=\"data row15 col1\" >-1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row16_col0\" class=\"data row16 col0\" >Use GPU</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row17_col0\" class=\"data row17 col0\" >Log Experiment</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row18_col0\" class=\"data row18 col0\" >Experiment Name</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row18_col1\" class=\"data row18 col1\" >reg-default-name</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row19_col0\" class=\"data row19 col0\" >USI</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row19_col1\" class=\"data row19 col1\" >f612</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row20_col0\" class=\"data row20 col0\" >Imputation Type</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row20_col1\" class=\"data row20 col1\" >simple</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row21_col0\" class=\"data row21 col0\" >Iterative Imputation Iteration</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row22_col0\" class=\"data row22 col0\" >Numeric Imputer</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row22_col1\" class=\"data row22 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row23_col0\" class=\"data row23 col0\" >Iterative Imputation Numeric Model</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row24_col0\" class=\"data row24 col0\" >Categorical Imputer</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row24_col1\" class=\"data row24 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row25_col0\" class=\"data row25 col0\" >Iterative Imputation Categorical Model</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row26_col0\" class=\"data row26 col0\" >Unknown Categoricals Handling</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row26_col1\" class=\"data row26 col1\" >least_frequent</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row27_col0\" class=\"data row27 col0\" >Normalize</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row27_col1\" class=\"data row27 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row28_col0\" class=\"data row28 col0\" >Normalize Method</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row28_col1\" class=\"data row28 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row29_col0\" class=\"data row29 col0\" >Transformation</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row29_col1\" class=\"data row29 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row30_col0\" class=\"data row30 col0\" >Transformation Method</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row30_col1\" class=\"data row30 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row31_col0\" class=\"data row31 col0\" >PCA</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row32_col0\" class=\"data row32 col0\" >PCA Method</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row33_col0\" class=\"data row33 col0\" >PCA Components</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row34_col0\" class=\"data row34 col0\" >Ignore Low Variance</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row35_col0\" class=\"data row35 col0\" >Combine Rare Levels</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row35_col1\" class=\"data row35 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row36_col0\" class=\"data row36 col0\" >Rare Level Threshold</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row36_col1\" class=\"data row36 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row37_col0\" class=\"data row37 col0\" >Numeric Binning</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row38_col0\" class=\"data row38 col0\" >Remove Outliers</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row38_col1\" class=\"data row38 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row39_col0\" class=\"data row39 col0\" >Outliers Threshold</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row39_col1\" class=\"data row39 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row40_col0\" class=\"data row40 col0\" >Remove Multicollinearity</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row41_col0\" class=\"data row41 col0\" >Multicollinearity Threshold</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row42_col0\" class=\"data row42 col0\" >Clustering</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row43_col0\" class=\"data row43 col0\" >Clustering Iteration</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row44_col0\" class=\"data row44 col0\" >Polynomial Features</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row44_col1\" class=\"data row44 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row45_col0\" class=\"data row45 col0\" >Polynomial Degree</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row45_col1\" class=\"data row45 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row46_col0\" class=\"data row46 col0\" >Trignometry Features</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row46_col1\" class=\"data row46 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row47_col0\" class=\"data row47 col0\" >Polynomial Threshold</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row47_col1\" class=\"data row47 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row48_col0\" class=\"data row48 col0\" >Group Features</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row48_col1\" class=\"data row48 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row49_col0\" class=\"data row49 col0\" >Feature Selection</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row50_col0\" class=\"data row50 col0\" >Features Selection Threshold</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row50_col1\" class=\"data row50 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row51_col0\" class=\"data row51 col0\" >Feature Interaction</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row51_col1\" class=\"data row51 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row52_col0\" class=\"data row52 col0\" >Feature Ratio</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row53_col0\" class=\"data row53 col0\" >Interaction Threshold</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row53_col1\" class=\"data row53 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row54_col0\" class=\"data row54 col0\" >Transform Target</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row54_col1\" class=\"data row54 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row55_col0\" class=\"data row55 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_ee787551_32cc_11eb_a9d5_3c528213d836row55_col1\" class=\"data row55 col1\" >box-cox</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15768c5e580>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_reg = setup(data = pycaret, target = 'DeathsDiff', session_id=0, normalize=False, \n",
    "                train_size=0.75, categorical_features=['Month','Day']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_0157953a_32cd_11eb_ab12_3c528213d836 th {\n",
       "          text-align: left;\n",
       "    }#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col6,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col0,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col6{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "        }#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col4,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col5,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col2,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col3,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col1,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col6{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  yellow;\n",
       "        }#T_0157953a_32cd_11eb_ab12_3c528213d836row0_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row1_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row2_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row3_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row4_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row5_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row6_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row7_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row8_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row9_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row10_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row11_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row13_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row14_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row15_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row16_col7,#T_0157953a_32cd_11eb_ab12_3c528213d836row17_col7{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  lightgrey;\n",
       "        }#T_0157953a_32cd_11eb_ab12_3c528213d836row12_col7{\n",
       "            text-align:  left;\n",
       "            text-align:  left;\n",
       "            background-color:  yellow;\n",
       "            background-color:  lightgrey;\n",
       "        }</style><table id=\"T_0157953a_32cd_11eb_ab12_3c528213d836\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>        <th class=\"col_heading level0 col7\" >TT (Sec)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row0\" class=\"row_heading level0 row0\" >lasso</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col0\" class=\"data row0 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col1\" class=\"data row0 col1\" >1.0044</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col2\" class=\"data row0 col2\" >2.6196</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col3\" class=\"data row0 col3\" >1.5108</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col4\" class=\"data row0 col4\" >0.2948</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col5\" class=\"data row0 col5\" >0.4911</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col6\" class=\"data row0 col6\" >0.5943</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row0_col7\" class=\"data row0 col7\" >0.0130</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row1\" class=\"row_heading level0 row1\" >en</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col0\" class=\"data row1 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col1\" class=\"data row1 col1\" >1.0042</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col2\" class=\"data row1 col2\" >2.6199</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col3\" class=\"data row1 col3\" >1.5109</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col4\" class=\"data row1 col4\" >0.2948</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col5\" class=\"data row1 col5\" >0.4909</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col6\" class=\"data row1 col6\" >0.5947</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row1_col7\" class=\"data row1 col7\" >0.0120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row2\" class=\"row_heading level0 row2\" >knn</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col0\" class=\"data row2 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col1\" class=\"data row2 col1\" >0.8153</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col2\" class=\"data row2 col2\" >2.2315</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col3\" class=\"data row2 col3\" >1.4357</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col4\" class=\"data row2 col4\" >0.2762</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col5\" class=\"data row2 col5\" >0.4397</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col6\" class=\"data row2 col6\" >0.7076</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row2_col7\" class=\"data row2 col7\" >0.0140</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row3\" class=\"row_heading level0 row3\" >huber</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col0\" class=\"data row3 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col1\" class=\"data row3 col1\" >0.8071</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col2\" class=\"data row3 col2\" >2.5101</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col3\" class=\"data row3 col3\" >1.4925</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col4\" class=\"data row3 col4\" >0.2610</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col5\" class=\"data row3 col5\" >0.4397</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col6\" class=\"data row3 col6\" >0.6261</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row3_col7\" class=\"data row3 col7\" >0.0290</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row4\" class=\"row_heading level0 row4\" >lightgbm</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col0\" class=\"data row4 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col1\" class=\"data row4 col1\" >0.8245</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col2\" class=\"data row4 col2\" >2.0951</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col3\" class=\"data row4 col3\" >1.3988</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col4\" class=\"data row4 col4\" >0.2433</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col5\" class=\"data row4 col5\" >0.4450</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col6\" class=\"data row4 col6\" >0.6732</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row4_col7\" class=\"data row4 col7\" >0.0260</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row5\" class=\"row_heading level0 row5\" >br</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col0\" class=\"data row5 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col1\" class=\"data row5 col1\" >0.9737</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col2\" class=\"data row5 col2\" >2.6449</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col3\" class=\"data row5 col3\" >1.5262</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col4\" class=\"data row5 col4\" >0.2267</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col5\" class=\"data row5 col5\" >0.4839</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col6\" class=\"data row5 col6\" >0.6006</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row5_col7\" class=\"data row5 col7\" >0.0150</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row6\" class=\"row_heading level0 row6\" >et</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col0\" class=\"data row6 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col1\" class=\"data row6 col1\" >0.7696</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col2\" class=\"data row6 col2\" >2.5556</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col3\" class=\"data row6 col3\" >1.5178</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col4\" class=\"data row6 col4\" >0.1749</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col5\" class=\"data row6 col5\" >0.4442</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col6\" class=\"data row6 col6\" >0.6348</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row6_col7\" class=\"data row6 col7\" >0.1230</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row7\" class=\"row_heading level0 row7\" >catboost</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col0\" class=\"data row7 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col1\" class=\"data row7 col1\" >0.8570</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col2\" class=\"data row7 col2\" >2.3486</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col3\" class=\"data row7 col3\" >1.4838</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col4\" class=\"data row7 col4\" >0.1551</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col5\" class=\"data row7 col5\" >0.4441</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col6\" class=\"data row7 col6\" >0.6759</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row7_col7\" class=\"data row7 col7\" >1.1140</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row8\" class=\"row_heading level0 row8\" >rf</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col0\" class=\"data row8 col0\" >Random Forest Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col1\" class=\"data row8 col1\" >0.8110</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col2\" class=\"data row8 col2\" >2.3499</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col3\" class=\"data row8 col3\" >1.4723</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col4\" class=\"data row8 col4\" >0.1445</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col5\" class=\"data row8 col5\" >0.4474</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col6\" class=\"data row8 col6\" >0.6468</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row8_col7\" class=\"data row8 col7\" >0.1410</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row9\" class=\"row_heading level0 row9\" >ada</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col0\" class=\"data row9 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col1\" class=\"data row9 col1\" >1.0312</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col2\" class=\"data row9 col2\" >2.3350</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col3\" class=\"data row9 col3\" >1.4824</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col4\" class=\"data row9 col4\" >0.1330</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col5\" class=\"data row9 col5\" >0.5379</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col6\" class=\"data row9 col6\" >0.5177</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row9_col7\" class=\"data row9 col7\" >0.0440</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row10\" class=\"row_heading level0 row10\" >gbr</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col0\" class=\"data row10 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col1\" class=\"data row10 col1\" >0.8746</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col2\" class=\"data row10 col2\" >2.5151</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col3\" class=\"data row10 col3\" >1.5329</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col4\" class=\"data row10 col4\" >0.0866</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col5\" class=\"data row10 col5\" >0.4669</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col6\" class=\"data row10 col6\" >0.7084</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row10_col7\" class=\"data row10 col7\" >0.0390</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row11\" class=\"row_heading level0 row11\" >omp</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col0\" class=\"data row11 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col1\" class=\"data row11 col1\" >0.8947</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col2\" class=\"data row11 col2\" >2.5398</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col3\" class=\"data row11 col3\" >1.5172</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col4\" class=\"data row11 col4\" >-0.0232</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col5\" class=\"data row11 col5\" >0.4715</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col6\" class=\"data row11 col6\" >0.6665</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row11_col7\" class=\"data row11 col7\" >0.0130</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row12\" class=\"row_heading level0 row12\" >ridge</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col0\" class=\"data row12 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col1\" class=\"data row12 col1\" >1.0666</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col2\" class=\"data row12 col2\" >2.9943</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col3\" class=\"data row12 col3\" >1.6623</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col4\" class=\"data row12 col4\" >-0.2045</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col5\" class=\"data row12 col5\" >0.5479</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col6\" class=\"data row12 col6\" >0.6564</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row12_col7\" class=\"data row12 col7\" >0.0100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row13\" class=\"row_heading level0 row13\" >llar</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col0\" class=\"data row13 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col1\" class=\"data row13 col1\" >1.4534</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col2\" class=\"data row13 col2\" >4.3269</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col3\" class=\"data row13 col3\" >2.0016</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col4\" class=\"data row13 col4\" >-0.2747</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col5\" class=\"data row13 col5\" >0.7435</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col6\" class=\"data row13 col6\" >0.4629</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row13_col7\" class=\"data row13 col7\" >0.0110</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row14\" class=\"row_heading level0 row14\" >dt</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col0\" class=\"data row14 col0\" >Decision Tree Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col1\" class=\"data row14 col1\" >0.9196</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col2\" class=\"data row14 col2\" >3.4565</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col3\" class=\"data row14 col3\" >1.8041</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col4\" class=\"data row14 col4\" >-0.2892</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col5\" class=\"data row14 col5\" >0.5540</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col6\" class=\"data row14 col6\" >0.7429</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row14_col7\" class=\"data row14 col7\" >0.0120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row15\" class=\"row_heading level0 row15\" >lr</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col0\" class=\"data row15 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col1\" class=\"data row15 col1\" >1.1351</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col2\" class=\"data row15 col2\" >3.2520</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col3\" class=\"data row15 col3\" >1.7385</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col4\" class=\"data row15 col4\" >-0.4266</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col5\" class=\"data row15 col5\" >0.5768</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col6\" class=\"data row15 col6\" >0.6966</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row15_col7\" class=\"data row15 col7\" >0.6000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row16\" class=\"row_heading level0 row16\" >par</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col1\" class=\"data row16 col1\" >2.7040</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col2\" class=\"data row16 col2\" >49.8622</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col3\" class=\"data row16 col3\" >4.9473</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col4\" class=\"data row16 col4\" >-15.6351</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col5\" class=\"data row16 col5\" >0.7104</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col6\" class=\"data row16 col6\" >1.8892</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row16_col7\" class=\"data row16 col7\" >0.0130</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0157953a_32cd_11eb_ab12_3c528213d836level0_row17\" class=\"row_heading level0 row17\" >lar</th>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col0\" class=\"data row17 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col1\" class=\"data row17 col1\" >7703.8101</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col2\" class=\"data row17 col2\" >1545888804.2501</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col3\" class=\"data row17 col3\" >13018.4320</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col4\" class=\"data row17 col4\" >-542510564.0134</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col5\" class=\"data row17 col5\" >2.5913</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col6\" class=\"data row17 col6\" >5098.4051</td>\n",
       "                        <td id=\"T_0157953a_32cd_11eb_ab12_3c528213d836row17_col7\" class=\"data row17 col7\" >0.0150</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x157665390d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "      normalize=False, positive=False, precompute=False, random_state=0,\n",
       "      selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "lrprediction = model1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.52302928,  0.52206879, -0.72297798,  0.63594962,  0.80806581,\n",
       "       -0.3672057 ,  3.11115833,  0.63505182,  0.19810741,  1.9885116 ,\n",
       "        2.17430917,  1.75831649, -0.5606298 ,  1.29492258, -0.28667605,\n",
       "       -0.25969866, -0.0579919 ,  0.40014286, -0.0264301 , -0.118329  ,\n",
       "        0.90519578, -0.6247919 ,  1.29413271,  1.1014569 , -0.1272126 ,\n",
       "        1.20515671,  1.89062302,  0.32272939,  0.91263411,  1.41336108,\n",
       "        0.30666462,  0.31243144,  2.98305746,  0.15288449,  0.6306367 ,\n",
       "       -0.41244754,  0.70707757,  2.91743896,  0.00654834, -0.12224363,\n",
       "        1.25507736,  1.35140372,  0.28827555,  0.24357661,  0.21489345,\n",
       "       -0.36687873, -0.81089688,  0.83234452,  2.7627294 , -0.03140478,\n",
       "        0.38029692,  4.80719401,  1.44298252, -0.80238205,  1.34335525,\n",
       "        0.27719264,  2.67624502,  0.27601275,  1.08075557, -0.20336378,\n",
       "       -0.51952615,  1.06713293,  0.42887705,  0.02500505,  2.31610025,\n",
       "        2.51743377, -0.72666368, -0.17666269,  0.98344387,  0.09863617,\n",
       "        3.15737503,  0.34253726,  1.27240564,  0.65861651,  2.43413688,\n",
       "        3.46900514,  2.62969856,  0.51160967])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrprediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  1., -1.,  1.,  1., -0.,  3.,  1.,  0.,  2.,  2.,  2., -1.,\n",
       "        1., -0., -0., -0.,  0., -0., -0.,  1., -1.,  1.,  1., -0.,  1.,\n",
       "        2.,  0.,  1.,  1.,  0.,  0.,  3.,  0.,  1., -0.,  1.,  3.,  0.,\n",
       "       -0.,  1.,  1.,  0.,  0.,  0., -0., -1.,  1.,  3., -0.,  0.,  5.,\n",
       "        1., -1.,  1.,  0.,  3.,  0.,  1., -0., -1.,  1.,  0.,  0.,  2.,\n",
       "        3., -1., -0.,  1.,  0.,  3.,  0.,  1.,  1.,  2.,  3.,  3.,  1.])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrprediction.round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0384615384615383"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test,lrprediction.round())\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.427747014866968"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31916996047430835"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2score = r2_score(y_test,lrprediction.round())\n",
    "r2score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,4))\n",
    "sns.regplot(x=y_test, y=lrprediction.round(), ax=ax)\n",
    "plt.title(\"Plot to compare actual vs predicted - Linear Regression\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extra Trees Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "et = ExtraTreesRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = et.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n",
       "                    max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                    max_samples=None, min_impurity_decrease=0.0,\n",
       "                    min_impurity_split=None, min_samples_leaf=1,\n",
       "                    min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                    n_estimators=100, n_jobs=None, oob_score=False,\n",
       "                    random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "etprediction = model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.17, 0.  , 0.  , 0.  , 0.  , 0.03, 3.21, 0.8 , 0.  , 1.71, 2.62,\n",
       "       1.28, 0.02, 0.  , 0.  , 0.01, 0.37, 0.32, 0.  , 0.48, 0.  , 0.  ,\n",
       "       1.16, 0.28, 0.  , 0.  , 2.32, 0.46, 0.05, 1.96, 0.  , 0.  , 0.03,\n",
       "       0.11, 0.02, 0.  , 0.  , 2.07, 0.  , 0.11, 0.03, 0.23, 0.08, 0.  ,\n",
       "       0.  , 0.  , 0.  , 0.  , 0.29, 0.  , 0.  , 4.35, 0.72, 0.  , 0.38,\n",
       "       0.  , 2.5 , 0.  , 0.04, 0.  , 0.  , 0.04, 0.  , 0.  , 1.16, 1.46,\n",
       "       0.  , 0.05, 0.44, 0.  , 1.54, 0.65, 0.  , 0.  , 1.79, 2.  , 1.89,\n",
       "       0.67])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etprediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 3., 1., 0., 2., 3., 1., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 1., 0., 0., 0., 2., 0., 0., 2., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       4., 1., 0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0.,\n",
       "       0., 0., 2., 1., 0., 0., 2., 2., 2., 1.])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etprediction.round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.282051282051282"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test,etprediction.round())\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5106459817082498"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2378129117259552"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2score = r2_score(y_test,etprediction.round())\n",
    "r2score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,4))\n",
    "sns.regplot(x=y_test, y=etprediction.round(), ax=ax)\n",
    "plt.title(\"Plot to compare actual vs predicted - Extra Trees Regressor\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using XGBoost (Scikit-Learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using RandomSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3 = XGBRegressor(random_state=0, n_estimators=100, objective='reg:squarederror')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'max_depth': np.arange(3,10,1),\n",
    "              'eta': np.arange(0.05,0.3,0.05),\n",
    "              'n_estimators':np.arange(100,1000,100),\n",
    "              'min_child_weight': np.arange(1,4,1),\n",
    "              'gamma':np.arange(0,10,2),\n",
    "              'subsample':np.arange(0.5,0.9,0.1),\n",
    "              'colsample_bytree':np.arange(0.5,0.9,0.1),\n",
    "              'reg_alpha':np.arange(0,1,0.1),\n",
    "              'reg_lambda':np.arange(0,1,0.1)\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "randm = RandomizedSearchCV(estimator=model3, param_distributions = parameters, cv = 5, n_iter = 20, \n",
    "                           n_jobs=-1, scoring='neg_root_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score=nan,\n",
       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, gamma=None,\n",
       "                                          gpu_id=None, importance_type='gain',\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=None,\n",
       "                                          max_delta_step=None, max_depth=None,\n",
       "                                          min_child_weight=None, missing=nan,\n",
       "                                          monotone_constraints=None,\n",
       "                                          n_...\n",
       "                                        'min_child_weight': array([1, 2, 3]),\n",
       "                                        'n_estimators': array([100, 200, 300, 400, 500, 600, 700, 800, 900]),\n",
       "                                        'reg_alpha': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),\n",
       "                                        'reg_lambda': array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),\n",
       "                                        'subsample': array([0.5, 0.6, 0.7, 0.8])},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False,\n",
       "                   scoring='neg_root_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.6, eta=0.05, gamma=4,\n",
       "             gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.0500000007, max_delta_step=0, max_depth=5,\n",
       "             min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=400, n_jobs=0, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=0,\n",
       "             reg_alpha=0.7000000000000001, reg_lambda=0.30000000000000004,\n",
       "             scale_pos_weight=1, subsample=0.5, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randm.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.3828418002128893"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randm.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.5,\n",
       " 'reg_lambda': 0.30000000000000004,\n",
       " 'reg_alpha': 0.7000000000000001,\n",
       " 'n_estimators': 400,\n",
       " 'min_child_weight': 3,\n",
       " 'max_depth': 5,\n",
       " 'gamma': 4,\n",
       " 'eta': 0.05,\n",
       " 'colsample_bytree': 0.6}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randm.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgbmodel = XGBRegressor(random_state=0, n_estimators=400, objective='reg:squarederror',\n",
    "                       subsample=0.5,reg_lambda=0.3,reg_alpha=0.7,min_child_weight=3,max_depth=5,\n",
    "                       gamma=4, eta=0.05,colsample_bytree=0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-rmse:1.75821\n",
      "Will train until validation_0-rmse hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-rmse:1.71623\n",
      "[2]\tvalidation_0-rmse:1.68752\n",
      "[3]\tvalidation_0-rmse:1.64542\n",
      "[4]\tvalidation_0-rmse:1.62503\n",
      "[5]\tvalidation_0-rmse:1.58318\n",
      "[6]\tvalidation_0-rmse:1.56379\n",
      "[7]\tvalidation_0-rmse:1.54993\n",
      "[8]\tvalidation_0-rmse:1.52237\n",
      "[9]\tvalidation_0-rmse:1.49689\n",
      "[10]\tvalidation_0-rmse:1.47791\n",
      "[11]\tvalidation_0-rmse:1.46083\n",
      "[12]\tvalidation_0-rmse:1.43904\n",
      "[13]\tvalidation_0-rmse:1.43089\n",
      "[14]\tvalidation_0-rmse:1.41257\n",
      "[15]\tvalidation_0-rmse:1.39626\n",
      "[16]\tvalidation_0-rmse:1.38596\n",
      "[17]\tvalidation_0-rmse:1.37691\n",
      "[18]\tvalidation_0-rmse:1.37286\n",
      "[19]\tvalidation_0-rmse:1.36382\n",
      "[20]\tvalidation_0-rmse:1.35836\n",
      "[21]\tvalidation_0-rmse:1.35815\n",
      "[22]\tvalidation_0-rmse:1.35958\n",
      "[23]\tvalidation_0-rmse:1.35307\n",
      "[24]\tvalidation_0-rmse:1.35303\n",
      "[25]\tvalidation_0-rmse:1.34221\n",
      "[26]\tvalidation_0-rmse:1.33919\n",
      "[27]\tvalidation_0-rmse:1.33155\n",
      "[28]\tvalidation_0-rmse:1.33006\n",
      "[29]\tvalidation_0-rmse:1.33051\n",
      "[30]\tvalidation_0-rmse:1.33587\n",
      "[31]\tvalidation_0-rmse:1.33602\n",
      "[32]\tvalidation_0-rmse:1.32577\n",
      "[33]\tvalidation_0-rmse:1.33098\n",
      "[34]\tvalidation_0-rmse:1.33098\n",
      "[35]\tvalidation_0-rmse:1.32998\n",
      "[36]\tvalidation_0-rmse:1.33081\n",
      "[37]\tvalidation_0-rmse:1.33100\n",
      "[38]\tvalidation_0-rmse:1.32762\n",
      "[39]\tvalidation_0-rmse:1.31556\n",
      "[40]\tvalidation_0-rmse:1.31411\n",
      "[41]\tvalidation_0-rmse:1.31006\n",
      "[42]\tvalidation_0-rmse:1.30838\n",
      "[43]\tvalidation_0-rmse:1.30557\n",
      "[44]\tvalidation_0-rmse:1.30499\n",
      "[45]\tvalidation_0-rmse:1.30563\n",
      "[46]\tvalidation_0-rmse:1.30046\n",
      "[47]\tvalidation_0-rmse:1.29726\n",
      "[48]\tvalidation_0-rmse:1.29286\n",
      "[49]\tvalidation_0-rmse:1.29207\n",
      "[50]\tvalidation_0-rmse:1.29202\n",
      "[51]\tvalidation_0-rmse:1.29470\n",
      "[52]\tvalidation_0-rmse:1.29148\n",
      "[53]\tvalidation_0-rmse:1.28896\n",
      "[54]\tvalidation_0-rmse:1.28999\n",
      "[55]\tvalidation_0-rmse:1.29026\n",
      "[56]\tvalidation_0-rmse:1.28967\n",
      "[57]\tvalidation_0-rmse:1.28944\n",
      "[58]\tvalidation_0-rmse:1.28951\n",
      "[59]\tvalidation_0-rmse:1.28778\n",
      "[60]\tvalidation_0-rmse:1.28743\n",
      "[61]\tvalidation_0-rmse:1.29161\n",
      "[62]\tvalidation_0-rmse:1.28673\n",
      "[63]\tvalidation_0-rmse:1.28983\n",
      "[64]\tvalidation_0-rmse:1.29113\n",
      "[65]\tvalidation_0-rmse:1.29351\n",
      "[66]\tvalidation_0-rmse:1.29551\n",
      "[67]\tvalidation_0-rmse:1.30079\n",
      "[68]\tvalidation_0-rmse:1.30007\n",
      "[69]\tvalidation_0-rmse:1.30075\n",
      "[70]\tvalidation_0-rmse:1.29939\n",
      "[71]\tvalidation_0-rmse:1.29610\n",
      "[72]\tvalidation_0-rmse:1.29620\n",
      "Stopping. Best iteration:\n",
      "[62]\tvalidation_0-rmse:1.28673\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.6, eta=0.05, gamma=4,\n",
       "             gpu_id=-1, importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.0500000007, max_delta_step=0, max_depth=5,\n",
       "             min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=400, n_jobs=0, num_parallel_tree=1,\n",
       "             objective='reg:squarederror', random_state=0, reg_alpha=0.7,\n",
       "             reg_lambda=0.3, scale_pos_weight=1, subsample=0.5,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbmodel.fit(X_train,y_train,eval_set=[(X_test,y_test)],eval_metric='rmse',early_stopping_rounds=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgbmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.693808  , 0.13319057, 0.05102912, 0.20768976, 0.20737144,\n",
       "       0.24536291, 2.4978304 , 0.986519  , 0.07461962, 2.6818886 ,\n",
       "       3.4523032 , 0.5945045 , 0.21168348, 0.43464845, 0.07461962,\n",
       "       0.43183863, 0.20026293, 1.6112388 , 0.13319057, 2.257338  ,\n",
       "       0.16788667, 0.07461962, 0.8828086 , 0.7581678 , 0.13319057,\n",
       "       0.35158795, 1.9784685 , 0.13319057, 0.33965206, 2.2176018 ,\n",
       "       0.13319057, 0.14977092, 2.1358585 , 0.3020173 , 0.31540662,\n",
       "       0.12845254, 0.08497405, 4.5722485 , 0.15236476, 0.22618872,\n",
       "       0.30875778, 2.0225163 , 0.1952821 , 0.07461962, 0.13319057,\n",
       "       0.13319057, 0.11582598, 0.19766909, 1.9202349 , 0.12877086,\n",
       "       0.20026293, 3.6903598 , 0.86935675, 0.13319057, 1.9299408 ,\n",
       "       0.08497405, 3.348646  , 0.07461962, 0.586531  , 0.07461962,\n",
       "       0.07461962, 0.5962136 , 0.25558794, 0.18085134, 0.89771414,\n",
       "       1.0833393 , 0.09924564, 0.15694445, 1.6898073 , 0.08497405,\n",
       "       2.3818989 , 0.12845254, 0.35158795, 0.39547715, 2.6168175 ,\n",
       "       3.8253388 , 3.27226   , 0.6519383 ], dtype=float32)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2., 0., 0., 0., 0., 0., 2., 1., 0., 3., 3., 1., 0., 0., 0., 0., 0.,\n",
       "       2., 0., 2., 0., 0., 1., 1., 0., 0., 2., 0., 0., 2., 0., 0., 2., 0.,\n",
       "       0., 0., 0., 5., 0., 0., 0., 2., 0., 0., 0., 0., 0., 0., 2., 0., 0.,\n",
       "       4., 1., 0., 2., 0., 3., 0., 1., 0., 0., 1., 0., 0., 1., 1., 0., 0.,\n",
       "       2., 0., 2., 0., 0., 0., 3., 4., 3., 1.], dtype=float32)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.round()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.655666687719013"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test,y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2867271224774166"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4470204145760609"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2score = r2_score(y_test,y_pred)\n",
    "r2score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.regplot(x=y_test, y=y_pred.round(), ax=ax)\n",
    "plt.title(\"Plot to compare actual vs predicted - XGBoost\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Feature Importances (Extra Tree and XGBoost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00002371, 0.01385562, 0.03296161, 0.00464944, 0.00149913,\n",
       "       0.00098501, 0.00020326, 0.00288917, 0.09225506, 0.06298913,\n",
       "       0.00828217, 0.07013692, 0.02320224, 0.01227223, 0.00075352,\n",
       "       0.00315433, 0.01444801, 0.02448371, 0.00714466, 0.00274756,\n",
       "       0.00176685, 0.00506808, 0.01110173, 0.00147041, 0.00071154,\n",
       "       0.00750516, 0.00548574, 0.00214961, 0.00813984, 0.00454469,\n",
       "       0.00369115, 0.04312089, 0.00354092, 0.00839973, 0.00377258,\n",
       "       0.00924342, 0.00740653, 0.02676879, 0.00381528, 0.02986567,\n",
       "       0.28322831, 0.15026656])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "et.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_importances = pd.Series(et.feature_importances_, index=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month_2        0.000024\n",
       "Month_3        0.013856\n",
       "Month_4        0.032962\n",
       "Month_5        0.004649\n",
       "Month_6        0.001499\n",
       "Month_7        0.000985\n",
       "Month_8        0.000203\n",
       "Month_9        0.002889\n",
       "Month_10       0.092255\n",
       "Month_11       0.062989\n",
       "Day_2          0.008282\n",
       "Day_3          0.070137\n",
       "Day_4          0.023202\n",
       "Day_5          0.012272\n",
       "Day_6          0.000754\n",
       "Day_7          0.003154\n",
       "Day_8          0.014448\n",
       "Day_9          0.024484\n",
       "Day_10         0.007145\n",
       "Day_11         0.002748\n",
       "Day_12         0.001767\n",
       "Day_13         0.005068\n",
       "Day_14         0.011102\n",
       "Day_15         0.001470\n",
       "Day_16         0.000712\n",
       "Day_17         0.007505\n",
       "Day_18         0.005486\n",
       "Day_19         0.002150\n",
       "Day_20         0.008140\n",
       "Day_21         0.004545\n",
       "Day_22         0.003691\n",
       "Day_23         0.043121\n",
       "Day_24         0.003541\n",
       "Day_25         0.008400\n",
       "Day_26         0.003773\n",
       "Day_27         0.009243\n",
       "Day_28         0.007407\n",
       "Day_29         0.026769\n",
       "Day_30         0.003815\n",
       "Day_31         0.029866\n",
       "ConfirmDiff    0.283228\n",
       "RecoverDiff    0.150267\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances.nlargest(10).plot(kind='barh', figsize=(10,10))\n",
    "plt.title('Extra Tree Feature Importances')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6', 'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11', 'Day_2', 'Day_3', 'Day_4', 'Day_5', 'Day_6', 'Day_7', 'Day_8', 'Day_9', 'Day_10', 'Day_11', 'Day_12', 'Day_13', 'Day_14', 'Day_15', 'Day_16', 'Day_17', 'Day_18', 'Day_19', 'Day_20', 'Day_21', 'Day_22', 'Day_23', 'Day_24', 'Day_25', 'Day_26', 'Day_27', 'Day_28', 'Day_29', 'Day_30', 'Day_31', 'ConfirmDiff', 'RecoverDiff'], dtype='object')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgbmodel.get_booster().feature_names = ['Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6', 'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11', 'Day_2', 'Day_3', 'Day_4', 'Day_5', 'Day_6', 'Day_7', 'Day_8', 'Day_9', 'Day_10', 'Day_11', 'Day_12', 'Day_13', 'Day_14', 'Day_15', 'Day_16', 'Day_17', 'Day_18', 'Day_19', 'Day_20', 'Day_21', 'Day_22', 'Day_23', 'Day_24', 'Day_25', 'Day_26', 'Day_27', 'Day_28', 'Day_29', 'Day_30', 'Day_31', 'ConfirmDiff', 'RecoverDiff']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "xgb.plot_importance(xgbmodel.get_booster(),ax=ax,title='XGBoost Feature importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.1s finished\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(xgbmodel,X,y,cv=5,verbose=1,scoring='neg_root_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4765106892218895"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Model Prediction on Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "testref = pd.read_csv(\"testcovid.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Month_2</th>\n",
       "      <th>Month_3</th>\n",
       "      <th>Month_4</th>\n",
       "      <th>Month_5</th>\n",
       "      <th>Month_6</th>\n",
       "      <th>Month_7</th>\n",
       "      <th>Month_8</th>\n",
       "      <th>Month_9</th>\n",
       "      <th>Month_10</th>\n",
       "      <th>Month_11</th>\n",
       "      <th>...</th>\n",
       "      <th>Day_25</th>\n",
       "      <th>Day_26</th>\n",
       "      <th>Day_27</th>\n",
       "      <th>Day_28</th>\n",
       "      <th>Day_29</th>\n",
       "      <th>Day_30</th>\n",
       "      <th>Day_31</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1109.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1315.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month_2  Month_3  Month_4  Month_5  Month_6  Month_7  Month_8  Month_9  Month_10  Month_11  ...  Day_25  Day_26  Day_27  Day_28  Day_29  Day_30  Day_31  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0        0        0        0        0        0        0        0        0         0         1  ...       0       1       0       0       0       0       0        935.0       2555.0         3.0\n",
       "1        0        0        0        0        0        0        0        0         0         1  ...       0       0       1       0       0       0       0       1109.0       1148.0         2.0\n",
       "2        0        0        0        0        0        0        0        0         0         1  ...       0       0       0       1       0       0       0       1315.0       1110.0         4.0\n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata = pd.read_csv(\"testcovid2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Month_2</th>\n",
       "      <th>Month_3</th>\n",
       "      <th>Month_4</th>\n",
       "      <th>Month_5</th>\n",
       "      <th>Month_6</th>\n",
       "      <th>Month_7</th>\n",
       "      <th>Month_8</th>\n",
       "      <th>Month_9</th>\n",
       "      <th>Month_10</th>\n",
       "      <th>Month_11</th>\n",
       "      <th>...</th>\n",
       "      <th>Day_24</th>\n",
       "      <th>Day_25</th>\n",
       "      <th>Day_26</th>\n",
       "      <th>Day_27</th>\n",
       "      <th>Day_28</th>\n",
       "      <th>Day_29</th>\n",
       "      <th>Day_30</th>\n",
       "      <th>Day_31</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>935</td>\n",
       "      <td>2555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1109</td>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1315</td>\n",
       "      <td>1110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month_2  Month_3  Month_4  Month_5  Month_6  Month_7  Month_8  Month_9  Month_10  Month_11  ...  Day_24  Day_25  Day_26  Day_27  Day_28  Day_29  Day_30  Day_31  ConfirmDiff  RecoverDiff\n",
       "0        0        0        0        0        0        0        0        0         0         1  ...       0       0       1       0       0       0       0       0          935         2555\n",
       "1        0        0        0        0        0        0        0        0         0         1  ...       0       0       0       1       0       0       0       0         1109         1148\n",
       "2        0        0        0        0        0        0        0        0         0         1  ...       0       0       0       0       1       0       0       0         1315         1110\n",
       "\n",
       "[3 rows x 42 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "testprediction = xgbmodel.predict(testdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.6429937, 3.0189664, 2.5625987], dtype=float32)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testprediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 3., 3.], dtype=float32)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testprediction.round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3.0\n",
       "1    2.0\n",
       "2    4.0\n",
       "Name: DeathsDiff, dtype: float64"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testref[\"DeathsDiff\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'covidmodelNN.sav'\n",
    "dump(xgbmodel,open(filename,'wb'))"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}