analysis_gov_regulation_impact/HealthEco_Component_Feature_Importance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "from path import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# For visualisation \n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Dependencies for preprocessing data for ML\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder,LabelEncoder\n",
    "\n",
    "# Dependencies for nueural network\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Dependencies for SVR model\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# random forest for feature importance on a regression problem\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# # imports for database connection\n",
    "# import sqlalchemy\n",
    "# from sqlalchemy.ext.automap import automap_base\n",
    "# from sqlalchemy import create_engine, func\n",
    "\n",
    "# # import these to view table column headers & rows\n",
    "# from sqlalchemy.orm import sessionmaker\n",
    "# from sqlalchemy import select\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the files\n",
    "file = Path(\"../analysis_gov_regulation_impact/Resources/HealthEcoColumns.csv\")\n",
    "\n",
    "# read the files\n",
    "df= pd.read_csv(file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0                                 int64\n",
      "date                                      object\n",
      "iso_code                                  object\n",
      "E1_Income support                        float64\n",
      "E1_Flag                                  float64\n",
      "E2_Debt/contract relief                  float64\n",
      "E3_Fiscal measures                       float64\n",
      "E4_International support                 float64\n",
      "H1_Public information campaigns          float64\n",
      "H1_Flag                                  float64\n",
      "H2_Testing policy                        float64\n",
      "H3_Contact tracing                       float64\n",
      "H4_Emergency investment in healthcare    float64\n",
      "H5_Investment in vaccines                float64\n",
      "total_cases                              float64\n",
      "total_deaths                             float64\n",
      "new_cases                                float64\n",
      "new_deaths                               float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Check StringencyColumns.csv\n",
    "print(df.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'date', 'iso_code', 'E1_Income support', 'E1_Flag',\n",
       "       'E2_Debt/contract relief', 'E3_Fiscal measures',\n",
       "       'E4_International support', 'H1_Public information campaigns',\n",
       "       'H1_Flag', 'H2_Testing policy', 'H3_Contact tracing',\n",
       "       'H4_Emergency investment in healthcare', 'H5_Investment in vaccines',\n",
       "       'total_cases', 'total_deaths', 'new_cases', 'new_deaths'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0                                 int64\n",
      "date                                       int32\n",
      "iso_code                                  object\n",
      "E1_Income support                        float64\n",
      "E1_Flag                                  float64\n",
      "E2_Debt/contract relief                  float64\n",
      "E3_Fiscal measures                       float64\n",
      "E4_International support                 float64\n",
      "H1_Public information campaigns          float64\n",
      "H1_Flag                                  float64\n",
      "H2_Testing policy                        float64\n",
      "H3_Contact tracing                       float64\n",
      "H4_Emergency investment in healthcare    float64\n",
      "H5_Investment in vaccines                float64\n",
      "total_cases                              float64\n",
      "total_deaths                             float64\n",
      "new_cases                                float64\n",
      "new_deaths                               float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Encode the date column to int\n",
    "le = LabelEncoder()\n",
    "df['date'] = le.fit_transform(df['date'])\n",
    "print(df.dtypes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>iso_code</th>\n",
       "      <th>E1_Income support</th>\n",
       "      <th>E2_Debt/contract relief</th>\n",
       "      <th>E3_Fiscal measures</th>\n",
       "      <th>E4_International support</th>\n",
       "      <th>H2_Testing policy</th>\n",
       "      <th>H3_Contact tracing</th>\n",
       "      <th>H4_Emergency investment in healthcare</th>\n",
       "      <th>H5_Investment in vaccines</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</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>79</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81</td>\n",
       "      <td>ABW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48523</th>\n",
       "      <td>239</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6251.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48524</th>\n",
       "      <td>240</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6292.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48525</th>\n",
       "      <td>241</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6388.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48526</th>\n",
       "      <td>242</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6406.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48527</th>\n",
       "      <td>243</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6412.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48528 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       date iso_code  E1_Income support  E2_Debt/contract relief  \\\n",
       "0        72      ABW                0.0                      0.0   \n",
       "1        78      ABW                0.0                      0.0   \n",
       "2        79      ABW                0.0                      0.0   \n",
       "3        80      ABW                0.0                      0.0   \n",
       "4        81      ABW                0.0                      0.0   \n",
       "...     ...      ...                ...                      ...   \n",
       "48523   239      ZWE                0.0                      0.0   \n",
       "48524   240      ZWE                0.0                      0.0   \n",
       "48525   241      ZWE                0.0                      0.0   \n",
       "48526   242      ZWE                0.0                      0.0   \n",
       "48527   243      ZWE                0.0                      0.0   \n",
       "\n",
       "       E3_Fiscal measures  E4_International support  H2_Testing policy  \\\n",
       "0                     0.0                       0.0                1.0   \n",
       "1                     0.0                       0.0                1.0   \n",
       "2                     0.0                       0.0                1.0   \n",
       "3                     0.0                       0.0                1.0   \n",
       "4                     0.0                       0.0                1.0   \n",
       "...                   ...                       ...                ...   \n",
       "48523                 0.0                       0.0                0.0   \n",
       "48524                 0.0                       0.0                0.0   \n",
       "48525                 0.0                       0.0                0.0   \n",
       "48526                 0.0                       0.0                0.0   \n",
       "48527                 0.0                       0.0                0.0   \n",
       "\n",
       "       H3_Contact tracing  H4_Emergency investment in healthcare  \\\n",
       "0                     0.0                                    0.0   \n",
       "1                     1.0                                    0.0   \n",
       "2                     1.0                                    0.0   \n",
       "3                     1.0                                    0.0   \n",
       "4                     1.0                                    0.0   \n",
       "...                   ...                                    ...   \n",
       "48523                 0.0                                    0.0   \n",
       "48524                 0.0                                    0.0   \n",
       "48525                 0.0                                    0.0   \n",
       "48526                 0.0                                    0.0   \n",
       "48527                 0.0                                    0.0   \n",
       "\n",
       "       H5_Investment in vaccines  total_cases  total_deaths  new_cases  \\\n",
       "0                            0.0          2.0           0.0        2.0   \n",
       "1                            0.0          2.0           0.0        0.0   \n",
       "2                            0.0          4.0           0.0        2.0   \n",
       "3                            0.0          4.0           0.0        0.0   \n",
       "4                            0.0          4.0           0.0        0.0   \n",
       "...                          ...          ...           ...        ...   \n",
       "48523                        0.0       6251.0         179.0       55.0   \n",
       "48524                        0.0       6292.0         189.0       41.0   \n",
       "48525                        0.0       6388.0         195.0       96.0   \n",
       "48526                        0.0       6406.0         196.0       18.0   \n",
       "48527                        0.0       6412.0         196.0        6.0   \n",
       "\n",
       "       new_deaths  \n",
       "0             0.0  \n",
       "1             0.0  \n",
       "2             0.0  \n",
       "3             0.0  \n",
       "4             0.0  \n",
       "...           ...  \n",
       "48523        13.0  \n",
       "48524        10.0  \n",
       "48525         6.0  \n",
       "48526         1.0  \n",
       "48527         0.0  \n",
       "\n",
       "[48528 rows x 14 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting only the relevant columns (minus the flags and H1;part of stringency Index)\n",
    "\n",
    "df=df[['date', 'iso_code', 'E1_Income support',\n",
    "       'E2_Debt/contract relief', 'E3_Fiscal measures',\n",
    "       'E4_International support','H2_Testing policy', 'H3_Contact tracing',\n",
    "       'H4_Emergency investment in healthcare', 'H5_Investment in vaccines',\n",
    "       'total_cases', 'total_deaths', 'new_cases', 'new_deaths']]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>E1_Income support</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E1_Income support</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.335554</td>\n",
       "      <td>0.420125</td>\n",
       "      <td>0.448207</td>\n",
       "      <td>0.449755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_cases</th>\n",
       "      <td>0.335554</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965935</td>\n",
       "      <td>0.893323</td>\n",
       "      <td>0.558573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_deaths</th>\n",
       "      <td>0.420125</td>\n",
       "      <td>0.965935</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.903863</td>\n",
       "      <td>0.624765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases</th>\n",
       "      <td>0.448207</td>\n",
       "      <td>0.893323</td>\n",
       "      <td>0.903863</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.721106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_deaths</th>\n",
       "      <td>0.449755</td>\n",
       "      <td>0.558573</td>\n",
       "      <td>0.624765</td>\n",
       "      <td>0.721106</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   E1_Income support  total_cases  total_deaths  new_cases  \\\n",
       "E1_Income support           1.000000     0.335554      0.420125   0.448207   \n",
       "total_cases                 0.335554     1.000000      0.965935   0.893323   \n",
       "total_deaths                0.420125     0.965935      1.000000   0.903863   \n",
       "new_cases                   0.448207     0.893323      0.903863   1.000000   \n",
       "new_deaths                  0.449755     0.558573      0.624765   0.721106   \n",
       "\n",
       "                   new_deaths  \n",
       "E1_Income support    0.449755  \n",
       "total_cases          0.558573  \n",
       "total_deaths         0.624765  \n",
       "new_cases            0.721106  \n",
       "new_deaths           1.000000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking corr for between \"income support\" and the target variables.\n",
    "df[['E1_Income support','total_cases', 'total_deaths', 'new_cases', 'new_deaths']].corr()\n",
    "\n",
    "# Finindg:Its a moderate positive corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking correlation for feature selection.\n",
    "\n",
    "def correlation_heatmap(df):\n",
    "    correlations = df.corr()\n",
    "    mask = np.zeros_like(correlations)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "    with sns.axes_style(\"white\"):\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(15,15))\n",
    "        sns.heatmap(correlations,cmap='viridis', mask=mask,vmax=1.0, center=0, fmt='.2f',\n",
    "                square=True, linewidths=.5, annot=True, cbar_kws={\"shrink\": .70})\n",
    "    plt.show();\n",
    "    \n",
    "correlation_heatmap(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DF Total Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>E1_Income support</th>\n",
       "      <th>E2_Debt/contract relief</th>\n",
       "      <th>E3_Fiscal measures</th>\n",
       "      <th>E4_International support</th>\n",
       "      <th>H2_Testing policy</th>\n",
       "      <th>H3_Contact tracing</th>\n",
       "      <th>H4_Emergency investment in healthcare</th>\n",
       "      <th>H5_Investment in vaccines</th>\n",
       "      <th>total_deaths</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   E1_Income support  E2_Debt/contract relief  E3_Fiscal measures  \\\n",
       "0                0.0                      0.0                 0.0   \n",
       "1                0.0                      0.0                 0.0   \n",
       "2                0.0                      0.0                 0.0   \n",
       "3                0.0                      0.0                 0.0   \n",
       "4                0.0                      0.0                 0.0   \n",
       "\n",
       "   E4_International support  H2_Testing policy  H3_Contact tracing  \\\n",
       "0                       0.0                1.0                 0.0   \n",
       "1                       0.0                1.0                 1.0   \n",
       "2                       0.0                1.0                 1.0   \n",
       "3                       0.0                1.0                 1.0   \n",
       "4                       0.0                1.0                 1.0   \n",
       "\n",
       "   H4_Emergency investment in healthcare  H5_Investment in vaccines  \\\n",
       "0                                    0.0                        0.0   \n",
       "1                                    0.0                        0.0   \n",
       "2                                    0.0                        0.0   \n",
       "3                                    0.0                        0.0   \n",
       "4                                    0.0                        0.0   \n",
       "\n",
       "   total_deaths  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df to test impact of some features on 'total deaths'(isolating on index features to see their impact)\n",
    "df_td=df[['E1_Income support',\n",
    "       'E2_Debt/contract relief', 'E3_Fiscal measures',\n",
    "       'E4_International support','H2_Testing policy', 'H3_Contact tracing',\n",
    "       'H4_Emergency investment in healthcare', 'H5_Investment in vaccines',\n",
    "       'total_deaths']]\n",
    "\n",
    "df_td.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_heatmap(df_td)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E1_Income support                        float64\n",
       "E2_Debt/contract relief                  float64\n",
       "E3_Fiscal measures                       float64\n",
       "E4_International support                 float64\n",
       "H2_Testing policy                        float64\n",
       "H3_Contact tracing                       float64\n",
       "H4_Emergency investment in healthcare    float64\n",
       "H5_Investment in vaccines                float64\n",
       "total_deaths                             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_td.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create features(X) and target(y) sets\n",
    "X = df_td.drop(['total_deaths'],axis=1).values\n",
    "\n",
    "y = df_td['total_deaths'].values\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into training and testing\n",
    "# train 95% of data, test 5%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)\n",
    "\n",
    "# scale data\n",
    "X_scaler = StandardScaler()\n",
    "X_scaler.fit(X_train)\n",
    "\n",
    "y_scaler= StandardScaler()\n",
    "y_scaler.fit(y_train)\n",
    "\n",
    "y_train_scaled=y_scaler.transform(y_train)\n",
    "y_test_scaled=y_scaler.transform(y_test)\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " the mean_squared error is :0.56327 and the r2_score is:0.394\n",
      "------------------------------------\n",
      "Feature: 0, Score: 0.60229\n",
      "Feature: 1, Score: 0.13593\n",
      "Feature: 2, Score: 0.01007\n",
      "Feature: 3, Score: 0.00107\n",
      "Feature: 4, Score: 0.18265\n",
      "Feature: 5, Score: 0.05667\n",
      "Feature: 6, Score: 0.00803\n",
      "Feature: 7, Score: 0.00329\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the model\n",
    "model = RandomForestRegressor()\n",
    "# fit the model\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# predict y\n",
    "y_pred=model.predict(X_test_scaled)\n",
    "# print(y_pred)\n",
    "\n",
    "# calculate mean square error\n",
    "mse=mean_squared_error(y_test_scaled,y_pred)\n",
    "\n",
    "# calculate r2 score\n",
    "r2=r2_score(y_test_scaled, y_pred)\n",
    "\n",
    "print(f' the mean_squared error is :{mse:.5f} and the r2_score is:{r2:.3f}')\n",
    "\n",
    "print('------------------------------------')\n",
    "\n",
    "# get importance\n",
    "importance = model.feature_importances_\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "    \n",
    "    \n",
    "print('--------------------------------------')\n",
    "\n",
    "# plot feature importance\n",
    "labels =[ 'Income support',\n",
    "       'Debt/contract relief', 'Fiscal measures',\n",
    "       'International support','Testing policy', 'Contact tracing',\n",
    "       'Emergency investment in healthcare', 'Investment in vaccines']\n",
    "\n",
    "plt.bar([x for x in range(len(importance))], importance*100)\n",
    "plt.xticks(range(len(importance)),labels, rotation=90, size=10)\n",
    "plt.ylabel('Score', size=14)\n",
    "plt.xlabel('Health & Economic Index', size=14)\n",
    "plt.title('Feature Importance for Total Deaths', size=16)\n",
    "plt.savefig('../analysis_gov_regulation_impact/plotly charts/images/Feature_imp_td(HE).png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60.2287351 , 13.59341514,  1.00693802,  0.10658881, 18.26493219,\n",
       "        5.66735638,  0.80347334,  0.32856102])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "sizes = [60.55675638, 13.32086447,  1.01575692,  0.12869346, 17.93738565,\n",
    "        5.88063721,  0.74596827,  0.41393765]\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=100,rotatelabels =True)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total cases Df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>E1_Income support</th>\n",
       "      <th>E2_Debt/contract relief</th>\n",
       "      <th>E3_Fiscal measures</th>\n",
       "      <th>E4_International support</th>\n",
       "      <th>H2_Testing policy</th>\n",
       "      <th>H3_Contact tracing</th>\n",
       "      <th>H4_Emergency investment in healthcare</th>\n",
       "      <th>H5_Investment in vaccines</th>\n",
       "      <th>total_cases</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   E1_Income support  E2_Debt/contract relief  E3_Fiscal measures  \\\n",
       "0                0.0                      0.0                 0.0   \n",
       "1                0.0                      0.0                 0.0   \n",
       "2                0.0                      0.0                 0.0   \n",
       "3                0.0                      0.0                 0.0   \n",
       "4                0.0                      0.0                 0.0   \n",
       "\n",
       "   E4_International support  H2_Testing policy  H3_Contact tracing  \\\n",
       "0                       0.0                1.0                 0.0   \n",
       "1                       0.0                1.0                 1.0   \n",
       "2                       0.0                1.0                 1.0   \n",
       "3                       0.0                1.0                 1.0   \n",
       "4                       0.0                1.0                 1.0   \n",
       "\n",
       "   H4_Emergency investment in healthcare  H5_Investment in vaccines  \\\n",
       "0                                    0.0                        0.0   \n",
       "1                                    0.0                        0.0   \n",
       "2                                    0.0                        0.0   \n",
       "3                                    0.0                        0.0   \n",
       "4                                    0.0                        0.0   \n",
       "\n",
       "   total_cases  \n",
       "0          2.0  \n",
       "1          2.0  \n",
       "2          4.0  \n",
       "3          4.0  \n",
       "4          4.0  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df to test impact of some features on 'total casess'\n",
    "df_tc=df[['E1_Income support',\n",
    "       'E2_Debt/contract relief', 'E3_Fiscal measures',\n",
    "       'E4_International support','H2_Testing policy', 'H3_Contact tracing',\n",
    "       'H4_Emergency investment in healthcare', 'H5_Investment in vaccines',\n",
    "       'total_cases']]\n",
    "df_tc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_heatmap(df_tc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create features(X) and target(y) sets\n",
    "X = df_tc.drop(['total_cases'],axis=1).values\n",
    "\n",
    "y = df_tc['total_cases'].values\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into training and testing\n",
    "# train 95% of data, test 5%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)\n",
    "\n",
    "# scale data\n",
    "X_scaler = StandardScaler()\n",
    "X_scaler.fit(X_train)\n",
    "\n",
    "y_scaler= StandardScaler()\n",
    "y_scaler.fit(y_train)\n",
    "\n",
    "y_train_scaled=y_scaler.transform(y_train)\n",
    "y_test_scaled=y_scaler.transform(y_test)\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " the mean_squared error is :0.70311 and the r2_score is:0.287\n",
      "------------------------------------\n",
      "Feature: 0, Score: 0.52166\n",
      "Feature: 1, Score: 0.15984\n",
      "Feature: 2, Score: 0.01109\n",
      "Feature: 3, Score: 0.00134\n",
      "Feature: 4, Score: 0.23823\n",
      "Feature: 5, Score: 0.05243\n",
      "Feature: 6, Score: 0.01029\n",
      "Feature: 7, Score: 0.00512\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the model\n",
    "model = RandomForestRegressor()\n",
    "# fit the model\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# predict y\n",
    "y_pred=model.predict(X_test_scaled)\n",
    "# print(y_pred)\n",
    "\n",
    "# calculate mean square error\n",
    "mse=mean_squared_error(y_test_scaled,y_pred)\n",
    "\n",
    "# calculate r2 score\n",
    "r2=r2_score(y_test_scaled, y_pred)\n",
    "\n",
    "print(f' the mean_squared error is :{mse:.5f} and the r2_score is:{r2:.3f}')\n",
    "\n",
    "print('------------------------------------')\n",
    "\n",
    "# get importance\n",
    "importance = model.feature_importances_\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "    \n",
    "    \n",
    "print('--------------------------------------')\n",
    "\n",
    "# plot feature importance\n",
    "labels =['Income support',\n",
    "       'Debt/contract relief', 'Fiscal measures',\n",
    "       'International support','Testing policy', 'Contact tracing',\n",
    "       'Emergency investment in healthcare', 'Investment in vaccines']\n",
    "\n",
    "plt.bar([x for x in range(len(importance))], importance*100)\n",
    "plt.xticks(range(len(importance)),labels, rotation=90, size=10)\n",
    "plt.ylabel('Score', size=14)\n",
    "plt.xlabel('Health & Economic Index', size=14)\n",
    "plt.title('Feature Importance for Total Cases', size=16)\n",
    "plt.savefig('../analysis_gov_regulation_impact/plotly charts/images/Feature_imp_tc(HE).png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([52.16593793, 15.98444484,  1.10877975,  0.13402973, 23.82304834,\n",
       "        5.24305462,  1.02892765,  0.51177714])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DF New Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>E1_Income support</th>\n",
       "      <th>E2_Debt/contract relief</th>\n",
       "      <th>E3_Fiscal measures</th>\n",
       "      <th>E4_International support</th>\n",
       "      <th>H2_Testing policy</th>\n",
       "      <th>H3_Contact tracing</th>\n",
       "      <th>H4_Emergency investment in healthcare</th>\n",
       "      <th>H5_Investment in vaccines</th>\n",
       "      <th>new_deaths</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   E1_Income support  E2_Debt/contract relief  E3_Fiscal measures  \\\n",
       "0                0.0                      0.0                 0.0   \n",
       "1                0.0                      0.0                 0.0   \n",
       "2                0.0                      0.0                 0.0   \n",
       "3                0.0                      0.0                 0.0   \n",
       "4                0.0                      0.0                 0.0   \n",
       "\n",
       "   E4_International support  H2_Testing policy  H3_Contact tracing  \\\n",
       "0                       0.0                1.0                 0.0   \n",
       "1                       0.0                1.0                 1.0   \n",
       "2                       0.0                1.0                 1.0   \n",
       "3                       0.0                1.0                 1.0   \n",
       "4                       0.0                1.0                 1.0   \n",
       "\n",
       "   H4_Emergency investment in healthcare  H5_Investment in vaccines  \\\n",
       "0                                    0.0                        0.0   \n",
       "1                                    0.0                        0.0   \n",
       "2                                    0.0                        0.0   \n",
       "3                                    0.0                        0.0   \n",
       "4                                    0.0                        0.0   \n",
       "\n",
       "   new_deaths  \n",
       "0         0.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df to test impact of some features on 'new deaths'\n",
    "df_nd=df[['E1_Income support',\n",
    "       'E2_Debt/contract relief', 'E3_Fiscal measures',\n",
    "       'E4_International support','H2_Testing policy', 'H3_Contact tracing',\n",
    "       'H4_Emergency investment in healthcare', 'H5_Investment in vaccines',\n",
    "       'new_deaths']]\n",
    "df_nd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_heatmap(df_nd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create features(X) and target(y) sets\n",
    "X = df_nd.drop(['new_deaths'],axis=1).values\n",
    "\n",
    "y = df_nd['new_deaths'].values\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into training and testing\n",
    "# train 95% of data, test 5%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)\n",
    "\n",
    "# scale data\n",
    "X_scaler = StandardScaler()\n",
    "X_scaler.fit(X_train)\n",
    "\n",
    "y_scaler= StandardScaler()\n",
    "y_scaler.fit(y_train)\n",
    "\n",
    "y_train_scaled=y_scaler.transform(y_train)\n",
    "y_test_scaled=y_scaler.transform(y_test)\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " the mean_squared error is :0.63219 and the r2_score is:0.385\n",
      "------------------------------------\n",
      "Feature: 0, Score: 0.64183\n",
      "Feature: 1, Score: 0.11606\n",
      "Feature: 2, Score: 0.01761\n",
      "Feature: 3, Score: 0.00193\n",
      "Feature: 4, Score: 0.02929\n",
      "Feature: 5, Score: 0.18200\n",
      "Feature: 6, Score: 0.00801\n",
      "Feature: 7, Score: 0.00327\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the model\n",
    "model = RandomForestRegressor()\n",
    "# fit the model\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# predict y\n",
    "y_pred=model.predict(X_test_scaled)\n",
    "# print(y_pred)\n",
    "\n",
    "# calculate mean square error\n",
    "mse=mean_squared_error(y_test_scaled,y_pred)\n",
    "\n",
    "# calculate r2 score\n",
    "r2=r2_score(y_test_scaled, y_pred)\n",
    "\n",
    "print(f' the mean_squared error is :{mse:.5f} and the r2_score is:{r2:.3f}')\n",
    "\n",
    "print('------------------------------------')\n",
    "\n",
    "# get importance\n",
    "importance = model.feature_importances_\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "    \n",
    "    \n",
    "print('--------------------------------------')\n",
    "\n",
    "# plot feature importance\n",
    "labels =['Income support',\n",
    "       'Debt/contract relief', 'Fiscal measures',\n",
    "       'International support','Testing policy', 'Contact tracing',\n",
    "       'Emergency investment in healthcare', 'Investment in vaccines']\n",
    "\n",
    "plt.bar([x for x in range(len(importance))], importance*100)\n",
    "plt.xticks(range(len(importance)),labels, rotation=90, size=10)\n",
    "plt.ylabel('Score', size=14)\n",
    "plt.xlabel('Health & Economic Index', size=14)\n",
    "plt.title('Feature Importance for Total Cases', size=16)\n",
    "plt.savefig('../analysis_gov_regulation_impact/plotly charts/images/Feature_imp_nd(HE).png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64.18275456, 11.60605788,  1.76144417,  0.19285352,  2.92924237,\n",
       "       18.19982273,  0.80085495,  0.32696983])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DF New Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>E1_Income support</th>\n",
       "      <th>E2_Debt/contract relief</th>\n",
       "      <th>E3_Fiscal measures</th>\n",
       "      <th>E4_International support</th>\n",
       "      <th>H2_Testing policy</th>\n",
       "      <th>H3_Contact tracing</th>\n",
       "      <th>H4_Emergency investment in healthcare</th>\n",
       "      <th>H5_Investment in vaccines</th>\n",
       "      <th>new_cases</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   E1_Income support  E2_Debt/contract relief  E3_Fiscal measures  \\\n",
       "0                0.0                      0.0                 0.0   \n",
       "1                0.0                      0.0                 0.0   \n",
       "2                0.0                      0.0                 0.0   \n",
       "3                0.0                      0.0                 0.0   \n",
       "4                0.0                      0.0                 0.0   \n",
       "\n",
       "   E4_International support  H2_Testing policy  H3_Contact tracing  \\\n",
       "0                       0.0                1.0                 0.0   \n",
       "1                       0.0                1.0                 1.0   \n",
       "2                       0.0                1.0                 1.0   \n",
       "3                       0.0                1.0                 1.0   \n",
       "4                       0.0                1.0                 1.0   \n",
       "\n",
       "   H4_Emergency investment in healthcare  H5_Investment in vaccines  new_cases  \n",
       "0                                    0.0                        0.0        2.0  \n",
       "1                                    0.0                        0.0        0.0  \n",
       "2                                    0.0                        0.0        2.0  \n",
       "3                                    0.0                        0.0        0.0  \n",
       "4                                    0.0                        0.0        0.0  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df to test impact of some features on 'new deaths'\n",
    "df_nc=df[['E1_Income support',\n",
    "       'E2_Debt/contract relief', 'E3_Fiscal measures',\n",
    "       'E4_International support','H2_Testing policy', 'H3_Contact tracing',\n",
    "       'H4_Emergency investment in healthcare', 'H5_Investment in vaccines',\n",
    "       'new_cases']]\n",
    "df_nc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_heatmap(df_nc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create features(X) and target(y) sets\n",
    "X = df_nc.drop(['new_cases'],axis=1).values\n",
    "\n",
    "y = df_nc['new_cases'].values\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into training and testing\n",
    "# train 95% of data, test 5%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)\n",
    "\n",
    "# scale data\n",
    "X_scaler = StandardScaler()\n",
    "X_scaler.fit(X_train)\n",
    "\n",
    "y_scaler= StandardScaler()\n",
    "y_scaler.fit(y_train)\n",
    "\n",
    "y_train_scaled=y_scaler.transform(y_train)\n",
    "y_test_scaled=y_scaler.transform(y_test)\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " the mean_squared error is :0.60446 and the r2_score is:0.392\n",
      "------------------------------------\n",
      "Feature: 0, Score: 0.63710\n",
      "Feature: 1, Score: 0.18748\n",
      "Feature: 2, Score: 0.01267\n",
      "Feature: 3, Score: 0.00104\n",
      "Feature: 4, Score: 0.08434\n",
      "Feature: 5, Score: 0.06408\n",
      "Feature: 6, Score: 0.00893\n",
      "Feature: 7, Score: 0.00436\n",
      "--------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the model\n",
    "model = RandomForestRegressor()\n",
    "# fit the model\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# predict y\n",
    "y_pred=model.predict(X_test_scaled)\n",
    "# print(y_pred)\n",
    "\n",
    "# calculate mean square error\n",
    "mse=mean_squared_error(y_test_scaled,y_pred)\n",
    "\n",
    "# calculate r2 score\n",
    "r2=r2_score(y_test_scaled, y_pred)\n",
    "\n",
    "print(f' the mean_squared error is :{mse:.5f} and the r2_score is:{r2:.3f}')\n",
    "\n",
    "print('------------------------------------')\n",
    "\n",
    "# get importance\n",
    "importance = model.feature_importances_\n",
    "\n",
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
    "    \n",
    "    \n",
    "print('--------------------------------------')\n",
    "\n",
    "# plot feature importance\n",
    "labels =['Income support',\n",
    "       'Debt/contract relief', 'Fiscal measures',\n",
    "       'International support','Testing policy', 'Contact tracing',\n",
    "       'Emergency investment in healthcare', 'Investment in vaccines']\n",
    "\n",
    "plt.bar([x for x in range(len(importance))], importance*100)\n",
    "plt.xticks(range(len(importance)),labels, rotation=90, size=10)\n",
    "plt.ylabel('Score', size=14)\n",
    "plt.xlabel('Health & Economic Index', size=14)\n",
    "plt.title('Feature Importance for Total Cases', size=16)\n",
    "plt.savefig('../analysis_gov_regulation_impact/plotly charts/images/Feature_imp_nc(HE).png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([63.7101816 , 18.74793541,  1.26690053,  0.10432169,  8.43415722,\n",
       "        6.40809009,  0.89290341,  0.43551006])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}