analysis_gov_regulation_impact/FeatureSelection_FeatureImportance_totaldeaths.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NNL:\n",
    "The best model for predicting total deaths was deep neural network with two hidden layers and 3x neurons. the mean_squared error is :0.0024 and the r2_score is:0.996\n",
    "\n",
    "# RandomForestRegressor:\n",
    " the mean_squared error is :0.00012 and the r2_score is:1.000\n",
    "\n",
    "# SVR:\n",
    "The best model was with linear kernel. the mean_squared error is :54975202.47 and the r2_score is:0.59"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"sqlite:///../analysis_gov_regulation_impact/Resources/covid_db.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reflect an existing database into a new model (creates base class for automap schema)\n",
    "Base = automap_base()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reflect the tables\n",
    "Base.prepare(engine, reflect=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['covid_stats', 'government_regulation']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Base.classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save references to each table\n",
    "covid_stats = Base.classes.covid_stats\n",
    "government_regulation = Base.classes.government_regulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create session (link) from Python to the database\n",
    "session = sessionmaker(bind=engine)()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View table column headers & rows - covid_stats\n",
    "covid_stats_table = select('*').select_from(covid_stats)\n",
    "covid_stats_result = session.execute(covid_stats_table).fetchall()\n",
    "# print(covid_stats.__table__.columns.keys())\n",
    "# print(covid_stats_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# View table column headers & rows - government_regulation\n",
    "government_regulation_table = select('*').select_from(government_regulation)\n",
    "government_regulation_result = session.execute(government_regulation_table).fetchall()\n",
    "# print(government_regulation.__table__.columns.keys())\n",
    "# print(government_regulation_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge covid_stats_table with government regulation_table\n",
    "joined = session.query(covid_stats.primary_key_reaction,\\\n",
    "                        covid_stats.date_updated,\\\n",
    "                        covid_stats.iso_code,\\\n",
    "                        covid_stats.continent,\\\n",
    "                        covid_stats.location,\\\n",
    "                        covid_stats.population,\\\n",
    "                        covid_stats.population_density,\\\n",
    "                        covid_stats.median_age,\\\n",
    "                        covid_stats.new_cases,\\\n",
    "                        covid_stats.percentage_of_Pop_New_Cases,\\\n",
    "                        covid_stats.new_deaths,\\\n",
    "                        covid_stats.percentage_of_Pop_New_Deaths,\\\n",
    "                        covid_stats.new_tests,\\\n",
    "                        covid_stats.percentage_of_Pop_New_Tests,\\\n",
    "                        covid_stats.total_cases_updated,\\\n",
    "                        covid_stats.percentage_of_Pop_Total_Cases,\\\n",
    "                        covid_stats.total_deaths_updated,\\\n",
    "                        covid_stats.percentage_of_Pop_Total_Deaths,\\\n",
    "                        covid_stats.total_tests_updated,\\\n",
    "                        covid_stats.percentage_of_Pop_Total_Tests,\\\n",
    "                       government_regulation.primary_key_response,\\\n",
    "                       government_regulation.StringencyIndex_updated,\\\n",
    "                       government_regulation.percentage_change_stringency,\\\n",
    "                       government_regulation.StringencyLegacyIndex_updated,\\\n",
    "                       government_regulation.GovernmentResponseIndex_updated,\\\n",
    "                       government_regulation.percentage_change_GovernmentResponse,\\\n",
    "                       government_regulation.ContainmentHealthIndex_updated,\\\n",
    "                       government_regulation.percentage_change_ContainmentHealth,\\\n",
    "                       government_regulation.EconomicSupportIndex_updated,\\\n",
    "                       government_regulation.percentage_change_EconomicSupport)\\\n",
    "        .join(government_regulation,government_regulation.primary_key_response == covid_stats.primary_key_reaction)\n",
    "\n",
    "# save the query results as a Pandas DataFrame and set the index\n",
    "df = pd.DataFrame(joined, columns=['primary_key_reaction',\n",
    "                                             'date_updated',\n",
    "                                             'iso_code',\n",
    "                                             'continent',\n",
    "                                             'location',\n",
    "                                             'population',\n",
    "                                             'population_density',\n",
    "                                             'median_age',\n",
    "                                             'new_cases',\n",
    "                                             'percentage_of_Pop_New_Cases',\n",
    "                                             'new_deaths',\n",
    "                                             'percentage_of_Pop_New_Deaths',\n",
    "                                             'new_tests',\n",
    "                                             'percentage_of_Pop_New_Tests',\n",
    "                                             'total_cases_updated',\n",
    "                                             'percentage_of_Pop_Total_Cases',\n",
    "                                             'total_deaths_updated',\n",
    "                                             'percentage_of_Pop_Total_Deaths',\n",
    "                                             'total_tests_updated',\n",
    "                                             'percentage_of_Pop_Total_Tests',\n",
    "                                             'primary_key_response',\n",
    "                                             'StringencyIndex_updated',\n",
    "                                             'percentage_change_stringency',\n",
    "                                             'StringencyLegacyIndex_updated',\n",
    "                                             'GovernmentResponseIndex_updated',\n",
    "                                             'percentage_change_GovernmentResponse',\n",
    "                                             'ContainmentHealthIndex_updated',\n",
    "                                             'percentage_change_ContainmentHealth',\n",
    "                                             'EconomicSupportIndex_updated',\n",
    "                                             'percentage_change_EconomicSupport'\n",
    "                                             ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "    .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>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>15.015601</td>\n",
       "      <td>34.85</td>\n",
       "      <td>15.016502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>44.44</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>26.076636</td>\n",
       "      <td>43.94</td>\n",
       "      <td>26.083214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>44.44</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.94</td>\n",
       "      <td>0.000000</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",
       "      <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>32272</th>\n",
       "      <td>ZWE2020-08-27</td>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>55</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-27</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32273</th>\n",
       "      <td>ZWE2020-08-28</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>41</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-28</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32274</th>\n",
       "      <td>ZWE2020-08-29</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>96</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-29</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32275</th>\n",
       "      <td>ZWE2020-08-30</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-30</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32276</th>\n",
       "      <td>ZWE2020-08-31</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-31</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32277 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      primary_key_reaction date_updated iso_code      continent  location  \\\n",
       "0            ABW2020-03-13   2020-03-13      ABW  North America     Aruba   \n",
       "1            ABW2020-03-19   2020-03-19      ABW  North America     Aruba   \n",
       "2            ABW2020-03-20   2020-03-20      ABW  North America     Aruba   \n",
       "3            ABW2020-03-21   2020-03-21      ABW  North America     Aruba   \n",
       "4            ABW2020-03-22   2020-03-22      ABW  North America     Aruba   \n",
       "...                    ...          ...      ...            ...       ...   \n",
       "32272        ZWE2020-08-27   2020-08-27      ZWE         Africa  Zimbabwe   \n",
       "32273        ZWE2020-08-28   2020-08-28      ZWE         Africa  Zimbabwe   \n",
       "32274        ZWE2020-08-29   2020-08-29      ZWE         Africa  Zimbabwe   \n",
       "32275        ZWE2020-08-30   2020-08-30      ZWE         Africa  Zimbabwe   \n",
       "32276        ZWE2020-08-31   2020-08-31      ZWE         Africa  Zimbabwe   \n",
       "\n",
       "       population  population_density  median_age  new_cases  \\\n",
       "0          106766             584.800        41.2          2   \n",
       "1          106766             584.800        41.2          0   \n",
       "2          106766             584.800        41.2          2   \n",
       "3          106766             584.800        41.2          0   \n",
       "4          106766             584.800        41.2          0   \n",
       "...           ...                 ...         ...        ...   \n",
       "32272    14862927              42.729        19.6         55   \n",
       "32273    14862927              42.729        19.6         41   \n",
       "32274    14862927              42.729        19.6         96   \n",
       "32275    14862927              42.729        19.6         18   \n",
       "32276    14862927              42.729        19.6          6   \n",
       "\n",
       "       percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                         0.001873  ...         ABW2020-03-13   \n",
       "1                         0.000000  ...         ABW2020-03-19   \n",
       "2                         0.001873  ...         ABW2020-03-20   \n",
       "3                         0.000000  ...         ABW2020-03-21   \n",
       "4                         0.000000  ...         ABW2020-03-22   \n",
       "...                            ...  ...                   ...   \n",
       "32272                     0.000370  ...         ZWE2020-08-27   \n",
       "32273                     0.000276  ...         ZWE2020-08-28   \n",
       "32274                     0.000646  ...         ZWE2020-08-29   \n",
       "32275                     0.000121  ...         ZWE2020-08-30   \n",
       "32276                     0.000040  ...         ZWE2020-08-31   \n",
       "\n",
       "       StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                         0.00                      0.000000   \n",
       "1                        33.33                      0.000000   \n",
       "2                        33.33                      0.000000   \n",
       "3                        44.44                     33.333333   \n",
       "4                        44.44                      0.000000   \n",
       "...                        ...                           ...   \n",
       "32272                    80.56                      0.000000   \n",
       "32273                    80.56                      0.000000   \n",
       "32274                    80.56                      0.000000   \n",
       "32275                    80.56                      0.000000   \n",
       "32276                    80.56                      0.000000   \n",
       "\n",
       "       StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                               0.00                             2.56   \n",
       "1                              42.86                            29.49   \n",
       "2                              42.86                            29.49   \n",
       "3                              60.71                            37.18   \n",
       "4                              60.71                            37.18   \n",
       "...                              ...                              ...   \n",
       "32272                          88.10                            66.03   \n",
       "32273                          88.10                            66.03   \n",
       "32274                          88.10                            66.03   \n",
       "32275                          88.10                            66.03   \n",
       "32276                          88.10                            66.03   \n",
       "\n",
       "       percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                                  0.000000                            3.03   \n",
       "1                                 15.015601                           34.85   \n",
       "2                                  0.000000                           34.85   \n",
       "3                                 26.076636                           43.94   \n",
       "4                                  0.000000                           43.94   \n",
       "...                                     ...                             ...   \n",
       "32272                              0.000000                           73.48   \n",
       "32273                              0.000000                           73.48   \n",
       "32274                              0.000000                           73.48   \n",
       "32275                              0.000000                           73.48   \n",
       "32276                              0.000000                           73.48   \n",
       "\n",
       "       percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                                 0.000000                           0.0   \n",
       "1                                15.016502                           0.0   \n",
       "2                                 0.000000                           0.0   \n",
       "3                                26.083214                           0.0   \n",
       "4                                 0.000000                           0.0   \n",
       "...                                    ...                           ...   \n",
       "32272                             0.000000                          25.0   \n",
       "32273                             0.000000                          25.0   \n",
       "32274                             0.000000                          25.0   \n",
       "32275                             0.000000                          25.0   \n",
       "32276                             0.000000                          25.0   \n",
       "\n",
       "       percentage_change_EconomicSupport  \n",
       "0                                    0.0  \n",
       "1                                    0.0  \n",
       "2                                    0.0  \n",
       "3                                    0.0  \n",
       "4                                    0.0  \n",
       "...                                  ...  \n",
       "32272                                0.0  \n",
       "32273                                0.0  \n",
       "32274                                0.0  \n",
       "32275                                0.0  \n",
       "32276                                0.0  \n",
       "\n",
       "[32277 rows x 30 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primary_key_reaction                     object\n",
      "date_updated                             object\n",
      "iso_code                                 object\n",
      "continent                                object\n",
      "location                                 object\n",
      "population                                int64\n",
      "population_density                      float64\n",
      "median_age                              float64\n",
      "new_cases                                 int64\n",
      "percentage_of_Pop_New_Cases             float64\n",
      "new_deaths                                int64\n",
      "percentage_of_Pop_New_Deaths            float64\n",
      "new_tests                                 int64\n",
      "percentage_of_Pop_New_Tests             float64\n",
      "total_cases_updated                       int64\n",
      "percentage_of_Pop_Total_Cases           float64\n",
      "total_deaths_updated                      int64\n",
      "percentage_of_Pop_Total_Deaths          float64\n",
      "total_tests_updated                       int64\n",
      "percentage_of_Pop_Total_Tests           float64\n",
      "primary_key_response                     object\n",
      "StringencyIndex_updated                 float64\n",
      "percentage_change_stringency            float64\n",
      "StringencyLegacyIndex_updated           float64\n",
      "GovernmentResponseIndex_updated         float64\n",
      "percentage_change_GovernmentResponse    float64\n",
      "ContainmentHealthIndex_updated          float64\n",
      "percentage_change_ContainmentHealth     float64\n",
      "EconomicSupportIndex_updated            float64\n",
      "percentage_change_EconomicSupport       float64\n",
      "dtype: object\n",
      "(32277, 30)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-13</td>\n",
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       "      <td>2.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>29.49</td>\n",
       "      <td>15.015601</td>\n",
       "      <td>34.85</td>\n",
       "      <td>15.016502</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>44.44</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>26.076636</td>\n",
       "      <td>43.94</td>\n",
       "      <td>26.083214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>44.44</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  primary_key_reaction date_updated iso_code      continent location  \\\n",
       "0        ABW2020-03-13   2020-03-13      ABW  North America    Aruba   \n",
       "1        ABW2020-03-19   2020-03-19      ABW  North America    Aruba   \n",
       "2        ABW2020-03-20   2020-03-20      ABW  North America    Aruba   \n",
       "3        ABW2020-03-21   2020-03-21      ABW  North America    Aruba   \n",
       "4        ABW2020-03-22   2020-03-22      ABW  North America    Aruba   \n",
       "\n",
       "   population  population_density  median_age  new_cases  \\\n",
       "0      106766               584.8        41.2          2   \n",
       "1      106766               584.8        41.2          0   \n",
       "2      106766               584.8        41.2          2   \n",
       "3      106766               584.8        41.2          0   \n",
       "4      106766               584.8        41.2          0   \n",
       "\n",
       "   percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                     0.001873  ...         ABW2020-03-13   \n",
       "1                     0.000000  ...         ABW2020-03-19   \n",
       "2                     0.001873  ...         ABW2020-03-20   \n",
       "3                     0.000000  ...         ABW2020-03-21   \n",
       "4                     0.000000  ...         ABW2020-03-22   \n",
       "\n",
       "   StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                     0.00                      0.000000   \n",
       "1                    33.33                      0.000000   \n",
       "2                    33.33                      0.000000   \n",
       "3                    44.44                     33.333333   \n",
       "4                    44.44                      0.000000   \n",
       "\n",
       "   StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                           0.00                             2.56   \n",
       "1                          42.86                            29.49   \n",
       "2                          42.86                            29.49   \n",
       "3                          60.71                            37.18   \n",
       "4                          60.71                            37.18   \n",
       "\n",
       "   percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                              0.000000                            3.03   \n",
       "1                             15.015601                           34.85   \n",
       "2                              0.000000                           34.85   \n",
       "3                             26.076636                           43.94   \n",
       "4                              0.000000                           43.94   \n",
       "\n",
       "   percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                             0.000000                           0.0   \n",
       "1                            15.016502                           0.0   \n",
       "2                             0.000000                           0.0   \n",
       "3                            26.083214                           0.0   \n",
       "4                             0.000000                           0.0   \n",
       "\n",
       "   percentage_change_EconomicSupport  \n",
       "0                                0.0  \n",
       "1                                0.0  \n",
       "2                                0.0  \n",
       "3                                0.0  \n",
       "4                                0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check merged_covid.csv\n",
    "print(df.dtypes)\n",
    "print(df.shape)\n",
    "\n",
    "# Display df\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_of_Pop_Total_Tests</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.227700e+04</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>3.227700e+04</td>\n",
       "      <td>3.227700e+04</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>3.227700e+04</td>\n",
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       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.400962e+07</td>\n",
       "      <td>214.197672</td>\n",
       "      <td>31.332131</td>\n",
       "      <td>778.920656</td>\n",
       "      <td>0.001991</td>\n",
       "      <td>26.154042</td>\n",
       "      <td>5.072572e-07</td>\n",
       "      <td>8.487867e+03</td>\n",
       "      <td>0.024842</td>\n",
       "      <td>4.745188e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.928935</td>\n",
       "      <td>58.044062</td>\n",
       "      <td>1.308332</td>\n",
       "      <td>64.012734</td>\n",
       "      <td>55.398830</td>\n",
       "      <td>1.274466</td>\n",
       "      <td>58.043087</td>\n",
       "      <td>1.226407</td>\n",
       "      <td>40.854943</td>\n",
       "      <td>0.337230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.764115e+08</td>\n",
       "      <td>728.593605</td>\n",
       "      <td>9.175007</td>\n",
       "      <td>4360.424882</td>\n",
       "      <td>0.005546</td>\n",
       "      <td>138.626066</td>\n",
       "      <td>2.001007e-06</td>\n",
       "      <td>5.284508e+04</td>\n",
       "      <td>0.074306</td>\n",
       "      <td>2.709722e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>5.493011</td>\n",
       "      <td>28.321004</td>\n",
       "      <td>19.904059</td>\n",
       "      <td>28.648400</td>\n",
       "      <td>24.321508</td>\n",
       "      <td>13.818227</td>\n",
       "      <td>25.638560</td>\n",
       "      <td>14.416320</td>\n",
       "      <td>32.776192</td>\n",
       "      <td>7.614218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.834000e+04</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>-2461.000000</td>\n",
       "      <td>-0.221254</td>\n",
       "      <td>-1918.000000</td>\n",
       "      <td>-6.790114e-05</td>\n",
       "      <td>-3.743000e+03</td>\n",
       "      <td>-0.039824</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-59.979210</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-59.947183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.822233e+06</td>\n",
       "      <td>32.333000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.810000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>44.870000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.132662e+07</td>\n",
       "      <td>82.805000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.331000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.740000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72.620000</td>\n",
       "      <td>62.180000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.892834e+07</td>\n",
       "      <td>204.430000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>0.001469</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.433856e-07</td>\n",
       "      <td>1.911000e+03</td>\n",
       "      <td>0.013440</td>\n",
       "      <td>1.297400e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.968671</td>\n",
       "      <td>80.560000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.710000</td>\n",
       "      <td>73.720000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>77.270000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.439324e+09</td>\n",
       "      <td>7915.731000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>78761.000000</td>\n",
       "      <td>0.189258</td>\n",
       "      <td>4928.000000</td>\n",
       "      <td>1.193442e-04</td>\n",
       "      <td>1.194086e+06</td>\n",
       "      <td>1.657412</td>\n",
       "      <td>5.997163e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>70.895970</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1965.107914</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>96.150000</td>\n",
       "      <td>655.459272</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>655.425220</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         population  population_density    median_age     new_cases  \\\n",
       "count  3.227700e+04        32277.000000  32277.000000  32277.000000   \n",
       "mean   5.400962e+07          214.197672     31.332131    778.920656   \n",
       "std    1.764115e+08          728.593605      9.175007   4360.424882   \n",
       "min    9.834000e+04            1.980000     15.100000  -2461.000000   \n",
       "25%    4.822233e+06           32.333000     23.500000      0.000000   \n",
       "50%    1.132662e+07           82.805000     31.100000     18.000000   \n",
       "75%    3.892834e+07          204.430000     39.800000    221.000000   \n",
       "max    1.439324e+09         7915.731000     48.200000  78761.000000   \n",
       "\n",
       "       percentage_of_Pop_New_Cases    new_deaths  \\\n",
       "count                 32277.000000  32277.000000   \n",
       "mean                      0.001991     26.154042   \n",
       "std                       0.005546    138.626066   \n",
       "min                      -0.221254  -1918.000000   \n",
       "25%                       0.000000      0.000000   \n",
       "50%                       0.000182      0.000000   \n",
       "75%                       0.001469      4.000000   \n",
       "max                       0.189258   4928.000000   \n",
       "\n",
       "       percentage_of_Pop_New_Deaths     new_tests  \\\n",
       "count                  3.227700e+04  3.227700e+04   \n",
       "mean                   5.072572e-07  8.487867e+03   \n",
       "std                    2.001007e-06  5.284508e+04   \n",
       "min                   -6.790114e-05 -3.743000e+03   \n",
       "25%                    0.000000e+00  0.000000e+00   \n",
       "50%                    0.000000e+00  0.000000e+00   \n",
       "75%                    2.433856e-07  1.911000e+03   \n",
       "max                    1.193442e-04  1.194086e+06   \n",
       "\n",
       "       percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "count                 32277.000000         3.227700e+04  ...   \n",
       "mean                      0.024842         4.745188e+04  ...   \n",
       "std                       0.074306         2.709722e+05  ...   \n",
       "min                      -0.039824         0.000000e+00  ...   \n",
       "25%                       0.000000         7.200000e+01  ...   \n",
       "50%                       0.000000         1.331000e+03  ...   \n",
       "75%                       0.013440         1.297400e+04  ...   \n",
       "max                       1.657412         5.997163e+06  ...   \n",
       "\n",
       "       percentage_of_Pop_Total_Tests  StringencyIndex_updated  \\\n",
       "count                   32277.000000             32277.000000   \n",
       "mean                        1.928935                58.044062   \n",
       "std                         5.493011                28.321004   \n",
       "min                         0.000000                 0.000000   \n",
       "25%                         0.000000                39.810000   \n",
       "50%                         0.000000                65.740000   \n",
       "75%                         0.968671                80.560000   \n",
       "max                        70.895970               100.000000   \n",
       "\n",
       "       percentage_change_stringency  StringencyLegacyIndex_updated  \\\n",
       "count                  32277.000000                   32277.000000   \n",
       "mean                       1.308332                      64.012734   \n",
       "std                       19.904059                      28.648400   \n",
       "min                     -100.000000                       0.000000   \n",
       "25%                        0.000000                      50.000000   \n",
       "50%                        0.000000                      72.620000   \n",
       "75%                        0.000000                      85.710000   \n",
       "max                     1965.107914                     100.000000   \n",
       "\n",
       "       GovernmentResponseIndex_updated  percentage_change_GovernmentResponse  \\\n",
       "count                     32277.000000                          32277.000000   \n",
       "mean                         55.398830                              1.274466   \n",
       "std                          24.321508                             13.818227   \n",
       "min                           0.000000                            -59.979210   \n",
       "25%                          44.870000                              0.000000   \n",
       "50%                          62.180000                              0.000000   \n",
       "75%                          73.720000                              0.000000   \n",
       "max                          96.150000                            655.459272   \n",
       "\n",
       "       ContainmentHealthIndex_updated  percentage_change_ContainmentHealth  \\\n",
       "count                    32277.000000                         32277.000000   \n",
       "mean                        58.043087                             1.226407   \n",
       "std                         25.638560                            14.416320   \n",
       "min                          0.000000                           -59.947183   \n",
       "25%                         44.700000                             0.000000   \n",
       "50%                         65.150000                             0.000000   \n",
       "75%                         77.270000                             0.000000   \n",
       "max                        100.000000                           655.425220   \n",
       "\n",
       "       EconomicSupportIndex_updated  percentage_change_EconomicSupport  \n",
       "count                  32277.000000                       32277.000000  \n",
       "mean                      40.854943                           0.337230  \n",
       "std                       32.776192                           7.614218  \n",
       "min                        0.000000                        -100.000000  \n",
       "25%                        0.000000                           0.000000  \n",
       "50%                       50.000000                           0.000000  \n",
       "75%                       75.000000                           0.000000  \n",
       "max                      100.000000                         400.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check merged df for null values.\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['primary_key_reaction', 'date_updated', 'iso_code', 'continent',\n",
       "       'location', 'population', 'population_density', 'median_age',\n",
       "       'new_cases', 'percentage_of_Pop_New_Cases', 'new_deaths',\n",
       "       'percentage_of_Pop_New_Deaths', 'new_tests',\n",
       "       'percentage_of_Pop_New_Tests', 'total_cases_updated',\n",
       "       'percentage_of_Pop_Total_Cases', 'total_deaths_updated',\n",
       "       'percentage_of_Pop_Total_Deaths', 'total_tests_updated',\n",
       "       'percentage_of_Pop_Total_Tests', 'primary_key_response',\n",
       "       'StringencyIndex_updated', 'percentage_change_stringency',\n",
       "       'StringencyLegacyIndex_updated', 'GovernmentResponseIndex_updated',\n",
       "       'percentage_change_GovernmentResponse',\n",
       "       'ContainmentHealthIndex_updated', 'percentage_change_ContainmentHealth',\n",
       "       'EconomicSupportIndex_updated', 'percentage_change_EconomicSupport'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "primary_key_reaction                     object\n",
      "date_updated                              int32\n",
      "iso_code                                 object\n",
      "continent                                object\n",
      "location                                 object\n",
      "population                                int64\n",
      "population_density                      float64\n",
      "median_age                              float64\n",
      "new_cases                                 int64\n",
      "percentage_of_Pop_New_Cases             float64\n",
      "new_deaths                                int64\n",
      "percentage_of_Pop_New_Deaths            float64\n",
      "new_tests                                 int64\n",
      "percentage_of_Pop_New_Tests             float64\n",
      "total_cases_updated                       int64\n",
      "percentage_of_Pop_Total_Cases           float64\n",
      "total_deaths_updated                      int64\n",
      "percentage_of_Pop_Total_Deaths          float64\n",
      "total_tests_updated                       int64\n",
      "percentage_of_Pop_Total_Tests           float64\n",
      "primary_key_response                     object\n",
      "StringencyIndex_updated                 float64\n",
      "percentage_change_stringency            float64\n",
      "StringencyLegacyIndex_updated           float64\n",
      "GovernmentResponseIndex_updated         float64\n",
      "percentage_change_GovernmentResponse    float64\n",
      "ContainmentHealthIndex_updated          float64\n",
      "percentage_change_ContainmentHealth     float64\n",
      "EconomicSupportIndex_updated            float64\n",
      "percentage_change_EconomicSupport       float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Encode the date column to int\n",
    "le = LabelEncoder()\n",
    "df['date_updated'] = le.fit_transform(df['date_updated'])\n",
    "print(df.dtypes)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking correlation for feature selection.\n",
    "\n",
    "def correlation_heatmap(df):\n",
    "    correlations = df.corr()\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(15,15))\n",
    "    sns.heatmap(correlations, 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": 52,
   "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>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>total_deaths_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.85</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.85</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.94</td>\n",
       "      <td>44.44</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106766</td>\n",
       "      <td>584.800</td>\n",
       "      <td>41.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.94</td>\n",
       "      <td>44.44</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32272</th>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>6251</td>\n",
       "      <td>25.0</td>\n",
       "      <td>73.48</td>\n",
       "      <td>80.56</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32273</th>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>6292</td>\n",
       "      <td>25.0</td>\n",
       "      <td>73.48</td>\n",
       "      <td>80.56</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32274</th>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>6388</td>\n",
       "      <td>25.0</td>\n",
       "      <td>73.48</td>\n",
       "      <td>80.56</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32275</th>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>6406</td>\n",
       "      <td>25.0</td>\n",
       "      <td>73.48</td>\n",
       "      <td>80.56</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32276</th>\n",
       "      <td>14862927</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>6412</td>\n",
       "      <td>25.0</td>\n",
       "      <td>73.48</td>\n",
       "      <td>80.56</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32277 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       population  population_density  median_age  total_cases_updated  \\\n",
       "0          106766             584.800        41.2                    2   \n",
       "1          106766             584.800        41.2                    2   \n",
       "2          106766             584.800        41.2                    4   \n",
       "3          106766             584.800        41.2                    4   \n",
       "4          106766             584.800        41.2                    4   \n",
       "...           ...                 ...         ...                  ...   \n",
       "32272    14862927              42.729        19.6                 6251   \n",
       "32273    14862927              42.729        19.6                 6292   \n",
       "32274    14862927              42.729        19.6                 6388   \n",
       "32275    14862927              42.729        19.6                 6406   \n",
       "32276    14862927              42.729        19.6                 6412   \n",
       "\n",
       "       EconomicSupportIndex_updated  ContainmentHealthIndex_updated  \\\n",
       "0                               0.0                            3.03   \n",
       "1                               0.0                           34.85   \n",
       "2                               0.0                           34.85   \n",
       "3                               0.0                           43.94   \n",
       "4                               0.0                           43.94   \n",
       "...                             ...                             ...   \n",
       "32272                          25.0                           73.48   \n",
       "32273                          25.0                           73.48   \n",
       "32274                          25.0                           73.48   \n",
       "32275                          25.0                           73.48   \n",
       "32276                          25.0                           73.48   \n",
       "\n",
       "       StringencyIndex_updated  total_deaths_updated  \n",
       "0                         0.00                     0  \n",
       "1                        33.33                     0  \n",
       "2                        33.33                     0  \n",
       "3                        44.44                     0  \n",
       "4                        44.44                     0  \n",
       "...                        ...                   ...  \n",
       "32272                    80.56                   179  \n",
       "32273                    80.56                   189  \n",
       "32274                    80.56                   195  \n",
       "32275                    80.56                   196  \n",
       "32276                    80.56                   196  \n",
       "\n",
       "[32277 rows x 8 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a df to test impact of some features on 'total deaths'\n",
    "df_death_ml=df[['population','population_density','median_age','total_cases_updated','EconomicSupportIndex_updated','ContainmentHealthIndex_updated','StringencyIndex_updated','total_deaths_updated']]\n",
    "df_death_ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>total_deaths_updated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.227700e+04</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>3.227700e+04</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "      <td>32277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.400962e+07</td>\n",
       "      <td>214.197672</td>\n",
       "      <td>31.332131</td>\n",
       "      <td>4.745188e+04</td>\n",
       "      <td>40.854943</td>\n",
       "      <td>58.043087</td>\n",
       "      <td>58.044062</td>\n",
       "      <td>2121.634662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.764115e+08</td>\n",
       "      <td>728.593605</td>\n",
       "      <td>9.175007</td>\n",
       "      <td>2.709722e+05</td>\n",
       "      <td>32.776192</td>\n",
       "      <td>25.638560</td>\n",
       "      <td>28.321004</td>\n",
       "      <td>10533.562293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.834000e+04</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</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>4.822233e+06</td>\n",
       "      <td>32.333000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.700000</td>\n",
       "      <td>39.810000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.132662e+07</td>\n",
       "      <td>82.805000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>1.331000e+03</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.150000</td>\n",
       "      <td>65.740000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.892834e+07</td>\n",
       "      <td>204.430000</td>\n",
       "      <td>39.800000</td>\n",
       "      <td>1.297400e+04</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>77.270000</td>\n",
       "      <td>80.560000</td>\n",
       "      <td>279.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.439324e+09</td>\n",
       "      <td>7915.731000</td>\n",
       "      <td>48.200000</td>\n",
       "      <td>5.997163e+06</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>183069.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         population  population_density    median_age  total_cases_updated  \\\n",
       "count  3.227700e+04        32277.000000  32277.000000         3.227700e+04   \n",
       "mean   5.400962e+07          214.197672     31.332131         4.745188e+04   \n",
       "std    1.764115e+08          728.593605      9.175007         2.709722e+05   \n",
       "min    9.834000e+04            1.980000     15.100000         0.000000e+00   \n",
       "25%    4.822233e+06           32.333000     23.500000         7.200000e+01   \n",
       "50%    1.132662e+07           82.805000     31.100000         1.331000e+03   \n",
       "75%    3.892834e+07          204.430000     39.800000         1.297400e+04   \n",
       "max    1.439324e+09         7915.731000     48.200000         5.997163e+06   \n",
       "\n",
       "       EconomicSupportIndex_updated  ContainmentHealthIndex_updated  \\\n",
       "count                  32277.000000                    32277.000000   \n",
       "mean                      40.854943                       58.043087   \n",
       "std                       32.776192                       25.638560   \n",
       "min                        0.000000                        0.000000   \n",
       "25%                        0.000000                       44.700000   \n",
       "50%                       50.000000                       65.150000   \n",
       "75%                       75.000000                       77.270000   \n",
       "max                      100.000000                      100.000000   \n",
       "\n",
       "       StringencyIndex_updated  total_deaths_updated  \n",
       "count             32277.000000          32277.000000  \n",
       "mean                 58.044062           2121.634662  \n",
       "std                  28.321004          10533.562293  \n",
       "min                   0.000000              0.000000  \n",
       "25%                  39.810000              1.000000  \n",
       "50%                  65.740000             26.000000  \n",
       "75%                  80.560000            279.000000  \n",
       "max                 100.000000         183069.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check stats\n",
    "df_death_ml.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32277, 8)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows with null values\n",
    "df_death_ml=df_death_ml.dropna(how='any')\n",
    "df_death_ml.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check for correlation\n",
    "correlation_heatmap(df_death_ml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVR MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create features(X) and target(y) sets\n",
    "X = df_death_ml.drop(['total_deaths_updated'],axis=1).values\n",
    "\n",
    "y =df_death_ml['total_deaths_updated'].values\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30663\n",
      "1614\n",
      "30663\n",
      "1614\n"
     ]
    }
   ],
   "source": [
    "# check number of samples (n_samples)\n",
    "print(len(X_train))\n",
    "print(len(X_test))\n",
    "print(len(y_train))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "print(len(df_death_ml.columns)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sklearn\\utils\\validation.py:73: 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",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()), ('svr', SVR())])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create SVR model\n",
    "n_samples, n_features = 30663, 7\n",
    "rng = np.random.RandomState(5)\n",
    "# y_train = rng.randn(n_samples)\n",
    "# X_train = rng.randn(n_samples, n_features)\n",
    "regr = make_pipeline(StandardScaler(), SVR(C=1.0, epsilon=0.1))\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.024576964527265988"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions using test data\n",
    "y_pred = regr.predict(X_test)\n",
    "\n",
    "# return R-squared for the prediction\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sklearn\\utils\\validation.py:73: 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",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('svr', SVR(kernel='linear'))])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create SVR model\n",
    "n_samples, n_features = 30663,len(df_death_ml.columns)-1\n",
    "rng = np.random.RandomState(5)\n",
    "# y_train = rng.randn(n_samples)\n",
    "# X_train = rng.randn(n_samples, n_features)\n",
    "regr = make_pipeline(StandardScaler(), SVR(kernel='linear',C=1.0, epsilon=0.1))\n",
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5012048467604424"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions using test data\n",
    "y_pred = regr.predict(X_test)\n",
    "\n",
    "# return R-squared for the prediction\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38654710.70638204"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse=mean_squared_error(y_test,y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sklearn\\utils\\validation.py:73: 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",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.35879918537441335"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create SVR model\n",
    "n_samples, n_features = 30663, len(df_death_ml.columns)-1\n",
    "rng = np.random.RandomState(5)\n",
    "# y_train = rng.randn(n_samples)\n",
    "# X_train = rng.randn(n_samples, n_features)\n",
    "regr = make_pipeline(StandardScaler(), SVR(kernel='poly', degree=3, gamma='scale', coef0=0.0, C=1.0, epsilon=0.1))\n",
    "regr.fit(X_train, y_train)\n",
    "\n",
    "# make predictions using test data\n",
    "# n_samples = 9, n_features = 6\n",
    "y_pred = regr.predict(X_test)\n",
    "\n",
    "# return R-squared for the prediction\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\sklearn\\utils\\validation.py:73: 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",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.02357222348548904"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create SVR model\n",
    "n_samples, n_features = 30663, len(df_death_ml.columns)-1\n",
    "rng = np.random.RandomState(5)\n",
    "\n",
    "regr = make_pipeline(StandardScaler(), SVR(kernel='sigmoid', gamma='scale', coef0=0.0, C=1.0, epsilon=0.1))\n",
    "regr.fit(X_train, y_train)\n",
    "\n",
    "# make predictions using test data\n",
    "\n",
    "y_pred = regr.predict(X_test)\n",
    "\n",
    "# return R-squared for the prediction\n",
    "regr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RandomForestRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create features(X) and target(y) sets\n",
    "X = df_death_ml.drop(['total_deaths_updated'],axis=1).values\n",
    "\n",
    "y = df_death_ml['total_deaths_updated'].values\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 69,
   "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 :0.00007 and the r2_score is:1.000\n",
      "------------------------------------\n",
      "Feature: 0, Score: 0.02155\n",
      "Feature: 1, Score: 0.00559\n",
      "Feature: 2, Score: 0.03417\n",
      "Feature: 3, Score: 0.83596\n",
      "Feature: 4, Score: 0.01846\n",
      "Feature: 5, Score: 0.00364\n",
      "Feature: 6, Score: 0.08064\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",
    "labels =['population','population density','median age','total cases','EconomicSupportIndex','ContainmentHealthIndex','StringencyIndex']\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('Features', size=14)\n",
    "plt.title('Feature Importance for Total Deaths', size=16)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.15489865,  0.55857699,  3.41692578, 83.59594739,  1.84591814,\n",
       "        0.36409349,  8.06363957])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the importance array for plots.\n",
    "importance*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NN Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create features(X) and target(y) sets\n",
    "X = df_death_ml.drop(['total_deaths_updated'],axis=1).values\n",
    "\n",
    "y = df_death_ml['total_deaths_updated'].values\n",
    "y = y.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_death_ml.columns)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\muzzn\\anaconda3\\envs\\PythonData\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 14)                112       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 15        \n",
      "=================================================================\n",
      "Total params: 127\n",
      "Trainable params: 127\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.2593 - mean_squared_error: 0.2593\n",
      "Epoch 2/100\n",
      "30663/30663 [==============================] - 1s 38us/sample - loss: 0.1230 - mean_squared_error: 0.1230\n",
      "Epoch 3/100\n",
      "30663/30663 [==============================] - 1s 37us/sample - loss: 0.1037 - mean_squared_error: 0.1037\n",
      "Epoch 4/100\n",
      "30663/30663 [==============================] - 1s 41us/sample - loss: 0.0928 - mean_squared_error: 0.0928\n",
      "Epoch 5/100\n",
      "30663/30663 [==============================] - 1s 41us/sample - loss: 0.0855 - mean_squared_error: 0.0855\n",
      "Epoch 6/100\n",
      "30663/30663 [==============================] - 1s 38us/sample - loss: 0.0794 - mean_squared_error: 0.0794\n",
      "Epoch 7/100\n",
      "30663/30663 [==============================] - 1s 39us/sample - loss: 0.0738 - mean_squared_error: 0.0738\n",
      "Epoch 8/100\n",
      "30663/30663 [==============================] - 1s 38us/sample - loss: 0.0698 - mean_squared_error: 0.0698\n",
      "Epoch 9/100\n",
      "30663/30663 [==============================] - 1s 39us/sample - loss: 0.0676 - mean_squared_error: 0.0676\n",
      "Epoch 10/100\n",
      "30663/30663 [==============================] - 1s 41us/sample - loss: 0.0653 - mean_squared_error: 0.0653\n",
      "Epoch 11/100\n",
      "30663/30663 [==============================] - 1s 40us/sample - loss: 0.0635 - mean_squared_error: 0.0635\n",
      "Epoch 12/100\n",
      "30663/30663 [==============================] - 1s 37us/sample - loss: 0.0620 - mean_squared_error: 0.0620\n",
      "Epoch 13/100\n",
      "30663/30663 [==============================] - 1s 37us/sample - loss: 0.0603 - mean_squared_error: 0.0603s - los\n",
      "Epoch 14/100\n",
      "30663/30663 [==============================] - 1s 41us/sample - loss: 0.0597 - mean_squared_error: 0.0597\n",
      "Epoch 15/100\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0586 - mean_squared_error: 0.0586\n",
      "Epoch 16/100\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0574 - mean_squared_error: 0.0574\n",
      "Epoch 17/100\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0569 - mean_squared_error: 0.0569\n",
      "Epoch 18/100\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0557 - mean_squared_error: 0.0557\n",
      "Epoch 19/100\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0552 - mean_squared_error: 0.0552\n",
      "Epoch 20/100\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0545 - mean_squared_error: 0.0545\n",
      "Epoch 21/100\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0535 - mean_squared_error: 0.0535\n",
      "Epoch 22/100\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.0530 - mean_squared_error: 0.0530\n",
      "Epoch 23/100\n",
      "30663/30663 [==============================] - 2s 60us/sample - loss: 0.0515 - mean_squared_error: 0.0515\n",
      "Epoch 24/100\n",
      "30663/30663 [==============================] - 2s 71us/sample - loss: 0.0502 - mean_squared_error: 0.0502\n",
      "Epoch 25/100\n",
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0499 - mean_squared_error: 0.0499\n",
      "Epoch 26/100\n",
      "30663/30663 [==============================] - 2s 71us/sample - loss: 0.0484 - mean_squared_error: 0.0484\n",
      "Epoch 27/100\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.0479 - mean_squared_error: 0.0479\n",
      "Epoch 28/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0469 - mean_squared_error: 0.0469\n",
      "Epoch 29/100\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0468 - mean_squared_error: 0.0468\n",
      "Epoch 30/100\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0457 - mean_squared_error: 0.0457\n",
      "Epoch 31/100\n",
      "30663/30663 [==============================] - 2s 68us/sample - loss: 0.0453 - mean_squared_error: 0.0453\n",
      "Epoch 32/100\n",
      "30663/30663 [==============================] - 2s 55us/sample - loss: 0.0450 - mean_squared_error: 0.0450\n",
      "Epoch 33/100\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0441 - mean_squared_error: 0.0441\n",
      "Epoch 34/100\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0436 - mean_squared_error: 0.0436\n",
      "Epoch 35/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0432 - mean_squared_error: 0.0432\n",
      "Epoch 36/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0425 - mean_squared_error: 0.0425\n",
      "Epoch 37/100\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0419 - mean_squared_error: 0.0419\n",
      "Epoch 38/100\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0412 - mean_squared_error: 0.0412\n",
      "Epoch 39/100\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0403 - mean_squared_error: 0.0403\n",
      "Epoch 40/100\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0378 - mean_squared_error: 0.0378\n",
      "Epoch 41/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0362 - mean_squared_error: 0.0362\n",
      "Epoch 42/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0357 - mean_squared_error: 0.0357\n",
      "Epoch 43/100\n",
      "30663/30663 [==============================] - 2s 54us/sample - loss: 0.0344 - mean_squared_error: 0.0344\n",
      "Epoch 44/100\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0338 - mean_squared_error: 0.0338\n",
      "Epoch 45/100\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0332 - mean_squared_error: 0.0332\n",
      "Epoch 46/100\n",
      "30663/30663 [==============================] - 2s 51us/sample - loss: 0.0324 - mean_squared_error: 0.0324\n",
      "Epoch 47/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0322 - mean_squared_error: 0.0322\n",
      "Epoch 48/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0316 - mean_squared_error: 0.0316\n",
      "Epoch 49/100\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0310 - mean_squared_error: 0.0310\n",
      "Epoch 50/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0307 - mean_squared_error: 0.0307\n",
      "Epoch 51/100\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0301 - mean_squared_error: 0.0301\n",
      "Epoch 52/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0300 - mean_squared_error: 0.0300\n",
      "Epoch 53/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0294 - mean_squared_error: 0.0294\n",
      "Epoch 54/100\n",
      "30663/30663 [==============================] - 2s 49us/sample - loss: 0.0292 - mean_squared_error: 0.0292\n",
      "Epoch 55/100\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0287 - mean_squared_error: 0.0287\n",
      "Epoch 56/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0287 - mean_squared_error: 0.0287\n",
      "Epoch 57/100\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0282 - mean_squared_error: 0.0282\n",
      "Epoch 58/100\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0281 - mean_squared_error: 0.0281\n",
      "Epoch 59/100\n",
      "30663/30663 [==============================] - 3s 85us/sample - loss: 0.0281 - mean_squared_error: 0.0281\n",
      "Epoch 60/100\n",
      "30663/30663 [==============================] - 2s 77us/sample - loss: 0.0279 - mean_squared_error: 0.0279\n",
      "Epoch 61/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0278 - mean_squared_error: 0.0278\n",
      "Epoch 62/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0273 - mean_squared_error: 0.0273\n",
      "Epoch 63/100\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0273 - mean_squared_error: 0.0273\n",
      "Epoch 64/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0271 - mean_squared_error: 0.0271\n",
      "Epoch 65/100\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0267 - mean_squared_error: 0.0267\n",
      "Epoch 66/100\n",
      "30663/30663 [==============================] - 2s 49us/sample - loss: 0.0267 - mean_squared_error: 0.0267\n",
      "Epoch 67/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0261 - mean_squared_error: 0.0261\n",
      "Epoch 68/100\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0265 - mean_squared_error: 0.0265\n",
      "Epoch 69/100\n",
      "30663/30663 [==============================] - 2s 51us/sample - loss: 0.0260 - mean_squared_error: 0.0260\n",
      "Epoch 70/100\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0256 - mean_squared_error: 0.0256\n",
      "Epoch 71/100\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0257 - mean_squared_error: 0.0257\n",
      "Epoch 72/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0252 - mean_squared_error: 0.0252\n",
      "Epoch 73/100\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0252 - mean_squared_error: 0.0252\n",
      "Epoch 74/100\n",
      "30663/30663 [==============================] - 2s 59us/sample - loss: 0.0251 - mean_squared_error: 0.0251\n",
      "Epoch 75/100\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0250 - mean_squared_error: 0.0250\n",
      "Epoch 76/100\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0246 - mean_squared_error: 0.0246\n",
      "Epoch 77/100\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0243 - mean_squared_error: 0.0243\n",
      "Epoch 78/100\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0241 - mean_squared_error: 0.0241\n",
      "Epoch 79/100\n",
      "30663/30663 [==============================] - 2s 55us/sample - loss: 0.0240 - mean_squared_error: 0.0240\n",
      "Epoch 80/100\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0238 - mean_squared_error: 0.0238\n",
      "Epoch 81/100\n",
      "30663/30663 [==============================] - 2s 49us/sample - loss: 0.0237 - mean_squared_error: 0.0237\n",
      "Epoch 82/100\n",
      "30663/30663 [==============================] - 2s 49us/sample - loss: 0.0236 - mean_squared_error: 0.0236\n",
      "Epoch 83/100\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0233 - mean_squared_error: 0.0233\n",
      "Epoch 84/100\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0232 - mean_squared_error: 0.0232\n",
      "Epoch 85/100\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0230 - mean_squared_error: 0.0230\n",
      "Epoch 86/100\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.0229 - mean_squared_error: 0.0229\n",
      "Epoch 87/100\n",
      "30663/30663 [==============================] - 2s 74us/sample - loss: 0.0230 - mean_squared_error: 0.0230\n",
      "Epoch 88/100\n",
      "30663/30663 [==============================] - 1s 40us/sample - loss: 0.0228 - mean_squared_error: 0.0228\n",
      "Epoch 89/100\n",
      "30663/30663 [==============================] - 1s 41us/sample - loss: 0.0223 - mean_squared_error: 0.0223\n",
      "Epoch 90/100\n",
      "30663/30663 [==============================] - 1s 42us/sample - loss: 0.0226 - mean_squared_error: 0.0226\n",
      "Epoch 91/100\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0222 - mean_squared_error: 0.0222\n",
      "Epoch 92/100\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0224 - mean_squared_error: 0.0224\n",
      "Epoch 93/100\n",
      "30663/30663 [==============================] - 2s 51us/sample - loss: 0.0223 - mean_squared_error: 0.0223\n",
      "Epoch 94/100\n",
      "30663/30663 [==============================] - 1s 42us/sample - loss: 0.0222 - mean_squared_error: 0.0222\n",
      "Epoch 95/100\n",
      "30663/30663 [==============================] - 1s 41us/sample - loss: 0.0222 - mean_squared_error: 0.0222\n",
      "Epoch 96/100\n",
      "30663/30663 [==============================] - 1s 42us/sample - loss: 0.0223 - mean_squared_error: 0.0223\n",
      "Epoch 97/100\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0218 - mean_squared_error: 0.0218\n",
      "Epoch 98/100\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0221 - mean_squared_error: 0.0221\n",
      "Epoch 99/100\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0216 - mean_squared_error: 0.0216\n",
      "Epoch 100/100\n",
      "30663/30663 [==============================] - 1s 40us/sample - loss: 0.0218 - mean_squared_error: 0.0218\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9789129159596608\n",
      "0.9735063069608486\n"
     ]
    }
   ],
   "source": [
    "# define model - shallow neural net\n",
    "number_hidden_nodes = (len(df_death_ml.columns)-1)*2\n",
    "number_input_features =len(df_death_ml.columns)-1\n",
    "\n",
    "nn = Sequential()\n",
    "nn.add(Dense(units=number_hidden_nodes, input_dim=number_input_features, activation=\"relu\"))\n",
    "nn.add(Dense(1, activation=\"linear\"))\n",
    "\n",
    "nn.summary()\n",
    "\n",
    "# compile model\n",
    "nn.compile(loss=\"mean_squared_error\", optimizer=\"adam\", metrics=[\"mse\"])\n",
    "\n",
    "# train model\n",
    "model_1 = nn.fit(X_train_scaled, y_train_scaled, epochs=100)\n",
    "\n",
    "# Plot the train and test loss function\n",
    "plt.plot(model_1.history[\"loss\"])\n",
    "plt.title(\"loss_function - 1 hidden layer\")\n",
    "plt.legend([\"loss\"])\n",
    "plt.show()\n",
    "\n",
    "y_train_pred = nn.predict(X_train_scaled)\n",
    "y_test_pred = nn.predict(X_test_scaled)\n",
    "\n",
    "print(r2_score(y_train_scaled, y_train_pred))\n",
    "print(r2_score(y_test_scaled, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 21)                168       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                220       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 399\n",
      "Trainable params: 399\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/200\n",
      "30663/30663 [==============================] - 3s 84us/sample - loss: 0.1707 - mean_squared_error: 0.1707\n",
      "Epoch 2/200\n",
      "30663/30663 [==============================] - 2s 59us/sample - loss: 0.0804 - mean_squared_error: 0.0804\n",
      "Epoch 3/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0599 - mean_squared_error: 0.0599\n",
      "Epoch 4/200\n",
      "30663/30663 [==============================] - 2s 51us/sample - loss: 0.0463 - mean_squared_error: 0.0463\n",
      "Epoch 5/200\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0356 - mean_squared_error: 0.0356\n",
      "Epoch 6/200\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0281 - mean_squared_error: 0.0281\n",
      "Epoch 7/200\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0230 - mean_squared_error: 0.0230\n",
      "Epoch 8/200\n",
      "30663/30663 [==============================] - 2s 54us/sample - loss: 0.0187 - mean_squared_error: 0.0187\n",
      "Epoch 9/200\n",
      "30663/30663 [==============================] - 2s 51us/sample - loss: 0.0165 - mean_squared_error: 0.0165\n",
      "Epoch 10/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0150 - mean_squared_error: 0.0150\n",
      "Epoch 11/200\n",
      "30663/30663 [==============================] - 2s 59us/sample - loss: 0.0135 - mean_squared_error: 0.0135\n",
      "Epoch 12/200\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 13/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 14/200\n",
      "30663/30663 [==============================] - 2s 75us/sample - loss: 0.0102 - mean_squared_error: 0.0102\n",
      "Epoch 15/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0099 - mean_squared_error: 0.0099\n",
      "Epoch 16/200\n",
      "30663/30663 [==============================] - 2s 61us/sample - loss: 0.0091 - mean_squared_error: 0.0091\n",
      "Epoch 17/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0088 - mean_squared_error: 0.0088\n",
      "Epoch 18/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0088 - mean_squared_error: 0.0088\n",
      "Epoch 19/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0076 - mean_squared_error: 0.0076\n",
      "Epoch 20/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0079 - mean_squared_error: 0.0079\n",
      "Epoch 21/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0073 - mean_squared_error: 0.0073\n",
      "Epoch 22/200\n",
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     ]
    },
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     ]
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      "Epoch 181/200\n",
      "30663/30663 [==============================] - 2s 49us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 182/200\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 183/200\n",
      "30663/30663 [==============================] - 2s 51us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 184/200\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0011 - mean_squared_error: 0.0011\n",
      "Epoch 185/200\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 186/200\n",
      "30663/30663 [==============================] - 2s 81us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 187/200\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 188/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 189/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 190/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 191/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 192/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 193/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 194/200\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 195/200\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 196/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 197/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 198/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30663/30663 [==============================] - 2s 72us/sample - loss: 9.8075e-04 - mean_squared_error: 9.8075e-04\n",
      "Epoch 199/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0010 - mean_squared_error: 0.0010\n",
      "Epoch 200/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9991178941860684\n",
      "0.9988474667692763\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net\n",
    "number_input_features = len(df_death_ml.columns)-1\n",
    "hidden_nodes_layer1 = (len(df_death_ml.columns)-1)*3\n",
    "hidden_nodes_layer2 =round(((len(df_death_ml.columns)-1)*3)/2)\n",
    "\n",
    "nn = Sequential()\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "# Second hidden layer\n",
    "nn.add(Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "# Output layer\n",
    "nn.add(Dense(units=1, activation=\"linear\"))\n",
    "\n",
    "\n",
    "nn.summary()\n",
    "\n",
    "# compile model\n",
    "nn.compile(loss=\"mean_squared_error\", optimizer=\"adam\", metrics=[\"mse\"])\n",
    "\n",
    "# train model\n",
    "model_1 = nn.fit(X_train_scaled, y_train_scaled, epochs=200)\n",
    "\n",
    "# Plot the train and test loss function\n",
    "plt.plot(model_1.history[\"loss\"])\n",
    "plt.title(\"loss_function - 2 hidden layer\")\n",
    "plt.legend([\"loss\"])\n",
    "plt.show()\n",
    "\n",
    "y_train_pred = nn.predict(X_train_scaled)\n",
    "y_test_pred = nn.predict(X_test_scaled)\n",
    "\n",
    "print(r2_score(y_train_scaled, y_train_pred))\n",
    "print(r2_score(y_test_scaled, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_5 (Dense)              (None, 21)                168       \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 10)                220       \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 5)                 55        \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 449\n",
      "Trainable params: 449\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/200\n",
      "30663/30663 [==============================] - 2s 71us/sample - loss: 0.1380 - mean_squared_error: 0.1380\n",
      "Epoch 2/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0722 - mean_squared_error: 0.0722\n",
      "Epoch 3/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0569 - mean_squared_error: 0.0569\n",
      "Epoch 4/200\n",
      "30663/30663 [==============================] - 2s 81us/sample - loss: 0.0468 - mean_squared_error: 0.0468\n",
      "Epoch 5/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0392 - mean_squared_error: 0.0392\n",
      "Epoch 6/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0311 - mean_squared_error: 0.0311\n",
      "Epoch 7/200\n",
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0245 - mean_squared_error: 0.0245\n",
      "Epoch 8/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0195 - mean_squared_error: 0.0195\n",
      "Epoch 9/200\n",
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0148 - mean_squared_error: 0.0148\n",
      "Epoch 10/200\n",
      "30663/30663 [==============================] - 2s 64us/sample - loss: 0.0130 - mean_squared_error: 0.0130\n",
      "Epoch 11/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0103 - mean_squared_error: 0.0103\n",
      "Epoch 12/200\n",
      "30663/30663 [==============================] - 2s 68us/sample - loss: 0.0092 - mean_squared_error: 0.0092\n",
      "Epoch 13/200\n",
      "30663/30663 [==============================] - 2s 67us/sample - loss: 0.0083 - mean_squared_error: 0.0083\n",
      "Epoch 14/200\n",
      "30663/30663 [==============================] - 2s 71us/sample - loss: 0.0076 - mean_squared_error: 0.0076\n",
      "Epoch 15/200\n",
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0078 - mean_squared_error: 0.0078\n",
      "Epoch 16/200\n",
      "30663/30663 [==============================] - 2s 64us/sample - loss: 0.0065 - mean_squared_error: 0.0065\n",
      "Epoch 17/200\n",
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0067 - mean_squared_error: 0.0067\n",
      "Epoch 18/200\n",
      "30663/30663 [==============================] - 2s 72us/sample - loss: 0.0050 - mean_squared_error: 0.0050\n",
      "Epoch 19/200\n",
      "30663/30663 [==============================] - 2s 81us/sample - loss: 0.0051 - mean_squared_error: 0.0051\n",
      "Epoch 20/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0047 - mean_squared_error: 0.0047\n",
      "Epoch 21/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0070 - mean_squared_error: 0.0070\n",
      "Epoch 22/200\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0043 - mean_squared_error: 0.0043\n",
      "Epoch 23/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0044 - mean_squared_error: 0.0044\n",
      "Epoch 24/200\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0039 - mean_squared_error: 0.0039\n",
      "Epoch 25/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0042 - mean_squared_error: 0.0042\n",
      "Epoch 26/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0079 - mean_squared_error: 0.0079\n",
      "Epoch 27/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0033 - mean_squared_error: 0.0033\n",
      "Epoch 28/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0038 - mean_squared_error: 0.0038\n",
      "Epoch 29/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0039 - mean_squared_error: 0.0039\n",
      "Epoch 30/200\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0030 - mean_squared_error: 0.0030\n",
      "Epoch 31/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0039 - mean_squared_error: 0.0039\n",
      "Epoch 32/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0032 - mean_squared_error: 0.0032\n",
      "Epoch 33/200\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0039 - mean_squared_error: 0.0039\n",
      "Epoch 34/200\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0031 - mean_squared_error: 0.0031\n",
      "Epoch 35/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0033 - mean_squared_error: 0.0033\n",
      "Epoch 36/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0034 - mean_squared_error: 0.0034\n",
      "Epoch 37/200\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0028 - mean_squared_error: 0.0028\n",
      "Epoch 38/200\n",
      "30663/30663 [==============================] - 2s 61us/sample - loss: 0.0026 - mean_squared_error: 0.0026\n",
      "Epoch 39/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0033 - mean_squared_error: 0.0033\n",
      "Epoch 40/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0057 - mean_squared_error: 0.0057\n",
      "Epoch 41/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0025 - mean_squared_error: 0.0025\n",
      "Epoch 42/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0027 - mean_squared_error: 0.0027\n",
      "Epoch 43/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0043 - mean_squared_error: 0.0043\n",
      "Epoch 44/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0023 - mean_squared_error: 0.0023\n",
      "Epoch 45/200\n",
      "30663/30663 [==============================] - 2s 61us/sample - loss: 0.0027 - mean_squared_error: 0.0027\n",
      "Epoch 46/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0025 - mean_squared_error: 0.0025\n",
      "Epoch 47/200\n",
      "30663/30663 [==============================] - 2s 61us/sample - loss: 0.0052 - mean_squared_error: 0.0052\n",
      "Epoch 48/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 49/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0028 - mean_squared_error: 0.0028\n",
      "Epoch 50/200\n",
      "30663/30663 [==============================] - 2s 60us/sample - loss: 0.0024 - mean_squared_error: 0.0024\n",
      "Epoch 51/200\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0030 - mean_squared_error: 0.0030\n",
      "Epoch 52/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0023 - mean_squared_error: 0.0023\n",
      "Epoch 53/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0027 - mean_squared_error: 0.0027\n",
      "Epoch 54/200\n",
      "30663/30663 [==============================] - 2s 67us/sample - loss: 0.0024 - mean_squared_error: 0.0024\n",
      "Epoch 55/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0025 - mean_squared_error: 0.0025\n",
      "Epoch 56/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0033 - mean_squared_error: 0.0033\n",
      "Epoch 57/200\n",
      "30663/30663 [==============================] - 2s 73us/sample - loss: 0.0023 - mean_squared_error: 0.0023\n",
      "Epoch 58/200\n",
      "30663/30663 [==============================] - 2s 49us/sample - loss: 0.0022 - mean_squared_error: 0.0022\n",
      "Epoch 59/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0026 - mean_squared_error: 0.0026\n",
      "Epoch 60/200\n",
      "30663/30663 [==============================] - 2s 76us/sample - loss: 0.0023 - mean_squared_error: 0.0023\n",
      "Epoch 61/200\n",
      "30663/30663 [==============================] - 3s 89us/sample - loss: 0.0026 - mean_squared_error: 0.0026\n",
      "Epoch 62/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30663/30663 [==============================] - ETA: 0s - loss: 0.0042 - mean_squared_error: 0.00 - 3s 82us/sample - loss: 0.0042 - mean_squared_error: 0.0042\n",
      "Epoch 63/200\n",
      "30663/30663 [==============================] - 2s 78us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 64/200\n",
      "30663/30663 [==============================] - 2s 76us/sample - loss: 0.0025 - mean_squared_error: 0.0025\n",
      "Epoch 65/200\n",
      "30663/30663 [==============================] - 3s 83us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 66/200\n",
      "30663/30663 [==============================] - 2s 75us/sample - loss: 0.0049 - mean_squared_error: 0.0049\n",
      "Epoch 67/200\n",
      "30663/30663 [==============================] - 2s 78us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 68/200\n",
      "30663/30663 [==============================] - 2s 71us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 69/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 70/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 71/200\n",
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 72/200\n",
      "30663/30663 [==============================] - 3s 96us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 73/200\n",
      "30663/30663 [==============================] - 3s 107us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 74/200\n",
      "30663/30663 [==============================] - 3s 93us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 75/200\n",
      "30663/30663 [==============================] - 3s 85us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 76/200\n",
      "30663/30663 [==============================] - 3s 83us/sample - loss: 0.0022 - mean_squared_error: 0.0022\n",
      "Epoch 77/200\n",
      "30663/30663 [==============================] - 3s 86us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 78/200\n",
      "30663/30663 [==============================] - 4s 118us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 79/200\n",
      "30663/30663 [==============================] - 4s 116us/sample - loss: 0.0019 - mean_squared_error: 0.0019 - loss: 0.0019 - mean_squared_error: \n",
      "Epoch 80/200\n",
      "30663/30663 [==============================] - 3s 105us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 81/200\n",
      "30663/30663 [==============================] - 2s 59us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 82/200\n",
      "30663/30663 [==============================] - 2s 57us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 83/200\n",
      "30663/30663 [==============================] - 2s 61us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 84/200\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0024 - mean_squared_error: 0.0024\n",
      "Epoch 85/200\n",
      "30663/30663 [==============================] - 2s 51us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 86/200\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 87/200\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.0022 - mean_squared_error: 0.0022\n",
      "Epoch 88/200\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 89/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0026 - mean_squared_error: 0.0026\n",
      "Epoch 90/200\n",
      "30663/30663 [==============================] - 2s 58us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 91/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 92/200\n",
      "30663/30663 [==============================] - 2s 60us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 93/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 94/200\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 95/200\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 96/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0024 - mean_squared_error: 0.0024\n",
      "Epoch 97/200\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 98/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 99/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 100/200\n",
      "30663/30663 [==============================] - 2s 68us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 101/200\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 102/200\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 103/200\n",
      "30663/30663 [==============================] - 1s 43us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 104/200\n",
      "30663/30663 [==============================] - ETA: 0s - loss: 0.0019 - mean_squared_error: 0.00 - 1s 43us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 105/200\n",
      "30663/30663 [==============================] - 1s 42us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 106/200\n",
      "30663/30663 [==============================] - 1s 44us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 107/200\n",
      "30663/30663 [==============================] - 1s 45us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 108/200\n",
      "30663/30663 [==============================] - 1s 47us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 109/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 110/200\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 111/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 112/200\n",
      "30663/30663 [==============================] - 2s 49us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 113/200\n",
      "30663/30663 [==============================] - 2s 55us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 114/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 115/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0030 - mean_squared_error: 0.0030\n",
      "Epoch 116/200\n",
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 117/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 118/200\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 119/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 120/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 121/200\n",
      "30663/30663 [==============================] - 1s 48us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 122/200\n",
      "30663/30663 [==============================] - 2s 54us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 123/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 124/200\n",
      "30663/30663 [==============================] - 2s 74us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 125/200\n",
      "30663/30663 [==============================] - 2s 79us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 126/200\n",
      "30663/30663 [==============================] - 2s 74us/sample - loss: 0.0027 - mean_squared_error: 0.0027\n",
      "Epoch 127/200\n",
      "30663/30663 [==============================] - 3s 83us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 128/200\n",
      "30663/30663 [==============================] - 2s 79us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 129/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30663/30663 [==============================] - 2s 73us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 130/200\n",
      "30663/30663 [==============================] - 2s 60us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 131/200\n",
      "30663/30663 [==============================] - 2s 75us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 132/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 133/200\n",
      "30663/30663 [==============================] - 2s 54us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 134/200\n",
      "30663/30663 [==============================] - 2s 59us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 135/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 136/200\n",
      "30663/30663 [==============================] - 2s 54us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 137/200\n",
      "30663/30663 [==============================] - 2s 75us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 138/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 139/200\n",
      "30663/30663 [==============================] - 2s 54us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 140/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0018 - mean_squared_error: 0.0018s - loss: 0.0018 - mean_squared_err\n",
      "Epoch 141/200\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 142/200\n",
      "30663/30663 [==============================] - 2s 74us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 143/200\n",
      "30663/30663 [==============================] - 2s 69us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 144/200\n",
      "30663/30663 [==============================] - 2s 74us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 145/200\n",
      "30663/30663 [==============================] - 2s 75us/sample - loss: 0.0021 - mean_squared_error: 0.0021\n",
      "Epoch 146/200\n",
      "30663/30663 [==============================] - 2s 77us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 147/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 148/200\n",
      "30663/30663 [==============================] - 3s 96us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 149/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 150/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 151/200\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 152/200\n",
      "30663/30663 [==============================] - 2s 59us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 153/200\n",
      "30663/30663 [==============================] - 1s 49us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 154/200\n",
      "30663/30663 [==============================] - 2s 55us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 155/200\n",
      "30663/30663 [==============================] - 2s 71us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 156/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 157/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 158/200\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 159/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 160/200\n",
      "30663/30663 [==============================] - 2s 78us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 161/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 162/200\n",
      "30663/30663 [==============================] - 2s 55us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 163/200\n",
      "30663/30663 [==============================] - 2s 73us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 164/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 165/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 166/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 167/200\n",
      "30663/30663 [==============================] - 2s 68us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 168/200\n",
      "30663/30663 [==============================] - 2s 67us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 169/200\n",
      "30663/30663 [==============================] - 2s 54us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 170/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 171/200\n",
      "30663/30663 [==============================] - 2s 52us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 172/200\n",
      "30663/30663 [==============================] - 2s 75us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 173/200\n",
      "30663/30663 [==============================] - 3s 85us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 174/200\n",
      "30663/30663 [==============================] - 2s 67us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 175/200\n",
      "30663/30663 [==============================] - 2s 67us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 176/200\n",
      "30663/30663 [==============================] - 2s 60us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 177/200\n",
      "30663/30663 [==============================] - 2s 62us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 178/200\n",
      "30663/30663 [==============================] - 2s 79us/sample - loss: 0.0017 - mean_squared_error: 0.0017\n",
      "Epoch 179/200\n",
      "30663/30663 [==============================] - 2s 65us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 180/200\n",
      "30663/30663 [==============================] - 2s 61us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 181/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 182/200\n",
      "30663/30663 [==============================] - 2s 67us/sample - loss: 0.0011 - mean_squared_error: 0.0011\n",
      "Epoch 183/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 184/200\n",
      "30663/30663 [==============================] - 2s 66us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 185/200\n",
      "30663/30663 [==============================] - 2s 70us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 186/200\n",
      "30663/30663 [==============================] - 3s 92us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 187/200\n",
      "30663/30663 [==============================] - 3s 87us/sample - loss: 0.0016 - mean_squared_error: 0.0016\n",
      "Epoch 188/200\n",
      "30663/30663 [==============================] - 2s 76us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 189/200\n",
      "30663/30663 [==============================] - 3s 88us/sample - loss: 0.0019 - mean_squared_error: 0.0019\n",
      "Epoch 190/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0011 - mean_squared_error: 0.0011\n",
      "Epoch 191/200\n",
      "30663/30663 [==============================] - 2s 68us/sample - loss: 0.0018 - mean_squared_error: 0.0018\n",
      "Epoch 192/200\n",
      "30663/30663 [==============================] - 2s 63us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 193/200\n",
      "30663/30663 [==============================] - 2s 58us/sample - loss: 0.0013 - mean_squared_error: 0.0013\n",
      "Epoch 194/200\n",
      "30663/30663 [==============================] - 2s 60us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 195/200\n",
      "30663/30663 [==============================] - 2s 53us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n",
      "Epoch 196/200\n",
      "30663/30663 [==============================] - 1s 46us/sample - loss: 0.0012 - mean_squared_error: 0.0012\n",
      "Epoch 197/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30663/30663 [==============================] - 2s 50us/sample - loss: 0.0015 - mean_squared_error: 0.0015\n",
      "Epoch 198/200\n",
      "30663/30663 [==============================] - 2s 68us/sample - loss: 0.0020 - mean_squared_error: 0.0020\n",
      "Epoch 199/200\n",
      "30663/30663 [==============================] - 2s 55us/sample - loss: 0.0010 - mean_squared_error: 0.0010\n",
      "Epoch 200/200\n",
      "30663/30663 [==============================] - 2s 56us/sample - loss: 0.0014 - mean_squared_error: 0.0014\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9955539062417118\n",
      "0.9936951251541238\n"
     ]
    }
   ],
   "source": [
    "# Define the model - deep neural net with 3 hidden layers\n",
    "number_input_features =len(df_death_ml.columns)-1\n",
    "hidden_nodes_layer1 = (len(df_death_ml.columns)-1)*3\n",
    "hidden_nodes_layer2 =round(((len(df_death_ml.columns)-1)*3)/2)\n",
    "hidden_nodes_layer3 =round(((len(df_death_ml.columns)-1)*3)/4)\n",
    "\n",
    "nn = Sequential()\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "# Second hidden layer\n",
    "nn.add(Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "# Third hidden layer\n",
    "nn.add(Dense(units=hidden_nodes_layer3, activation=\"relu\"))\n",
    "# Output layer\n",
    "nn.add(Dense(units=1, activation=\"linear\"))\n",
    "\n",
    "\n",
    "nn.summary()\n",
    "\n",
    "# compile model\n",
    "nn.compile(loss=\"mean_squared_error\", optimizer=\"adam\", metrics=[\"mse\"])\n",
    "\n",
    "# train model\n",
    "model_1 = nn.fit(X_train_scaled, y_train_scaled, epochs=200)\n",
    "\n",
    "# Plot the train and test loss function\n",
    "plt.plot(model_1.history[\"loss\"])\n",
    "plt.title(\"loss_function - 3 hidden layer\")\n",
    "plt.legend([\"loss\"])\n",
    "plt.show()\n",
    "\n",
    "y_train_pred = nn.predict(X_train_scaled)\n",
    "y_test_pred = nn.predict(X_test_scaled)\n",
    "\n",
    "print(r2_score(y_train_scaled, y_train_pred))\n",
    "print(r2_score(y_test_scaled, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.20658332]\n",
      " [-0.2052077 ]\n",
      " [-0.20680988]\n",
      " ...\n",
      " [-0.20265085]\n",
      " [-0.20401871]\n",
      " [-0.17722699]]\n",
      "[[-0.18518436]\n",
      " [-0.1993852 ]\n",
      " [-0.15883571]\n",
      " ...\n",
      " [-0.21514842]\n",
      " [-0.20224187]\n",
      " [ 0.19594029]]\n"
     ]
    }
   ],
   "source": [
    "# Print y_train and y_test predicted[0]\n",
    "print(y_train_pred)\n",
    "print(y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-49.980076 ]\n",
      " [-35.414158 ]\n",
      " [-52.379    ]\n",
      " ...\n",
      " [ -8.3406725]\n",
      " [-22.824558 ]\n",
      " [260.86124  ]]\n",
      "[[ 1.7660416e+02]\n",
      " [ 2.6237698e+01]\n",
      " [ 4.5559830e+02]\n",
      " ...\n",
      " [-1.4067197e+02]\n",
      " [-4.0103502e+00]\n",
      " [ 4.2121641e+03]]\n"
     ]
    }
   ],
   "source": [
    "# Convert the scaled predictions to actual values.\n",
    "print(y_scaler.inverse_transform(y_train_pred))\n",
    "print(y_scaler.inverse_transform(y_test_pred))"
   ]
  },
  {
   "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
}