ML_Models/DNN_and_RNN_ML_Models/DNN_and_RNN_ML_Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Modeling of US COVID New Cases and New Deaths:  Using Deep Neural Network and Recurrent Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import train_test_split\n",
    "# from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from datetime import datetime\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.python.keras.utils import np_utils\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, Activation, Flatten\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop\n",
    "from tensorflow.keras.layers import SimpleRNN\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load Dataset\n",
    "\n",
    "### a. Connect to Provisional pgAdmin Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>2/25/2020</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2/26/2020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2/27/2020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2/28/2020</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "      location iso_code continent       date  total_cases  new_cases  new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  gdp_per_capita  extreme_poverty  cardiovasc_death_rate  diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  hospital_beds_per_thousand  life_expectancy  human_development_index\n",
       "0  Afghanistan      AFG      Asia  2/24/2020          1.0        1.0                 0.0           0.0         0.0                  0.0  ...        1803.987              0.0                597.029                 9.59             0.0           0.0                  37.746                         0.5            64.83                    0.511\n",
       "1  Afghanistan      AFG      Asia  2/25/2020          1.0        0.0                 0.0           0.0         0.0                  0.0  ...        1803.987              0.0                597.029                 9.59             0.0           0.0                  37.746                         0.5            64.83                    0.511\n",
       "2  Afghanistan      AFG      Asia  2/26/2020          1.0        0.0                 0.0           0.0         0.0                  0.0  ...        1803.987              0.0                597.029                 9.59             0.0           0.0                  37.746                         0.5            64.83                    0.511\n",
       "3  Afghanistan      AFG      Asia  2/27/2020          1.0        0.0                 0.0           0.0         0.0                  0.0  ...        1803.987              0.0                597.029                 9.59             0.0           0.0                  37.746                         0.5            64.83                    0.511\n",
       "4  Afghanistan      AFG      Asia  2/28/2020          1.0        0.0                 0.0           0.0         0.0                  0.0  ...        1803.987              0.0                597.029                 9.59             0.0           0.0                  37.746                         0.5            64.83                    0.511\n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create Connection String to SQL \n",
    "password = \"BoomerSooner2!\"\n",
    "\n",
    "db_string = f\"postgres://postgres:{password}@127.0.0.1:5432/Final_Project\"\n",
    "## Make above a config.py for password\n",
    "\n",
    "engine = create_engine(db_string)\n",
    "    \n",
    "\n",
    "# Connect to PostgreSQL server\n",
    "\n",
    "dbConnection = engine.connect();\n",
    "\n",
    "# Read data from PostgreSQL database table and load into a DataFrame instance\n",
    "\n",
    "covid_data_final_df = pd.read_sql(\"select * from \\\"covid_data_final\\\"\", dbConnection)\n",
    "\n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "\n",
    "\n",
    "# Close the database connection\n",
    "\n",
    "dbConnection.close();\n",
    "\n",
    "# Proof of Connection:\n",
    "covid_data_final_df.head()\n",
    "\n",
    "\n",
    "\n",
    "# _______________________________________\n",
    "\n",
    "# From CSV.....\n",
    "\n",
    "\n",
    "# # Import Dataset\n",
    "# covid_data_raw_df = pd.read_csv('owid-covid-data.csv')\n",
    "\n",
    "# covid_data_raw_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b. Preprocess Data\n",
    "\n",
    "•\tProvisionally, select desired columns\n",
    "\n",
    "•\tClean data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
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       "  </thead>\n",
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       "      <td>0.0</td>\n",
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       "      <th>71615</th>\n",
       "      <td>1/23/2020</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>71616</th>\n",
       "      <td>1/24/2020</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>71617</th>\n",
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       "      <td>0.0</td>\n",
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       "      <th>71618</th>\n",
       "      <td>1/26/2020</td>\n",
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      "text/plain": [
       "            date  new_cases\n",
       "71614  1/22/2020        0.0\n",
       "71615  1/23/2020        0.0\n",
       "71616  1/24/2020        1.0\n",
       "71617  1/25/2020        0.0\n",
       "71618  1/26/2020        3.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For proof-of-concept, focus on 1 country\n",
    "covid_data_poc_df = covid_data_final_df[covid_data_final_df[\"location\"] == \"United States\"]\n",
    "\n",
    "\n",
    "# Convert NAN to 0\n",
    "covid_data_poc_df = covid_data_poc_df.fillna(0)\n",
    "\n",
    "\n",
    "covid_data_new_cases_df = covid_data_poc_df[['date', 'new_cases']]\n",
    "covid_data_new_cases_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(421, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data_new_cases_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmhan_uricwmy\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smooth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.333333</td>\n",
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       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "            date  new_cases  new_cases_smooth\n",
       "71614  1/22/2020        0.0          0.000000\n",
       "71615  1/23/2020        0.0          0.000000\n",
       "71616  1/24/2020        1.0          0.333333\n",
       "71617  1/25/2020        0.0          0.250000\n",
       "71618  1/26/2020        3.0          0.800000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Smooth Data with Rolling_Average\n",
    "covid_data_new_cases_df['new_cases_smooth'] = covid_data_new_cases_df['new_cases'].rolling(7, min_periods=1).mean()\n",
    "covid_data_new_cases_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# covid_data_new_cases_df.head(500)\n",
    "covid_data_new_cases_df.to_csv('New_Cases_Smooth.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'New Cases')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# colors for the line plot\n",
    "colors = ['green', 'red']\n",
    "\n",
    "# line plot\n",
    "covid_data_new_cases_df.plot(x = \"date\",color=colors, linewidth=3, figsize=(12,6))\n",
    "\n",
    "# modify ticks size\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels =['New Cases - Raw', 'New Cases - Rolling Avg' ], fontsize=14)\n",
    "\n",
    "# title and labels\n",
    "plt.title('US CoVID Cases', fontsize=20)\n",
    "plt.xlabel('Date', fontsize=16)\n",
    "plt.ylabel('New Cases', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72030</th>\n",
       "      <td>3/13/2021</td>\n",
       "      <td>1725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72031</th>\n",
       "      <td>3/14/2021</td>\n",
       "      <td>572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72032</th>\n",
       "      <td>3/15/2021</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72033</th>\n",
       "      <td>3/16/2021</td>\n",
       "      <td>1286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72034</th>\n",
       "      <td>3/17/2021</td>\n",
       "      <td>1173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  new_deaths\n",
       "72030  3/13/2021      1725.0\n",
       "72031  3/14/2021       572.0\n",
       "72032  3/15/2021       740.0\n",
       "72033  3/16/2021      1286.0\n",
       "72034  3/17/2021      1173.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data_new_deaths_df = covid_data_poc_df[['date', 'new_deaths']]\n",
    "covid_data_new_deaths_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(421, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data_new_deaths_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmhan_uricwmy\\anaconda3\\envs\\PythonData\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smooth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71614</th>\n",
       "      <td>1/22/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71615</th>\n",
       "      <td>1/23/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71616</th>\n",
       "      <td>1/24/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71617</th>\n",
       "      <td>1/25/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71618</th>\n",
       "      <td>1/26/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  new_deaths  new_deaths_smooth\n",
       "71614  1/22/2020         0.0                0.0\n",
       "71615  1/23/2020         0.0                0.0\n",
       "71616  1/24/2020         0.0                0.0\n",
       "71617  1/25/2020         0.0                0.0\n",
       "71618  1/26/2020         0.0                0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Smooth Data with Rolling_Average\n",
    "covid_data_new_deaths_df['new_deaths_smooth'] = covid_data_new_deaths_df['new_deaths'].rolling(7, min_periods=1).mean()\n",
    "covid_data_new_deaths_df.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'New Deaths')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# colors for the line plot\n",
    "colors = ['green', 'red']\n",
    "\n",
    "# line plot\n",
    "covid_data_new_deaths_df.plot(x = \"date\",color=colors, linewidth=3, figsize=(12,6))\n",
    "\n",
    "# modify ticks size\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels =['New Deaths - Raw', 'New Deaths - Rolling Avg' ], fontsize=14)\n",
    "\n",
    "# title and labels\n",
    "plt.title('US CoVID Deaths', fontsize=20)\n",
    "plt.xlabel('Date', fontsize=16)\n",
    "plt.ylabel('New Deaths', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Develop Model: Deep Neural  (DNN)\n",
    "\n",
    "**DNN ~ treats time-series as linear model: {X(i) …X(i+t)}**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. New Cases\n",
    "\n",
    "#### Step 1: Data Preprocessing\n",
    "\n",
    "**Incorporates a helper function, convert2matrix, to reshape dataset into a 2-D input shape of DNN. (A full connected net — the main difference between RNN or LTSM)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert2matrix(data_arr, look_back):\n",
    " X, Y =[], []\n",
    " for i in range(len(data_arr)-look_back):\n",
    "  d=i+look_back  \n",
    "  X.append(data_arr[i:d,0])\n",
    "  Y.append(data_arr[d,0])\n",
    " return np.array(X), np.array(Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>new_cases_smooth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/22/2020</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/23/2020</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/24/2020</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/25/2020</th>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/26/2020</th>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           new_cases_smooth\n",
       "date                       \n",
       "1/22/2020          0.000000\n",
       "1/23/2020          0.000000\n",
       "1/24/2020          0.333333\n",
       "1/25/2020          0.250000\n",
       "1/26/2020          0.800000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data_new_cases_df = covid_data_new_cases_df.set_index('date')\n",
    "del covid_data_new_cases_df['new_cases']\n",
    "covid_data_new_cases_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train length:  316\n",
      "Train length + Test Length :  406\n",
      "Train length + Test Length + Look Back:  421\n",
      "Number of data observations:  421\n",
      "\n",
      "trainX shape:  (301, 15)\n",
      "trainY shape:  (301,)\n",
      "testX shape:  (90, 15)\n",
      "testY shape:  (90,)\n"
     ]
    }
   ],
   "source": [
    "# setup look_back window \n",
    "look_back = 15\n",
    "\n",
    "#Split data set into testing dataset and train dataset\n",
    "train_size = round(.75*len(covid_data_new_cases_df))\n",
    "train, test =covid_data_new_cases_df.values[0:train_size,:],covid_data_new_cases_df.values[train_size:len(covid_data_new_cases_df.values),:]\n",
    "\n",
    "#convert dataset into right shape in order to input into the DNN\n",
    "trainXCases, trainYCases = convert2matrix(train, look_back)\n",
    "testXCases, testYCases = convert2matrix(test, look_back)\n",
    "\n",
    "\n",
    "print(\"Train length: \",train_size)\n",
    "print(\"Train length + Test Length : \",train_size + len(testXCases))\n",
    "print(\"Train length + Test Length + Look Back: \",train_size + len(testXCases) + look_back)\n",
    "print(\"Number of data observations: \",len(covid_data_new_cases_df))\n",
    "print(\"\")\n",
    "print(\"trainX shape: \", trainXCases.shape)\n",
    "print(\"trainY shape: \", trainYCases.shape)\n",
    "print(\"testX shape: \", testXCases.shape)\n",
    "print(\"testY shape: \", testYCases.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Define neural network shape and compile model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def model_dnn(look_back):\n",
    "    model=Sequential()\n",
    "    model.add(Dense(units=32, input_dim=look_back, activation='relu'))\n",
    "    model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error',  optimizer='adam',metrics = ['mse', 'mae'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Fit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11/11 [==============================] - 1s 53ms/step - loss: 2296929617.1868 - mse: 2296929617.1868 - mae: 32893.4446 - val_loss: 30848956416.0000 - val_mse: 30848956416.0000 - val_mae: 159997.0938\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 1176161006.6303 - mse: 1176161006.6303 - mae: 23438.3334 - val_loss: 14354499584.0000 - val_mse: 14354499584.0000 - val_mae: 107404.2344\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 682229683.0100 - mse: 682229683.0100 - mae: 17385.8566 - val_loss: 6667044864.0000 - val_mse: 6667044864.0000 - val_mae: 68611.4688\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 384895768.1485 - mse: 384895768.1485 - mae: 12920.4169 - val_loss: 2488669184.0000 - val_mse: 2488669184.0000 - val_mae: 36249.1328\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 168983179.2706 - mse: 168983180.2706 - mae: 8137.6719 - val_loss: 690873152.0000 - val_mse: 690873152.0000 - val_mae: 18466.2090\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 71515066.7465 - mse: 71515066.7465 - mae: 4691.9114 - val_loss: 662766016.0000 - val_mse: 662766016.0000 - val_mae: 21883.3887\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 43.1904 - mse: 43.1904 - mae: 2.650 - 0s 5ms/step - loss: 47945410.9325 - mse: 47945410.9325 - mae: 4348.5051 - val_loss: 1090448640.0000 - val_mse: 1090448640.0000 - val_mae: 28421.1992\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 45862899.5170 - mse: 45862899.5170 - mae: 4522.7291 - val_loss: 1170467456.0000 - val_mse: 1170467456.0000 - val_mae: 29546.0273\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 45179101.9873 - mse: 45179101.9873 - mae: 4469.5580 - val_loss: 997091712.0000 - val_mse: 997091712.0000 - val_mae: 27079.6992\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 44617589.8157 - mse: 44617587.8157 - mae: 4308.7649 - val_loss: 821749888.0000 - val_mse: 821749888.0000 - val_mae: 24486.5312\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 44037120.1447 - mse: 44037120.1447 - mae: 4150.6684 - val_loss: 711899520.0000 - val_mse: 711899520.0000 - val_mae: 22868.9590\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 42843257.4646 - mse: 42843257.4646 - mae: 4017.8807 - val_loss: 637824768.0000 - val_mse: 637824768.0000 - val_mae: 21716.9902\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 39704983.4423 - mse: 39704983.4423 - mae: 3802.0389 - val_loss: 534407680.0000 - val_mse: 534407680.0000 - val_mae: 19846.9121\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 34196842.2435 - mse: 34196842.2435 - mae: 3493.4959 - val_loss: 475148928.0000 - val_mse: 475148928.0000 - val_mae: 18714.5449\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 30011502.6953 - mse: 30011502.6953 - mae: 3222.1716 - val_loss: 413688576.0000 - val_mse: 413688576.0000 - val_mae: 17309.7812\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 26368034.8332 - mse: 26368034.8332 - mae: 3054.7900 - val_loss: 431707424.0000 - val_mse: 431707424.0000 - val_mae: 17759.9531\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 24310719.2925 - mse: 24310719.2925 - mae: 2967.5993 - val_loss: 419231424.0000 - val_mse: 419231424.0000 - val_mae: 17492.3105\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 22819120.7624 - mse: 22819120.7624 - mae: 2891.0269 - val_loss: 417799008.0000 - val_mse: 417799008.0000 - val_mae: 17470.3008\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 21901547.0682 - mse: 21901547.0682 - mae: 2837.1068 - val_loss: 400386624.0000 - val_mse: 400386624.0000 - val_mae: 17086.7246\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 21094916.2952 - mse: 21094915.7119 - mae: 2776.0466 - val_loss: 389535936.0000 - val_mse: 389535936.0000 - val_mae: 16843.5977\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 20262602.1900 - mse: 20262602.1900 - mae: 2726.6782 - val_loss: 383024704.0000 - val_mse: 383024704.0000 - val_mae: 16707.8477\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 19635786.9189 - mse: 19635786.9189 - mae: 2681.4254 - val_loss: 369067424.0000 - val_mse: 369067424.0000 - val_mae: 16383.4902\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 19031032.6498 - mse: 19031032.6498 - mae: 2632.1594 - val_loss: 360541184.0000 - val_mse: 360541184.0000 - val_mae: 16190.3027\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 18348042.6305 - mse: 18348042.6305 - mae: 2591.2528 - val_loss: 354486944.0000 - val_mse: 354486944.0000 - val_mae: 16056.0791\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 17906874.6120 - mse: 17906874.6120 - mae: 2550.5934 - val_loss: 339951840.0000 - val_mse: 339951840.0000 - val_mae: 15693.7207\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 17358013.7621 - mse: 17358013.7621 - mae: 2505.2526 - val_loss: 333223328.0000 - val_mse: 333223328.0000 - val_mae: 15531.5176\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 16868860.1612 - mse: 16868860.1612 - mae: 2467.4111 - val_loss: 323744608.0000 - val_mse: 323744608.0000 - val_mae: 15288.8857\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 16416872.4778 - mse: 16416872.4778 - mae: 2428.0126 - val_loss: 314029280.0000 - val_mse: 314029280.0000 - val_mae: 15032.5176\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 15954618.2113 - mse: 15954618.2113 - mae: 2390.0169 - val_loss: 306140128.0000 - val_mse: 306140128.0000 - val_mae: 14820.4668\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 15515467.0278 - mse: 15515467.0278 - mae: 2353.4350 - val_loss: 297858560.0000 - val_mse: 297858560.0000 - val_mae: 14590.0723\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 15094118.5112 - mse: 15094118.0112 - mae: 2316.4345 - val_loss: 289802112.0000 - val_mse: 289802112.0000 - val_mae: 14358.2969\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 14684105.8232 - mse: 14684105.8232 - mae: 2284.5626 - val_loss: 282493824.0000 - val_mse: 282493824.0000 - val_mae: 14144.2207\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 14283629.8088 - mse: 14283629.8088 - mae: 2244.5472 - val_loss: 275518592.0000 - val_mse: 275518592.0000 - val_mae: 13933.3057\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 13901320.7943 - mse: 13901320.7943 - mae: 2210.3555 - val_loss: 268774528.0000 - val_mse: 268774528.0000 - val_mae: 13723.5654\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 13532417.7790 - mse: 13532417.7790 - mae: 2176.7872 - val_loss: 262377680.0000 - val_mse: 262377680.0000 - val_mae: 13519.9609\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 13177373.0135 - mse: 13177372.8469 - mae: 2144.0949 - val_loss: 256276576.0000 - val_mse: 256276576.0000 - val_mae: 13323.2275\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 12833421.7480 - mse: 12833422.2480 - mae: 2112.0370 - val_loss: 250583296.0000 - val_mse: 250583296.0000 - val_mae: 13143.9971\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 12504114.3164 - mse: 12504114.3164 - mae: 2081.1290 - val_loss: 244973984.0000 - val_mse: 244973984.0000 - val_mae: 12969.9023\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 12188137.8854 - mse: 12188137.8854 - mae: 2050.5624 - val_loss: 239658912.0000 - val_mse: 239658912.0000 - val_mae: 12804.8555\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11883852.7880 - mse: 11883852.7880 - mae: 2020.7817 - val_loss: 234693952.0000 - val_mse: 234693952.0000 - val_mae: 12648.5469\n",
      "Epoch 41/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 5ms/step - loss: 11592567.4408 - mse: 11592567.4408 - mae: 1992.0785 - val_loss: 229974080.0000 - val_mse: 229974080.0000 - val_mae: 12496.1377\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11313334.2605 - mse: 11313334.2605 - mae: 1965.0348 - val_loss: 225552768.0000 - val_mse: 225552768.0000 - val_mae: 12349.1768\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11046361.7053 - mse: 11046361.7053 - mae: 1939.6581 - val_loss: 221388640.0000 - val_mse: 221388640.0000 - val_mae: 12206.3486\n",
      "Epoch 44/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 10791759.4420 - mse: 10791759.4420 - mae: 1915.3798 - val_loss: 217536560.0000 - val_mse: 217536560.0000 - val_mae: 12074.9775\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 10547325.3040 - mse: 10547325.3040 - mae: 1891.5854 - val_loss: 213981616.0000 - val_mse: 213981616.0000 - val_mae: 11950.3428\n",
      "Epoch 46/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 10316429.4578 - mse: 10316429.4578 - mae: 1869.2798 - val_loss: 210384800.0000 - val_mse: 210384800.0000 - val_mae: 11820.7676\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 10096536.4869 - mse: 10096536.4869 - mae: 1847.5559 - val_loss: 207502160.0000 - val_mse: 207502160.0000 - val_mae: 11712.7500\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 9880566.7244 - mse: 9880566.7244 - mae: 1827.0203 - val_loss: 204366592.0000 - val_mse: 204366592.0000 - val_mae: 11592.7100\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 9706342.7957 - mse: 9706342.7957 - mae: 1807.7048 - val_loss: 200549280.0000 - val_mse: 200549280.0000 - val_mae: 11444.1514\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 9494354.3674 - mse: 9494354.3674 - mae: 1787.1394 - val_loss: 199492320.0000 - val_mse: 199492320.0000 - val_mae: 11394.2803\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 9361798.7723 - mse: 9361798.7723 - mae: 1771.8174 - val_loss: 194143120.0000 - val_mse: 194143120.0000 - val_mae: 11181.1143\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 9151490.2205 - mse: 9151490.2205 - mae: 1750.2275 - val_loss: 195866256.0000 - val_mse: 195866256.0000 - val_mae: 11233.4170\n",
      "Epoch 53/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 9070147.5003 - mse: 9070147.5003 - mae: 1741.4836 - val_loss: 187620016.0000 - val_mse: 187620016.0000 - val_mae: 10897.4795\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8869365.6165 - mse: 8869365.6165 - mae: 1716.9361 - val_loss: 193742432.0000 - val_mse: 193742432.0000 - val_mae: 11138.5947\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8886161.6871 - mse: 8886161.6871 - mae: 1722.4050 - val_loss: 179964864.0000 - val_mse: 179964864.0000 - val_mae: 10543.9922\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8763901.4720 - mse: 8763901.7220 - mae: 1695.0317 - val_loss: 187349648.0000 - val_mse: 187349648.0000 - val_mae: 10842.5166\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 9098535.8734 - mse: 9098535.8734 - mae: 1750.3074 - val_loss: 168698480.0000 - val_mse: 168698480.0000 - val_mae: 9998.6982\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 9547609.1641 - mse: 9547609.1641 - mae: 1734.2732 - val_loss: 211170464.0000 - val_mse: 211170464.0000 - val_mae: 11771.4336\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11174104.9773 - mse: 11174104.9773 - mae: 1961.3743 - val_loss: 154701264.0000 - val_mse: 154701264.0000 - val_mae: 9254.4102\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 13752910.9478 - mse: 13752910.9478 - mae: 2060.5473 - val_loss: 280864672.0000 - val_mse: 280864672.0000 - val_mae: 13740.2012\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 16866353.6203 - mse: 16866354.8703 - mae: 2405.0917 - val_loss: 162795312.0000 - val_mse: 162795312.0000 - val_mae: 8873.7939\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 12315023.6268 - mse: 12315023.6268 - mae: 1922.8859 - val_loss: 394389504.0000 - val_mse: 394389504.0000 - val_mae: 16961.5273\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11386508.3552 - mse: 11386508.3552 - mae: 1969.2146 - val_loss: 171138704.0000 - val_mse: 171138704.0000 - val_mae: 10556.4463\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 9306592.4802 - mse: 9306592.4802 - mae: 1833.2130 - val_loss: 277758624.0000 - val_mse: 277758624.0000 - val_mae: 14088.3193\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8871128.6949 - mse: 8871128.6949 - mae: 1858.8250 - val_loss: 213906912.0000 - val_mse: 213906912.0000 - val_mae: 12101.7305\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8288238.2337 - mse: 8288238.2337 - mae: 1720.4439 - val_loss: 198419600.0000 - val_mse: 198419600.0000 - val_mae: 11517.1113\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8072518.5160 - mse: 8072518.5160 - mae: 1685.2448 - val_loss: 207403984.0000 - val_mse: 207403984.0000 - val_mae: 11820.0830\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7847622.5872 - mse: 7847622.5872 - mae: 1656.3716 - val_loss: 191060224.0000 - val_mse: 191060224.0000 - val_mae: 11228.1123\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7778875.2076 - mse: 7778875.2076 - mae: 1638.9289 - val_loss: 196109808.0000 - val_mse: 196109808.0000 - val_mae: 11420.3955\n"
     ]
    }
   ],
   "source": [
    "model=model_dnn(look_back)\n",
    "history=model.fit(trainXCases,trainYCases, epochs=100, batch_size=30, verbose=1, validation_data=(testXCases,testYCases),callbacks=[EarlyStopping(monitor='val_loss', patience=10)],shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_loss(history):\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Test Loss')\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epochs')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Root Mean Squared Error(RMSE): 3301.96; Train Mean Absolute Error(MAE) : 2182.09 \n",
      "Test Root Mean Squared Error(RMSE): 14003.92; Test Mean Absolute Error(MAE) : 11420.40 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_predict_Cases = model.predict(trainXCases)\n",
    "test_predict_Cases = model.predict(testXCases)\n",
    "\n",
    "\n",
    "\n",
    "train_score = model.evaluate(trainXCases, trainYCases, verbose=0)\n",
    "print('Train Root Mean Squared Error(RMSE): %.2f; Train Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(train_score[1]), train_score[2]))\n",
    "test_score = model.evaluate(testXCases, testYCases, verbose=0)\n",
    "print('Test Root Mean Squared Error(RMSE): %.2f; Test Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(test_score[1]), test_score[2]))\n",
    "model_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5. Plot Prediction Vs. Actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction_plot(testYCases, test_predict_Cases):\n",
    "      len_prediction=[x for x in range(len(testYCases))]\n",
    "      plt.figure(figsize=(8,4))\n",
    "      plt.plot(len_prediction, testYCases, marker='.', label=\"actual\")\n",
    "      plt.plot(len_prediction, test_predict_Cases, 'r', label=\"prediction\")\n",
    "      plt.tight_layout()\n",
    "      plt.subplots_adjust(left=0.07)\n",
    "      plt.ylabel('New Cases', size=15)\n",
    "      plt.xlabel('Days', size=15)\n",
    "      plt.legend(fontsize=15)\n",
    "      plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_plot(testYCases, test_predict_Cases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. New Deaths\n",
    "\n",
    "#### Step 1: Data Preprocessing\n",
    "\n",
    "**Incorporates a helper function, convert2matrix, to reshape dataset into a 2-D input shape of DNN. (A full connected net — the main difference between RNN or LTSM)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert2matrix(data_arr, look_back):\n",
    " X, Y =[], []\n",
    " for i in range(len(data_arr)-look_back):\n",
    "  d=i+look_back  \n",
    "  X.append(data_arr[i:d,0])\n",
    "  Y.append(data_arr[d,0])\n",
    " return np.array(X), np.array(Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "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>new_deaths_smooth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/22/2020</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/23/2020</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/24/2020</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/25/2020</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/26/2020</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           new_deaths_smooth\n",
       "date                        \n",
       "1/22/2020                0.0\n",
       "1/23/2020                0.0\n",
       "1/24/2020                0.0\n",
       "1/25/2020                0.0\n",
       "1/26/2020                0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data_new_deaths_df = covid_data_new_deaths_df.set_index('date')\n",
    "del covid_data_new_deaths_df['new_deaths']\n",
    "covid_data_new_deaths_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train length:  316\n",
      "Train length + Test Length :  406\n",
      "Train length + Test Length + Look Back:  421\n",
      "Number of data observations:  421\n",
      "\n",
      "trainX shape:  (301, 15)\n",
      "trainY shape:  (301,)\n",
      "testX shape:  (90, 15)\n",
      "testY shape:  (90,)\n"
     ]
    }
   ],
   "source": [
    "# setup look_back window \n",
    "look_back = 15\n",
    "\n",
    "#Split data set into testing dataset and train dataset\n",
    "train_size = round(.75*len(covid_data_new_deaths_df))\n",
    "train, test =covid_data_new_deaths_df.values[0:train_size,:],covid_data_new_deaths_df.values[train_size:len(covid_data_new_deaths_df.values),:]\n",
    "\n",
    "#convert dataset into right shape in order to input into the DNN\n",
    "trainXDeaths, trainYDeaths = convert2matrix(train, look_back)\n",
    "testXDeaths, testYDeaths = convert2matrix(test, look_back)\n",
    "\n",
    "\n",
    "print(\"Train length: \",train_size)\n",
    "print(\"Train length + Test Length : \",train_size + len(testXDeaths))\n",
    "print(\"Train length + Test Length + Look Back: \",train_size + len(testXDeaths) + look_back)\n",
    "print(\"Number of data observations: \",len(covid_data_new_deaths_df))\n",
    "print(\"\")\n",
    "print(\"trainX shape: \", trainXDeaths.shape)\n",
    "print(\"trainY shape: \", trainYDeaths.shape)\n",
    "print(\"testX shape: \", testXDeaths.shape)\n",
    "print(\"testY shape: \", testYDeaths.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Define neural network shape and compile model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def model_dnn(look_back):\n",
    "    model=Sequential()\n",
    "    model.add(Dense(units=32, input_dim=look_back, activation='relu'))\n",
    "    model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error',  optimizer='adam',metrics = ['mse', 'mae'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Fit Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11/11 [==============================] - 0s 16ms/step - loss: 2663470.3366 - mse: 2663470.3366 - mae: 1131.4482 - val_loss: 8382330.5000 - val_mse: 8382330.5000 - val_mae: 2841.7104\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 665628.4528 - mse: 665628.4528 - mae: 555.6881 - val_loss: 789429.2500 - val_mse: 789429.2500 - val_mae: 858.5960\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 31506.8261 - mse: 31506.8261 - mae: 111.2700 - val_loss: 98616.8359 - val_mse: 98616.8359 - val_mae: 264.0988\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 35971.7995 - mse: 35971.7995 - mae: 125.5990 - val_loss: 252568.6250 - val_mse: 252568.6250 - val_mae: 453.6712\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 37642.7805 - mse: 37642.7805 - mae: 123.4141 - val_loss: 78591.1641 - val_mse: 78591.1719 - val_mae: 235.8130\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11341.3404 - mse: 11341.3404 - mae: 63.9451 - val_loss: 35352.7852 - val_mse: 35352.7852 - val_mae: 143.7472\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6482.6946 - mse: 6482.6946 - mae: 49.1396 - val_loss: 39658.8242 - val_mse: 39658.8242 - val_mae: 151.1429\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6171.9881 - mse: 6171.9881 - mae: 46.8893 - val_loss: 34717.0508 - val_mse: 34717.0508 - val_mae: 144.1666\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 6315.5625 - mse: 6315.5625 - mae: 46.7090 - val_loss: 35732.1133 - val_mse: 35732.1133 - val_mae: 150.1943\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6786.8819 - mse: 6786.8819 - mae: 48.9071 - val_loss: 35589.3789 - val_mse: 35589.3789 - val_mae: 149.7173\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6501.1360 - mse: 6501.1360 - mae: 47.9351 - val_loss: 34719.1289 - val_mse: 34719.1289 - val_mae: 146.9280\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 6090.2690 - mse: 6090.2690 - mae: 46.4896 - val_loss: 34511.3711 - val_mse: 34511.3711 - val_mae: 146.3237\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 5852.7521 - mse: 5852.7521 - mae: 45.7939 - val_loss: 34541.6328 - val_mse: 34541.6328 - val_mae: 146.7762\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5672.4239 - mse: 5672.4239 - mae: 45.3454 - val_loss: 34548.0273 - val_mse: 34548.0273 - val_mae: 147.0647\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5341.6104 - mse: 5341.6104 - mae: 44.4388 - val_loss: 32606.1367 - val_mse: 32606.1367 - val_mae: 142.1329\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6049.7337 - mse: 6049.7337 - mae: 46.8538 - val_loss: 31780.9023 - val_mse: 31780.9023 - val_mae: 139.2455\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4761.0949 - mse: 4761.0949 - mae: 42.1559 - val_loss: 31059.1387 - val_mse: 31059.1387 - val_mae: 138.2855\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5033.1982 - mse: 5033.1982 - mae: 43.2105 - val_loss: 30491.4219 - val_mse: 30491.4219 - val_mae: 136.7761\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 4578.1952 - mse: 4578.1952 - mae: 41.4478 - val_loss: 30332.9785 - val_mse: 30332.9785 - val_mae: 136.5982\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4524.2027 - mse: 4524.2027 - mae: 41.2179 - val_loss: 30235.3203 - val_mse: 30235.3203 - val_mae: 136.4672\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 4418.3959 - mse: 4418.3959 - mae: 40.8166 - val_loss: 30043.9473 - val_mse: 30043.9473 - val_mae: 135.9487\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 4267.3366 - mse: 4267.3366 - mae: 40.1934 - val_loss: 29935.4473 - val_mse: 29935.4473 - val_mae: 135.7027\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4184.8330 - mse: 4184.8330 - mae: 39.8537 - val_loss: 29806.6172 - val_mse: 29806.6172 - val_mae: 135.3737\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4086.9470 - mse: 4086.9470 - mae: 39.4318 - val_loss: 29675.2578 - val_mse: 29675.2578 - val_mae: 135.0594\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3986.2270 - mse: 3986.2270 - mae: 38.9540 - val_loss: 29522.6504 - val_mse: 29522.6504 - val_mae: 134.7906\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3793.7989 - mse: 3793.7989 - mae: 38.1026 - val_loss: 29406.5664 - val_mse: 29406.5664 - val_mae: 134.5275\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3609.5922 - mse: 3609.5922 - mae: 36.9764 - val_loss: 29203.3613 - val_mse: 29203.3613 - val_mae: 133.9514\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3417.9241 - mse: 3417.9241 - mae: 36.0648 - val_loss: 29196.3340 - val_mse: 29196.3340 - val_mae: 134.3323\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3412.4198 - mse: 3412.4198 - mae: 35.9263 - val_loss: 29001.7441 - val_mse: 29001.7441 - val_mae: 133.6868\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3280.9129 - mse: 3280.9129 - mae: 35.2536 - val_loss: 28917.4082 - val_mse: 28917.4082 - val_mae: 133.4611\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3228.7090 - mse: 3228.7090 - mae: 34.9253 - val_loss: 28829.0664 - val_mse: 28829.0664 - val_mae: 133.3719\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3189.9939 - mse: 3189.9939 - mae: 34.6492 - val_loss: 28738.1777 - val_mse: 28738.1777 - val_mae: 133.1353\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3139.3899 - mse: 3139.3899 - mae: 34.3344 - val_loss: 28661.1445 - val_mse: 28661.1445 - val_mae: 132.9006\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3087.1667 - mse: 3087.1667 - mae: 33.9996 - val_loss: 28531.9023 - val_mse: 28531.9023 - val_mae: 132.6654\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3039.0487 - mse: 3039.0487 - mae: 33.6749 - val_loss: 28418.9688 - val_mse: 28418.9688 - val_mae: 132.4869\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3005.0754 - mse: 3005.0754 - mae: 33.3860 - val_loss: 28367.9297 - val_mse: 28367.9277 - val_mae: 132.3394\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2965.8613 - mse: 2965.8613 - mae: 33.0805 - val_loss: 28279.8008 - val_mse: 28279.8008 - val_mae: 132.0627\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2915.7426 - mse: 2915.7426 - mae: 32.7291 - val_loss: 28186.5059 - val_mse: 28186.5020 - val_mae: 131.9425\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2897.4686 - mse: 2897.4686 - mae: 32.5151 - val_loss: 28123.8359 - val_mse: 28123.8359 - val_mae: 131.7457\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2854.1492 - mse: 2854.1492 - mae: 32.1857 - val_loss: 28028.8145 - val_mse: 28028.8145 - val_mae: 131.5297\n",
      "Epoch 41/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2829.9106 - mse: 2829.9106 - mae: 31.9532 - val_loss: 27957.7617 - val_mse: 27957.7617 - val_mae: 131.3516\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2789.2254 - mse: 2789.2252 - mae: 31.6369 - val_loss: 27851.1426 - val_mse: 27851.1426 - val_mae: 131.1605\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2770.0213 - mse: 2770.0213 - mae: 31.4373 - val_loss: 27821.6523 - val_mse: 27821.6523 - val_mae: 131.0473\n",
      "Epoch 44/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2742.3760 - mse: 2742.3760 - mae: 31.2293 - val_loss: 27764.7480 - val_mse: 27764.7441 - val_mae: 130.7726\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2707.7171 - mse: 2707.7171 - mae: 30.9857 - val_loss: 27675.0547 - val_mse: 27675.0547 - val_mae: 130.6673\n",
      "Epoch 46/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 5ms/step - loss: 2686.5474 - mse: 2686.5476 - mae: 30.8036 - val_loss: 27570.3613 - val_mse: 27570.3613 - val_mae: 130.5128\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2674.3900 - mse: 2674.3903 - mae: 30.6487 - val_loss: 27533.9219 - val_mse: 27533.9219 - val_mae: 130.3444\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2652.1777 - mse: 2652.1776 - mae: 30.5057 - val_loss: 27495.8340 - val_mse: 27495.8340 - val_mae: 130.1201\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2633.6108 - mse: 2633.6108 - mae: 30.3763 - val_loss: 27407.2109 - val_mse: 27407.2109 - val_mae: 129.9995\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2617.4783 - mse: 2617.4783 - mae: 30.2437 - val_loss: 27304.8008 - val_mse: 27304.8008 - val_mae: 129.8397\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2610.8289 - mse: 2610.8289 - mae: 30.1843 - val_loss: 27265.9785 - val_mse: 27265.9746 - val_mae: 129.6744\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2587.8301 - mse: 2587.8301 - mae: 30.0566 - val_loss: 27197.9082 - val_mse: 27197.9082 - val_mae: 129.5112\n",
      "Epoch 53/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2587.4879 - mse: 2587.4879 - mae: 30.0232 - val_loss: 27150.5938 - val_mse: 27150.5938 - val_mae: 129.3609\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2566.8285 - mse: 2566.8285 - mae: 29.8967 - val_loss: 27054.2051 - val_mse: 27054.2051 - val_mae: 129.1564\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2564.9892 - mse: 2564.9892 - mae: 29.8499 - val_loss: 27026.4785 - val_mse: 27026.4785 - val_mae: 129.0262\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2548.1026 - mse: 2548.1026 - mae: 29.7401 - val_loss: 26932.7148 - val_mse: 26932.7148 - val_mae: 128.8072\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2544.2099 - mse: 2544.2099 - mae: 29.6461 - val_loss: 26915.4336 - val_mse: 26915.4336 - val_mae: 128.7093\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2532.6275 - mse: 2532.6275 - mae: 29.5413 - val_loss: 26805.7949 - val_mse: 26805.7949 - val_mae: 128.4546\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2515.5375 - mse: 2515.5375 - mae: 29.3991 - val_loss: 26745.1797 - val_mse: 26745.1797 - val_mae: 128.4066\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2526.4218 - mse: 2526.4218 - mae: 29.3938 - val_loss: 26695.2949 - val_mse: 26695.2949 - val_mae: 128.1545\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2503.8745 - mse: 2503.8745 - mae: 29.2528 - val_loss: 26683.9883 - val_mse: 26683.9883 - val_mae: 127.9934\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2498.0458 - mse: 2498.0458 - mae: 29.1840 - val_loss: 26592.8145 - val_mse: 26592.8145 - val_mae: 127.8299\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2486.9121 - mse: 2486.9121 - mae: 29.0848 - val_loss: 26522.4727 - val_mse: 26522.4727 - val_mae: 127.7260\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2490.1603 - mse: 2490.1603 - mae: 29.0601 - val_loss: 26467.4004 - val_mse: 26467.4004 - val_mae: 127.4976\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2465.3355 - mse: 2465.3355 - mae: 28.9159 - val_loss: 26412.6309 - val_mse: 26412.6309 - val_mae: 127.3999\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2477.3451 - mse: 2477.3451 - mae: 28.9295 - val_loss: 26358.4277 - val_mse: 26358.4277 - val_mae: 127.1748\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2449.2629 - mse: 2449.2629 - mae: 28.7619 - val_loss: 26280.1719 - val_mse: 26280.1719 - val_mae: 127.1038\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2464.8945 - mse: 2464.8945 - mae: 28.7912 - val_loss: 26250.9336 - val_mse: 26250.9336 - val_mae: 126.8859\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2439.2304 - mse: 2439.2304 - mae: 28.6484 - val_loss: 26203.9199 - val_mse: 26203.9199 - val_mae: 126.8215\n",
      "Epoch 70/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2448.0970 - mse: 2448.0971 - mae: 28.6507 - val_loss: 26148.6602 - val_mse: 26148.6602 - val_mae: 126.6231\n",
      "Epoch 71/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2427.3673 - mse: 2427.3673 - mae: 28.5210 - val_loss: 26049.2500 - val_mse: 26049.2500 - val_mae: 126.5071\n",
      "Epoch 72/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2424.6666 - mse: 2424.6666 - mae: 28.4565 - val_loss: 25976.0840 - val_mse: 25976.0840 - val_mae: 126.4196\n",
      "Epoch 73/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2433.2534 - mse: 2433.2534 - mae: 28.4629 - val_loss: 25972.0410 - val_mse: 25972.0410 - val_mae: 126.2721\n",
      "Epoch 74/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2415.5028 - mse: 2415.5028 - mae: 28.3673 - val_loss: 25942.9004 - val_mse: 25942.9004 - val_mae: 126.1068\n",
      "Epoch 75/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2408.3415 - mse: 2408.3415 - mae: 28.3011 - val_loss: 25843.7168 - val_mse: 25843.7168 - val_mae: 125.9831\n",
      "Epoch 76/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2400.4721 - mse: 2400.4721 - mae: 28.2115 - val_loss: 25768.2910 - val_mse: 25768.2910 - val_mae: 125.9002\n",
      "Epoch 77/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2404.7232 - mse: 2404.7232 - mae: 28.1980 - val_loss: 25713.7832 - val_mse: 25713.7832 - val_mae: 125.6888\n",
      "Epoch 78/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2380.3515 - mse: 2380.3515 - mae: 28.0563 - val_loss: 25664.3887 - val_mse: 25664.3887 - val_mae: 125.6530\n",
      "Epoch 79/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2397.4681 - mse: 2397.4681 - mae: 28.1056 - val_loss: 25610.6641 - val_mse: 25610.6641 - val_mae: 125.4157\n",
      "Epoch 80/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2369.0946 - mse: 2369.0946 - mae: 27.9606 - val_loss: 25564.8418 - val_mse: 25564.8418 - val_mae: 125.4081\n",
      "Epoch 81/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2389.5539 - mse: 2389.5539 - mae: 28.0162 - val_loss: 25494.8047 - val_mse: 25494.8047 - val_mae: 125.0841\n",
      "Epoch 82/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2358.4591 - mse: 2358.4591 - mae: 27.8531 - val_loss: 25459.8672 - val_mse: 25459.8672 - val_mae: 125.1347\n",
      "Epoch 83/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2381.4097 - mse: 2381.4097 - mae: 27.9321 - val_loss: 25379.2148 - val_mse: 25379.2148 - val_mae: 124.7449\n",
      "Epoch 84/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2348.4089 - mse: 2348.4089 - mae: 27.7567 - val_loss: 25351.2109 - val_mse: 25351.2109 - val_mae: 124.8453\n",
      "Epoch 85/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2373.1084 - mse: 2373.1084 - mae: 27.8531 - val_loss: 25258.3242 - val_mse: 25258.3242 - val_mae: 124.4106\n",
      "Epoch 86/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2335.8960 - mse: 2335.8960 - mae: 27.6488 - val_loss: 25186.8828 - val_mse: 25186.8828 - val_mae: 124.5117\n",
      "Epoch 87/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2356.1308 - mse: 2356.1308 - mae: 27.7106 - val_loss: 25068.5996 - val_mse: 25068.5996 - val_mae: 124.1586\n",
      "Epoch 88/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2352.4985 - mse: 2352.4985 - mae: 27.6558 - val_loss: 25118.2422 - val_mse: 25118.2422 - val_mae: 124.2175\n",
      "Epoch 89/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2342.9882 - mse: 2342.9882 - mae: 27.6338 - val_loss: 25076.8359 - val_mse: 25076.8359 - val_mae: 123.9597\n",
      "Epoch 90/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2335.0992 - mse: 2335.0992 - mae: 27.5830 - val_loss: 24969.7324 - val_mse: 24969.7324 - val_mae: 123.8232\n",
      "Epoch 91/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 5ms/step - loss: 2331.1416 - mse: 2331.1416 - mae: 27.5147 - val_loss: 24887.3105 - val_mse: 24887.3105 - val_mae: 123.6723\n",
      "Epoch 92/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2334.8372 - mse: 2334.8372 - mae: 27.4905 - val_loss: 24887.1172 - val_mse: 24887.1172 - val_mae: 123.5827\n",
      "Epoch 93/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2313.0775 - mse: 2313.0775 - mae: 27.3907 - val_loss: 24796.3281 - val_mse: 24796.3281 - val_mae: 123.4376\n",
      "Epoch 94/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2333.3230 - mse: 2333.3230 - mae: 27.4405 - val_loss: 24733.8340 - val_mse: 24733.8340 - val_mae: 123.1935\n",
      "Epoch 95/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2298.0505 - mse: 2298.0505 - mae: 27.2725 - val_loss: 24698.8574 - val_mse: 24698.8574 - val_mae: 123.2108\n",
      "Epoch 96/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2323.0320 - mse: 2323.0320 - mae: 27.3927 - val_loss: 24627.5781 - val_mse: 24627.5781 - val_mae: 122.9027\n",
      "Epoch 97/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2294.1677 - mse: 2294.1677 - mae: 27.2142 - val_loss: 24587.3770 - val_mse: 24587.3770 - val_mae: 122.8884\n",
      "Epoch 98/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2311.0301 - mse: 2311.0301 - mae: 27.2879 - val_loss: 24520.9160 - val_mse: 24520.9160 - val_mae: 122.5960\n",
      "Epoch 99/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2287.6464 - mse: 2287.6464 - mae: 27.1520 - val_loss: 24486.1172 - val_mse: 24486.1172 - val_mae: 122.6174\n",
      "Epoch 100/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2300.5112 - mse: 2300.5112 - mae: 27.2093 - val_loss: 24420.2422 - val_mse: 24420.2422 - val_mae: 122.3240\n"
     ]
    }
   ],
   "source": [
    "model=model_dnn(look_back)\n",
    "history=model.fit(trainXDeaths,trainYDeaths, epochs=100, batch_size=30, verbose=1, validation_data=(testXDeaths,testYDeaths),callbacks=[EarlyStopping(monitor='val_loss', patience=10)],shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_loss(history):\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Test Loss')\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epochs')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Root Mean Squared Error(RMSE): 49.23; Train Mean Absolute Error(MAE) : 31.80 \n",
      "Test Root Mean Squared Error(RMSE): 156.27; Test Mean Absolute Error(MAE) : 122.32 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_predict_Deaths = model.predict(trainXDeaths)\n",
    "test_predict_Deaths = model.predict(testXDeaths)\n",
    "\n",
    "\n",
    "\n",
    "train_score = model.evaluate(trainXDeaths, trainYDeaths, verbose=0)\n",
    "print('Train Root Mean Squared Error(RMSE): %.2f; Train Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(train_score[1]), train_score[2]))\n",
    "test_score = model.evaluate(testXDeaths, testYDeaths, verbose=0)\n",
    "print('Test Root Mean Squared Error(RMSE): %.2f; Test Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(test_score[1]), test_score[2]))\n",
    "model_loss(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5. Plot Prediction Vs. Actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prediction_plot(testYDeaths, test_predict_Deaths):\n",
    "      len_prediction=[x for x in range(len(testYDeaths))]\n",
    "      plt.figure(figsize=(8,4))\n",
    "      plt.plot(len_prediction, testYDeaths, marker='.', label=\"actual\")\n",
    "      plt.plot(len_prediction, test_predict_Deaths, 'r', label=\"prediction\")\n",
    "      plt.tight_layout()\n",
    "      plt.subplots_adjust(left=0.07)\n",
    "      plt.ylabel('New Deaths', size=15)\n",
    "      plt.xlabel('Days', size=15)\n",
    "      plt.legend(fontsize=15)\n",
    "      plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_plot(testYDeaths, test_predict_Deaths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Develop Model: Recurrent Neural Net (RNN)\n",
    "\n",
    "\n",
    "**RNN ~ time-series datasets are sequences**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. New Cases\n",
    "\n",
    "#### Step 1: Data Preprocessing\n",
    "\n",
    "**Incorporates a helper function, convert2matrix, to reshape dataset into\n",
    "RNN input shape: (batch_size, window size, input_features)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert2matrix(data_arr, look_back):\n",
    "   X, Y =[], []\n",
    "   for i in range(len(data_arr)-look_back):\n",
    "       d=i+look_back  \n",
    "       X.append(data_arr[i:d,])\n",
    "       Y.append(data_arr[d,])\n",
    "   return np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train length:  316\n",
      "Train length + Test Length :  406\n",
      "Train length + Test Length + Look Back:  421\n",
      "Number of data observations:  421\n",
      "\n",
      "trainX shape:  (301, 1, 15)\n",
      "trainY shape:  (301, 1)\n",
      "testX shape:  (90, 1, 15)\n",
      "testY shape:  (90, 1)\n"
     ]
    }
   ],
   "source": [
    "# setup look_back window \n",
    "look_back = 15 \n",
    "\n",
    "#Split data set into testing dataset and train dataset\n",
    "train_size = round(.75*len(covid_data_new_cases_df))\n",
    "train, test = covid_data_new_cases_df.values[0:train_size,:], covid_data_new_cases_df.values[train_size:len(covid_data_new_cases_df.values),:]\n",
    "\n",
    "#convert dataset into right shape in order to input into the DNN\n",
    "trainXCases_RNN, trainYCases_RNN = convert2matrix(train, look_back)\n",
    "testXCases_RNN,testYCases_RNN =convert2matrix(test, look_back)\n",
    "\n",
    "trainXCases_RNN = np.reshape(trainXCases_RNN, (trainXCases_RNN.shape[0], 1, trainXCases_RNN.shape[1]))\n",
    "testXCases_RNN = np.reshape(testXCases_RNN, (testXCases_RNN.shape[0], 1, testXCases_RNN.shape[1]))\n",
    "\n",
    "print(\"Train length: \",train_size)\n",
    "print(\"Train length + Test Length : \",train_size + len(testXCases_RNN))\n",
    "print(\"Train length + Test Length + Look Back: \",train_size + len(testXCases_RNN) + look_back)\n",
    "print(\"Number of data observations: \",len(covid_data_new_cases_df))\n",
    "print(\"\")\n",
    "print(\"trainX shape: \", trainXCases_RNN.shape)\n",
    "print(\"trainY shape: \", trainYCases_RNN.shape)\n",
    "print(\"testX shape: \", testXCases_RNN.shape)\n",
    "print(\"testY shape: \", testYCases_RNN.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_rnn(look_back):\n",
    "  model=Sequential()\n",
    "  model.add(SimpleRNN(units=32, input_shape=(1,look_back), activation=\"relu\"))\n",
    "  model.add(Dense(8, activation='relu'))\n",
    "  model.add(Dense(1))\n",
    "  model.compile(loss='mean_squared_error',  optimizer='adam',metrics = ['mse', 'mae'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11/11 [==============================] - 1s 24ms/step - loss: 97257311.6307 - mse: 97257311.6307 - mae: 6994.7178 - val_loss: 1188097024.0000 - val_mse: 1188097024.0000 - val_mae: 29891.3691\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 27648927.8596 - mse: 27648927.8596 - mae: 3602.0623 - val_loss: 879597824.0000 - val_mse: 879597824.0000 - val_mae: 25308.4531\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 28617150.6445 - mse: 28617150.6445 - mae: 3111.2160 - val_loss: 463696640.0000 - val_mse: 463696640.0000 - val_mae: 18217.0176\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 22970501.0957 - mse: 22970501.0957 - mae: 3112.2669 - val_loss: 968609792.0000 - val_mse: 968609792.0000 - val_mae: 27010.2637\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 28145776.7184 - mse: 28145776.6767 - mae: 2906.2894 - val_loss: 277243328.0000 - val_mse: 277243328.0000 - val_mae: 14115.0801\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 20172327.7599 - mse: 20172327.7599 - mae: 2823.2764 - val_loss: 989403904.0000 - val_mse: 989403904.0000 - val_mae: 27682.1445\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 20453388.6569 - mse: 20453388.6569 - mae: 2678.7731 - val_loss: 278603552.0000 - val_mse: 278603552.0000 - val_mae: 14167.0332\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 14730163.7979 - mse: 14730163.7979 - mae: 2484.3906 - val_loss: 692493184.0000 - val_mse: 692493184.0000 - val_mae: 22735.8398\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 14034435.8424 - mse: 14034435.8424 - mae: 2429.2706 - val_loss: 353672992.0000 - val_mse: 353672992.0000 - val_mae: 15937.9834\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 12173956.4509 - mse: 12173956.4509 - mae: 2286.5806 - val_loss: 452934144.0000 - val_mse: 452934144.0000 - val_mae: 18124.9863\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 12018983.9689 - mse: 12018983.9689 - mae: 2200.3649 - val_loss: 290037312.0000 - val_mse: 290037312.0000 - val_mae: 14425.8135\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 10176124.5773 - mse: 10176124.5773 - mae: 2113.5616 - val_loss: 143438256.0000 - val_mse: 143438256.0000 - val_mae: 9834.3154\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6244813.8271 - mse: 6244813.8271 - mae: 1476.6313 - val_loss: 133164296.0000 - val_mse: 133164296.0000 - val_mae: 9300.5762\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 23807148.2033 - mse: 23807148.2033 - mae: 3019.8017 - val_loss: 242169280.0000 - val_mse: 242169280.0000 - val_mae: 12804.5498\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 40226399.6885 - mse: 40226399.6885 - mae: 3685.5414 - val_loss: 141522768.0000 - val_mse: 141522768.0000 - val_mae: 8203.7246\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 65700657.5036 - mse: 65700657.5036 - mae: 4513.4907 - val_loss: 1426881408.0000 - val_mse: 1426881408.0000 - val_mae: 33992.9688\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 35819919.3423 - mse: 35819919.3423 - mae: 3084.9542 - val_loss: 427375584.0000 - val_mse: 427375584.0000 - val_mae: 17031.7148\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 10573857.4646 - mse: 10573857.7980 - mae: 1885.3891 - val_loss: 524133024.0000 - val_mse: 524133024.0000 - val_mae: 19631.0898\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 9148445.9622 - mse: 9148445.9622 - mae: 2041.5035 - val_loss: 161265344.0000 - val_mse: 161265344.0000 - val_mae: 10452.9473\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 7559414.5535 - mse: 7559414.5535 - mae: 1571.5373 - val_loss: 103564960.0000 - val_mse: 103564960.0000 - val_mae: 8136.2368\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - ETA: 0s - loss: 25.2021 - mse: 25.2021 - mae: 2.003 - 0s 6ms/step - loss: 7047712.6418 - mse: 7047712.6418 - mae: 1605.7619 - val_loss: 206492032.0000 - val_mse: 206492032.0000 - val_mae: 11731.8281\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 6329619.0761 - mse: 6329619.0761 - mae: 1570.0690 - val_loss: 125375864.0000 - val_mse: 125375864.0000 - val_mae: 9075.7393\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5887666.3094 - mse: 5887666.3094 - mae: 1463.6813 - val_loss: 140504272.0000 - val_mse: 140504272.0000 - val_mae: 9637.5625\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 5721029.8930 - mse: 5721029.8930 - mae: 1465.2500 - val_loss: 138458448.0000 - val_mse: 138458448.0000 - val_mae: 9539.0186\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5355121.3159 - mse: 5355121.3159 - mae: 1410.3337 - val_loss: 126697144.0000 - val_mse: 126697144.0000 - val_mae: 9058.8750\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5209531.9718 - mse: 5209531.9718 - mae: 1386.2413 - val_loss: 130497448.0000 - val_mse: 130497448.0000 - val_mae: 9194.7236\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4957990.2904 - mse: 4957990.2904 - mae: 1355.0798 - val_loss: 122549544.0000 - val_mse: 122549544.0000 - val_mae: 8856.0381\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4823659.9883 - mse: 4823659.9883 - mae: 1331.3426 - val_loss: 123506760.0000 - val_mse: 123506760.0000 - val_mae: 8881.0088\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 4661961.7883 - mse: 4661961.7883 - mae: 1309.2843 - val_loss: 119540312.0000 - val_mse: 119540312.0000 - val_mae: 8698.4111\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 4572840.3622 - mse: 4572840.3622 - mae: 1294.6020 - val_loss: 119390136.0000 - val_mse: 119390136.0000 - val_mae: 8678.5146\n"
     ]
    }
   ],
   "source": [
    "model=model_rnn(look_back)\n",
    "history=model.fit(trainXCases_RNN,trainYCases_RNN, epochs=100, batch_size=30, verbose=1, validation_data=(testXCases_RNN,testYCases_RNN),callbacks=[EarlyStopping(monitor='val_loss', patience=10)],shuffle=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    " def model_loss(history):\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Test Loss')\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epochs')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Root Mean Squared Error(RMSE): 2816.90; Train Mean Absolute Error(MAE) : 1786.26 \n",
      "Test Root Mean Squared Error(RMSE): 10926.58; Test Mean Absolute Error(MAE) : 8678.52 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_predict_Cases_RNN = model.predict(trainXCases_RNN)\n",
    "test_predict_Cases_RNN = model.predict(testXCases_RNN)\n",
    "\n",
    "\n",
    "train_scoreCases_RNN = model.evaluate(trainXCases_RNN, trainYCases_RNN, verbose=0)\n",
    "print('Train Root Mean Squared Error(RMSE): %.2f; Train Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(train_scoreCases_RNN[1]), train_scoreCases_RNN[2]))\n",
    "test_scoreCases_RNN = model.evaluate(testXCases_RNN, testYCases_RNN, verbose=0)\n",
    "print('Test Root Mean Squared Error(RMSE): %.2f; Test Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(test_scoreCases_RNN[1]), test_scoreCases_RNN[2]))\n",
    "model_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "   def prediction_plot(testYCases_RNN, test_predict_Cases_RNN):\n",
    "      len_prediction=[x for x in range(len(testYCases_RNN))]\n",
    "      plt.figure(figsize=(8,4))\n",
    "      plt.plot(len_prediction, testYCases_RNN, marker='.', label=\"actual\")\n",
    "      plt.plot(len_prediction, test_predict_Cases_RNN, 'r', label=\"prediction\")\n",
    "      plt.tight_layout()\n",
    "      plt.subplots_adjust(left=0.07)\n",
    "      plt.ylabel('New Cases', size=15)\n",
    "      plt.xlabel('Time step', size=15)\n",
    "      plt.legend(fontsize=15)\n",
    "      plt.show();\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_plot(testYCases_RNN, test_predict_Cases_RNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. New Deaths\n",
    "\n",
    "#### Step 1: Data Preprocessing\n",
    "\n",
    "**Incorporates a helper function, convert2matrix, to reshape dataset into\n",
    "RNN input shape: (batch_size, window size, input_features)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert2matrix(data_arr, look_back):\n",
    "   X, Y =[], []\n",
    "   for i in range(len(data_arr)-look_back):\n",
    "       d=i+look_back  \n",
    "       X.append(data_arr[i:d,])\n",
    "       Y.append(data_arr[d,])\n",
    "   return np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train length:  316\n",
      "Train length + Test Length :  406\n",
      "Train length + Test Length + Look Back:  421\n",
      "Number of data observations:  421\n",
      "\n",
      "trainX shape:  (301, 1, 15)\n",
      "trainY shape:  (301, 1)\n",
      "testX shape:  (90, 1, 15)\n",
      "testY shape:  (90, 1)\n"
     ]
    }
   ],
   "source": [
    "# setup look_back window \n",
    "look_back = 15 \n",
    "\n",
    "#Split data set into testing dataset and train dataset\n",
    "train_size = round(.75*len(covid_data_new_deaths_df))\n",
    "train, test = covid_data_new_deaths_df.values[0:train_size,:], covid_data_new_deaths_df.values[train_size:len(covid_data_new_deaths_df.values),:]\n",
    "\n",
    "#convert dataset into right shape in order to input into the DNN\n",
    "trainXDeaths_RNN, trainYDeaths_RNN = convert2matrix(train, look_back)\n",
    "testXDeaths_RNN,testYDeaths_RNN =convert2matrix(test, look_back)\n",
    "\n",
    "trainXDeaths_RNN = np.reshape(trainXDeaths_RNN, (trainXDeaths_RNN.shape[0], 1, trainXDeaths_RNN.shape[1]))\n",
    "testXDeaths_RNN = np.reshape(testXDeaths_RNN, (testXDeaths_RNN.shape[0], 1, testXDeaths_RNN.shape[1]))\n",
    "\n",
    "\n",
    "\n",
    "print(\"Train length: \",train_size)\n",
    "print(\"Train length + Test Length : \",train_size + len(testXDeaths_RNN))\n",
    "print(\"Train length + Test Length + Look Back: \",train_size + len(testXDeaths_RNN) + look_back)\n",
    "print(\"Number of data observations: \",len(covid_data_new_deaths_df))\n",
    "print(\"\")\n",
    "print(\"trainX shape: \", trainXDeaths_RNN.shape)\n",
    "print(\"trainY shape: \", trainYDeaths_RNN.shape)\n",
    "print(\"testX shape: \", testXDeaths_RNN.shape)\n",
    "print(\"testY shape: \", testYDeaths_RNN.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_rnn(look_back):\n",
    "  model=Sequential()\n",
    "  model.add(SimpleRNN(units=32, input_shape=(1,look_back), activation=\"relu\"))\n",
    "  model.add(Dense(8, activation='relu'))\n",
    "  model.add(Dense(1))\n",
    "  model.compile(loss='mean_squared_error',  optimizer='adam',metrics = ['mse', 'mae'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11/11 [==============================] - 1s 26ms/step - loss: 56994.2493 - mse: 56994.2493 - mae: 137.5224 - val_loss: 240389.4219 - val_mse: 240389.4375 - val_mae: 431.0346\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 46227.6091 - mse: 46227.6091 - mae: 134.0111 - val_loss: 71509.5312 - val_mse: 71509.5312 - val_mae: 211.6065\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 24296.9152 - mse: 24296.9152 - mae: 80.0115 - val_loss: 51198.7109 - val_mse: 51198.7109 - val_mae: 190.8424\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 21085.0805 - mse: 21085.0805 - mae: 84.6698 - val_loss: 44636.3047 - val_mse: 44636.3047 - val_mae: 177.7788\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 15992.4440 - mse: 15992.4440 - mae: 68.1455 - val_loss: 42323.5156 - val_mse: 42323.5156 - val_mae: 169.7870\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 14380.5479 - mse: 14380.5479 - mae: 66.1743 - val_loss: 41944.9570 - val_mse: 41944.9609 - val_mae: 168.9706\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 13395.7397 - mse: 13395.7399 - mae: 65.7080 - val_loss: 40876.0430 - val_mse: 40876.0430 - val_mae: 163.0110\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 12277.6689 - mse: 12277.6689 - mae: 63.0522 - val_loss: 40271.8164 - val_mse: 40271.8203 - val_mae: 163.0860\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 12086.0180 - mse: 12086.0180 - mae: 63.6846 - val_loss: 39760.3867 - val_mse: 39760.3867 - val_mae: 159.5362\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11561.3861 - mse: 11561.3861 - mae: 62.0583 - val_loss: 39222.8906 - val_mse: 39222.8906 - val_mae: 159.5138\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 11530.9963 - mse: 11530.9963 - mae: 62.4554 - val_loss: 38922.8789 - val_mse: 38922.8789 - val_mae: 157.5564\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11206.8160 - mse: 11206.8160 - mae: 61.4142 - val_loss: 38490.1445 - val_mse: 38490.1406 - val_mae: 157.3034\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 11086.7661 - mse: 11086.7661 - mae: 61.4995 - val_loss: 38129.7461 - val_mse: 38129.7461 - val_mae: 155.9970\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 10859.1684 - mse: 10859.1684 - mae: 60.8596 - val_loss: 37681.0781 - val_mse: 37681.0781 - val_mae: 155.3110\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 10700.8579 - mse: 10700.8579 - mae: 60.6614 - val_loss: 37284.2617 - val_mse: 37284.2617 - val_mae: 154.3174\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 10514.4019 - mse: 10514.4019 - mae: 60.1972 - val_loss: 36832.8906 - val_mse: 36832.8906 - val_mae: 153.3663\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 10104.4898 - mse: 10104.4898 - mae: 57.7307 - val_loss: 40517.0703 - val_mse: 40517.0703 - val_mae: 169.6960\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 12315.6961 - mse: 12315.6961 - mae: 71.0303 - val_loss: 34706.2734 - val_mse: 34706.2734 - val_mae: 145.3600\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 10976.6999 - mse: 10976.6999 - mae: 62.7826 - val_loss: 30079.6504 - val_mse: 30079.6504 - val_mae: 139.1342\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8354.4552 - mse: 8354.4552 - mae: 53.6764 - val_loss: 29671.9395 - val_mse: 29671.9395 - val_mae: 140.6615\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 9555.4047 - mse: 9555.4047 - mae: 58.3888 - val_loss: 29746.9492 - val_mse: 29746.9492 - val_mae: 134.6521\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 8580.2001 - mse: 8580.2001 - mae: 53.7348 - val_loss: 28313.6230 - val_mse: 28313.6230 - val_mae: 132.5460\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8210.5985 - mse: 8210.5985 - mae: 53.4196 - val_loss: 27560.0742 - val_mse: 27560.0742 - val_mae: 131.0325\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 8380.2565 - mse: 8380.2565 - mae: 54.0135 - val_loss: 26874.5059 - val_mse: 26874.5059 - val_mae: 128.3953\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 8105.6083 - mse: 8105.6083 - mae: 52.8020 - val_loss: 26173.4648 - val_mse: 26173.4648 - val_mae: 127.0271\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7922.3637 - mse: 7922.3637 - mae: 52.3683 - val_loss: 25537.0176 - val_mse: 25537.0176 - val_mae: 125.2669\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 7831.7608 - mse: 7831.7608 - mae: 52.0461 - val_loss: 24910.6523 - val_mse: 24910.6523 - val_mae: 123.2687\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7649.2934 - mse: 7649.2934 - mae: 51.3894 - val_loss: 24277.5664 - val_mse: 24277.5664 - val_mae: 121.4167\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7493.7804 - mse: 7493.7804 - mae: 50.8986 - val_loss: 23689.7109 - val_mse: 23689.7109 - val_mae: 119.6928\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7364.5724 - mse: 7364.5724 - mae: 50.4168 - val_loss: 23083.0938 - val_mse: 23083.0938 - val_mae: 117.8512\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7220.0231 - mse: 7220.0231 - mae: 49.8974 - val_loss: 22521.7910 - val_mse: 22521.7910 - val_mae: 116.2733\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 7098.6533 - mse: 7098.6533 - mae: 49.4885 - val_loss: 22025.7949 - val_mse: 22025.7949 - val_mae: 114.8524\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 6990.5515 - mse: 6990.5515 - mae: 49.1211 - val_loss: 21591.7598 - val_mse: 21591.7598 - val_mae: 113.6604\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6881.5035 - mse: 6881.5035 - mae: 48.7648 - val_loss: 21196.4961 - val_mse: 21196.4961 - val_mae: 112.5767\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6770.8012 - mse: 6770.8012 - mae: 48.4222 - val_loss: 20823.2148 - val_mse: 20823.2148 - val_mae: 111.5432\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6667.6991 - mse: 6667.6991 - mae: 48.0760 - val_loss: 20473.8672 - val_mse: 20473.8672 - val_mae: 110.5478\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 6553.8869 - mse: 6553.8869 - mae: 47.6892 - val_loss: 20140.6562 - val_mse: 20140.6562 - val_mae: 109.6261\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6451.6813 - mse: 6451.6815 - mae: 47.3652 - val_loss: 19817.4492 - val_mse: 19817.4492 - val_mae: 108.7161\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6356.7762 - mse: 6356.7762 - mae: 47.0306 - val_loss: 19508.6562 - val_mse: 19508.6562 - val_mae: 107.8357\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6246.5942 - mse: 6246.5942 - mae: 46.6063 - val_loss: 19223.4453 - val_mse: 19223.4453 - val_mae: 107.0913\n",
      "Epoch 41/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6127.7107 - mse: 6127.7107 - mae: 46.2073 - val_loss: 18956.8555 - val_mse: 18956.8555 - val_mae: 106.3776\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 6024.8319 - mse: 6024.8319 - mae: 45.8732 - val_loss: 18701.3848 - val_mse: 18701.3848 - val_mae: 105.6182\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5916.6205 - mse: 5916.6205 - mae: 45.5014 - val_loss: 18450.4980 - val_mse: 18450.4980 - val_mae: 104.8752\n",
      "Epoch 44/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 5812.3538 - mse: 5812.3539 - mae: 45.1416 - val_loss: 18221.8301 - val_mse: 18221.8301 - val_mae: 104.1883\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5712.4258 - mse: 5712.4258 - mae: 44.7966 - val_loss: 18018.0801 - val_mse: 18018.0801 - val_mae: 103.5602\n",
      "Epoch 46/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 5ms/step - loss: 5588.0941 - mse: 5588.0941 - mae: 44.3563 - val_loss: 17809.7500 - val_mse: 17809.7500 - val_mae: 102.8790\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5484.3564 - mse: 5484.3564 - mae: 44.0346 - val_loss: 17616.8672 - val_mse: 17616.8672 - val_mae: 102.2082\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5400.5628 - mse: 5400.5628 - mae: 43.7630 - val_loss: 17428.3398 - val_mse: 17428.3398 - val_mae: 101.5142\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 5301.9866 - mse: 5301.9865 - mae: 43.3850 - val_loss: 17244.6758 - val_mse: 17244.6758 - val_mae: 100.7992\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5193.1870 - mse: 5193.1870 - mae: 42.9941 - val_loss: 17068.3730 - val_mse: 17068.3730 - val_mae: 100.0734\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 5106.0670 - mse: 5106.0676 - mae: 42.6954 - val_loss: 16905.2676 - val_mse: 16905.2676 - val_mae: 99.3624\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 5022.6064 - mse: 5022.6064 - mae: 42.3829 - val_loss: 16779.8633 - val_mse: 16779.8633 - val_mae: 98.8034\n",
      "Epoch 53/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 4934.1454 - mse: 4934.1454 - mae: 42.0478 - val_loss: 16668.8770 - val_mse: 16668.8770 - val_mae: 98.3123\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4853.4244 - mse: 4853.4244 - mae: 41.7459 - val_loss: 16554.5684 - val_mse: 16554.5684 - val_mae: 97.7855\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 4778.5379 - mse: 4778.5379 - mae: 41.4640 - val_loss: 16427.4258 - val_mse: 16427.4258 - val_mae: 97.2275\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4705.8103 - mse: 4705.8103 - mae: 41.1719 - val_loss: 16311.5010 - val_mse: 16311.5010 - val_mae: 96.7468\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4620.9033 - mse: 4620.9033 - mae: 40.8250 - val_loss: 16220.3789 - val_mse: 16220.3789 - val_mae: 96.3841\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4529.6776 - mse: 4529.6775 - mae: 40.4472 - val_loss: 16129.4385 - val_mse: 16129.4385 - val_mae: 96.1925\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4432.5916 - mse: 4432.5916 - mae: 40.0278 - val_loss: 15998.2197 - val_mse: 15998.2207 - val_mae: 96.0921\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4333.2439 - mse: 4333.2439 - mae: 39.6197 - val_loss: 15871.2607 - val_mse: 15871.2607 - val_mae: 95.6953\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4176.7710 - mse: 4176.7710 - mae: 38.8231 - val_loss: 15672.0977 - val_mse: 15672.0977 - val_mae: 94.6165\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4211.0351 - mse: 4211.0351 - mae: 39.0406 - val_loss: 15481.9580 - val_mse: 15481.9580 - val_mae: 94.0899\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4183.6632 - mse: 4183.6632 - mae: 38.9387 - val_loss: 15327.0498 - val_mse: 15327.0498 - val_mae: 93.5786\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4012.0714 - mse: 4012.0714 - mae: 38.0424 - val_loss: 15136.2168 - val_mse: 15136.2168 - val_mae: 92.6108\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4044.1490 - mse: 4044.1490 - mae: 38.2869 - val_loss: 14945.2773 - val_mse: 14945.2773 - val_mae: 92.1435\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 4027.8357 - mse: 4027.8357 - mae: 38.2595 - val_loss: 14803.0586 - val_mse: 14803.0586 - val_mae: 91.6922\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3876.8364 - mse: 3876.8364 - mae: 37.4267 - val_loss: 14652.8213 - val_mse: 14652.8213 - val_mae: 90.6294\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3883.4721 - mse: 3883.4721 - mae: 37.5267 - val_loss: 14470.9307 - val_mse: 14470.9307 - val_mae: 90.2747\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3867.4904 - mse: 3867.4904 - mae: 37.5397 - val_loss: 14329.0166 - val_mse: 14329.0166 - val_mae: 89.9261\n",
      "Epoch 70/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3754.5804 - mse: 3754.5804 - mae: 36.9118 - val_loss: 14207.4072 - val_mse: 14207.4072 - val_mae: 88.8400\n",
      "Epoch 71/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3755.9221 - mse: 3755.9221 - mae: 36.9493 - val_loss: 14029.7627 - val_mse: 14029.7607 - val_mae: 88.4859\n",
      "Epoch 72/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3720.4918 - mse: 3720.4918 - mae: 36.8753 - val_loss: 13892.0420 - val_mse: 13892.0420 - val_mae: 88.2777\n",
      "Epoch 73/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3635.0199 - mse: 3635.0199 - mae: 36.3993 - val_loss: 13805.0527 - val_mse: 13805.0527 - val_mae: 87.1682\n",
      "Epoch 74/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3629.3965 - mse: 3629.3965 - mae: 36.3688 - val_loss: 13628.7109 - val_mse: 13628.7109 - val_mae: 86.8536\n",
      "Epoch 75/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 3581.9160 - mse: 3581.9160 - mae: 36.2312 - val_loss: 13493.2305 - val_mse: 13493.2305 - val_mae: 86.6981\n",
      "Epoch 76/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3515.7060 - mse: 3515.7060 - mae: 35.8600 - val_loss: 13438.4766 - val_mse: 13438.4766 - val_mae: 85.8336\n",
      "Epoch 77/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3506.6747 - mse: 3506.6747 - mae: 35.7905 - val_loss: 13264.6602 - val_mse: 13264.6602 - val_mae: 85.4423\n",
      "Epoch 78/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3452.4576 - mse: 3452.4576 - mae: 35.6175 - val_loss: 13121.5068 - val_mse: 13121.5068 - val_mae: 85.6144\n",
      "Epoch 79/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3451.6449 - mse: 3451.6449 - mae: 35.6489 - val_loss: 13088.2832 - val_mse: 13088.2832 - val_mae: 84.5551\n",
      "Epoch 80/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3374.4161 - mse: 3374.4161 - mae: 35.1726 - val_loss: 12947.9844 - val_mse: 12947.9844 - val_mae: 84.5097\n",
      "Epoch 81/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3291.9719 - mse: 3291.9719 - mae: 34.7301 - val_loss: 12893.9111 - val_mse: 12893.9111 - val_mae: 83.9659\n",
      "Epoch 82/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3259.2821 - mse: 3259.2821 - mae: 34.5814 - val_loss: 12748.2891 - val_mse: 12748.2891 - val_mae: 83.5846\n",
      "Epoch 83/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3193.3424 - mse: 3193.3424 - mae: 34.1913 - val_loss: 12719.5928 - val_mse: 12719.5928 - val_mae: 83.1995\n",
      "Epoch 84/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3237.6912 - mse: 3237.6912 - mae: 34.4664 - val_loss: 12504.4502 - val_mse: 12504.4502 - val_mae: 82.6841\n",
      "Epoch 85/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3217.3186 - mse: 3217.3186 - mae: 34.4851 - val_loss: 12348.1670 - val_mse: 12348.1670 - val_mae: 82.6564\n",
      "Epoch 86/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3242.6647 - mse: 3242.6647 - mae: 34.6750 - val_loss: 12408.9824 - val_mse: 12408.9824 - val_mae: 81.8108\n",
      "Epoch 87/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3129.5700 - mse: 3129.5700 - mae: 33.8877 - val_loss: 12313.6055 - val_mse: 12313.6055 - val_mae: 81.6245\n",
      "Epoch 88/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2984.8554 - mse: 2984.8554 - mae: 33.0425 - val_loss: 12339.5889 - val_mse: 12339.5889 - val_mae: 81.5412\n",
      "Epoch 89/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3005.2763 - mse: 3005.2763 - mae: 33.2222 - val_loss: 12136.3594 - val_mse: 12136.3594 - val_mae: 80.8920\n",
      "Epoch 90/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2892.5005 - mse: 2892.5005 - mae: 32.5866 - val_loss: 12110.8027 - val_mse: 12110.8027 - val_mae: 80.8115\n",
      "Epoch 91/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 6ms/step - loss: 2995.8793 - mse: 2995.8793 - mae: 33.2361 - val_loss: 11850.6973 - val_mse: 11850.6973 - val_mae: 80.2586\n",
      "Epoch 92/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 3034.2784 - mse: 3034.2784 - mae: 33.6310 - val_loss: 11701.6670 - val_mse: 11701.6670 - val_mae: 79.9878\n",
      "Epoch 93/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 3065.8287 - mse: 3065.8287 - mae: 33.8474 - val_loss: 11864.3926 - val_mse: 11864.3926 - val_mae: 79.5500\n",
      "Epoch 94/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2928.0547 - mse: 2928.0547 - mae: 32.8210 - val_loss: 11775.5830 - val_mse: 11775.5830 - val_mae: 79.4502\n",
      "Epoch 95/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2759.9336 - mse: 2759.9336 - mae: 31.8613 - val_loss: 11832.3877 - val_mse: 11832.3877 - val_mae: 79.5111\n",
      "Epoch 96/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2773.7737 - mse: 2773.7737 - mae: 32.0353 - val_loss: 11586.0107 - val_mse: 11586.0107 - val_mae: 78.9386\n",
      "Epoch 97/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2703.9462 - mse: 2703.9462 - mae: 31.6265 - val_loss: 11581.0322 - val_mse: 11581.0322 - val_mae: 78.6981\n",
      "Epoch 98/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2808.8425 - mse: 2808.8425 - mae: 32.2829 - val_loss: 11370.1768 - val_mse: 11370.1768 - val_mae: 77.9327\n",
      "Epoch 99/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 2726.7641 - mse: 2726.7641 - mae: 31.7024 - val_loss: 11462.3604 - val_mse: 11462.3604 - val_mae: 77.9070\n",
      "Epoch 100/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 2791.3573 - mse: 2791.3573 - mae: 32.1358 - val_loss: 11206.2676 - val_mse: 11206.2676 - val_mae: 77.4482\n"
     ]
    }
   ],
   "source": [
    "model=model_rnn(look_back)\n",
    "history=model.fit(trainXDeaths_RNN,trainYDeaths_RNN, epochs=100, batch_size=30, verbose=1, validation_data=(testXDeaths_RNN,testYDeaths_RNN),callbacks=[EarlyStopping(monitor='val_loss', patience=10)],shuffle=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    " def model_loss(history):\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Test Loss')\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epochs')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Root Mean Squared Error(RMSE): 43.32; Train Mean Absolute Error(MAE) : 28.57 \n",
      "Test Root Mean Squared Error(RMSE): 105.86; Test Mean Absolute Error(MAE) : 77.45 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_predict_Deaths_RNN = model.predict(trainXDeaths_RNN)\n",
    "test_predict_Deaths_RNN = model.predict(testXDeaths_RNN)\n",
    "\n",
    "\n",
    "train_scoreDeaths_RNN = model.evaluate(trainXDeaths_RNN, trainYDeaths_RNN, verbose=0)\n",
    "print('Train Root Mean Squared Error(RMSE): %.2f; Train Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(train_scoreDeaths_RNN[1]), train_scoreDeaths_RNN[2]))\n",
    "test_scoreDeaths_RNN = model.evaluate(testXDeaths_RNN, testYDeaths_RNN, verbose=0)\n",
    "print('Test Root Mean Squared Error(RMSE): %.2f; Test Mean Absolute Error(MAE) : %.2f ' \n",
    "% (np.sqrt(test_scoreDeaths_RNN[1]), test_scoreDeaths_RNN[2]))\n",
    "model_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "   def prediction_plot(testYDeaths_RNN, test_predict_Deaths_RNN):\n",
    "      len_prediction=[x for x in range(len(testYDeaths_RNN))]\n",
    "      plt.figure(figsize=(8,4))\n",
    "      plt.plot(len_prediction, testYDeaths_RNN, marker='.', label=\"actual\")\n",
    "      plt.plot(len_prediction, test_predict_Deaths_RNN, 'r', label=\"prediction\")\n",
    "      plt.tight_layout()\n",
    "      plt.subplots_adjust(left=0.07)\n",
    "      plt.ylabel('New Deaths', size=15)\n",
    "      plt.xlabel('Time step', size=15)\n",
    "      plt.legend(fontsize=15)\n",
    "      plt.show();\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_plot(testYDeaths_RNN, test_predict_Deaths_RNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Test Multiple Plots\n",
    "\n",
    "def prediction_plot(testYDeaths_RNN, test_predict_Deaths_RNN, test_predict_Deaths):\n",
    "    len_prediction=[x for x in range(len(testYDeaths_RNN))]\n",
    "    plt.figure(figsize=(8,4))\n",
    "    plt.plot(len_prediction, testYDeaths_RNN, marker='.', label=\"actual\")\n",
    "    plt.plot(len_prediction, test_predict_Deaths_RNN, 'r', label=\"RNN prediction\")\n",
    "    plt.plot(len_prediction, test_predict_Deaths, 'g', label=\"DNN prediction\")\n",
    "    plt.tight_layout()\n",
    "    plt.subplots_adjust(left=0.07)\n",
    "    plt.ylabel('New Deaths', size=15)\n",
    "    plt.xlabel('Time step', size=15)\n",
    "    plt.legend(fontsize=15)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_plot(testYDeaths_RNN, test_predict_Deaths_RNN, test_predict_Deaths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collate Model Results into DF and Save to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>DNN Model Cases</th>\n",
       "      <th>DNN Model Deaths</th>\n",
       "      <th>RNN Model Cases</th>\n",
       "      <th>RNN Model Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-18</th>\n",
       "      <td>225848.171875</td>\n",
       "      <td>2693.013428</td>\n",
       "      <td>225178.015625</td>\n",
       "      <td>2768.837891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-19</th>\n",
       "      <td>224576.203125</td>\n",
       "      <td>2837.284668</td>\n",
       "      <td>224396.546875</td>\n",
       "      <td>2741.830078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-20</th>\n",
       "      <td>221697.625000</td>\n",
       "      <td>2753.085449</td>\n",
       "      <td>222570.859375</td>\n",
       "      <td>2768.466064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-21</th>\n",
       "      <td>218463.750000</td>\n",
       "      <td>2797.016357</td>\n",
       "      <td>223343.515625</td>\n",
       "      <td>2735.833252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-22</th>\n",
       "      <td>218603.593750</td>\n",
       "      <td>2753.304688</td>\n",
       "      <td>225997.578125</td>\n",
       "      <td>2815.534180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DNN Model Cases  DNN Model Deaths  RNN Model Cases  RNN Model Deaths\n",
       "date                                                                            \n",
       "2020-12-18    225848.171875       2693.013428    225178.015625       2768.837891\n",
       "2020-12-19    224576.203125       2837.284668    224396.546875       2741.830078\n",
       "2020-12-20    221697.625000       2753.085449    222570.859375       2768.466064\n",
       "2020-12-21    218463.750000       2797.016357    223343.515625       2735.833252\n",
       "2020-12-22    218603.593750       2753.304688    225997.578125       2815.534180"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collate Model Predictions / Convert Time Step into Dates\n",
    "\n",
    "predict_array = np.concatenate((test_predict_Cases, test_predict_Deaths, test_predict_Cases_RNN, test_predict_Deaths_RNN ), axis=1)\n",
    "\n",
    "predict_df = pd.DataFrame(predict_array, columns = ['DNN Model Cases','DNN Model Deaths','RNN Model Cases', 'RNN Model Deaths'])\n",
    "\n",
    "predict_df['date'] = pd.date_range(start='12/18/2020', periods=len(predict_df), freq='D')\n",
    "\n",
    "predict_df = predict_df[['date', 'DNN Model Cases','DNN Model Deaths','RNN Model Cases', 'RNN Model Deaths']]\n",
    "\n",
    "predict_df = predict_df.set_index('date')\n",
    "\n",
    "\n",
    "# predict_df.dtypes\n",
    "predict_df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>new_cases_smooth</th>\n",
       "      <th>new_deaths_smooth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            new_cases_smooth  new_deaths_smooth\n",
       "date                                           \n",
       "2020-01-22          0.000000                0.0\n",
       "2020-01-23          0.000000                0.0\n",
       "2020-01-24          0.333333                0.0\n",
       "2020-01-25          0.250000                0.0\n",
       "2020-01-26          0.800000                0.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collate Smoothed Actual data into DF\n",
    "\n",
    "actuals_smoothed_df = pd.merge(covid_data_new_cases_df, covid_data_new_deaths_df, left_index=True, right_index=True, how='inner')\n",
    "\n",
    "actuals_smoothed_df['date1'] = pd.date_range(start='01/22/2020', periods=len(actuals_smoothed_df), freq='D')\n",
    "\n",
    "actuals_smoothed_df = actuals_smoothed_df.set_index('date1')\n",
    "\n",
    "actuals_smoothed_df.index.names = ['date']\n",
    "\n",
    "actuals_smoothed_df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_cases_smooth</th>\n",
       "      <th>new_deaths_smooth</th>\n",
       "      <th>DNN Model Cases</th>\n",
       "      <th>DNN Model Deaths</th>\n",
       "      <th>RNN Model Cases</th>\n",
       "      <th>RNN Model Deaths</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-20</th>\n",
       "      <td>67529.571429</td>\n",
       "      <td>1866.714286</td>\n",
       "      <td>81696.867188</td>\n",
       "      <td>1788.505249</td>\n",
       "      <td>73428.570312</td>\n",
       "      <td>1865.074829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-21</th>\n",
       "      <td>66391.142857</td>\n",
       "      <td>1879.285714</td>\n",
       "      <td>78631.101562</td>\n",
       "      <td>1787.289551</td>\n",
       "      <td>71655.460938</td>\n",
       "      <td>1838.592407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-22</th>\n",
       "      <td>66681.000000</td>\n",
       "      <td>1930.428571</td>\n",
       "      <td>75892.898438</td>\n",
       "      <td>1759.854736</td>\n",
       "      <td>67589.625000</td>\n",
       "      <td>1817.207153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-23</th>\n",
       "      <td>68042.571429</td>\n",
       "      <td>2007.428571</td>\n",
       "      <td>73261.820312</td>\n",
       "      <td>1775.304688</td>\n",
       "      <td>65757.960938</td>\n",
       "      <td>1822.515137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-24</th>\n",
       "      <td>68699.142857</td>\n",
       "      <td>2118.142857</td>\n",
       "      <td>71094.976562</td>\n",
       "      <td>1788.945312</td>\n",
       "      <td>64938.664062</td>\n",
       "      <td>1894.126343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            new_cases_smooth  new_deaths_smooth  DNN Model Cases  DNN Model Deaths  RNN Model Cases  RNN Model Deaths\n",
       "date                                                                                                                 \n",
       "2020-01-22          0.000000           0.000000              NaN               NaN              NaN               NaN\n",
       "2020-01-23          0.000000           0.000000              NaN               NaN              NaN               NaN\n",
       "2020-01-24          0.333333           0.000000              NaN               NaN              NaN               NaN\n",
       "2020-01-25          0.250000           0.000000              NaN               NaN              NaN               NaN\n",
       "2020-01-26          0.800000           0.000000              NaN               NaN              NaN               NaN\n",
       "...                      ...                ...              ...               ...              ...               ...\n",
       "2021-02-20      67529.571429        1866.714286     81696.867188       1788.505249     73428.570312       1865.074829\n",
       "2021-02-21      66391.142857        1879.285714     78631.101562       1787.289551     71655.460938       1838.592407\n",
       "2021-02-22      66681.000000        1930.428571     75892.898438       1759.854736     67589.625000       1817.207153\n",
       "2021-02-23      68042.571429        2007.428571     73261.820312       1775.304688     65757.960938       1822.515137\n",
       "2021-02-24      68699.142857        2118.142857     71094.976562       1788.945312     64938.664062       1894.126343\n",
       "\n",
       "[400 rows x 6 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge Model Results and Actuals into DF\n",
    "\n",
    "Summary_Data_df = pd.merge(actuals_smoothed_df, predict_df, left_index=True, right_index=True, how='left')\n",
    "\n",
    "Summary_Data_df.head(400)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Summary_Data_df to CSV for import into Tableau\n",
    "\n",
    "Summary_Data_df.to_csv('Summary_Data_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Model Summary Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. New Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# colors for the line plot\n",
    "colors = ['b','c', 'y']\n",
    "\n",
    "# line plot\n",
    "Summary_Data_df.plot(y = [\"new_cases_smooth\", \"DNN Model Cases\", \"RNN Model Cases\"], color=colors, linewidth=3, figsize=(12,6))\n",
    "\n",
    "# modify ticks size\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels =['New Cases', 'DNN Model Cases', 'RNN Model Cases'], fontsize=14)\n",
    "\n",
    "# title and labels\n",
    "plt.title('US CoVID Cases', fontsize=20)\n",
    "plt.xlabel('Date', fontsize=16)\n",
    "plt.ylabel('New Cases', fontsize=16)\n",
    "\n",
    "plt.savefig('Cases_Model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. New Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# colors for the line plot\n",
    "colors = ['b', 'g', 'r']\n",
    "\n",
    "# line plot\n",
    "Summary_Data_df.plot(y = [\"new_deaths_smooth\", \"DNN Model Deaths\", \"RNN Model Deaths\"],color=colors, linewidth=3, figsize=(12,6))\n",
    "\n",
    "# modify ticks size\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels =['New Deaths', 'DNN Model Deaths', 'RNN Model Deaths'], fontsize=14)\n",
    "\n",
    "# title and labels\n",
    "plt.title('US CoVID Deaths', fontsize=20)\n",
    "plt.xlabel('Date', fontsize=16)\n",
    "plt.ylabel('New Deaths', fontsize=16)\n",
    "\n",
    "\n",
    "plt.savefig('Deaths_Model.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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