DNN Covid-19 Malaysia Part A.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Definition and Model Training (Deep Neural Network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This Notebook is for Non-Normalised Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Field          | Description                                                                           |\n",
    "|----------------|---------------------------------------------------------------------------------------|\n",
    "|Date |Date of incident\t|\n",
    "|Province/State |If there is a breakdown in states\t|\n",
    "|Lat |Latitude\t|\n",
    "|Lon |Longitude\t|\n",
    "|Confirmed |Confirmed cases\t|\n",
    "|Recovered |Recovered cases\t|\n",
    "|Deaths |Death cases\t|\n",
    "|Active |Active cases\t|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary and Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm      | MSE | RMSE | R2 |\n",
    "|----------------|-----|------|----|\n",
    "|Deep Neural Network | 2.423  | 1.556 |  0.191 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(60000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 60 seconds\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style('darkgrid')\n",
    "sns.set(font_scale=1.2)\n",
    "#sets the default autosave frequency in seconds\n",
    "%autosave 60\n",
    "\n",
    "# from sklearn.neural_network import MLPClassifier, MLPRegressor\n",
    "# from sklearn.utils import shuffle\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV, RandomizedSearchCV,StratifiedKFold\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, OneHotEncoder\n",
    "from sklearn.metrics import confusion_matrix, classification_report, mean_absolute_error, mean_squared_error,r2_score\n",
    "from sklearn.metrics import plot_confusion_matrix, plot_precision_recall_curve, plot_roc_curve, accuracy_score\n",
    "from sklearn.metrics import auc, f1_score, precision_score, recall_score, roc_auc_score\n",
    "\n",
    "import tensorflow as tf\n",
    "# from tensorflow import keras\n",
    "# from tensorflow.keras.models import Sequential, save_model\n",
    "# from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, Activation\n",
    "# from tensorflow.keras.wrappers.scikit_learn import KerasRegressor, KerasClassifier\n",
    "# from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "#For ANN and DNN\n",
    "import keras\n",
    "from keras.models import save_model, load_model, Sequential\n",
    "from keras.layers import Activation, BatchNormalization, Dense, Dropout, Flatten\n",
    "from keras.optimizers import Adam, Adagrad, RMSprop, SGD\n",
    "from keras.wrappers.scikit_learn import KerasRegressor, KerasClassifier\n",
    "from keras.regularizers import l1,l2,l1_l2\n",
    "\n",
    "#For Tuning\n",
    "from keras.callbacks import EarlyStopping, LearningRateScheduler, ModelCheckpoint, ReduceLROnPlateau, TensorBoard\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pickle\n",
    "from pickle import dump, load\n",
    "\n",
    "pd.set_option('display.max_columns',None)\n",
    "#pd.set_option('display.max_rows',None)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "np.random.seed(0)\n",
    "np.set_printoptions(suppress=True)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"traincovid.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>1405.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>1884.0</td>\n",
       "      <td>883.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>2188.0</td>\n",
       "      <td>1673.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2348.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0        1   22          0.0          0.0         0.0\n",
       "1        1   23          0.0          0.0         0.0\n",
       "2        1   24          0.0          0.0         0.0\n",
       "3        1   25          3.0          0.0         0.0\n",
       "4        1   26          1.0          0.0         0.0\n",
       "..     ...  ...          ...          ...         ...\n",
       "304     11   21       1041.0       1405.0         3.0\n",
       "305     11   22       1096.0       1104.0         3.0\n",
       "306     11   23       1884.0        883.0         2.0\n",
       "307     11   24       2188.0       1673.0         4.0\n",
       "308     11   25        970.0       2348.0         4.0\n",
       "\n",
       "[309 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month          0\n",
       "Day            0\n",
       "ConfirmDiff    0\n",
       "RecoverDiff    0\n",
       "DeathsDiff     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 309 entries, 0 to 308\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Month        309 non-null    int64  \n",
      " 1   Day          309 non-null    int64  \n",
      " 2   ConfirmDiff  309 non-null    float64\n",
      " 3   RecoverDiff  309 non-null    float64\n",
      " 4   DeathsDiff   309 non-null    float64\n",
      "dtypes: float64(3), int64(2)\n",
      "memory usage: 12.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>309.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.265372</td>\n",
       "      <td>15.857605</td>\n",
       "      <td>193.582524</td>\n",
       "      <td>150.488673</td>\n",
       "      <td>1.116505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.943317</td>\n",
       "      <td>8.814048</td>\n",
       "      <td>359.375952</td>\n",
       "      <td>306.753450</td>\n",
       "      <td>1.988445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2188.000000</td>\n",
       "      <td>2348.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Month         Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "count  309.000000  309.000000   309.000000   309.000000  309.000000\n",
       "mean     6.265372   15.857605   193.582524   150.488673    1.116505\n",
       "std      2.943317    8.814048   359.375952   306.753450    1.988445\n",
       "min      1.000000    1.000000     0.000000     0.000000    0.000000\n",
       "25%      4.000000    8.000000     7.000000     6.000000    0.000000\n",
       "50%      6.000000   16.000000    33.000000    28.000000    0.000000\n",
       "75%      9.000000   23.000000   150.000000    96.000000    1.000000\n",
       "max     11.000000   31.000000  2188.000000  2348.000000   12.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 5)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Day', 'ConfirmDiff', 'RecoverDiff', 'DeathsDiff'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50, figsize=(20,10))\n",
    "plt.suptitle('Feature Distribution', x=0.5, y=1.02, ha='center', fontsize='large')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(figsize=(20,5))\n",
    "plt.suptitle('BoxPlot', x=0.5, y=1.02, ha='center', fontsize='large')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pairplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "sns.pairplot(df.sample(150))\n",
    "plt.suptitle('Pairplots of features', x=0.5, y=1.02, ha='center', fontsize='large')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098535</td>\n",
       "      <td>0.604269</td>\n",
       "      <td>0.545945</td>\n",
       "      <td>0.341420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>-0.098535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>0.035592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <td>0.604269</td>\n",
       "      <td>0.002108</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875959</td>\n",
       "      <td>0.641523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecoverDiff</th>\n",
       "      <td>0.545945</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>0.875959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.561145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeathsDiff</th>\n",
       "      <td>0.341420</td>\n",
       "      <td>0.035592</td>\n",
       "      <td>0.641523</td>\n",
       "      <td>0.561145</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Month       Day  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "Month        1.000000 -0.098535     0.604269     0.545945    0.341420\n",
       "Day         -0.098535  1.000000     0.002108     0.027499    0.035592\n",
       "ConfirmDiff  0.604269  0.002108     1.000000     0.875959    0.641523\n",
       "RecoverDiff  0.545945  0.027499     0.875959     1.000000    0.561145\n",
       "DeathsDiff   0.341420  0.035592     0.641523     0.561145    1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.heatmap(df.corr(),cmap=\"coolwarm\",annot=True,fmt='.2f',linewidths=2)\n",
    "plt.title(\"HeatMap\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Treat Duplicate Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated(keep='first').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"traincovid2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_2</th>\n",
       "      <th>Month_3</th>\n",
       "      <th>Month_4</th>\n",
       "      <th>Month_5</th>\n",
       "      <th>Month_6</th>\n",
       "      <th>Month_7</th>\n",
       "      <th>Month_8</th>\n",
       "      <th>Month_9</th>\n",
       "      <th>Month_10</th>\n",
       "      <th>Month_11</th>\n",
       "      <th>Day_2</th>\n",
       "      <th>Day_3</th>\n",
       "      <th>Day_4</th>\n",
       "      <th>Day_5</th>\n",
       "      <th>Day_6</th>\n",
       "      <th>Day_7</th>\n",
       "      <th>Day_8</th>\n",
       "      <th>Day_9</th>\n",
       "      <th>Day_10</th>\n",
       "      <th>Day_11</th>\n",
       "      <th>Day_12</th>\n",
       "      <th>Day_13</th>\n",
       "      <th>Day_14</th>\n",
       "      <th>Day_15</th>\n",
       "      <th>Day_16</th>\n",
       "      <th>Day_17</th>\n",
       "      <th>Day_18</th>\n",
       "      <th>Day_19</th>\n",
       "      <th>Day_20</th>\n",
       "      <th>Day_21</th>\n",
       "      <th>Day_22</th>\n",
       "      <th>Day_23</th>\n",
       "      <th>Day_24</th>\n",
       "      <th>Day_25</th>\n",
       "      <th>Day_26</th>\n",
       "      <th>Day_27</th>\n",
       "      <th>Day_28</th>\n",
       "      <th>Day_29</th>\n",
       "      <th>Day_30</th>\n",
       "      <th>Day_31</th>\n",
       "      <th>ConfirmDiff</th>\n",
       "      <th>RecoverDiff</th>\n",
       "      <th>DeathsDiff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month_2  Month_3  Month_4  Month_5  Month_6  Month_7  Month_8  Month_9  Month_10  Month_11  Day_2  Day_3  Day_4  Day_5  Day_6  Day_7  Day_8  Day_9  Day_10  Day_11  Day_12  Day_13  Day_14  Day_15  Day_16  Day_17  Day_18  Day_19  Day_20  Day_21  Day_22  Day_23  Day_24  Day_25  Day_26  Day_27  Day_28  Day_29  Day_30  Day_31  ConfirmDiff  RecoverDiff  DeathsDiff\n",
       "0        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "1        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "2        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0          0.0          0.0         0.0\n",
       "3        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0          3.0          0.0         0.0\n",
       "4        0        0        0        0        0        0        0        0         0         0      0      0      0      0      0      0      0      0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0          1.0          0.0         0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(309, 43)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.iloc[:,0:42]\n",
    "y = df2.iloc[:,42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        [   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        [   0.,    0.,    0., ...,    0.,    0.,    0.],\n",
       "        ...,\n",
       "        [   0.,    0.,    0., ...,    0., 1884.,  883.],\n",
       "        [   0.,    0.,    0., ...,    0., 2188., 1673.],\n",
       "        [   0.,    0.,    0., ...,    0.,  970., 2348.]]),\n",
       " array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  2.,  0.,  0.,  1.,  1.,  6.,  4.,  2.,  4.,  3.,\n",
       "         3.,  1.,  8.,  2.,  6.,  2.,  5.,  3.,  4.,  4.,  1.,  1.,  2.,\n",
       "         2.,  3.,  3.,  3.,  1.,  5.,  1.,  1.,  2.,  2.,  1.,  0.,  3.,\n",
       "         1.,  2.,  1.,  2.,  0.,  1.,  1.,  0.,  2.,  1.,  0.,  2.,  0.,\n",
       "         1.,  1.,  0.,  0.,  1.,  0.,  1.,  0.,  2.,  1.,  0.,  1.,  0.,\n",
       "         0.,  1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  1.,  0.,  1.,\n",
       "         1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         1.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,  0.,  0.,  3.,  0.,\n",
       "         0.,  0.,  1.,  0.,  0.,  2.,  0.,  0.,  1.,  0.,  0.,  4.,  0.,\n",
       "         5.,  6.,  3.,  2.,  2.,  4.,  4.,  3.,  6.,  4.,  7.,  3.,  3.,\n",
       "         6.,  5., 10.,  7.,  8.,  7.,  2.,  8.,  0.,  3.,  0.,  0.,  2.,\n",
       "        12.,  8.,  6.,  2.,  3.,  4.,  8.,  6.,  2.,  1.,  1.,  2.,  3.,\n",
       "         4.,  5.,  4.,  4.,  3.,  3.,  3.,  2.,  4.,  4.]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.values, y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X.values, y.values, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((231, 42), (78, 42), (231,), (78,))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling (If not using Batch Normalization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train_scaled = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_test_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "#model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "#model.add(BatchNormalization())\n",
    "#model.add(Dropout(0.2))\n",
    "model.add(Dense(units=1,activation='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 100)               4300      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 14,501\n",
      "Trainable params: 14,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointcb = keras.callbacks.ModelCheckpoint(\"BestModelPartA.h5\",save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystoppingcb = keras.callbacks.EarlyStopping(patience=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = Adam(learning_rate=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/800\n",
      "6/6 - 0s - loss: 4119.8086 - mse: 4118.7930 - val_loss: 3451.2778 - val_mse: 3450.2622\n",
      "Epoch 2/800\n",
      "6/6 - 0s - loss: 3962.5627 - mse: 3961.5471 - val_loss: 3323.4192 - val_mse: 3322.4043\n",
      "Epoch 3/800\n",
      "6/6 - 0s - loss: 3806.2603 - mse: 3805.2449 - val_loss: 3202.3857 - val_mse: 3201.3711\n",
      "Epoch 4/800\n",
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      "Epoch 85/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 147.4668 - mse: 146.4887 - val_loss: 103.8943 - val_mse: 102.9164\n",
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      "Epoch 173/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 16.7815 - mse: 15.8397 - val_loss: 24.6110 - val_mse: 23.6693\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 263/800\n",
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      "Epoch 345/800\n",
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      "Epoch 346/800\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 355/800\n",
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      "Epoch 447/800\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 - 0s - loss: 3.4719 - mse: 2.6279 - val_loss: 6.1560 - val_mse: 5.3123\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 540/800\n",
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      "6/6 - 0s - loss: 3.0840 - mse: 2.2705 - val_loss: 5.4914 - val_mse: 4.6780\n",
      "Epoch 547/800\n",
      "6/6 - 0s - loss: 3.0943 - mse: 2.2811 - val_loss: 5.5095 - val_mse: 4.6965\n",
      "Epoch 548/800\n",
      "6/6 - 0s - loss: 3.0842 - mse: 2.2713 - val_loss: 5.4856 - val_mse: 4.6728\n",
      "Epoch 549/800\n",
      "6/6 - 0s - loss: 3.0776 - mse: 2.2650 - val_loss: 5.4745 - val_mse: 4.6621\n",
      "Epoch 550/800\n",
      "6/6 - 0s - loss: 3.0747 - mse: 2.2624 - val_loss: 5.4673 - val_mse: 4.6551\n",
      "Epoch 551/800\n",
      "6/6 - 0s - loss: 3.0737 - mse: 2.2616 - val_loss: 5.4581 - val_mse: 4.6462\n",
      "Epoch 552/800\n",
      "6/6 - 0s - loss: 3.0732 - mse: 2.2615 - val_loss: 5.4612 - val_mse: 4.6496\n",
      "Epoch 553/800\n",
      "6/6 - 0s - loss: 3.0697 - mse: 2.2583 - val_loss: 5.4563 - val_mse: 4.6450\n",
      "Epoch 554/800\n",
      "6/6 - 0s - loss: 3.0669 - mse: 2.2558 - val_loss: 5.4555 - val_mse: 4.6446\n",
      "Epoch 555/800\n",
      "6/6 - 0s - loss: 3.0676 - mse: 2.2568 - val_loss: 5.4436 - val_mse: 4.6329\n",
      "Epoch 556/800\n",
      "6/6 - 0s - loss: 3.0673 - mse: 2.2567 - val_loss: 5.4330 - val_mse: 4.6226\n",
      "Epoch 557/800\n",
      "6/6 - 0s - loss: 3.0625 - mse: 2.2523 - val_loss: 5.4400 - val_mse: 4.6299\n",
      "Epoch 558/800\n",
      "6/6 - 0s - loss: 3.0600 - mse: 2.2500 - val_loss: 5.4345 - val_mse: 4.6247\n",
      "Epoch 559/800\n",
      "6/6 - 0s - loss: 3.0611 - mse: 2.2514 - val_loss: 5.4263 - val_mse: 4.6168\n",
      "Epoch 560/800\n",
      "6/6 - 0s - loss: 3.0571 - mse: 2.2477 - val_loss: 5.4377 - val_mse: 4.6284\n",
      "Epoch 561/800\n",
      "6/6 - 0s - loss: 3.0542 - mse: 2.2451 - val_loss: 5.4376 - val_mse: 4.6287\n",
      "Epoch 562/800\n",
      "6/6 - 0s - loss: 3.0527 - mse: 2.2439 - val_loss: 5.4353 - val_mse: 4.6267\n",
      "Epoch 563/800\n",
      "6/6 - 0s - loss: 3.0527 - mse: 2.2442 - val_loss: 5.4266 - val_mse: 4.6183\n",
      "Epoch 564/800\n",
      "6/6 - 0s - loss: 3.0487 - mse: 2.2404 - val_loss: 5.4269 - val_mse: 4.6189\n",
      "Epoch 565/800\n",
      "6/6 - 0s - loss: 3.0483 - mse: 2.2404 - val_loss: 5.4183 - val_mse: 4.6105\n",
      "Epoch 566/800\n",
      "6/6 - 0s - loss: 3.0467 - mse: 2.2391 - val_loss: 5.4223 - val_mse: 4.6148\n",
      "Epoch 567/800\n",
      "6/6 - 0s - loss: 3.0442 - mse: 2.2369 - val_loss: 5.4183 - val_mse: 4.6111\n",
      "Epoch 568/800\n",
      "6/6 - 0s - loss: 3.0422 - mse: 2.2351 - val_loss: 5.4056 - val_mse: 4.5987\n",
      "Epoch 569/800\n",
      "6/6 - 0s - loss: 3.0398 - mse: 2.2331 - val_loss: 5.4010 - val_mse: 4.5944\n",
      "Epoch 570/800\n",
      "6/6 - 0s - loss: 3.0393 - mse: 2.2328 - val_loss: 5.3916 - val_mse: 4.5853\n",
      "Epoch 571/800\n",
      "6/6 - 0s - loss: 3.0376 - mse: 2.2314 - val_loss: 5.3845 - val_mse: 4.5785\n",
      "Epoch 572/800\n",
      "6/6 - 0s - loss: 3.0352 - mse: 2.2293 - val_loss: 5.3913 - val_mse: 4.5856\n",
      "Epoch 573/800\n",
      "6/6 - 0s - loss: 3.0332 - mse: 2.2275 - val_loss: 5.3896 - val_mse: 4.5841\n",
      "Epoch 574/800\n",
      "6/6 - 0s - loss: 3.0320 - mse: 2.2267 - val_loss: 5.3894 - val_mse: 4.5842\n",
      "Epoch 575/800\n",
      "6/6 - 0s - loss: 3.0306 - mse: 2.2256 - val_loss: 5.3775 - val_mse: 4.5726\n",
      "Epoch 576/800\n",
      "6/6 - 0s - loss: 3.0287 - mse: 2.2240 - val_loss: 5.3757 - val_mse: 4.5711\n",
      "Epoch 577/800\n",
      "6/6 - 0s - loss: 3.0335 - mse: 2.2291 - val_loss: 5.3589 - val_mse: 4.5546\n",
      "Epoch 578/800\n",
      "6/6 - 0s - loss: 3.0268 - mse: 2.2226 - val_loss: 5.3662 - val_mse: 4.5623\n",
      "Epoch 579/800\n",
      "6/6 - 0s - loss: 3.0243 - mse: 2.2205 - val_loss: 5.3636 - val_mse: 4.5599\n",
      "Epoch 580/800\n",
      "6/6 - 0s - loss: 3.0219 - mse: 2.2183 - val_loss: 5.3761 - val_mse: 4.5727\n",
      "Epoch 581/800\n",
      "6/6 - 0s - loss: 3.0218 - mse: 2.2185 - val_loss: 5.3703 - val_mse: 4.5671\n",
      "Epoch 582/800\n",
      "6/6 - 0s - loss: 3.0194 - mse: 2.2164 - val_loss: 5.3736 - val_mse: 4.5708\n",
      "Epoch 583/800\n",
      "6/6 - 0s - loss: 3.0176 - mse: 2.2149 - val_loss: 5.3721 - val_mse: 4.5695\n",
      "Epoch 584/800\n",
      "6/6 - 0s - loss: 3.0171 - mse: 2.2146 - val_loss: 5.3638 - val_mse: 4.5615\n",
      "Epoch 585/800\n",
      "6/6 - 0s - loss: 3.0148 - mse: 2.2127 - val_loss: 5.3617 - val_mse: 4.5597\n",
      "Epoch 586/800\n",
      "6/6 - 0s - loss: 3.0128 - mse: 2.2109 - val_loss: 5.3673 - val_mse: 4.5656\n",
      "Epoch 587/800\n",
      "6/6 - 0s - loss: 3.0138 - mse: 2.2122 - val_loss: 5.3705 - val_mse: 4.5691\n",
      "Epoch 00587: early stopping\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(X_train, y_train,epochs=800,\n",
    "                          validation_split=0.2, verbose=2, callbacks=[checkpointcb,earlystoppingcb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'verbose': 2, 'epochs': 800, 'steps': 6}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_history.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  4.559748649597168,\n",
       "  4.5655837059021,\n",
       "  4.569131851196289]}"
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     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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    "model_history.history"
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  {
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    "hist = pd.DataFrame(model_history.history)\n",
    "hist"
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  {
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   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist['epoch'] = model_history.epoch"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
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       "            loss          mse     val_loss      val_mse  epoch\n",
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(model_history.history['loss'])\n",
    "plt.plot(model_history.history['val_loss'])\n",
    "plt.title(\"Model Training\")\n",
    "plt.ylabel(\"Training and Validation Loss\")\n",
    "plt.xlabel(\"Epoch Number\")\n",
    "plt.legend(['Training Loss', 'Validation Loss'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(model_history.history['mse'])\n",
    "plt.plot(model_history.history['val_mse'])\n",
    "plt.title(\"Model Training - MSE\")\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.xlabel(\"Epoch Number\")\n",
    "plt.legend(['MSE','VAL_MSE'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[-0.06692149, -0.11918116,  0.00647938, ..., -0.0463139 ,\n",
       "          0.09368964, -0.16854094],\n",
       "        [ 0.05740365,  0.00807465, -0.17234413, ...,  0.06566545,\n",
       "         -0.16051328, -0.13977562],\n",
       "        [ 0.03110347, -0.02175083,  0.02451196, ..., -0.11600856,\n",
       "          0.18329461,  0.08509453],\n",
       "        ...,\n",
       "        [ 0.03880645,  0.09782134,  0.18210599, ...,  0.08798633,\n",
       "         -0.16941778,  0.01249855],\n",
       "        [ 0.11023546, -0.04211644,  0.15408164, ...,  0.09474277,\n",
       "         -0.03555257,  0.06336047],\n",
       "        [-0.15282373,  0.1216042 , -0.16169126, ...,  0.11059742,\n",
       "          0.09820643, -0.07787107]], dtype=float32),\n",
       " array([-0.0050367 ,  0.0057361 , -0.00192996, -0.00214218,  0.00374088,\n",
       "         0.00388337,  0.00567919, -0.00170535,  0.00070381, -0.00689378,\n",
       "         0.00543956,  0.01581135,  0.00616075,  0.00193988,  0.00419267,\n",
       "        -0.00331371, -0.00450264,  0.00560008,  0.01336006,  0.00784956,\n",
       "         0.00106189, -0.00596037, -0.00286763,  0.00723396, -0.00376203,\n",
       "        -0.00509891,  0.00192765,  0.00097815, -0.00066744, -0.00469941,\n",
       "        -0.01491817, -0.00049102,  0.00298632, -0.00273825,  0.00096033,\n",
       "        -0.00672221,  0.00471769, -0.00527557,  0.00520677,  0.00436814,\n",
       "         0.00595919,  0.00222888, -0.00438795,  0.00618835,  0.00293152,\n",
       "         0.01369347,  0.0046662 ,  0.00661061, -0.00856426,  0.0061499 ,\n",
       "        -0.00386507,  0.00210933, -0.00474657,  0.00559242,  0.00547927,\n",
       "         0.00214138,  0.00835476, -0.00514303,  0.00364806, -0.00436244,\n",
       "        -0.00408117, -0.0085962 ,  0.00107222, -0.00552727,  0.00461269,\n",
       "        -0.00247697, -0.0025538 ,  0.00693881, -0.00249612,  0.00466043,\n",
       "        -0.0011562 ,  0.00081194,  0.00321999, -0.00126281,  0.00648455,\n",
       "        -0.00468673, -0.00189219, -0.01038424, -0.00002253,  0.00148641,\n",
       "        -0.00501963,  0.00123138, -0.00151466, -0.00387145, -0.00612714,\n",
       "        -0.00231125,  0.00597441, -0.00240223,  0.00116373,  0.00454374,\n",
       "         0.00384843,  0.00329027, -0.00355941,  0.00417888, -0.00176681,\n",
       "        -0.00739277,  0.00763472, -0.00560932,  0.00584368, -0.00542123],\n",
       "       dtype=float32),\n",
       " array([[ 0.0179388 , -0.09295921,  0.12354421, ...,  0.07020293,\n",
       "         -0.10245834, -0.12865208],\n",
       "        [ 0.05806442,  0.04401294,  0.09890251, ...,  0.15028065,\n",
       "          0.16907662,  0.08465107],\n",
       "        [ 0.11305827,  0.03156068,  0.14243452, ...,  0.00535111,\n",
       "         -0.03415229,  0.0641544 ],\n",
       "        ...,\n",
       "        [ 0.16799307, -0.05228799, -0.07698134, ...,  0.07654466,\n",
       "          0.09417668,  0.10768475],\n",
       "        [ 0.06958847,  0.04072955,  0.14748767, ...,  0.12383068,\n",
       "         -0.01577906, -0.08006766],\n",
       "        [ 0.10588089,  0.00865349, -0.02511424, ...,  0.01933053,\n",
       "         -0.01661848,  0.01357626]], dtype=float32),\n",
       " array([-0.00290215,  0.00721631, -0.0034876 ,  0.00522459, -0.00539645,\n",
       "        -0.01289506, -0.01346471, -0.00493843,  0.00519098,  0.00493228,\n",
       "        -0.00424107,  0.00530977,  0.00477421, -0.00495508, -0.00495807,\n",
       "        -0.00058637, -0.01240844,  0.00512769,  0.00505565,  0.00061412,\n",
       "         0.00483486, -0.01076483, -0.00710617, -0.0005836 , -0.00500103,\n",
       "        -0.00496955, -0.01322378,  0.01148133, -0.00500341, -0.00212564,\n",
       "         0.02416029,  0.00084283,  0.00532361,  0.00168238,  0.00402742,\n",
       "         0.00087139,  0.        ,  0.00032552, -0.0013239 ,  0.00507729,\n",
       "         0.00509705, -0.01137773,  0.00072997,  0.01210183,  0.00555676,\n",
       "        -0.00414948,  0.0051337 ,  0.00628288,  0.0217368 ,  0.00550904,\n",
       "        -0.00222294,  0.00508358,  0.00426696, -0.00335418,  0.00056086,\n",
       "         0.        , -0.00339361, -0.00485787, -0.00500436, -0.00504616,\n",
       "        -0.00498241, -0.00491671, -0.0054395 , -0.01119232, -0.00966717,\n",
       "        -0.00281474,  0.00367388, -0.00404825, -0.00385823, -0.00653877,\n",
       "        -0.00441082, -0.00372645, -0.00041777,  0.00637347,  0.00165427,\n",
       "         0.01468014, -0.00539759,  0.00620594,  0.        ,  0.01036723,\n",
       "         0.00219306,  0.00108964,  0.00494218, -0.0049989 , -0.00534647,\n",
       "         0.00508055, -0.00469416,  0.01227204, -0.00443336,  0.00219077,\n",
       "        -0.00411663,  0.01578879, -0.00762428,  0.0050556 , -0.00284704,\n",
       "         0.00183356,  0.00068756, -0.00501034,  0.00510741,  0.00611015],\n",
       "       dtype=float32),\n",
       " array([[-0.14785807],\n",
       "        [ 0.12231623],\n",
       "        [-0.00238066],\n",
       "        [ 0.18846594],\n",
       "        [-0.04307169],\n",
       "        [-0.2300941 ],\n",
       "        [ 0.09036354],\n",
       "        [-0.06268132],\n",
       "        [ 0.04245079],\n",
       "        [ 0.2003136 ],\n",
       "        [-0.17680526],\n",
       "        [ 0.0248916 ],\n",
       "        [ 0.09582332],\n",
       "        [-0.23809977],\n",
       "        [-0.0636276 ],\n",
       "        [ 0.03374527],\n",
       "        [-0.07878704],\n",
       "        [ 0.07637405],\n",
       "        [ 0.24536511],\n",
       "        [-0.21648343],\n",
       "        [ 0.19513804],\n",
       "        [-0.10586401],\n",
       "        [-0.02086056],\n",
       "        [ 0.11685456],\n",
       "        [-0.15919109],\n",
       "        [-0.12299158],\n",
       "        [ 0.22135863],\n",
       "        [ 0.24863423],\n",
       "        [-0.22239625],\n",
       "        [-0.00416952],\n",
       "        [ 0.01950584],\n",
       "        [ 0.217599  ],\n",
       "        [ 0.18481399],\n",
       "        [ 0.07198253],\n",
       "        [ 0.06771594],\n",
       "        [ 0.1665416 ],\n",
       "        [ 0.14886484],\n",
       "        [-0.00192831],\n",
       "        [ 0.10382697],\n",
       "        [ 0.14568514],\n",
       "        [ 0.11989952],\n",
       "        [-0.19997025],\n",
       "        [-0.15378475],\n",
       "        [-0.09417084],\n",
       "        [ 0.2424759 ],\n",
       "        [-0.07797949],\n",
       "        [ 0.06206232],\n",
       "        [ 0.0065717 ],\n",
       "        [ 0.09534227],\n",
       "        [ 0.07173148],\n",
       "        [ 0.07236681],\n",
       "        [ 0.12001739],\n",
       "        [ 0.19073345],\n",
       "        [-0.17270698],\n",
       "        [ 0.02529016],\n",
       "        [-0.069327  ],\n",
       "        [-0.01644886],\n",
       "        [-0.20528129],\n",
       "        [-0.20445007],\n",
       "        [-0.09722018],\n",
       "        [-0.19289073],\n",
       "        [-0.05168707],\n",
       "        [-0.22626981],\n",
       "        [-0.21994588],\n",
       "        [-0.17354701],\n",
       "        [-0.14938849],\n",
       "        [-0.24563487],\n",
       "        [-0.00196588],\n",
       "        [-0.14584386],\n",
       "        [-0.12944664],\n",
       "        [-0.13896921],\n",
       "        [ 0.18674292],\n",
       "        [ 0.09051076],\n",
       "        [ 0.07555529],\n",
       "        [ 0.18189043],\n",
       "        [-0.24899463],\n",
       "        [-0.15757743],\n",
       "        [ 0.00918794],\n",
       "        [-0.22455129],\n",
       "        [-0.1635428 ],\n",
       "        [ 0.22179256],\n",
       "        [ 0.08625703],\n",
       "        [ 0.2445817 ],\n",
       "        [-0.19281271],\n",
       "        [-0.193432  ],\n",
       "        [ 0.14260483],\n",
       "        [-0.09445037],\n",
       "        [ 0.15463966],\n",
       "        [-0.05927893],\n",
       "        [-0.15517698],\n",
       "        [-0.07415263],\n",
       "        [ 0.0115212 ],\n",
       "        [ 0.02725275],\n",
       "        [ 0.23516424],\n",
       "        [ 0.09976053],\n",
       "        [ 0.02591992],\n",
       "        [-0.18524128],\n",
       "        [-0.21759205],\n",
       "        [ 0.10536728],\n",
       "        [-0.11459419]], dtype=float32),\n",
       " array([0.00503275], dtype=float32)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 998us/step - loss: 3.1042 - mse: 2.3028\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_mse = model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 2.3028433322906494\n"
     ]
    }
   ],
   "source": [
    "print(\"Test MSE: {}\".format(test_mse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.7172812 ],\n",
       "       [ 0.06949318],\n",
       "       [ 0.1189203 ],\n",
       "       [-0.02114202],\n",
       "       [ 0.09577706]], dtype=float32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 0.],\n",
       "       [ 0.],\n",
       "       [-0.],\n",
       "       [ 0.]], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.round()[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.423076923076923"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test,y_pred.round())\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5566235649883124"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1907114624505929"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = r2_score(y_test,y_pred.round())\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = len(X_test)\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = X_test.shape[1]\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.7804347826086955"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adjusted R2 Score\n",
    "adjr2 = 1-(1-r2)*(n-1)/(n-p-1)\n",
    "adjr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "sns.regplot(x=y_test, y=y_pred.round(), ax=ax)\n",
    "plt.title(\"Plot to compare actual vs predicted\")\n",
    "plt.ylabel(\"Predicted\")\n",
    "plt.xlabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"dnnPartA.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a model (regression or classfier) first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_regressor():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=1,activation='linear'))\n",
    "    optimizer = Adam(learning_rate=0.00001)\n",
    "    model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KerasRegressor(build_fn=build_regressor, epochs=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "kfold = StratifiedKFold(n_splits=5,shuffle=True,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:   32.0s finished\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(estimator=model,X=X_train, y=y_train, cv=kfold, n_jobs=-1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.45266533, -2.98656297, -3.08224034, -2.71152568, -4.56542969])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.9596848011016847"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4040409434972103"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9596848011016847"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = abs(cv.mean())\n",
    "mse_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9898956759342146"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse = np.sqrt(mse_mean)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a regressor or classifier function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_regressor(optimizer):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "    #model.add(BatchNormalization())\n",
    "    #model.add(Dropout(0.2))\n",
    "    model.add(Dense(units=1,activation='linear'))\n",
    "    optimizer = Adam(learning_rate=0.00001)\n",
    "    model.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KerasRegressor(build_fn=build_regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {'batch_size':[1,2,5],\n",
    "          'epochs' : [100,200,300],\n",
    "          'optimizer' : ['Adam', 'RMSprop', 'SGD']\n",
    "         }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use RandomSearch CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearch = RandomizedSearchCV(estimator=model, param_distributions=params,n_iter=10,\n",
    "                                  scoring='neg_root_mean_squared_error',n_jobs=-1,cv=5,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearchcv = randomsearch.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearchcv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomsearchcv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = Sequential()\n",
    "model2.add(Dense(units=100,activation='relu',input_dim=42))\n",
    "#model2.add(BatchNormalization())\n",
    "#model2.add(Dropout(0.2))\n",
    "model2.add(Dense(units=100,activation='relu',kernel_regularizer='l2'))\n",
    "#model2.add(BatchNormalization())\n",
    "#model2.add(Dropout(0.2))\n",
    "model2.add(Dense(units=1,activation='linear'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointcb = keras.callbacks.ModelCheckpoint(\"BestModelPartA.h5\",save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystoppingcb = keras.callbacks.EarlyStopping(patience=10, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = SGD(learning_rate=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.compile(optimizer=optimizer, loss='mean_squared_error', metrics=[\"mse\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_history_2 = model2.fit(X_train, y_train,epochs=200,batch_size=2,\n",
    "                          validation_split=0.2, verbose=2, callbacks=[checkpointcb,earlystoppingcb])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_history_2.params"
   ]
  }
 ],
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