LP5 DL Assignment 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82712757",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ishant Kadam\n",
    "# COBA029\n",
    "\n",
    "# Importing Necesarry Packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import keras\n",
    "from keras.layers import Dense, Activation,Dropout\n",
    "from keras.models import Sequential\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5a750c54",
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  PRICE  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
       "2     17.8  392.83   4.03   34.7  \n",
       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading Data\n",
    "boston = load_boston()\n",
    "data = pd.DataFrame(boston.data)\n",
    "data.columns = boston.feature_names\n",
    "data['PRICE'] = boston.target \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d55efd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 14)\n",
      "CRIM       float64\n",
      "ZN         float64\n",
      "INDUS      float64\n",
      "CHAS       float64\n",
      "NOX        float64\n",
      "RM         float64\n",
      "AGE        float64\n",
      "DIS        float64\n",
      "RAD        float64\n",
      "TAX        float64\n",
      "PTRATIO    float64\n",
      "B          float64\n",
      "LSTAT      float64\n",
      "PRICE      float64\n",
      "dtype: object\n",
      "CRIM       0\n",
      "ZN         0\n",
      "INDUS      0\n",
      "CHAS       0\n",
      "NOX        0\n",
      "RM         0\n",
      "AGE        0\n",
      "DIS        0\n",
      "RAD        0\n",
      "TAX        0\n",
      "PTRATIO    0\n",
      "B          0\n",
      "LSTAT      0\n",
      "PRICE      0\n",
      "dtype: int64\n",
      "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
      "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
      "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
      "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
      "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
      "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
      "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
      "\n",
      "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
      "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
      "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
      "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
      "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
      "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
      "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
      "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
      "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
      "\n",
      "            LSTAT       PRICE  \n",
      "count  506.000000  506.000000  \n",
      "mean    12.653063   22.532806  \n",
      "std      7.141062    9.197104  \n",
      "min      1.730000    5.000000  \n",
      "25%      6.950000   17.025000  \n",
      "50%     11.360000   21.200000  \n",
      "75%     16.955000   25.000000  \n",
      "max     37.970000   50.000000  \n"
     ]
    }
   ],
   "source": [
    "# Data Exploration\n",
    "print(data.shape)\n",
    "print(data.dtypes)\n",
    "print(data.isnull().sum())\n",
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d59f79cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data Visualization\n",
    "sns.displot(data.PRICE)\n",
    "\n",
    "correlation = data.corr()\n",
    "correlation.loc['PRICE']\n",
    "\n",
    "fig,axes = plt.subplots(figsize=(15,12))\n",
    "sns.heatmap(correlation,square = True,annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3c3c3062",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting Data into testing and training data\n",
    "X = data.iloc[:,:-1]\n",
    "y= data.PRICE\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 4)\n",
    "\n",
    "# Normalizing the data\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f2f8bd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               1792      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 16)                528       \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,673\n",
      "Trainable params: 12,673\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Model Building\n",
    "model = Sequential()\n",
    "model.add(Dense(128,activation  = 'relu',input_dim =13))\n",
    "model.add(Dense(64,activation  = 'relu'))\n",
    "model.add(Dense(32,activation  = 'relu'))\n",
    "model.add(Dense(16,activation  = 'relu'))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer = 'adam',loss = 'mean_squared_error')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f285526d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "13/13 [==============================] - 1s 1ms/step - loss: 572.2520\n",
      "Epoch 2/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 541.6292\n",
      "Epoch 3/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 473.6244\n",
      "Epoch 4/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 312.4244\n",
      "Epoch 5/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 112.6376\n",
      "Epoch 6/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 61.5438\n",
      "Epoch 7/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 38.5271\n",
      "Epoch 8/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 28.4949\n",
      "Epoch 9/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 24.2137\n",
      "Epoch 10/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 21.3957\n",
      "Epoch 11/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 19.1263\n",
      "Epoch 12/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 17.6134\n",
      "Epoch 13/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 16.6028\n",
      "Epoch 14/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 15.3106\n",
      "Epoch 15/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 14.5605\n",
      "Epoch 16/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 13.8504\n",
      "Epoch 17/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 13.3533\n",
      "Epoch 18/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 12.9414\n",
      "Epoch 19/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 12.3699\n",
      "Epoch 20/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 12.1141\n",
      "Epoch 21/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 11.8200\n",
      "Epoch 22/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 11.4488\n",
      "Epoch 23/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 11.1978\n",
      "Epoch 24/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 10.7235\n",
      "Epoch 25/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 10.5966\n",
      "Epoch 26/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 10.4092\n",
      "Epoch 27/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 10.0838\n",
      "Epoch 28/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 10.0911\n",
      "Epoch 29/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 9.9088\n",
      "Epoch 30/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 9.5246\n",
      "Epoch 31/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 9.4161\n",
      "Epoch 32/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 9.1052\n",
      "Epoch 33/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 8.8125\n",
      "Epoch 34/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 8.7604\n",
      "Epoch 35/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 8.5864\n",
      "Epoch 36/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 8.4900\n",
      "Epoch 37/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 8.2849\n",
      "Epoch 38/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 8.0817\n",
      "Epoch 39/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 8.0983\n",
      "Epoch 40/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 7.8683\n",
      "Epoch 41/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 7.7130\n",
      "Epoch 42/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 7.8757\n",
      "Epoch 43/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 7.7122\n",
      "Epoch 44/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 7.5595\n",
      "Epoch 45/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 7.3817\n",
      "Epoch 46/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 7.3316\n",
      "Epoch 47/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 6.9273\n",
      "Epoch 48/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 7.1339\n",
      "Epoch 49/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 6.7271\n",
      "Epoch 50/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 6.4854\n",
      "Epoch 51/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 6.5049\n",
      "Epoch 52/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 6.6566\n",
      "Epoch 53/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 6.5210\n",
      "Epoch 54/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 6.3395\n",
      "Epoch 55/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 6.1529\n",
      "Epoch 56/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 6.1376\n",
      "Epoch 57/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 5.9419\n",
      "Epoch 58/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 5.9670\n",
      "Epoch 59/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 5.7985\n",
      "Epoch 60/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 5.6095\n",
      "Epoch 61/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 5.7304\n",
      "Epoch 62/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 5.7601\n",
      "Epoch 63/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 5.8569\n",
      "Epoch 64/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 5.4628\n",
      "Epoch 65/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 5.4347\n",
      "Epoch 66/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 5.1674\n",
      "Epoch 67/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 5.0461\n",
      "Epoch 68/100\n",
      "13/13 [==============================] - 0s 839us/step - loss: 5.3222\n",
      "Epoch 69/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 5.1464\n",
      "Epoch 70/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.9569\n",
      "Epoch 71/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 5.2188\n",
      "Epoch 72/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 5.0008\n",
      "Epoch 73/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 4.8560\n",
      "Epoch 74/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.8404\n",
      "Epoch 75/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 4.7328\n",
      "Epoch 76/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.8707\n",
      "Epoch 77/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 4.5290\n",
      "Epoch 78/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.7465\n",
      "Epoch 79/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.6810\n",
      "Epoch 80/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.6079\n",
      "Epoch 81/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.3668\n",
      "Epoch 82/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 4.2827\n",
      "Epoch 83/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 4.4679\n",
      "Epoch 84/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 4.5822\n",
      "Epoch 85/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.1652\n",
      "Epoch 86/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 4.1822\n",
      "Epoch 87/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 4.2009\n",
      "Epoch 88/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.2067\n",
      "Epoch 89/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 4.0624\n",
      "Epoch 90/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 3.9742\n",
      "Epoch 91/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 4.0157\n",
      "Epoch 92/100\n",
      "13/13 [==============================] - 0s 1ms/step - loss: 4.2554\n",
      "Epoch 93/100\n",
      "13/13 [==============================] - 0s 917us/step - loss: 3.9088\n",
      "Epoch 94/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 3.9607\n",
      "Epoch 95/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 3.8336\n",
      "Epoch 96/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 3.6856\n",
      "Epoch 97/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 3.7677\n",
      "Epoch 98/100\n",
      "13/13 [==============================] - 0s 918us/step - loss: 3.7348\n",
      "Epoch 99/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 3.5505\n",
      "Epoch 100/100\n",
      "13/13 [==============================] - 0s 834us/step - loss: 3.5618\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2ac03d1d9d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the data to the model\n",
    "model.fit(X_train, y_train, epochs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "52ec3d3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step\n",
      "R2 Score =  0.898351679499482\n",
      "RMSE Score =  3.072789229093155\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model\n",
    "y_pred = model.predict(X_test)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_pred)))\n",
    "print(\"R2 Score = \", r2)\n",
    "print(\"RMSE Score = \", rmse)"
   ]
  }
 ],
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