ANN_kc_house_regressiong.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf22410f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a665802",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/kc_house_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a4bcbf2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf967dc2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f86337f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db2b6b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "886dc8ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "\n",
    "df['month'] = df['date'].apply(lambda date:date.month)\n",
    "df['year'] = df['date'].apply(lambda date:date.year)\n",
    "df = df.drop('id',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eaf4ccbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('date',axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40a33330",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('zipcode',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74a7bb8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('price',axis=1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6bfa86e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "230fc863",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fde78f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0da798dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c83fd24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2290</td>\n",
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       "      <td>1.5</td>\n",
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       "      <td>1921</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5917</td>\n",
       "      <td>-122.290</td>\n",
       "      <td>2390</td>\n",
       "      <td>6300</td>\n",
       "      <td>9</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20546</th>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1630</td>\n",
       "      <td>2520</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1630</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5493</td>\n",
       "      <td>-121.998</td>\n",
       "      <td>1630</td>\n",
       "      <td>3131</td>\n",
       "      <td>6</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21555</th>\n",
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       "      <td>3.50</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1950</td>\n",
       "      <td>900</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5252</td>\n",
       "      <td>-122.192</td>\n",
       "      <td>2850</td>\n",
       "      <td>5708</td>\n",
       "      <td>5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9795</th>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1540</td>\n",
       "      <td>6632</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1070</td>\n",
       "      <td>470</td>\n",
       "      <td>1959</td>\n",
       "      <td>0</td>\n",
       "      <td>47.4973</td>\n",
       "      <td>-122.252</td>\n",
       "      <td>2510</td>\n",
       "      <td>6618</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12625</th>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2010</td>\n",
       "      <td>7200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1010</td>\n",
       "      <td>1000</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5591</td>\n",
       "      <td>-122.267</td>\n",
       "      <td>2010</td>\n",
       "      <td>7200</td>\n",
       "      <td>10</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1350</td>\n",
       "      <td>7827</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1350</td>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>0</td>\n",
       "      <td>47.3786</td>\n",
       "      <td>-122.219</td>\n",
       "      <td>1900</td>\n",
       "      <td>7827</td>\n",
       "      <td>8</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8006</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1430</td>\n",
       "      <td>4000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>930</td>\n",
       "      <td>500</td>\n",
       "      <td>1949</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5233</td>\n",
       "      <td>-122.284</td>\n",
       "      <td>1110</td>\n",
       "      <td>4000</td>\n",
       "      <td>7</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17745</th>\n",
       "      <td>2</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1800</td>\n",
       "      <td>7191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>990</td>\n",
       "      <td>810</td>\n",
       "      <td>1952</td>\n",
       "      <td>0</td>\n",
       "      <td>47.4828</td>\n",
       "      <td>-122.191</td>\n",
       "      <td>1940</td>\n",
       "      <td>7400</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17931</th>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1500</td>\n",
       "      <td>11233</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1500</td>\n",
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       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>47.7279</td>\n",
       "      <td>-121.967</td>\n",
       "      <td>1580</td>\n",
       "      <td>14013</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
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       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1160</td>\n",
       "      <td>5038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>740</td>\n",
       "      <td>420</td>\n",
       "      <td>1942</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5304</td>\n",
       "      <td>-122.380</td>\n",
       "      <td>1160</td>\n",
       "      <td>5076</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15117 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view   \n",
       "7452          4       1.00         2290      6300     1.5           0     4  \\\n",
       "20546         3       2.25         1630      2520     2.0           0     0   \n",
       "21555         4       3.50         2850      5577     2.0           0     0   \n",
       "9795          3       1.75         1540      6632     1.0           0     0   \n",
       "12625         4       2.25         2010      7200     1.0           0     1   \n",
       "...         ...        ...          ...       ...     ...         ...   ...   \n",
       "5695          3       1.50         1350      7827     1.0           0     0   \n",
       "8006          2       1.00         1430      4000     1.0           0     0   \n",
       "17745         2       1.75         1800      7191     1.0           0     3   \n",
       "17931         3       2.00         1500     11233     1.0           0     0   \n",
       "13151         2       1.00         1160      5038     1.0           0     0   \n",
       "\n",
       "       condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated   \n",
       "7452           4      7        2150            140      1921             0  \\\n",
       "20546          3      7        1630              0      2005             0   \n",
       "21555          3      8        1950            900      2014             0   \n",
       "9795           3      7        1070            470      1959             0   \n",
       "12625          4      8        1010           1000      1950             0   \n",
       "...          ...    ...         ...            ...       ...           ...   \n",
       "5695           4      7        1350              0      1968             0   \n",
       "8006           3      7         930            500      1949             0   \n",
       "17745          4      7         990            810      1952             0   \n",
       "17931          3      7        1500              0      1987             0   \n",
       "13151          5      7         740            420      1942             0   \n",
       "\n",
       "           lat     long  sqft_living15  sqft_lot15  month  year  \n",
       "7452   47.5917 -122.290           2390        6300      9  2014  \n",
       "20546  47.5493 -121.998           1630        3131      6  2014  \n",
       "21555  47.5252 -122.192           2850        5708      5  2015  \n",
       "9795   47.4973 -122.252           2510        6618      2  2015  \n",
       "12625  47.5591 -122.267           2010        7200     10  2014  \n",
       "...        ...      ...            ...         ...    ...   ...  \n",
       "5695   47.3786 -122.219           1900        7827      8  2014  \n",
       "8006   47.5233 -122.284           1110        4000      7  2014  \n",
       "17745  47.4828 -122.191           1940        7400      4  2015  \n",
       "17931  47.7279 -121.967           1580       14013      4  2015  \n",
       "13151  47.5304 -122.380           1160        5076      1  2015  \n",
       "\n",
       "[15117 rows x 19 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58c7f5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train= scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4afa4233",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-28 20:19:21.909071: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-06-28 20:19:21.953394: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-06-28 20:19:21.953757: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-06-28 20:19:22.697240: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d919316",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(19,activation='relu'))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "457b46de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/400\n",
      "60/60 [==============================] - 1s 5ms/step - loss: 430242365440.0000 - val_loss: 418921185280.0000\n",
      "Epoch 2/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 427290656768.0000 - val_loss: 402143739904.0000\n",
      "Epoch 3/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 272017489920.0000 - val_loss: 103886446592.0000\n",
      "Epoch 4/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 98924322816.0000 - val_loss: 92958269440.0000\n",
      "Epoch 5/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 93530603520.0000 - val_loss: 88514641920.0000\n",
      "Epoch 6/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 88420925440.0000 - val_loss: 83265708032.0000\n",
      "Epoch 7/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 82152382464.0000 - val_loss: 76474146816.0000\n",
      "Epoch 8/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 74361356288.0000 - val_loss: 67960406016.0000\n",
      "Epoch 9/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 65388154880.0000 - val_loss: 59339980800.0000\n",
      "Epoch 10/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 57621864448.0000 - val_loss: 53145522176.0000\n",
      "Epoch 11/400\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 52652261376.0000 - val_loss: 49632649216.0000\n",
      "Epoch 12/400\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 49460461568.0000 - val_loss: 47028633600.0000\n",
      "Epoch 13/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 47323308032.0000 - val_loss: 45521874944.0000\n",
      "Epoch 14/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 45770235904.0000 - val_loss: 43497029632.0000\n",
      "Epoch 15/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 43951550464.0000 - val_loss: 42114830336.0000\n",
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     ]
    },
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     "text": [
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     ]
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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      "Epoch 318/400\n",
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      "Epoch 319/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18252177408.0000 - val_loss: 18844004352.0000\n",
      "Epoch 320/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18581682176.0000 - val_loss: 18479341568.0000\n",
      "Epoch 321/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18476824576.0000 - val_loss: 18179895296.0000\n",
      "Epoch 322/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18295416832.0000 - val_loss: 18737741824.0000\n",
      "Epoch 323/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18379898880.0000 - val_loss: 18232479744.0000\n",
      "Epoch 324/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18270316544.0000 - val_loss: 18063644672.0000\n",
      "Epoch 325/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18369386496.0000 - val_loss: 18995879936.0000\n",
      "Epoch 326/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 19396151296.0000 - val_loss: 18235944960.0000\n",
      "Epoch 327/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18439380992.0000 - val_loss: 18435561472.0000\n",
      "Epoch 328/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18129172480.0000 - val_loss: 18423412736.0000\n",
      "Epoch 329/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18028357632.0000 - val_loss: 18492379136.0000\n",
      "Epoch 330/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18632183808.0000 - val_loss: 18137761792.0000\n",
      "Epoch 331/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18112806912.0000 - val_loss: 18278885376.0000\n",
      "Epoch 332/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18190063616.0000 - val_loss: 18186049536.0000\n",
      "Epoch 333/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18295621632.0000 - val_loss: 18213097472.0000\n",
      "Epoch 334/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17996001280.0000 - val_loss: 18591039488.0000\n",
      "Epoch 335/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18015006720.0000 - val_loss: 18324473856.0000\n",
      "Epoch 336/400\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 18035355648.0000 - val_loss: 18237925376.0000\n",
      "Epoch 337/400\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 18192590848.0000 - val_loss: 18447286272.0000\n",
      "Epoch 338/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18220234752.0000 - val_loss: 18488776704.0000\n",
      "Epoch 339/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18028593152.0000 - val_loss: 18108557312.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 340/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17868419072.0000 - val_loss: 18099030016.0000\n",
      "Epoch 341/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17942099968.0000 - val_loss: 18059038720.0000\n",
      "Epoch 342/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17775597568.0000 - val_loss: 18063423488.0000\n",
      "Epoch 343/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17859059712.0000 - val_loss: 18581706752.0000\n",
      "Epoch 344/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18234664960.0000 - val_loss: 18164314112.0000\n",
      "Epoch 345/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17740578816.0000 - val_loss: 18688169984.0000\n",
      "Epoch 346/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 18103578624.0000 - val_loss: 18458662912.0000\n",
      "Epoch 347/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17988214784.0000 - val_loss: 18019528704.0000\n",
      "Epoch 348/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17758199808.0000 - val_loss: 17927090176.0000\n",
      "Epoch 349/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17778116608.0000 - val_loss: 18049503232.0000\n",
      "Epoch 350/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17596307456.0000 - val_loss: 18147823616.0000\n",
      "Epoch 351/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17650712576.0000 - val_loss: 18155745280.0000\n",
      "Epoch 352/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17982720000.0000 - val_loss: 17929635840.0000\n",
      "Epoch 353/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17642420224.0000 - val_loss: 18047602688.0000\n",
      "Epoch 354/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17661679616.0000 - val_loss: 18336266240.0000\n",
      "Epoch 355/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17708234752.0000 - val_loss: 18130929664.0000\n",
      "Epoch 356/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17616797696.0000 - val_loss: 17964599296.0000\n",
      "Epoch 357/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17657753600.0000 - val_loss: 18598350848.0000\n",
      "Epoch 358/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17455241216.0000 - val_loss: 17926846464.0000\n",
      "Epoch 359/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17468469248.0000 - val_loss: 18163132416.0000\n",
      "Epoch 360/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17564096512.0000 - val_loss: 17979205632.0000\n",
      "Epoch 361/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17741293568.0000 - val_loss: 17956235264.0000\n",
      "Epoch 362/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17412481024.0000 - val_loss: 17866082304.0000\n",
      "Epoch 363/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17421176832.0000 - val_loss: 17839976448.0000\n",
      "Epoch 364/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17419304960.0000 - val_loss: 18084085760.0000\n",
      "Epoch 365/400\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 17477203968.0000 - val_loss: 19251775488.0000\n",
      "Epoch 366/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17512036352.0000 - val_loss: 17819658240.0000\n",
      "Epoch 367/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17397637120.0000 - val_loss: 18251257856.0000\n",
      "Epoch 368/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17392683008.0000 - val_loss: 18659479552.0000\n",
      "Epoch 369/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17583343616.0000 - val_loss: 18066595840.0000\n",
      "Epoch 370/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17678622720.0000 - val_loss: 18197270528.0000\n",
      "Epoch 371/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17249380352.0000 - val_loss: 18058309632.0000\n",
      "Epoch 372/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17236342784.0000 - val_loss: 18023073792.0000\n",
      "Epoch 373/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17520572416.0000 - val_loss: 17934649344.0000\n",
      "Epoch 374/400\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 17125094400.0000 - val_loss: 17842577408.0000\n",
      "Epoch 375/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17320110080.0000 - val_loss: 20661843968.0000\n",
      "Epoch 376/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 22983651328.0000 - val_loss: 18547867648.0000\n",
      "Epoch 377/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17509009408.0000 - val_loss: 18046914560.0000\n",
      "Epoch 378/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17379440640.0000 - val_loss: 18075631616.0000\n",
      "Epoch 379/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17448777728.0000 - val_loss: 17796786176.0000\n",
      "Epoch 380/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17125453824.0000 - val_loss: 17884184576.0000\n",
      "Epoch 381/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17144658944.0000 - val_loss: 17696571392.0000\n",
      "Epoch 382/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17017732096.0000 - val_loss: 18098501632.0000\n",
      "Epoch 383/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17115889664.0000 - val_loss: 17854068736.0000\n",
      "Epoch 384/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17434707968.0000 - val_loss: 18292582400.0000\n",
      "Epoch 385/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17104873472.0000 - val_loss: 18133882880.0000\n",
      "Epoch 386/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17092464640.0000 - val_loss: 18183294976.0000\n",
      "Epoch 387/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17053649920.0000 - val_loss: 17635862528.0000\n",
      "Epoch 388/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17182969856.0000 - val_loss: 17654290432.0000\n",
      "Epoch 389/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17119407104.0000 - val_loss: 18632439808.0000\n",
      "Epoch 390/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17245960192.0000 - val_loss: 18222559232.0000\n",
      "Epoch 391/400\n",
      "60/60 [==============================] - 0s 3ms/step - loss: 16899832832.0000 - val_loss: 17820061696.0000\n",
      "Epoch 392/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17001780224.0000 - val_loss: 17821411328.0000\n",
      "Epoch 393/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17169088512.0000 - val_loss: 17581008896.0000\n",
      "Epoch 394/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17084238848.0000 - val_loss: 17672556544.0000\n",
      "Epoch 395/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 17101892608.0000 - val_loss: 17765937152.0000\n",
      "Epoch 396/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 16876299264.0000 - val_loss: 17635272704.0000\n",
      "Epoch 397/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 16804237312.0000 - val_loss: 17510494208.0000\n",
      "Epoch 398/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 16966050816.0000 - val_loss: 17737621504.0000\n",
      "Epoch 399/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 16906958848.0000 - val_loss: 17549649920.0000\n",
      "Epoch 400/400\n",
      "60/60 [==============================] - 0s 2ms/step - loss: 16782284800.0000 - val_loss: 17466314752.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fddc439b010>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train,y=y_train.values,\n",
    "          validation_data=(X_test,y_test.values),\n",
    "          batch_size=256,epochs=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4a0f395f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "203/203 [==============================] - 0s 707us/step\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b93d68a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cadacd0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d51b256",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test=(np.array(y_test)).reshape(6480,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa66129c",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=np.sqrt((1-np.power((np.sum(np.abs(y_pred-y_test))/np.sum(np.abs(y_test))),2)))*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b5861881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.90911966803499"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5e5c0fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "203/203 [==============================] - 0s 888us/step - loss: 17466314752.0000\n",
      "Mean Squared Error:  17466314752.0\n"
     ]
    }
   ],
   "source": [
    "loss = model.evaluate(X_test, y_test)\n",
    "print('Mean Squared Error: ', loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ee288da",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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