Deep Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "150c2227",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cbf83d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('log_transformed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "daa023f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>LotFrontage_nan</th>\n",
       "      <th>MasVnrArea_nan</th>\n",
       "      <th>GarageYrBlt_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
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       "      <td>1.386294</td>\n",
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       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
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       "      <td>1.386294</td>\n",
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       "      <td>1.609438</td>\n",
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       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
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       "<p>5 rows × 84 columns</p>\n",
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      "text/plain": [
       "         Id  MSSubClass  MSZoning  LotFrontage   LotArea    Street     Alley  \\\n",
       "0  0.693147    4.110874  1.386294     4.189655  9.042040  0.693147  1.098612   \n",
       "1  1.098612    3.044522  1.386294     4.394449  9.169623  0.693147  1.098612   \n",
       "2  1.386294    4.110874  1.386294     4.234107  9.328212  0.693147  1.098612   \n",
       "3  1.609438    4.262680  1.386294     4.110874  9.164401  0.693147  1.098612   \n",
       "4  1.791759    4.110874  1.386294     4.442651  9.565284  0.693147  1.098612   \n",
       "\n",
       "   LotShape  LandContour  Utilities  ...  MiscFeature  MiscVal    MoSold  \\\n",
       "0  0.000000     0.693147   0.693147  ...     1.386294      0.0  1.098612   \n",
       "1  0.000000     0.693147   0.693147  ...     1.386294      0.0  1.791759   \n",
       "2  0.693147     0.693147   0.693147  ...     1.386294      0.0  2.302585   \n",
       "3  0.693147     0.693147   0.693147  ...     1.386294      0.0  1.098612   \n",
       "4  0.693147     0.693147   0.693147  ...     1.386294      0.0  2.564949   \n",
       "\n",
       "     YrSold  SaleType  SaleCondition  SalePrice  LotFrontage_nan  \\\n",
       "0  7.605392  1.609438       1.609438  12.247699              0.0   \n",
       "1  7.604894  1.609438       1.609438  12.109016              0.0   \n",
       "2  7.605392  1.609438       1.609438  12.317171              0.0   \n",
       "3  7.604396  1.609438       0.693147  11.849405              0.0   \n",
       "4  7.605392  1.609438       1.609438  12.429220              0.0   \n",
       "\n",
       "   MasVnrArea_nan  GarageYrBlt_nan  \n",
       "0             0.0              0.0  \n",
       "1             0.0              0.0  \n",
       "2             0.0              0.0  \n",
       "3             0.0              0.0  \n",
       "4             0.0              0.0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fae9bc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.SalePrice.copy()\n",
    "x = data.drop(['Id','SalePrice'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "581d8ebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd58d424",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a45467eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41abefdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(Dense(units=20, kernel_initializer = 'he_uniform', activation = 'relu', input_dim = 82 ))\n",
    "model.add(Dense(units=20, kernel_initializer = 'he_uniform', activation = 'relu' ))\n",
    "model.add(Dense(units=20, kernel_initializer = 'he_uniform', activation = 'relu' ))\n",
    "model.add(Dense(units=20, kernel_initializer = 'he_uniform', activation = 'relu' ))\n",
    "model.add(Dense(units=20, kernel_initializer = 'he_uniform', activation = 'relu' ))\n",
    "model.add(Dense(units=20, kernel_initializer = 'he_uniform', activation = 'relu' ))\n",
    "model.add(Dense(units=1, kernel_initializer = 'glorot_uniform', activation = 'linear' ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a2c4eb7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 20)                1660      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 20)                420       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 20)                420       \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 20)                420       \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 20)                420       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 20)                420       \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1)                 21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3,781\n",
      "Trainable params: 3,781\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d8d9554a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "020c0aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = keras.callbacks.ModelCheckpoint(filepath=os.getcwd(), monitor='val_loss', save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "36682e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss = 'MeanSquaredError', metrics=['mean_squared_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7cc3845e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      " 80/110 [====================>.........] - ETA: 0s - loss: 11.2416 - mean_squared_error: 11.2416 INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 2s 14ms/step - loss: 8.3701 - mean_squared_error: 8.3701 - val_loss: 0.5212 - val_mean_squared_error: 0.5212\n",
      "Epoch 2/250\n",
      "110/110 [==============================] - ETA: 0s - loss: 0.3634 - mean_squared_error: 0.3634INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 9ms/step - loss: 0.3634 - mean_squared_error: 0.3634 - val_loss: 0.2893 - val_mean_squared_error: 0.2893\n",
      "Epoch 3/250\n",
      " 85/110 [======================>.......] - ETA: 0s - loss: 0.1994 - mean_squared_error: 0.1994INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 10ms/step - loss: 0.1966 - mean_squared_error: 0.1966 - val_loss: 0.1502 - val_mean_squared_error: 0.1502\n",
      "Epoch 4/250\n",
      "106/110 [===========================>..] - ETA: 0s - loss: 0.1290 - mean_squared_error: 0.1290INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 9ms/step - loss: 0.1284 - mean_squared_error: 0.1284 - val_loss: 0.1112 - val_mean_squared_error: 0.1112\n",
      "Epoch 5/250\n",
      " 85/110 [======================>.......] - ETA: 0s - loss: 0.1066 - mean_squared_error: 0.1066INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 10ms/step - loss: 0.1030 - mean_squared_error: 0.1030 - val_loss: 0.0943 - val_mean_squared_error: 0.0943\n",
      "Epoch 6/250\n",
      " 89/110 [=======================>......] - ETA: 0s - loss: 0.0894 - mean_squared_error: 0.0894INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 11ms/step - loss: 0.0855 - mean_squared_error: 0.0855 - val_loss: 0.0690 - val_mean_squared_error: 0.0690\n",
      "Epoch 7/250\n",
      " 75/110 [===================>..........] - ETA: 0s - loss: 0.0768 - mean_squared_error: 0.0768INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 10ms/step - loss: 0.0762 - mean_squared_error: 0.0762 - val_loss: 0.0670 - val_mean_squared_error: 0.0670\n",
      "Epoch 8/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0656 - mean_squared_error: 0.0656 - val_loss: 0.0707 - val_mean_squared_error: 0.0707\n",
      "Epoch 9/250\n",
      "106/110 [===========================>..] - ETA: 0s - loss: 0.0631 - mean_squared_error: 0.0631INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 13ms/step - loss: 0.0629 - mean_squared_error: 0.0629 - val_loss: 0.0573 - val_mean_squared_error: 0.0573\n",
      "Epoch 10/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0595 - mean_squared_error: 0.0595 - val_loss: 0.0662 - val_mean_squared_error: 0.0662\n",
      "Epoch 11/250\n",
      "107/110 [============================>.] - ETA: 0s - loss: 0.0563 - mean_squared_error: 0.0563INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 12ms/step - loss: 0.0565 - mean_squared_error: 0.0565 - val_loss: 0.0556 - val_mean_squared_error: 0.0556\n",
      "Epoch 12/250\n",
      " 98/110 [=========================>....] - ETA: 0s - loss: 0.0552 - mean_squared_error: 0.0552INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 9ms/step - loss: 0.0542 - mean_squared_error: 0.0542 - val_loss: 0.0481 - val_mean_squared_error: 0.0481\n",
      "Epoch 13/250\n",
      " 99/110 [==========================>...] - ETA: 0s - loss: 0.0500 - mean_squared_error: 0.0500INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 10ms/step - loss: 0.0499 - mean_squared_error: 0.0499 - val_loss: 0.0438 - val_mean_squared_error: 0.0438\n",
      "Epoch 14/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0466 - mean_squared_error: 0.0466 - val_loss: 0.0642 - val_mean_squared_error: 0.0642\n",
      "Epoch 15/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0471 - mean_squared_error: 0.0471 - val_loss: 0.0444 - val_mean_squared_error: 0.0444\n",
      "Epoch 16/250\n",
      " 99/110 [==========================>...] - ETA: 0s - loss: 0.0510 - mean_squared_error: 0.0510INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 11ms/step - loss: 0.0505 - mean_squared_error: 0.0505 - val_loss: 0.0414 - val_mean_squared_error: 0.0414\n",
      "Epoch 17/250\n",
      " 94/110 [========================>.....] - ETA: 0s - loss: 0.0432 - mean_squared_error: 0.0432INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 10ms/step - loss: 0.0430 - mean_squared_error: 0.0430 - val_loss: 0.0398 - val_mean_squared_error: 0.0398\n",
      "Epoch 18/250\n",
      " 82/110 [=====================>........] - ETA: 0s - loss: 0.0415 - mean_squared_error: 0.0415INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 12ms/step - loss: 0.0395 - mean_squared_error: 0.0395 - val_loss: 0.0397 - val_mean_squared_error: 0.0397\n",
      "Epoch 19/250\n",
      "101/110 [==========================>...] - ETA: 0s - loss: 0.0393 - mean_squared_error: 0.0393INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 10ms/step - loss: 0.0404 - mean_squared_error: 0.0404 - val_loss: 0.0362 - val_mean_squared_error: 0.0362\n",
      "Epoch 20/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0395 - mean_squared_error: 0.0395 - val_loss: 0.0373 - val_mean_squared_error: 0.0373\n",
      "Epoch 21/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0459 - mean_squared_error: 0.0459 - val_loss: 0.0532 - val_mean_squared_error: 0.0532\n",
      "Epoch 22/250\n",
      " 98/110 [=========================>....] - ETA: 0s - loss: 0.0408 - mean_squared_error: 0.0408INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 13ms/step - loss: 0.0407 - mean_squared_error: 0.0407 - val_loss: 0.0337 - val_mean_squared_error: 0.0337\n",
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      "110/110 [==============================] - 1s 12ms/step - loss: 0.0317 - mean_squared_error: 0.0317 - val_loss: 0.0316 - val_mean_squared_error: 0.0316\n",
      "Epoch 24/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0324 - mean_squared_error: 0.0324 - val_loss: 0.0376 - val_mean_squared_error: 0.0376\n",
      "Epoch 25/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0331 - mean_squared_error: 0.0331 - val_loss: 0.0499 - val_mean_squared_error: 0.0499\n",
      "Epoch 26/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0361 - mean_squared_error: 0.0361 - val_loss: 0.0514 - val_mean_squared_error: 0.0514\n",
      "Epoch 27/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0324 - mean_squared_error: 0.0324 - val_loss: 0.0413 - val_mean_squared_error: 0.0413\n",
      "Epoch 28/250\n"
     ]
    },
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      "110/110 [==============================] - 1s 12ms/step - loss: 0.0307 - mean_squared_error: 0.0307 - val_loss: 0.0315 - val_mean_squared_error: 0.0315\n",
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      "110/110 [==============================] - 1s 11ms/step - loss: 0.0341 - mean_squared_error: 0.0341 - val_loss: 0.0273 - val_mean_squared_error: 0.0273\n",
      "Epoch 30/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0279 - mean_squared_error: 0.0279 - val_loss: 0.0401 - val_mean_squared_error: 0.0401\n",
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      "110/110 [==============================] - 1s 12ms/step - loss: 0.0296 - mean_squared_error: 0.0296 - val_loss: 0.0253 - val_mean_squared_error: 0.0253\n",
      "Epoch 32/250\n",
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      "110/110 [==============================] - 1s 11ms/step - loss: 0.0286 - mean_squared_error: 0.0286 - val_loss: 0.0241 - val_mean_squared_error: 0.0241\n",
      "Epoch 34/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0267 - mean_squared_error: 0.0267 - val_loss: 0.0265 - val_mean_squared_error: 0.0265\n",
      "Epoch 35/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0283 - mean_squared_error: 0.0283 - val_loss: 0.0533 - val_mean_squared_error: 0.0533\n",
      "Epoch 36/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0284 - mean_squared_error: 0.0284 - val_loss: 0.0270 - val_mean_squared_error: 0.0270\n",
      "Epoch 37/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0251 - mean_squared_error: 0.0251 - val_loss: 0.0313 - val_mean_squared_error: 0.0313\n",
      "Epoch 38/250\n",
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      "110/110 [==============================] - 2s 15ms/step - loss: 0.0274 - mean_squared_error: 0.0274 - val_loss: 0.0236 - val_mean_squared_error: 0.0236\n",
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      "110/110 [==============================] - 0s 3ms/step - loss: 0.0325 - mean_squared_error: 0.0325 - val_loss: 0.0296 - val_mean_squared_error: 0.0296\n",
      "Epoch 44/250\n",
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      "110/110 [==============================] - 0s 3ms/step - loss: 0.0309 - mean_squared_error: 0.0309 - val_loss: 0.0316 - val_mean_squared_error: 0.0316\n",
      "Epoch 46/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0275 - mean_squared_error: 0.0275 - val_loss: 0.0275 - val_mean_squared_error: 0.0275\n",
      "Epoch 47/250\n",
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      "Epoch 48/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0221 - mean_squared_error: 0.0221 - val_loss: 0.0334 - val_mean_squared_error: 0.0334\n",
      "Epoch 49/250\n",
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      "110/110 [==============================] - 1s 10ms/step - loss: 0.0226 - mean_squared_error: 0.0226 - val_loss: 0.0205 - val_mean_squared_error: 0.0205\n",
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      "110/110 [==============================] - 1s 10ms/step - loss: 0.0227 - mean_squared_error: 0.0227 - val_loss: 0.0197 - val_mean_squared_error: 0.0197\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0225 - mean_squared_error: 0.0225 - val_loss: 0.0224 - val_mean_squared_error: 0.0224\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0244 - mean_squared_error: 0.0244 - val_loss: 0.0225 - val_mean_squared_error: 0.0225\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0282 - mean_squared_error: 0.0282 - val_loss: 0.0203 - val_mean_squared_error: 0.0203\n",
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      "110/110 [==============================] - 0s 3ms/step - loss: 0.0230 - mean_squared_error: 0.0230 - val_loss: 0.0691 - val_mean_squared_error: 0.0691\n",
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     ]
    },
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0219 - mean_squared_error: 0.0219 - val_loss: 0.0208 - val_mean_squared_error: 0.0208\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0313 - val_mean_squared_error: 0.0313\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0226 - mean_squared_error: 0.0226 - val_loss: 0.0259 - val_mean_squared_error: 0.0259\n",
      "Epoch 71/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0246 - mean_squared_error: 0.0246 - val_loss: 0.0675 - val_mean_squared_error: 0.0675\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0278 - mean_squared_error: 0.0278 - val_loss: 0.0260 - val_mean_squared_error: 0.0260\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0206 - mean_squared_error: 0.0206 - val_loss: 0.0217 - val_mean_squared_error: 0.0217\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0204 - mean_squared_error: 0.0204 - val_loss: 0.0208 - val_mean_squared_error: 0.0208\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0256 - mean_squared_error: 0.0256 - val_loss: 0.0884 - val_mean_squared_error: 0.0884\n",
      "Epoch 76/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0268 - mean_squared_error: 0.0268 - val_loss: 0.0224 - val_mean_squared_error: 0.0224\n",
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      "110/110 [==============================] - 1s 10ms/step - loss: 0.0185 - mean_squared_error: 0.0185 - val_loss: 0.0196 - val_mean_squared_error: 0.0196\n",
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      "110/110 [==============================] - 1s 11ms/step - loss: 0.0214 - mean_squared_error: 0.0214 - val_loss: 0.0182 - val_mean_squared_error: 0.0182\n",
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      "110/110 [==============================] - 1s 10ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0170 - val_mean_squared_error: 0.0170\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0183 - val_mean_squared_error: 0.0183\n",
      "Epoch 91/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0172 - mean_squared_error: 0.0172 - val_loss: 0.0233 - val_mean_squared_error: 0.0233\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0197 - mean_squared_error: 0.0197 - val_loss: 0.0265 - val_mean_squared_error: 0.0265\n",
      "Epoch 93/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0202 - mean_squared_error: 0.0202 - val_loss: 0.0773 - val_mean_squared_error: 0.0773\n",
      "Epoch 94/250\n",
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      "Epoch 95/250\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0194 - mean_squared_error: 0.0194 - val_loss: 0.0204 - val_mean_squared_error: 0.0204\n",
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      "Epoch 107/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0196 - mean_squared_error: 0.0196 - val_loss: 0.0391 - val_mean_squared_error: 0.0391\n",
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      "Epoch 109/250\n",
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      "Epoch 110/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0155 - mean_squared_error: 0.0155 - val_loss: 0.0230 - val_mean_squared_error: 0.0230\n",
      "Epoch 111/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0154 - mean_squared_error: 0.0154 - val_loss: 0.0185 - val_mean_squared_error: 0.0185\n",
      "Epoch 112/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0195 - mean_squared_error: 0.0195 - val_loss: 0.0248 - val_mean_squared_error: 0.0248\n",
      "Epoch 113/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0177 - mean_squared_error: 0.0177 - val_loss: 0.0184 - val_mean_squared_error: 0.0184\n"
     ]
    },
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      "Epoch 114/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0222 - val_mean_squared_error: 0.0222\n",
      "Epoch 115/250\n",
      "110/110 [==============================] - ETA: 0s - loss: 0.0192 - mean_squared_error: 0.0192INFO:tensorflow:Assets written to: C:\\Users\\rishi\\Desktop\\Projects\\kaggle\\house-prices-advanced-regression-techniques\\assets\n",
      "110/110 [==============================] - 1s 9ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0169 - val_mean_squared_error: 0.0169\n",
      "Epoch 116/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0176 - mean_squared_error: 0.0176 - val_loss: 0.0217 - val_mean_squared_error: 0.0217\n",
      "Epoch 117/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0173 - mean_squared_error: 0.0173 - val_loss: 0.0238 - val_mean_squared_error: 0.0238\n",
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      "110/110 [==============================] - 2s 15ms/step - loss: 0.0162 - mean_squared_error: 0.0162 - val_loss: 0.0166 - val_mean_squared_error: 0.0166\n",
      "Epoch 119/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0176 - val_mean_squared_error: 0.0176\n",
      "Epoch 120/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0246 - val_mean_squared_error: 0.0246\n",
      "Epoch 121/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0236 - mean_squared_error: 0.0236 - val_loss: 0.0339 - val_mean_squared_error: 0.0339\n",
      "Epoch 122/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0174 - mean_squared_error: 0.0174 - val_loss: 0.0187 - val_mean_squared_error: 0.0187\n",
      "Epoch 123/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0188 - val_mean_squared_error: 0.0188\n",
      "Epoch 124/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0161 - mean_squared_error: 0.0161 - val_loss: 0.0182 - val_mean_squared_error: 0.0182\n",
      "Epoch 125/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0178 - mean_squared_error: 0.0178 - val_loss: 0.0173 - val_mean_squared_error: 0.0173\n",
      "Epoch 126/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0140 - mean_squared_error: 0.0140 - val_loss: 0.0174 - val_mean_squared_error: 0.0174\n",
      "Epoch 127/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0173 - mean_squared_error: 0.0173 - val_loss: 0.0210 - val_mean_squared_error: 0.0210\n",
      "Epoch 128/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0211 - mean_squared_error: 0.0211 - val_loss: 0.0178 - val_mean_squared_error: 0.0178\n",
      "Epoch 129/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0215 - val_mean_squared_error: 0.0215\n",
      "Epoch 130/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0173 - mean_squared_error: 0.0173 - val_loss: 0.0648 - val_mean_squared_error: 0.0648\n",
      "Epoch 131/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0198 - mean_squared_error: 0.0198 - val_loss: 0.0208 - val_mean_squared_error: 0.0208\n",
      "Epoch 132/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0162 - mean_squared_error: 0.0162 - val_loss: 0.0265 - val_mean_squared_error: 0.0265\n",
      "Epoch 133/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0140 - mean_squared_error: 0.0140 - val_loss: 0.0183 - val_mean_squared_error: 0.0183\n",
      "Epoch 134/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0177 - mean_squared_error: 0.0177 - val_loss: 0.0173 - val_mean_squared_error: 0.0173\n",
      "Epoch 135/250\n",
      "110/110 [==============================] - ETA: 0s - loss: 0.0127 - mean_squared_error: 0.01 - 0s 2ms/step - loss: 0.0142 - mean_squared_error: 0.0142 - val_loss: 0.0192 - val_mean_squared_error: 0.0192\n",
      "Epoch 136/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0168 - mean_squared_error: 0.0168 - val_loss: 0.0193 - val_mean_squared_error: 0.0193\n",
      "Epoch 137/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0145 - mean_squared_error: 0.0145 - val_loss: 0.0250 - val_mean_squared_error: 0.0250\n",
      "Epoch 138/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0179 - mean_squared_error: 0.0179 - val_loss: 0.0220 - val_mean_squared_error: 0.0220\n",
      "Epoch 139/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0175 - mean_squared_error: 0.0175 - val_loss: 0.0168 - val_mean_squared_error: 0.0168\n",
      "Epoch 140/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0187 - val_mean_squared_error: 0.0187\n",
      "Epoch 141/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0170 - mean_squared_error: 0.0170 - val_loss: 0.0206 - val_mean_squared_error: 0.0206\n",
      "Epoch 142/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0135 - mean_squared_error: 0.0135 - val_loss: 0.0282 - val_mean_squared_error: 0.0282\n",
      "Epoch 143/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0238 - val_mean_squared_error: 0.0238\n",
      "Epoch 144/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0200 - mean_squared_error: 0.0200 - val_loss: 0.0196 - val_mean_squared_error: 0.0196\n",
      "Epoch 145/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0149 - mean_squared_error: 0.0149 - val_loss: 0.0283 - val_mean_squared_error: 0.0283\n",
      "Epoch 146/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0233 - mean_squared_error: 0.0233 - val_loss: 0.0171 - val_mean_squared_error: 0.0171\n",
      "Epoch 147/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0177 - mean_squared_error: 0.0177 - val_loss: 0.0227 - val_mean_squared_error: 0.0227\n",
      "Epoch 148/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0194 - val_mean_squared_error: 0.0194\n",
      "Epoch 149/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0206 - val_mean_squared_error: 0.0206\n",
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      "110/110 [==============================] - 1s 11ms/step - loss: 0.0147 - mean_squared_error: 0.0147 - val_loss: 0.0162 - val_mean_squared_error: 0.0162\n",
      "Epoch 151/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0157 - mean_squared_error: 0.0157 - val_loss: 0.0219 - val_mean_squared_error: 0.0219\n",
      "Epoch 152/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0149 - mean_squared_error: 0.0149 - val_loss: 0.0219 - val_mean_squared_error: 0.0219\n",
      "Epoch 153/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0198 - mean_squared_error: 0.0198 - val_loss: 0.0265 - val_mean_squared_error: 0.0265\n",
      "Epoch 154/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0176 - mean_squared_error: 0.0176 - val_loss: 0.0217 - val_mean_squared_error: 0.0217\n",
      "Epoch 155/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0139 - mean_squared_error: 0.0139 - val_loss: 0.0167 - val_mean_squared_error: 0.0167\n",
      "Epoch 156/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0150 - mean_squared_error: 0.0150 - val_loss: 0.0171 - val_mean_squared_error: 0.0171\n",
      "Epoch 157/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0137 - mean_squared_error: 0.0137 - val_loss: 0.0182 - val_mean_squared_error: 0.0182\n",
      "Epoch 158/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0192 - val_mean_squared_error: 0.0192\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 159/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0148 - mean_squared_error: 0.0148 - val_loss: 0.0313 - val_mean_squared_error: 0.0313\n",
      "Epoch 160/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0167 - mean_squared_error: 0.0167 - val_loss: 0.0222 - val_mean_squared_error: 0.0222\n",
      "Epoch 161/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0178 - mean_squared_error: 0.0178 - val_loss: 0.0406 - val_mean_squared_error: 0.0406\n",
      "Epoch 162/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0181 - mean_squared_error: 0.0181 - val_loss: 0.0190 - val_mean_squared_error: 0.0190\n",
      "Epoch 163/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0192 - mean_squared_error: 0.0192 - val_loss: 0.0201 - val_mean_squared_error: 0.0201\n",
      "Epoch 164/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0232 - val_mean_squared_error: 0.0232\n",
      "Epoch 165/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0129 - mean_squared_error: 0.0129 - val_loss: 0.0320 - val_mean_squared_error: 0.0320\n",
      "Epoch 166/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0169 - mean_squared_error: 0.0169 - val_loss: 0.0180 - val_mean_squared_error: 0.0180\n",
      "Epoch 167/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0142 - mean_squared_error: 0.0142 - val_loss: 0.0203 - val_mean_squared_error: 0.0203\n",
      "Epoch 168/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0133 - mean_squared_error: 0.0133 - val_loss: 0.0166 - val_mean_squared_error: 0.0166\n",
      "Epoch 169/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0160 - mean_squared_error: 0.0160 - val_loss: 0.0258 - val_mean_squared_error: 0.0258\n",
      "Epoch 170/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0157 - mean_squared_error: 0.0157 - val_loss: 0.0186 - val_mean_squared_error: 0.0186\n",
      "Epoch 171/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0179 - mean_squared_error: 0.0179 - val_loss: 0.0178 - val_mean_squared_error: 0.0178\n",
      "Epoch 172/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0142 - mean_squared_error: 0.0142 - val_loss: 0.0285 - val_mean_squared_error: 0.0285\n",
      "Epoch 173/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0147 - mean_squared_error: 0.0147 - val_loss: 0.0236 - val_mean_squared_error: 0.0236\n",
      "Epoch 174/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0153 - mean_squared_error: 0.0153 - val_loss: 0.0230 - val_mean_squared_error: 0.0230\n",
      "Epoch 175/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0165 - mean_squared_error: 0.0165 - val_loss: 0.0195 - val_mean_squared_error: 0.0195\n",
      "Epoch 176/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0133 - mean_squared_error: 0.0133 - val_loss: 0.0246 - val_mean_squared_error: 0.0246\n",
      "Epoch 177/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0177 - mean_squared_error: 0.0177 - val_loss: 0.0229 - val_mean_squared_error: 0.0229\n",
      "Epoch 178/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0146 - mean_squared_error: 0.0146 - val_loss: 0.0196 - val_mean_squared_error: 0.0196\n",
      "Epoch 179/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0147 - mean_squared_error: 0.0147 - val_loss: 0.0179 - val_mean_squared_error: 0.0179\n",
      "Epoch 180/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0207 - mean_squared_error: 0.0207 - val_loss: 0.0194 - val_mean_squared_error: 0.0194\n",
      "Epoch 181/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0145 - mean_squared_error: 0.0145 - val_loss: 0.0176 - val_mean_squared_error: 0.0176\n",
      "Epoch 182/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0134 - mean_squared_error: 0.0134 - val_loss: 0.0163 - val_mean_squared_error: 0.0163\n",
      "Epoch 183/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0148 - mean_squared_error: 0.0148 - val_loss: 0.0228 - val_mean_squared_error: 0.0228\n",
      "Epoch 184/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0240 - val_mean_squared_error: 0.0240\n",
      "Epoch 185/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0150 - mean_squared_error: 0.0150 - val_loss: 0.0189 - val_mean_squared_error: 0.0189\n",
      "Epoch 186/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0143 - mean_squared_error: 0.0143 - val_loss: 0.0176 - val_mean_squared_error: 0.0176\n",
      "Epoch 187/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0184 - mean_squared_error: 0.0184 - val_loss: 0.0202 - val_mean_squared_error: 0.0202\n",
      "Epoch 188/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0139 - mean_squared_error: 0.0139 - val_loss: 0.0173 - val_mean_squared_error: 0.0173\n",
      "Epoch 189/250\n",
      "110/110 [==============================] - 0s 3ms/step - loss: 0.0138 - mean_squared_error: 0.0138 - val_loss: 0.0170 - val_mean_squared_error: 0.0170\n",
      "Epoch 190/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0142 - mean_squared_error: 0.0142 - val_loss: 0.0176 - val_mean_squared_error: 0.0176\n",
      "Epoch 191/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0168 - val_mean_squared_error: 0.0168\n",
      "Epoch 192/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0247 - val_mean_squared_error: 0.0247\n",
      "Epoch 193/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0154 - mean_squared_error: 0.0154 - val_loss: 0.0168 - val_mean_squared_error: 0.0168\n",
      "Epoch 194/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0135 - mean_squared_error: 0.0135 - val_loss: 0.0171 - val_mean_squared_error: 0.0171\n",
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      "110/110 [==============================] - 1s 10ms/step - loss: 0.0141 - mean_squared_error: 0.0141 - val_loss: 0.0157 - val_mean_squared_error: 0.0157\n",
      "Epoch 196/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0154 - mean_squared_error: 0.0154 - val_loss: 0.0169 - val_mean_squared_error: 0.0169\n",
      "Epoch 197/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0228 - val_mean_squared_error: 0.0228\n",
      "Epoch 198/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0135 - mean_squared_error: 0.0135 - val_loss: 0.0305 - val_mean_squared_error: 0.0305\n",
      "Epoch 199/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0165 - mean_squared_error: 0.0165 - val_loss: 0.0162 - val_mean_squared_error: 0.0162\n",
      "Epoch 200/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0166 - mean_squared_error: 0.0166 - val_loss: 0.0166 - val_mean_squared_error: 0.0166\n",
      "Epoch 201/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0155 - mean_squared_error: 0.0155 - val_loss: 0.0226 - val_mean_squared_error: 0.0226\n",
      "Epoch 202/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0136 - mean_squared_error: 0.0136 - val_loss: 0.0165 - val_mean_squared_error: 0.0165\n",
      "Epoch 203/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0180 - val_mean_squared_error: 0.0180\n",
      "Epoch 204/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0146 - mean_squared_error: 0.0146 - val_loss: 0.0290 - val_mean_squared_error: 0.0290\n",
      "Epoch 205/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0145 - mean_squared_error: 0.0145 - val_loss: 0.0224 - val_mean_squared_error: 0.0224\n",
      "Epoch 206/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0143 - mean_squared_error: 0.0143 - val_loss: 0.0267 - val_mean_squared_error: 0.0267\n",
      "Epoch 207/250\n"
     ]
    },
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0158 - mean_squared_error: 0.0158 - val_loss: 0.0267 - val_mean_squared_error: 0.0267\n",
      "Epoch 208/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0140 - mean_squared_error: 0.0140 - val_loss: 0.0232 - val_mean_squared_error: 0.0232\n",
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      "110/110 [==============================] - 0s 2ms/step - loss: 0.0125 - mean_squared_error: 0.0125 - val_loss: 0.0195 - val_mean_squared_error: 0.0195\n",
      "Epoch 210/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0158 - mean_squared_error: 0.0158 - val_loss: 0.0208 - val_mean_squared_error: 0.0208\n",
      "Epoch 211/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0145 - mean_squared_error: 0.0145 - val_loss: 0.0353 - val_mean_squared_error: 0.0353\n",
      "Epoch 212/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0177 - val_mean_squared_error: 0.0177\n",
      "Epoch 213/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0124 - mean_squared_error: 0.0124 - val_loss: 0.0249 - val_mean_squared_error: 0.0249\n",
      "Epoch 214/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0171 - mean_squared_error: 0.0171 - val_loss: 0.0301 - val_mean_squared_error: 0.0301\n",
      "Epoch 215/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0139 - mean_squared_error: 0.0139 - val_loss: 0.0206 - val_mean_squared_error: 0.0206\n",
      "Epoch 216/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0153 - mean_squared_error: 0.0153 - val_loss: 0.0169 - val_mean_squared_error: 0.0169\n",
      "Epoch 217/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0138 - mean_squared_error: 0.0138 - val_loss: 0.0166 - val_mean_squared_error: 0.0166\n",
      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 223/250\n",
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      "Epoch 225/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0122 - mean_squared_error: 0.0122 - val_loss: 0.0182 - val_mean_squared_error: 0.0182\n",
      "Epoch 226/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0157 - mean_squared_error: 0.0157 - val_loss: 0.0233 - val_mean_squared_error: 0.0233\n",
      "Epoch 227/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0163 - mean_squared_error: 0.0163 - val_loss: 0.0387 - val_mean_squared_error: 0.0387\n",
      "Epoch 228/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0177 - mean_squared_error: 0.0177 - val_loss: 0.0208 - val_mean_squared_error: 0.0208\n",
      "Epoch 229/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0151 - mean_squared_error: 0.0151 - val_loss: 0.0164 - val_mean_squared_error: 0.0164\n",
      "Epoch 230/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0151 - mean_squared_error: 0.0151 - val_loss: 0.0186 - val_mean_squared_error: 0.0186\n",
      "Epoch 231/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0107 - mean_squared_error: 0.0107 - val_loss: 0.0171 - val_mean_squared_error: 0.0171\n",
      "Epoch 232/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0159 - mean_squared_error: 0.0159 - val_loss: 0.0206 - val_mean_squared_error: 0.0206\n",
      "Epoch 233/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0257 - val_mean_squared_error: 0.0257\n",
      "Epoch 234/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0139 - mean_squared_error: 0.0139 - val_loss: 0.0242 - val_mean_squared_error: 0.0242\n",
      "Epoch 235/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0150 - mean_squared_error: 0.0150 - val_loss: 0.0199 - val_mean_squared_error: 0.0199\n",
      "Epoch 236/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0119 - mean_squared_error: 0.0119 - val_loss: 0.0182 - val_mean_squared_error: 0.0182\n",
      "Epoch 237/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0366 - val_mean_squared_error: 0.0366\n",
      "Epoch 238/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0122 - mean_squared_error: 0.0122 - val_loss: 0.0419 - val_mean_squared_error: 0.0419\n",
      "Epoch 239/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0121 - mean_squared_error: 0.0121 - val_loss: 0.0228 - val_mean_squared_error: 0.0228\n",
      "Epoch 240/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0143 - mean_squared_error: 0.0143 - val_loss: 0.0273 - val_mean_squared_error: 0.0273\n",
      "Epoch 241/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0154 - mean_squared_error: 0.0154 - val_loss: 0.0214 - val_mean_squared_error: 0.0214\n",
      "Epoch 242/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0130 - mean_squared_error: 0.0130 - val_loss: 0.0183 - val_mean_squared_error: 0.0183\n",
      "Epoch 243/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0196 - val_mean_squared_error: 0.0196\n",
      "Epoch 244/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0156 - mean_squared_error: 0.0156 - val_loss: 0.0203 - val_mean_squared_error: 0.0203\n",
      "Epoch 245/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0144 - mean_squared_error: 0.0144 - val_loss: 0.0163 - val_mean_squared_error: 0.0163\n",
      "Epoch 246/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0110 - mean_squared_error: 0.0110 - val_loss: 0.0195 - val_mean_squared_error: 0.0195\n",
      "Epoch 247/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0117 - mean_squared_error: 0.0117 - val_loss: 0.0181 - val_mean_squared_error: 0.0181\n",
      "Epoch 248/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0127 - mean_squared_error: 0.0127 - val_loss: 0.0176 - val_mean_squared_error: 0.0176\n",
      "Epoch 249/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0131 - mean_squared_error: 0.0131 - val_loss: 0.0241 - val_mean_squared_error: 0.0241\n",
      "Epoch 250/250\n",
      "110/110 [==============================] - 0s 2ms/step - loss: 0.0102 - mean_squared_error: 0.0102 - val_loss: 0.0170 - val_mean_squared_error: 0.0170\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(x_train, y_train, validation_data=(x_test, y_test), batch_size = 10, epochs = 250, callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9d587bef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x173b0d7fbe0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_weights(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3e0922c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009824377103606058"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_train, model.predict(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06c758cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09911799586152889"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(0.009824377103606058)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20ed9de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01567061761270809"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test, model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3893484d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12518233746303067"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(0.01567061761270809)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7c210fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('log_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f63345bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
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       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
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       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
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       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
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       "      <td>9.565775</td>\n",
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       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
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       "      <td>9.433564</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>4.110874</td>\n",
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       "      <td>4.317488</td>\n",
       "      <td>9.534668</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>4.369448</td>\n",
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       "      <td>1.098612</td>\n",
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       "      <td>0.693147</td>\n",
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       "      <td>3.784190</td>\n",
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      "text/plain": [
       "         Id  MSSubClass  MSZoning  LotFrontage   LotArea    Street     Alley  \\\n",
       "0  7.287561    3.044522  1.098612     4.394449  9.360741  0.693147  1.098612   \n",
       "1  7.288244    3.044522  1.386294     4.406719  9.565775  0.693147  1.098612   \n",
       "2  7.288928    4.110874  1.386294     4.317488  9.534668  0.693147  1.098612   \n",
       "3  7.289611    4.110874  1.386294     4.369448  9.208238  0.693147  1.098612   \n",
       "4  7.290293    4.795791  1.386294     3.784190  8.518392  0.693147  1.098612   \n",
       "\n",
       "   LotShape  LandContour  Utilities  ...     Fence  MiscFeature   MiscVal  \\\n",
       "0  0.000000     0.693147   0.693147  ...  1.098612     1.386294  0.000000   \n",
       "1  0.693147     0.693147   0.693147  ...  1.609438     1.098612  9.433564   \n",
       "2  0.693147     0.693147   0.693147  ...  1.098612     1.386294  0.000000   \n",
       "3  0.693147     0.693147   0.693147  ...  1.609438     1.386294  0.000000   \n",
       "4  0.693147     1.386294   0.693147  ...  1.609438     1.386294  0.000000   \n",
       "\n",
       "     MoSold    YrSold  SaleType  SaleCondition  LotFrontage_nan  \\\n",
       "0  1.945910  7.606387  1.609438       1.609438              0.0   \n",
       "1  1.945910  7.606387  1.609438       1.609438              0.0   \n",
       "2  1.386294  7.606387  1.609438       1.609438              0.0   \n",
       "3  1.945910  7.606387  1.609438       1.609438              0.0   \n",
       "4  0.693147  7.606387  1.609438       1.609438              0.0   \n",
       "\n",
       "   MasVnrArea_nan  GarageYrBlt_nan  \n",
       "0             0.0              0.0  \n",
       "1             0.0              0.0  \n",
       "2             0.0              0.0  \n",
       "3             0.0              0.0  \n",
       "4             0.0              0.0  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a0931f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop('Id', axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "58d4f458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 82)"
      ]
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     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
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    "test.shape"
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  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ff8941d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a04a0e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11.636502 ],\n",
       "       [12.084483 ],\n",
       "       [12.147181 ],\n",
       "       ...,\n",
       "       [12.1037855],\n",
       "       [11.812609 ],\n",
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     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "score"
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  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b700af6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = np.e**score - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fe070f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = pd.read_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1869b295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SalePrice'], dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7aab8fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.SalePrice = score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "84adb075",
   "metadata": {},
   "outputs": [
    {
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       "     Id      SalePrice\n",
       "0  1461  113152.648438\n",
       "1  1462  177101.296875\n",
       "2  1463  188560.625000\n",
       "3  1464  195599.750000\n",
       "4  1465  199028.953125"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0f743fce",
   "metadata": {},
   "outputs": [],
   "source": [
    "nn.to_csv('NN_SUBMISSION.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f26784",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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