House Price Prediction.ipynb

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   "source": [
    "# Advanced House Price Prediction\n",
    "In this notebook we use advanced regression models to predict house prices\n",
    "\n",
    "[Dataset on kaggle](https://www.kaggle.com/datasets/shivachandel/kc-house-data)\n",
    "\n",
    "## Contents\n",
    "* Data Cleaning\n",
    "* Data Understanding and EDA\n",
    "* Data Preprocessing\n",
    "* Modeling\n",
    "* Evaluation\n",
    "* Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "9def4435",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a6d1c5d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('kc_house_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "905e2ab4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13399196",
   "metadata": {
    "pycharm": {
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   "outputs": [
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</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>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
       "0  221900.0         3       1.00         1180      5650     1.0           0   \n",
       "1  538000.0         3       2.25         2570      7242     2.0           0   \n",
       "2  180000.0         2       1.00          770     10000     1.0           0   \n",
       "\n",
       "   view  condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
       "0     0          3      7      1180.0              0      1955             0   \n",
       "1     0          3      7      2170.0            400      1951          1991   \n",
       "2     0          3      6       770.0              0      1933             0   \n",
       "\n",
       "   zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0    98178  47.5112 -122.257           1340        5650  \n",
       "1    98125  47.7210 -122.319           1690        7639  \n",
       "2    98028  47.7379 -122.233           2720        8062  "
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.head(3)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa5a4993",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21613, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "da7f3a52",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21613 non-null  int64  \n",
      " 1   date           21613 non-null  object \n",
      " 2   price          21613 non-null  float64\n",
      " 3   bedrooms       21613 non-null  int64  \n",
      " 4   bathrooms      21613 non-null  float64\n",
      " 5   sqft_living    21613 non-null  int64  \n",
      " 6   sqft_lot       21613 non-null  int64  \n",
      " 7   floors         21613 non-null  float64\n",
      " 8   waterfront     21613 non-null  int64  \n",
      " 9   view           21613 non-null  int64  \n",
      " 10  condition      21613 non-null  int64  \n",
      " 11  grade          21613 non-null  int64  \n",
      " 12  sqft_above     21611 non-null  float64\n",
      " 13  sqft_basement  21613 non-null  int64  \n",
      " 14  yr_built       21613 non-null  int64  \n",
      " 15  yr_renovated   21613 non-null  int64  \n",
      " 16  zipcode        21613 non-null  int64  \n",
      " 17  lat            21613 non-null  float64\n",
      " 18  long           21613 non-null  float64\n",
      " 19  sqft_living15  21613 non-null  int64  \n",
      " 20  sqft_lot15     21613 non-null  int64  \n",
      "dtypes: float64(6), int64(14), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
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   "source": [
    "data.info()"
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  },
  {
   "cell_type": "markdown",
   "id": "49263097",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b4ca8cf",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "data.drop(['id', 'date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07688464",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       2\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "zipcode          0\n",
       "lat              0\n",
       "long             0\n",
       "sqft_living15    0\n",
       "sqft_lot15       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f98dd9",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Just 2 NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cde8f274",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "132570ec",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
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       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
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       "      <td>1180.0</td>\n",
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       "      <td>1955</td>\n",
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       "      <td>98178</td>\n",
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       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
       "0  221900.0         3       1.00         1180      5650     1.0           0   \n",
       "1  538000.0         3       2.25         2570      7242     2.0           0   \n",
       "2  180000.0         2       1.00          770     10000     1.0           0   \n",
       "3  604000.0         4       3.00         1960      5000     1.0           0   \n",
       "4  510000.0         3       2.00         1680      8080     1.0           0   \n",
       "\n",
       "   view  condition  grade  sqft_above  sqft_basement  yr_built  yr_renovated  \\\n",
       "0     0          3      7      1180.0              0      1955             0   \n",
       "1     0          3      7      2170.0            400      1951          1991   \n",
       "2     0          3      6       770.0              0      1933             0   \n",
       "3     0          5      7      1050.0            910      1965             0   \n",
       "4     0          3      8      1680.0              0      1987             0   \n",
       "\n",
       "   zipcode      lat     long  sqft_living15  sqft_lot15  \n",
       "0    98178  47.5112 -122.257           1340        5650  \n",
       "1    98125  47.7210 -122.319           1690        7639  \n",
       "2    98028  47.7379 -122.233           2720        8062  \n",
       "3    98136  47.5208 -122.393           1360        5000  \n",
       "4    98074  47.6168 -122.045           1800        7503  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13cd81e6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "cols = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a9dd120c",
   "metadata": {
    "pycharm": {
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   "outputs": [
    {
     "data": {
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dToeAgAAEBwcDADIyMpCcnIyamhp4e3sjNjYWAJCSkoKEhASsWrUKvXv3xtKlSwEAs2fPRkJCAsLCwtC9e3dkZGRYsGpERER3jlYn9J07d5r/9vPzw+bNm2+ZZ9CgQdi4ceMt5V5eXlizZs0t5e7u7nj//fdbGwIRERE1gT3FERERSQATOhERkQQwoRMREUlAm2+KI6I7U0xMDC5dumTuJOrNN99EbW0tFi9eDJ1Oh5CQEMydOxfAtXEdkpKSUFtbC19fX6SmpkIul6O0tBTx8fGorKzE/fffj4yMDNx11122rBaRZPAMnYhaJIRASUkJ8vLyzP8efPBBi43rQEQdx4RORC06deoUACAuLg5jxozB2rVrLTquAxF1HBM6EbWoqqoKfn5+WLlyJT799FN88cUXKC0ttdi4DkTUcfwNnYha5OPjAx8fH/N0ZGQkli9fjmHDhpnLLDmuQ3OaG7/BHlmqe14pdvMrxToBtqsXEzoRtejw4cPQ6/Xw8/MDcC1Je3l5WWxch7ZoavwGwD4ThCXGCXDk8QaaIsU6Adavl5OTrMmDWl5yJ6IWVVdXIz09HTqdDjU1NcjJycGrr75qsXEdiKjjeIZORC0aOXIkjh49irFjx8JkMiEqKgo+Pj4WG9eBiDqOCZ2IWmXOnDmYM2dOozJLjetARB3HS+5EREQSwIROREQkAR265M6uIImIiOxDu8/Q2RUkERGR/Wh3QmdXkERERPaj3QmdXUESERHZj3b/hm4vXUE21w2kpXuN6sxeqOyxx6vWYNxERLbR7oRuL11BNtUN5PXu9yy5o+6sbgodtUtExt35eCBCRNe1+5I7u4IkIiKyH+0+Q2dXkERERPajQ8+hsytIIiIi+8Ce4oiIiCSACZ2IiEgCmNCJiIgkgAmdiIhIApjQiYiIJIAJnYiISAKY0ImIiCSACZ2IiEgCOtSxzJ2kQW/scL/Z9ToDqqvqLBQRERHRfzGht5KLwhma1/I6tIwt74TDMYcAISIie8dL7kRERBLAhE5ERCQBTOhEREQSwIROREQkAXaR0Lds2YLQ0FAEBgZi3bp1tg6HiKyMbZ7I8mx+l3tZWRkyMzPx1VdfwcXFBRMmTMATTzyBBx54wNahEZEVsM0TWYfNE3pBQQGefPJJuLu7AwCCgoKg1Wrxyiuv2DYwK2jLs+zNzcfn2cmR3Ultnqgz2TyhX7x4EUql0jytUqlw7NixVr/fyUnW4muqHl3bH+ANOrocF4Uz/vb29g7Hser1/+1wJzcAoNMZUFNT3+Hl3Ki57WHPHDVuR2TNNg/YT3sHLNMhlU5nACDN76gU6wRYt17NLdvmCd1kMkEm+2+AQohG0y3p0eOuJl/r2dMNAPBxcmD7A7yBJZZjqVgswdVVDldXN4su8/o6dzSOGrcjsmabB+yrvVuCq+u13bQUv6NSrBNgu3rZ/KY4T09PlJeXm6fLy8uhUqlsGBERWRPbPJF12DyhP/XUU9i/fz8uXbqEuro6bN++Hf7+/rYOi4ishG2eyDpsfsm9V69emDt3LmJjY6HX6xEZGYlHHnnE1mERkZWwzRNZh0wIIWwdBBEREXWMzS+5ExERUccxoRMREUkAEzoREZEEMKETERFJgCQTuqMO/LBixQqEhYUhLCwM6enptg6nzZYsWYKEhARbh9EmO3fuREREBEJCQvD222/bOpxWy8vLM39XlixZYutw7IKjtvvbqampgVqtxrlz5wBc6y5Xo9EgMDAQmZmZNo6ufW63f3P0er377rsIDQ1FWFgYPvnkEwA2rpOQmN9//12MHDlSXL58WdTW1gqNRiNOnjxp67BatG/fPvH8888LnU4nGhoaRGxsrNi+fbutw2q1goIC8cQTT4jXX3/d1qG02pkzZ8Tw4cPFhQsXRENDg5g4caLYtWuXrcNq0dWrV8Vjjz0mKisrhV6vF5GRkWLfvn22DsumHLXd385PP/0k1Gq18Pb2FmfPnhV1dXUiICBAnDlzRuj1ehEXF+cQ39Mb3W7/tmXLFoeu14EDB8SECROEXq8XdXV1YuTIkaKoqMimdZLcGfqNAz9069bNPPCDvVMqlUhISICLiwsUCgX69++P0tJSW4fVKleuXEFmZiamT59u61DaZMeOHQgNDYWnpycUCgUyMzMxZMgQW4fVIqPRCJPJhLq6OhgMBhgMBri6uto6LJty1HZ/O9nZ2UhJSTH3nnfs2DH07dsXffr0gVwuh0ajcbi63W7/VlJS4tD1evzxx/H5559DLpejsrISRqMRVVVVNq2T5BL67QZ+KCsrs2FErTNgwAAMHToUAFBSUoJt27YhICDAtkG10oIFCzB37lzcfffdtg6lTU6fPg2j0Yjp06cjPDwc69evx5/+9Cdbh9UiNzc3zJ49GyEhIQgICICXlxceffRRW4dlU47a7m9n4cKF8PX1NU9LoW6327/JZDKHr5dCocDy5csRFhYGPz8/m28rySX0jg78YGsnT55EXFwc5s2bh/vuu69dyzhw4ADUanWb3rNixQp88803AICEhAR8/PHHrXrfl19+id69e8PPz6/Ncdqa0WjE/v37sWjRImzYsAHHjh1DTk6OrcNq0YkTJ7Bp0yZ899132Lt3L5ycnFq9vaTK0dt9c6xVtwsXLkCtViM8PBw//vgj4uLicOnSpWbfc+O+5d1330Vubm6z84eHh6Oqqso8feP+rU+fPpLYZrNmzcL+/ftx4cIFlJSU2LROkkvojjzww5EjRzBlyhS89tprGDduXKd+9oEDB2AwGNr8vvz8fOzbtw/h4eFYvnw5du7ciUWLFlkhQsu755574OfnBw8PD3Tp0gWjR49u0zCetvL999/Dz88PPXv2hIuLCyIiInDw4EFbh2VTjtzuW2Ktuh04cAD33HMP8vLy4OPjg3379rXp/bNnz8bYsWObnScvL8985e7m/Zujb7PffvsNRUVFAICuXbsiMDAQBw4csGmdJJfQHXXghwsXLuDll19GRkYGwsLCOry8q1evYtasWQgPD0dMTAyKi4tRXFyMF154AX/9618xcuRIzJgxAzqdDuvWrUNhYSHS09OxY8cOAMCPP/6ICRMmYPTo0ZgxYwauXr0KABg8eDBmz56NoKAgHD9+HC+//DK6desGk8kEJycneHt7IzExEQCwcuVKhIaGQqPRYNasWeYvekxMDNLS0jBhwgQEBgbio48+Qlpamvlu819++QUAsH37dowbNw4REREYP348Dh061OH1cqORI0fi+++/R1VVFYxGI/bu3Qtvb2+LfoY1DBo0CAUFBbh69SqEENi5cycefvhhW4dlU47a7ltjyJAhKC4uNv9EtHXr1tvWrba21tzmx40bh+TkZJhMJrz77rsYPXo0xo8fjyVLliAmJgY//PADli1bhuPHjyMmJgbz588HAEyePBkXLlxoVVzXr+Rt2LCh0f0zv/32G0aMGAGj0YgHH3wQly5dwj//+U9MnjwZf/7zn7Fq1SpERkaie/fuKC4uRkFBAaKiopCamorCwkJMmTIFX331lWVWnhWdO3cOycnJaGhoQENDA7799ltMmDChVdvKajrt9rtOtHnzZhEWFiYCAwPFBx98YOtwWuWtt94SQ4cOFWPGjDH/W79+fbuW9cMPP4hBgwaJI0eOCCGE+OKLL0RkZKRIS0sTubm5QgghGhoahFqtFlqtVgghxKRJk8S2bduEEEK8/vrrIjIyUly9elUYDAYxbtw4kZOTI4QQYuDAgea/L126JPz8/MRPP/0khBAiKytLDB48WJw5c0Zs3LhRPP/886K2tlYIIcTy5ctFXFyc+bNeeeUVIcS1O3oHDhwovv32WyGEEAsXLhTJyclCCCH+93//V/z4449CCCH27t0r3nvvvXatj+Z8+eWX5u9KamqqMBqNFv8Ma1i9erUICgoSarVazJ8/X9TX19s6JJtzxHbfnJEjR4qzZ88KIa49RaLRaERgYKBYuHChMJlMt8yfk5NjbmMGg0EkJSWJDz74QISGhorq6mrR0NAgXnzxRTFp0iQhhBCbNm0SU6dONb9/4MCBorKystmYfvjhBxEWFiaEuLaf+Oijj0R1dbXw9fUVFy9eFEIIkZ6eLpYuXdpomVOmTBEDBw4UISEhYsyYMeLxxx8X48ePFwUFBeKRRx4RTz75pFi4cKE4efKkGDJkiNi0aVMH117nWL58uQgJCRFqtVosX75cCNG6bWUtNh9tzRo0Gg00Go2tw2iT5ORkJCcnW2x5Dz74oPlGqXHjxuEf//gH/vnPf+Knn37Chx9+iJKSEly8eNF85n2z0aNHo2vXrgCu3dBy429r12/YOXbsGP785z+b7wyfMWMGjh07hoMHD2LPnj2IiIhAt27dAACxsbF4//330dDQAAB49tlnAQB9+vQBAIwYMQIA8Oc//9l8+TgsLAyvvPIKAgIC8PTTT+Oll16y2Pq5LjIyEpGRkRZfrrVNnToVU6dOtXUYdsUR231zdu7caf7bz88Pmzdvbnb+YcOGITMzEzExMXjqqacwefJk/Otf/8Kzzz4LNzc3AMDzzz+Pzz77zKJxurm54dlnn8XmzZsxZcoUbNmy5ZZ+ADQaDUwmk/mzN23ahB07duAvf/kLGhoacPjwYSgUCgDAk08+adH4rGnmzJmYOXNmo7LWbCtrkdwld7rGyanxppXJZEhKSkJ2dja8vLwwZcoUeHt7QzQx2J5cLm/03hvnu56kjUbjLTd8CCFgMBhuuZHHZDI1+o3excWl0fuuN+YbzZ07F+vXr8fgwYPx1VdfITo6uqVqE92x+vTpgx07dmDq1KmoqanBCy+8gH//+9+N2u7t2pkl/PWvf0Vubi727t2L/v37mw/Ub9SlSxfz39f3Kc7OzgDQKMbrZdR2TOgS9csvv5hv2NiwYQOGDRuGgoICvPzyywgNDQUAHD16FEajEcC1RtTWm+KGDh2KU6dOmW8kO3nyJA4dOoTHH38cI0aMwKZNm8xXANasWYPHHnvslkTeFIPBgFGjRqGurg4TJ05ESkoKfvnlF/MZPhE1tn79esyfPx/Dhw9HfHw8hg8fjtGjR0Or1eKPP/6AyWRq9q709uwDrrv+SNrKlSsxfvz4Vr/Pzc0Njz76qPk387Nnz2L//v0Oebe7PZDkJXcC+vXrhxUrVuDs2bPo2bMn0tLSsGvXLvNNbG5ubnjsscdw5swZAMCoUaOwdOlS6PX6Vn+Gh4cH3n33Xbz11luor6+HTCbD4sWLcf/996Nv3764cOECxo8fD5PJhL59+yIjI6PVy5bL5UhMTMTf//53yOVyyGQyLFq0qNUHBER3mrFjx+LgwYMIDQ1F165d0bt3b8TExKBHjx6IioqCq6srvLy8mnx/cHAwYmJi8N5772HgwIFt/vzx48cjKysLo0ePbtP7lixZgqSkJKxfvx69evXCvffe2+hsnlpPJpq65kpERJKi1Wqxbt06rFmzxtahmK1atQqBgYHo378/qqurMWbMGHz44Yd44IEHbB2aw+EZOpGN1dTUYMKECXj//fdx7733NjnfqVOnkJKSgj/++ANKpRJLly51iJ7tyLHMmTMHxcXFt30tMzMT/fr1s+jn3XfffZg7dy6cnJxgNBrx0ksvMZm3E8/QiWzo6NGjSE5ORnFxMbRabZMJXQiB4OBgJCUlwd/fHxkZGRBCID4+vpMjJiJ7xTN0Ihu6PhDHvHnzzGW5ubn47LPPYDKZ4O3tjZSUFJw8eRLdunUzd1Ixffr0Rl1qEhHxDJ3IDowaNQqff/456urqkJKSgk8++QSurq5455130LVrV9x3333IycmBUqlEUVER+vXrhzfeeAPu7u62Dp2I7AQfWyOyA7169YKzszMKCwuh0+nwyiuv4KWXXsLx48dx+fJlKBQKnDt3DrGxscjJyYG3tzdWr15t67CJyI7wDJ2IiEgCHP439MuXa2EyNX1M0rOnGyorazoxoo5hvNblaPECzcfs5CRDjx53dXJEtnVzm3fEbWoNXA//JeV10Vybd/iEbjKJZhP69XkcCeO1LkeLF3DMmK3ldm2e6+carof/uhPXBX9DJyIikgCHP0O/E3W/uyu6uHZ809XrDKiuqrNARETSwjZGjogJ3QF1cZVD81peh5ez5Z1wVFsgHiKpYRsjR8RL7kR0i5qaGqjVapw7dw4AUFBQAI1Gg8DAQGRmZprnKyoqQkREBIKCgpCUlGQerau0tBTR0dEIDg7GjBkzUFtbCwCoqqrC1KlTERISgujoaJSXl3d+5YgkigmdiBo5evQoJk6ciJKSEgBAfX09EhMTkZWVhfz8fBQWFmL37t0AgPj4eCxYsABff/01hBDIzs4GAKSmpiIqKgparRaDBw9GVlYWAGDZsmXw9fXFtm3bMH78eCxcuNAmdSSSIib0TtT97q5QKrs3+w9Aq+Yhspbr3dGqVCoAwLFjx9C3b1/06dMHcrkcGo0GWq0W58+fR319vXks7IiICGi1Wuj1ehw6dAhBQUGNygFg165d0Gg0AAC1Wo09e/a0acheImoaf0PvRJb8XY7IWm4+a7548SKUSqV5WqVSoays7JZypVKJsrIyXL58GW5ubpDL5Y3Kb16WXC6Hm5sbLl26hF69elm7WkSSx4RORM0ymUyQyWTmaSEEZDJZk+XX/7/RzdM3vsfJqW0XCnv2dLulzJ6vXHVmbPa8HjrbnbgumNCJqFmenp6Nbl4rLy+HSqW6pbyiogIqlQoeHh6orq6G0WiEs7OzeX7g2tl9RUUFPD09YTAYUFtb2+YBZioraxp1GqJUdkd5uWXvJbdkMrB0bE2xxnpwVFJeF05Ostse1AL8DZ2IWjBkyBAUFxfj9OnTMBqN2Lp1K/z9/eHl5QVXV1ccOXIEAJCXlwd/f38oFAr4+voiPz8fwLXhYK8P+xoQEIDc3FwAQH5+Pnx9faFQKGxSLyKp4Rk6ETXL1dUVaWlpmDlzJnQ6HQICAhAcHAwAyMjIQHJyMmpqauDt7Y3Y2FgAQEpKChISErBq1Sr07t0bS5cuBQDMnj0bCQkJCAsLQ/fu3ZGRkWGzehFJDRM6Ed3Wzp07zX/7+flh8+bNt8wzaNAgbNy48ZZyLy8vrFmz5pZyd3d3vP/++5YNlIgA8JI7ERGRJHQooe/cuRMREREICQnB22+/DcByPUoRERFR67U7oZ89exYpKSnIysrC5s2b8Z///Ae7d++2WI9SRERE1HrtTug7duxAaGgoPD09oVAokJmZia5du1qsRykiIiJqvXbfFHf69GkoFApMnz4dFy5cwDPPPIMBAwZYrEcpIiIiar12J3Sj0YjDhw9jzZo16NatG2bMmIEuXbpYrUeppjT1gP2N7sQeg1rLEuvG0davo8ULOGbMRNS52p3Q77nnHvj5+cHDwwMAMHr0aGi1Wjg7O5vn6UiPUq11c69RN7OnHoPscafc0XVjT+u3NRwtXqD5mJvrNYqI7izt/g195MiR+P7771FVVQWj0Yi9e/ciODjYYj1KERERUeu1+wx9yJAhePHFFxEVFQW9Xo+nn34aEydORL9+/SzSoxQRERG1Xod6iouMjERkZGSjMkv1KEVEREStx57iiIiIJIAJnYiISAKY0ImIiCSAo621Uve7u6KLK1cXERHZJ2aoVuriKofmtbwOLWPLO+EWioaIbocH3nQn4zefiCTDEgfeAA++yTHxN3QiIiIJYEInIiKSACZ0IiIiCWBCJyIikgAmdCIiIglgQiciIpIAJnQiIiIJYEInIiKSAIsk9CVLliAhIQEAUFBQAI1Gg8DAQGRmZprnKSoqQkREBIKCgpCUlASDwQAAKC0tRXR0NIKDgzFjxgzU1tZaIiQiIqI7SocT+v79+5GTkwMAqK+vR2JiIrKyspCfn4/CwkLs3r0bABAfH48FCxbg66+/hhAC2dnZAIDU1FRERUVBq9Vi8ODByMrK6mhIREREd5wOJfQrV64gMzMT06dPBwAcO3YMffv2RZ8+fSCXy6HRaKDVanH+/HnU19dj6NChAICIiAhotVro9XocOnQIQUFBjcqJiIiobTqU0BcsWIC5c+fi7rvvBgBcvHgRSqXS/LpKpUJZWdkt5UqlEmVlZbh8+TLc3Nwgl8sblRMREVHbtHtwli+//BK9e/eGn58fvvrqKwCAyWSCTCYzzyOEgEwma7L8+v83unm6JT17urU4j1LZvU3LvJNYYt042vp1tHgBx4yZiDpXuxN6fn4+ysvLER4ejj/++ANXr17F+fPn4ezsbJ6nvLwcKpUKnp6eKC8vN5dXVFRApVLBw8MD1dXVMBqNcHZ2Ns/fFpWVNTCZRJOvK5XdUV5e3fYK3mY5UtTRdWOp9dtZHC1eoPmYnZxkrTqoJSLpa/cl908++QRbt25FXl4eZs2ahVGjRuGjjz5CcXExTp8+DaPRiK1bt8Lf3x9eXl5wdXXFkSNHAAB5eXnw9/eHQqGAr68v8vPzAQC5ubnw9/e3TM2IiIjuIBYdD93V1RVpaWmYOXMmdDodAgICEBwcDADIyMhAcnIyampq4O3tjdjYWABASkoKEhISsGrVKvTu3RtLly61ZEhEZCExMTG4dOmS+Z6XN998E7W1tVi8eDF0Oh1CQkIwd+5cANceU01KSkJtbS18fX2RmpoKuVyO0tJSxMfHo7KyEvfffz8yMjJw11132bJaRJJhkYQeERGBiIgIAICfnx82b958yzyDBg3Cxo0bbyn38vLCmjVrLBEGEVmJEAIlJSX47rvvzAm9vr4ewcHBWLNmDXr37o1p06Zh9+7dCAgIQHx8PN5++20MHToUiYmJyM7ORlRUlPkx1bCwMKxcuRJZWVmIj4+3ce2IpIE9xRFRi06dOgUAiIuLw5gxY7B27Vo+pkpkZ5jQiahFVVVV8PPzw8qVK/Hpp5/iiy++QGlpKR9TJbIjFv0NnYikycfHBz4+PubpyMhILF++HMOGDTOXdcZjqsDtH1W156dQOjM2e14Pne1OXBdM6ETUosOHD0Ov18PPzw/AtSTt5eXV6HHUznhMFbj1UdUbH+uzx514Zz0m6YiPZFqLlNdFc4+q8pI7EbWouroa6enp0Ol0qKmpQU5ODl599VU+pkpkR3iGTkQtGjlyJI4ePYqxY8fCZDIhKioKPj4+fEyVyI4woRNRq8yZMwdz5sxpVMbHVInsBy+5ExERSQATOhERkQQwoRMREUkAEzoREZEEMKETERFJABM6ERGRBDChExERSQATOhERkQR0KKGvWLECYWFhCAsLQ3p6OgCgoKAAGo0GgYGByMzMNM9bVFSEiIgIBAUFISkpCQaDAQBQWlqK6OhoBAcHY8aMGaitre1ISERERHekdif0goICfP/998jJyUFubi5+/vlnbN26FYmJicjKykJ+fj4KCwuxe/duAEB8fDwWLFiAr7/+GkIIZGdnAwBSU1MRFRUFrVaLwYMHIysryzI1IyIiuoO0O6ErlUokJCTAxcUFCoUC/fv3R0lJCfr27Ys+ffpALpdDo9FAq9Xi/PnzqK+vx9ChQwEAERER0Gq10Ov1OHToEIKCghqVExERUdu0O6EPGDDAnKBLSkqwbds2yGQyKJVK8zwqlQplZWW4ePFio3KlUomysjJcvnwZbm5ukMvljcqJiIiobTo8OMvJkycxbdo0zJs3D87OzigpKTG/JoSATCaDyWSCTCa7pfz6/ze6ebolTY0LeyN7HCPZXlhi3Tja+nW0eAHHjJmIOleHEvqRI0cwa9YsJCYmIiwsDAcPHkR5ebn59fLycqhUKnh6ejYqr6iogEqlgoeHB6qrq2E0GuHs7Gyevy0qK2tgMokmX7fUQPdS3aF2dN1Yav12FkeLF2g+ZicnWasOask2GvTGDu876nUGVFfVWSgikrJ2J/QLFy7g5ZdfRmZmJvz8/AAAQ4YMQXFxMU6fPo17770XW7duxXPPPQcvLy+4urriyJEjGDZsGPLy8uDv7w+FQgFfX1/k5+dDo9EgNzcX/v7+FqscEZEtuSicoXktr0PL2PJOOBzrEJRspd0J/eOPP4ZOp0NaWpq5bMKECUhLS8PMmTOh0+kQEBCA4OBgAEBGRgaSk5NRU1MDb29vxMbGAgBSUlKQkJCAVatWoXfv3li6dGkHq0RERHTnaXdCT05ORnJy8m1f27x58y1lgwYNwsaNG28p9/Lywpo1a9obBhEREYE9xREREUlCh+9yJ8dliRt2GvRGC0VDREQdwYR+B7PUDTtERGR7vOROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBTOhEREQSwIROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBTOhEREQSYBeDs2zZsgWrVq2CwWDA5MmTER0dbeuQqJUsMWIbANTrDKiuqrNAROQI2OZbry1trLn52Makz+YJvaysDJmZmfjqq6/g4uKCCRMm4IknnsADDzxg69CoFSwxYhtwbdS2agvEQ/aPbb5t2MaotWx+yb2goABPPvkk3N3d0a1bNwQFBUGr1do6LCKyErZ5Iuuw+Rn6xYsXoVQqzdMqlQrHjh1r9fudnGTNvm6pS8IAoOrR1S6WYW/LscQyLLWddA1GuLo4NztPS5/TmmVYKpbWaNAbm/yet/T9t0fWaPM3ltlT27DUchytjbW4DJ0BNTX1HY7Fza0LXF1vn8baUldLxdMZmmvzMiGE6MRYbrFq1SrodDrMmTMHAJCdnY3CwkK8+eabtgyLiKyEbZ7IOmx+yd3T0xPl5eXm6fLycqhUKhtGRETWxDZPZB02T+hPPfUU9u/fj0uXLqGurg7bt2+Hv7+/rcMiIithmyeyDpv/ht6rVy/MnTsXsbGx0Ov1iIyMxCOPPGLrsIjIStjmiazD5r+hExERUcfZ/JI7ERERdRwTOhERkQQwoRMREUkAEzoREZEESDqhb9myBaGhoQgMDMS6detsHU6LVqxYgbCwMISFhSE9Pd3W4bTKkiVLkJCQYOswWmXnzp2IiIhASEgI3n77bVuH06K8vDzz92HJkiW2DsfuOFr7bo+YmBiEhYUhPDwc4eHhOHr0KAoKCqDRaBAYGIjMzEzzvEVFRYiIiEBQUBCSkpJgMBgAAKWlpYiOjkZwcDBmzJiB2tpaW1WnXWpqaqBWq3Hu3DkAsFj9q6qqMHXqVISEhCA6OrpR3wgOS0jU77//LkaOHCkuX74samtrhUajESdPnrR1WE3at2+feP7554VOpxMNDQ0iNjZWbN++3dZhNaugoEA88cQT4vXXX7d1KC06c+aMGD58uLhw4YJoaGgQEydOFLt27bJ1WE26evWqeOyxx0RlZaXQ6/UiMjJS7Nu3z9Zh2Q1Ha9/tYTKZxPDhw4VerzeX1dXViYCAAHHmzBmh1+tFXFyc+XscFhYmfvzxRyGEEPPnzxfr1q0TQggxdepUsXXrViGEECtWrBDp6emdW5EO+Omnn4RarRbe3t7i7NmzFq1/amqqWL16tRBCiJycHDF79uzOrZwVSPYM3dEGgFAqlUhISICLiwsUCgX69++P0tJSW4fVpCtXriAzMxPTp0+3dSitsmPHDoSGhsLT0xMKhQKZmZkYMmSIrcNqktFohMlkQl1dHQwGAwwGA1xdXW0dlt1wtPbdHqdOnQIAxMXFYcyYMVi7di2OHTuGvn37ok+fPpDL5dBoNNBqtTh//jzq6+sxdOhQAEBERAS0Wi30ej0OHTqEoKCgRuWOIjs7GykpKeaeBC1Z/127dkGj0QAA1Go19uzZA71e3/mVtCDJJvTbDQBRVlZmw4iaN2DAAPOXsaSkBNu2bUNAQIBtg2rGggULMHfuXNx99922DqVVTp8+DaPRiOnTpyM8PBzr16/Hn/70J1uH1SQ3NzfMnj0bISEhCAgIgJeXFx599FFbh2U3HK19t0dVVRX8/PywcuVKfPrpp/jiiy9QWlp623rfvD6USiXKyspw+fJluLm5QS6XNyp3FAsXLoSvr695uqnt3p763/geuVwONzc3XLp0qTOqZTWSTegmkwky2X9HpRFCNJq2VydPnkRcXBzmzZuH++67z9bhAAAuXLgAtVqN8PBw/PjjjwgNDUWPHj3g5+fX5HsOHDgAtVrd4rJXrFiBb775xpLh3pbRaMT+/fuxaNEibNiwAceOHUNOTo7VP7e9Tpw4gU2bNuG7777D3r174eTkhI8//tjWYdkNR23fbeHj44P09HR0794dHh4eiIyMxPLly29b76bWx+3Wy/XplJQUjBo1CiNGjMDx48c7p1Id1FQ921P/mwkh4OTk2CnRsaNvhiMOAHHkyBFMmTIFr732GsaNG2frcMwOHDiAe+65B3l5efDx8cFvv/2GgwcPIjw8HMuXL8fOnTuxaNGidi/7+s0r1nTPPffAz88PHh4e6NKlC0aPHt2mITs72/fffw8/Pz/07NkTLi4uiIiIwMGDB20dlt1wxPbdVocPH8b+/fvN00IIeHl53bbeN6+PiooKqFQqeHh4oLq6GkajsdH8ALBhwwasX78eCoWik2rUcU1t9/bUX6VSoaKiAgBgMBhQW1sLd3f3zquMFUg2oTvaABAXLlzAyy+/jIyMDISFhVn1s2prazFr1iyEh4dj3LhxSE5OhslkwrvvvovRo0dj/PjxWLJkCWJiYvDDDz9g2bJlOH78OGJiYjB//nwA1y5Rvf/++5g1axZGjRqFxMTEJj+vuroaf//736FWq6HRaJCeng6DwYB169ahsLAQ6enp2LFjh1XrPHLkSHz//feoqqqC0WjE3r174e3tbdXP7IhBgwahoKAAV69ehRACO3fuxMMPP2zrsOyGo7Xv9qiurkZ6ejp0Oh1qamqQk5ODV199FcXFxeafkLZu3Qp/f394eXnB1dUVR44cAXDtCQl/f38oFAr4+voiPz8fAJCbmwt/f39ERUVBCIGXXnoJFy5cMH/mhg0boFarMWbMGMTFxaG4uNgcy+3aMAAMHjwYs2fPRlBQEI4fP47ly5dDo9EgIiICf/vb33Dx4kWLrZMhQ4ZYpP4AEBAQgNzcXABAfn4+fH19Herg5rZscy9e59i8ebMICwsTgYGB4oMPPrB1OM166623xNChQ8WYMWPM/9avX2+Vz8rJyRFxcXFCCCEMBoNISkoSH3zwgQgNDRXV1dWioaFBvPjii2LSpElCCCE2bdokpk6dan7/wIEDRWVlpfm1293l/sMPP4iwsDAhhBDz5s0Tb731ljCZTEKn04m4uDjz3aWTJk0S27Zts0o9b/bll1+avw+pqanCaDR2yue21+rVq0VQUJBQq9Vi/vz5or6+3tYh2RVHat/tlZmZKYKDg0VgYKD49NNPhRDXni7RaDQiMDBQLFy4UJhMJiGEEEVFReK5554TQUFB4tVXXxU6nU4IIcS5c+fEpEmTREhIiIiLixNXrlwRQvy3HY8cOVIcO3ZMFBQUiNGjRzdq2yEhIcJkMjXbhgcOHChycnKEEEKUlpaKRx991PzZH3/8sdixY0eH18PIkSPF2bNnLVr/y5cvi2nTponQ0FDx/PPPm5fvyCSd0On2zpw5I/z9/cWkSZNEVlaW+PXXX0VqaqrIzMw0z7Njx45WJfSm3JjQn3zySVFcXGx+bfv27SI6OloI0bkJnYj+6+aEvmTJErF06dJG8zz66KPizJkzzbbhgQMHmpOh0WgUUVFRIjQ0VKSlpYmCgoJOqw9J+LE1alqfPn2wY8cOTJ06FTU1NXjhhRfw73//G+KGgfcseenp5htWTCZTp/xuTkStZzKZbikTQsBgMLTYhrt16wYAcHJywtq1a7F48WK4u7tj0aJFDtNJlhQwod+B1q9fj/nz52P48OGIj4/H8OHDMXr0aGi1Wvzxxx8wmUzm35Zux9nZuU0Jefjw4Vi7di2EEGhoaEB2djaeeuqpdi2LiKxjxIgRyM/PNz+6tWnTJri7u6Nv377NtuEbnThxAmq1Gv3798e0adMwZcoUh7mDXgrktg6AOt/YsWNx8OBBhIaGomvXrujduzdiYmLQo0cPREVFwdXVFV5eXk2+Pzg4GDExMXjvvfcwcODAFj8vOTkZb7/9NjQaDfR6PUaMGGHukGbUqFFYunQp9Hq9Xd3ZT3SnefrppzFlyhRMnjwZJpMJHh4eWL16NZycnJptwzcaNGgQQkJC8Nxzz6Fbt27o0qULkpOTbVCbO5NM3Hidlej/02q1WLduHdasWWPrUIiIqBV4hk7tNmfOHPNjLTfLzMxEv379OjkiIqI7F8/QiYiIJIA3xREREUkAEzoREZEEMKETERFJABM6ERGRBDj8Xe6XL9fCZGr6vr6ePd1QWVnTiRF1DinWi3VqOycnGXr0uMtqy7dHzbV5KX6HLIXrpmmOtG6aa/MOn9BNJtFsQr8+jxRJsV6sE7WkpTbP9d00rpumSWHd8JI7ERGRBDj8GXpLGvRGKJXdO7ycep0B1VV1FoiIiKyF7Z3uZK1K6CtWrMC2bdsAXBsUft68eSgoKMDixYuh0+kQEhKCuXPnAgCKioqQlJSE2tpa+Pr6IjU1FXK5HKWlpYiPj0dlZSXuv/9+ZGRk4K677kJVVRX+/ve/4+zZs/Dw8MCyZcugVCotVkEXhTM0r+V1eDlb3glHtQXiISLrYXunO1mLl9wLCgrw/fffIycnB7m5ufj555+xdetWJCYmIisrC/n5+SgsLMTu3bsBAPHx8ViwYAG+/vprCCGQnZ0NAEhNTUVUVBS0Wi0GDx6MrKwsAMCyZcvg6+uLbdu2Yfz48Vi4cKEVq0tErVFTUwO1Wo1z584BuLYf0Gg0CAwMRGZmpnm+oqIiREREICgoCElJSeaR80pLSxEdHY3g4GDMmDEDtbW1AICqqipMnToVISEhiI6ORnl5eedXjkiiWkzoSqUSCQkJcHFxgUKhQP/+/VFSUoK+ffuiT58+kMvl0Gg00Gq1OH/+POrr6zF06FAAQEREBLRaLfR6PQ4dOoSgoKBG5QCwa9cuaDQaAIBarcaePXug1+utVF0iasnRo0cxceJElJSUAADq6+t5AE/kAFpM6AMGDDAn6JKSEmzbtg0ymazRZXGVSoWysjJcvHixUblSqURZWRkuX74MNzc3yOXyRuUAGr1HLpfDzc3NPB4vEXW+7OxspKSkQKVSAQCOHTvGA3giB9Dqm+JOnjyJadOmYd68eXB2djYfvQOAEAIymQwmkwkymeyW8uv/3+jm6Rvf4+TU+pvve/Z0a/W8HWWJm20syd7isQTWyfZuPmu++UDdGgfwvXr1sna1iCSvVQn9yJEjmDVrFhITExEWFoaDBw82+u2rvLwcKpUKnp6ejcorKiqgUqng4eGB6upqGI1GODs7m+cHru0cKioq4OnpCYPBgNraWri7u7e6ApWVNc0+P2jJnWl5uf3cJqNUdrereCyBdWo7JyeZ1Q9qmzpQt8UBPNB5B/GOdiDWGlKsk6VIYd20mNAvXLiAl19+GZmZmfDz8wMADBkyBMXFxTh9+jTuvfdebN26Fc899xy8vLzg6uqKI0eOYNiwYcjLy4O/vz8UCgV8fX2Rn58PjUaD3Nxc+Pv7A7h213xubi6mT5+O/Px8+Pr6QqFQWLfWRNRqNx+o2/IAHmj+IF6qB/CWIMUDZktxpHXT3EF8i4fGH3/8MXQ6HdLS0hAeHo7w8HB89dVXSEtLw8yZMxEaGop+/fohODgYAJCRkYHFixcjODgYV69eRWxsLAAgJSUF2dnZCA0NxeHDhzFnzhwAwOzZs/HTTz8hLCwM69evx4IFCyxUbSKyhBsP4I1GI7Zu3Qp/f/9GB/AAbnsAD+C2B/AAeABPZGEyIYRD93fXmkvulnou1Z6O4BzpiLK1WKe2s+Yl91GjRuHzzz/Hvffei/3795v7nQgICMD8+fMhk8lw4sQJJCcno6amBt7e3li8eDFcXFxw/vx5JCQkoLKyEr1798bSpUvxpz/9CVeuXEFCQgLOnj2L7t27IyMjA/fee2+b4mrpDF2K7d0SpNi+LMWR1k1zbV7yPcURUfvs3LnT/Lefnx82b958yzyDBg3Cxo0bbyn38vLCmjVrbil3d3fH+++/b9lAiQgA+3InIiKSBCZ0IiIiCWBCJyIikgAmdCIiIglgQiciIpIAJnQiIiIJYEInIiKSACZ0IiIiCWBCJyIikgD2FEdEdAdo0BstMnhNvc6A6qo6C0RElsaETkR0B3BROFusn3vH6PX8zsNL7kRERBLAhE5ERCQBTOhEREQSwIROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBTOhEREQSwIROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBTOhEREQSwIROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBrUroNTU1UKvVOHfuHACgoKAAGo0GgYGByMzMNM9XVFSEiIgIBAUFISkpCQaDAQBQWlqK6OhoBAcHY8aMGaitrQUAVFVVYerUqQgJCUF0dDTKy8stXT8ispCYmBiEhYUhPDwc4eHhOHr0qMX2BUTUcS0m9KNHj2LixIkoKSkBANTX1yMxMRFZWVnIz89HYWEhdu/eDQCIj4/HggUL8PXXX0MIgezsbABAamoqoqKioNVqMXjwYGRlZQEAli1bBl9fX2zbtg3jx4/HwoULrVRNIuoIIQRKSkqQl5dn/vfggw9abF9ARB3XYkLPzs5GSkoKVCoVAODYsWPo27cv+vTpA7lcDo1GA61Wi/Pnz6O+vh5Dhw4FAERERECr1UKv1+PQoUMICgpqVA4Au3btgkajAQCo1Wrs2bMHer3eGvUkog44deoUACAuLg5jxozB2rVrLbovIKKOk7c0w81nzRcvXoRSqTRPq1QqlJWV3VKuVCpRVlaGy5cvw83NDXK5vFH5zcuSy+Vwc3PDpUuX0KtXr47XjIgspqqqCn5+fnjjjTeg1+sRGxuLF1980WL7AiLquBYT+s1MJhNkMpl5WggBmUzWZPn1/2908/SN73Fyatt9ej17urVp/o5QKrt32me1hr3FYwmsk33y8fGBj4+PeToyMhLLly/HsGHDzGWW3Bc0p7PavBS2m7VIcd1IoU5tTuienp6Nbl4rLy+HSqW6pbyiogIqlQoeHh6orq6G0WiEs7OzeX7g2hF9RUUFPD09YTAYUFtbC3d39zbFU1lZA5NJNPm6JTdSeXm1xZbVUUpld7uKxxJYp7ZzcpJ1SoI7fPgw9Ho9/Pz8AFxL0l5eXhbbF7RFc21equ3dErhumuZI+57m2nybH1sbMmQIiouLcfr0aRiNRmzduhX+/v7w8vKCq6srjhw5AgDIy8uDv78/FAoFfH19kZ+fDwDIzc2Fv78/ACAgIAC5ubkAgPz8fPj6+kKhULSnjkRkRdXV1UhPT4dOp0NNTQ1ycnLw6quvWmxfQEQd1+YzdFdXV6SlpWHmzJnQ6XQICAhAcHAwACAjIwPJycmoqamBt7c3YmNjAQApKSlISEjAqlWr0Lt3byxduhQAMHv2bCQkJCAsLAzdu3dHRkaGBatGRJYycuRIHD16FGPHjoXJZEJUVBR8fHwsti8goo6TCSGavl7tAFpzyV3zWl6HP2fLO+F2dUnGkS4RtRbr1HaddcndnrR0yV2K7d0SuG6a5kj7HoteciciIiL7w4ROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBTOhEREQSwIROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBTOhEREQSwIROREQkAUzoREREEsCETkREJAFM6ERERBLAhE5ERCQBTOhEREQSwIROREQkAXJbB0BEJFXd7+6KLq4d283W6wyorqqzUEQkZUzoRERW0sVVDs1reR1axpZ3wlFtoXhI2njJnYiISAKY0ImIiCSACZ2IiEgCmNCJiIgkgAmdiIhIApjQiYiIJIAJnYiISAKY0ImIiCSAHcsQEdEdrUFvhFLZvUPLsIce/ewioW/ZsgWrVq2CwWDA5MmTER0dbeuQiMiK2ObJnrgonCXRo5/NE3pZWRkyMzPx1VdfwcXFBRMmTMATTzyBBx54wNahEZEVsM0TWYfNf0MvKCjAk08+CXd3d3Tr1g1BQUHQarW2DouIrIRtnsg6bH6GfvHiRSiVSvO0SqXCsWPHWv1+JydZi/OoenRtV2w3ssRvLDqdATU19R2O5brW1N3RsE72s2xrsXabt0R7b83ntJYl4rGnWICOx+Pm1gWuHRyFDrDsPtWetlN7P8PmCd1kMkEm+2+AQohG0y3p0eOuFuf5ODmwXbFZmqurHK6ubhZbXs+elluWvWCdpM/abd5S7d1S280S8dhTLID9fKctuU+1p+3UXja/5O7p6Yny8nLzdHl5OVQqlQ0jIiJrYpsnsg6bJ/SnnnoK+/fvx6VLl1BXV4ft27fD39/f1mERkZWwzRNZh80vuffq1Qtz585FbGws9Ho9IiMj8cgjj9g6LCKyErZ5IuuQCSGErYMgIiKijrH5JXciIiLqOCZ0IiIiCWBCJyIikgAmdCIiIglgQiciIpIAyST0LVu2IDQ0FIGBgVi3bt0trxcVFSEiIgJBQUFISkqCwWCwQZRt01KdVqxYgZEjRyI8PBzh4eG3ncce1dTUQK1W49y5c7e85ojb6brm6uWo28rRNLcN7lQrVqxAWFgYwsLCkJ6ebutw7Mq7776L0NBQhIWF4ZNPPrF1OB0nJOD3338XI0eOFJcvXxa1tbVCo9GIkydPNponLCxM/Pjjj0IIIebPny/WrVtng0hbrzV1mjZtmvj3v/9towjb56effhJqtVp4e3uLs2fP3vK6o22n61qqlyNuK0fT0ja4E+3bt088//zzQqfTiYaGBhEbGyu2b99u67DswoEDB8SECROEXq8XdXV1YuTIkeK3336zdVgdIokz9JZGbzp//jzq6+sxdOhQAEBERITdj+7UmhGpCgsLsXr1amg0Grz55pvQ6XQ2irb1srOzkZKSctuuPh1xO13XXL0Ax9xWjqalbXAnUiqVSEhIgIuLCxQKBfr374/S0lJbh2UXHn/8cXz++eeQy+WorKyE0WhEt27dbB1Wh0giod9u9KaysrImX1cqlY1et0ct1am2thYPPfQQ4uPjkZOTg6qqKmRlZdki1DZZuHAhfH19b/uaI26n65qrl6NuK0fT3Da4Uw0YMMB8gFxSUoJt27YhICDAtkHZEYVCgeXLlyMsLAx+fn7o1auXrUPqEEkk9JZGb+ro6E620FLMd911Fz788EP0798fcrkccXFx2L17ty1CtRhH3E6tIcVtRY7l5MmTiIuLw7x583DffffZOhy7MmvWLOzfvx8XLlxAdna2rcPpEEkk9JZGb7r59YqKCru/LNdSnUpLS7Fx40bztBACcrnNu+bvEEfcTq0hxW1FjuPIkSOYMmUKXnvtNYwbN87W4diN3377DUVFRQCArl27IjAwEL/88ouNo+oYSST0lkZv8vLygqurK44cOQIAyMvLs/vRnVqqU5cuXfB///d/OHv2LIQQWLduHZ599lkbRtxxjridWkOK24ocw4ULF/Dyyy8jIyMDYWFhtg7Hrpw7dw7JycloaGhAQ0MDvv32WwwbNszWYXWIJBL6jaM3jR07Fmq1Go888gheeuklHD9+HACQkZGBxYsXIzg4GFevXkVsbKyNo25eS3Xy8PDAm2++iRkzZiA4OBhCCLzwwgu2DrtdHHk7NUeK24ocy8cffwydToe0tDTzI5P/+te/bB2WXQgICMAzzzyDsWPH4rnnnoOPj4/DH/RwtDUiIiIJkMQZOhER0Z2OCZ2IiEgCmNCJiIgkgAmdiIhIApjQiWystQOKnDp1CjExMRgzZgz+9re/4Y8//uikCInIETChE9nQ0aNHMXHiRJSUlDQ7nxACM2bMwEsvvYTNmzfjoYcewgcffNA5QRKRQ2B3VUQ2dH1AkXnz5pnLcnNz8dlnn8FkMsHb2xspKSk4efIkunXrZu5oZ/r06aiqqrJV2ERkh/gcOpEdGDVqFD7//HPU1dUhJSUFn3zyCVxdXfHOO++ga9euuO+++5CTkwOlUomioiL069cPb7zxBtzd3W0dOhHZCV5yJ7IjBw4cwOnTp/HXv/4V4eHh+Pbbb3Hq1CkYDAYcPHgQEydORE5ODvr06YO0tDRbh0tEdoSX3InsiNFoREhICJKTkwFcG3rVaDTi559/Rt++ffHwww8DANRqNWbNmmXLUInIzvAMnciOPPHEE9ixYwcqKyshhMA//vEPfPbZZ/Dx8cGlS5dw4sQJAMDOnTvh7e1t42iJyJ7wDJ3IjgwaNAivvPIKJk+eDJPJhIceeghTp06Fq6srVq5cieTkZNTV1cHT0xPp6em2DpeI7AhviiMiIpIAXnInIiKSACZ0IiIiCWBCJyIikgAmdCIiIglgQiciIpIAJnQiIiIJYEInIiKSACZ0IiIiCfh/D2yWDVFyPg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[cols[0:6]].hist(figsize=(8, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028133bb",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "34f63276",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price : 4028\n",
      "bedrooms : 13\n",
      "bathrooms : 30\n",
      "sqft_living : 1038\n",
      "sqft_lot : 9782\n",
      "floors : 6\n"
     ]
    }
   ],
   "source": [
    "for col in cols[0:6]:\n",
    "    print(col, ':', data[col].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f3e2a876",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[cols[6:12]].hist(figsize=(8, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e766447e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "waterfront : 2\n",
      "view : 5\n",
      "condition : 5\n",
      "grade : 12\n",
      "sqft_above : 946\n",
      "sqft_basement : 306\n"
     ]
    }
   ],
   "source": [
    "for col in cols[6:12]:\n",
    "    print(col, ':', data[col].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "355581c6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[cols[12:18]].hist(figsize=(8, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7d6eec76",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yr_built : 116\n",
      "yr_renovated : 70\n",
      "zipcode : 70\n",
      "lat : 5034\n",
      "long : 752\n",
      "sqft_living15 : 777\n"
     ]
    }
   ],
   "source": [
    "for col in cols[12:18]:\n",
    "    print(col, ':', data[col].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c3cc765d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[cols[18]].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c2a0dabf",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sqft_lot15 : 8689\n"
     ]
    }
   ],
   "source": [
    "print(cols[18], ':', data[cols[18]].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0ed45e",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "For sqft_basement and yr_renovated zero values are considered as NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b497f6e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.7706723427884 % of yr_renovated is null\n"
     ]
    }
   ],
   "source": [
    "print(len(data[data['yr_renovated'] == 0]) / len(data) * 100, '% of yr_renovated is null')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6c1f70e8",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.732960066632735 % of sqft_basement is null\n"
     ]
    }
   ],
   "source": [
    "print(len(data[data['sqft_basement'] == 0]) / len(data) * 100, '% of sqft_basement is null')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c6e09a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "So we remove these columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "65cf7a04",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data.drop(['yr_renovated', 'sqft_basement'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04fdc540",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---\n",
    "## Data Understanding and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fb66e820",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</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>yr_built</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>1933</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1965</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  \\\n",
       "0  221900.0         3       1.00         1180      5650     1.0           0   \n",
       "1  538000.0         3       2.25         2570      7242     2.0           0   \n",
       "2  180000.0         2       1.00          770     10000     1.0           0   \n",
       "3  604000.0         4       3.00         1960      5000     1.0           0   \n",
       "4  510000.0         3       2.00         1680      8080     1.0           0   \n",
       "\n",
       "   view  condition  grade  sqft_above  yr_built  zipcode      lat     long  \\\n",
       "0     0          3      7      1180.0      1955    98178  47.5112 -122.257   \n",
       "1     0          3      7      2170.0      1951    98125  47.7210 -122.319   \n",
       "2     0          3      6       770.0      1933    98028  47.7379 -122.233   \n",
       "3     0          5      7      1050.0      1965    98136  47.5208 -122.393   \n",
       "4     0          3      8      1680.0      1987    98074  47.6168 -122.045   \n",
       "\n",
       "   sqft_living15  sqft_lot15  \n",
       "0           1340        5650  \n",
       "1           1690        7639  \n",
       "2           2720        8062  \n",
       "3           1360        5000  \n",
       "4           1800        7503  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9681f2ac",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21611, 17)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266b6d42",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0db41ffb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 15))\n",
    "sns.heatmap(data.corr(), annot=True, cmap='Reds')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7c881b",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Average house price for number of bedrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "46640ec9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "Number of bedrooms=%{marker.color}<br>Price=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": [
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           2,
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          ],
          "coloraxis": "coloraxis",
          "pattern": {
           "shape": ""
          }
         },
         "name": "",
         "offsetgroup": "",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
         "x": [
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          2,
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          4,
          5,
          6,
          7,
          8,
          9,
          10,
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         ],
         "xaxis": "x",
         "y": [
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         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "Number of bedrooms"
          }
         },
         "colorscale": [
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          ],
          [
           1,
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          ]
         ]
        },
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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             ],
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             ],
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             ],
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             ],
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             ],
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             ],
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             ],
             [
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             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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             ],
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             ],
             [
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             ],
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             ],
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             ],
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              "#fdca26"
             ],
             [
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             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
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             ],
             [
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             ],
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             ],
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             ],
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             ],
             [
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             ],
             [
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             ],
             [
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              "#fdca26"
             ],
             [
              1,
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             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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             ],
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             ],
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    "bedrooms_price_df = pd.DataFrame({'Number of bedrooms' : sorted(data['bedrooms'].unique()),\n",
    "                                  'Price' : data.groupby('bedrooms')['price'].mean()})\n",
    "fig = px.bar(bedrooms_price_df, x='Number of bedrooms',  y='Price', title='The average house price for each number of bedrooms',\n",
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    "### Average house price for each grade"
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    "grade_price_df = pd.DataFrame({'The grade of house' : sorted(data['grade'].unique()),\n",
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    "fig = px.bar(grade_price_df, x='The grade of house', y='Price', color='The grade of house',\n",
    "             title='The average house price for each grade of houses')\n",
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    "### Average house price for each number of bathrooms"
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  {
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   "execution_count": 61,
   "id": "5db45114",
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    "grade_price_df = pd.DataFrame({'The number of bathrooms' : sorted(data['bathrooms'].unique()),\n",
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    "fig = px.bar(grade_price_df, x='The number of bathrooms', y='Price', color='The number of bathrooms',\n",
    "             title='The average house price for each number of bathrooms')\n",
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    "### Average house price for each view grade of houses"
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    "grade_price_df = pd.DataFrame({'The view grade' : sorted(data['view'].unique()),\n",
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    "fig = px.bar(grade_price_df, x='The view grade', y='Price', color='The view grade',\n",
    "             title='The average house price for each view grade of houses')\n",
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  {
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   "source": [
    "---\n",
    "## Data Preprocessing"
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  },
  {
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   "execution_count": 66,
   "id": "2372dfdd",
   "metadata": {
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    "X = data.drop(['price', 'zipcode'], axis=1)\n",
    "y = data['price']"
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  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "18a5e1f0",
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       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>770.0</td>\n",
       "      <td>1933</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1965</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1987</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "0         3       1.00         1180      5650     1.0           0     0   \n",
       "1         3       2.25         2570      7242     2.0           0     0   \n",
       "2         2       1.00          770     10000     1.0           0     0   \n",
       "3         4       3.00         1960      5000     1.0           0     0   \n",
       "4         3       2.00         1680      8080     1.0           0     0   \n",
       "\n",
       "   condition  grade  sqft_above  yr_built      lat     long  sqft_living15  \\\n",
       "0          3      7      1180.0      1955  47.5112 -122.257           1340   \n",
       "1          3      7      2170.0      1951  47.7210 -122.319           1690   \n",
       "2          3      6       770.0      1933  47.7379 -122.233           2720   \n",
       "3          5      7      1050.0      1965  47.5208 -122.393           1360   \n",
       "4          3      8      1680.0      1987  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d000833",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ef1a439d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n",
    "X = ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "63a97a75",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "y = np.log(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0772287f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21611, 15)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2ac5556b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21611,)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ed5c20f2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "de616f88",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5622521",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---\n",
    "## Modeling and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2341c9db",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 1. XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "84ba6092",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "data_matrix = xgb.DMatrix(data=X, label=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "35c744ea",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.5, enable_categorical=False,\n",
       "             eval_metric='mae', gamma=0.1, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.001, max_delta_step=0,\n",
       "             max_depth=3, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=10000, n_jobs=-1,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=14,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=0.5,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=0)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_reg = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=10000, max_depth=3, colsample_bytree=.5,\n",
    "                          learning_rate=0.001, random_state=14, subsample=.5, n_jobs=-1, eval_metric='mae',\n",
    "                          verbosity=0, gamma=0.1)\n",
    "xgb_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "4b55a9df",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8855990221554072"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2b85c60b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8780646527590441"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfdd9073",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "r2 score for xgboost is 0.878"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60f1d52",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 2. Catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "d959d64b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85cd1ec79ab8490fad1af61b80bfa025",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x2dcbc025790>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catb_reg = CatBoostRegressor(loss_function='RMSE', iterations=1000, depth=3, colsample_bylevel=.4,\n",
    "                             eval_metric='MAE', subsample=.5, random_state=10, verbose=0, eta=.2)\n",
    "catb_reg.fit(X_train, y_train, plot=True, eval_set=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "14b82121",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9257715795550736"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catb_reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "030428f7",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9017355052760399"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catb_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c6cb88",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "r2 score for Catboost regressor is 0.90"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa4dde4",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 3. Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "dcc34ef1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=3, n_estimators=10000, n_jobs=-1,\n",
       "                      random_state=14)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_reg = RandomForestRegressor(n_estimators=10000, max_depth=3, random_state=14, n_jobs=-1)\n",
    "rf_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "73465669",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6962014965825829"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train, rf_reg.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "bd4fc38d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6875274138573244"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, rf_reg.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec98cc3a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "r2 score is 0.68 for random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e6574a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### 4. SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "9b772bab",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.5)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr = SVR(kernel='rbf', C=0.5, epsilon=0.1, gamma='scale')\n",
    "svr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "495aa535",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8792788102869707"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train, svr.predict(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "0e7d8ef6",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8601592847181123"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, svr.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d68d97",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "r2 score for SVR is 0.86"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5feeac8d",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "---\n",
    "## Comparison\n",
    "In this part we compare MAE and r2 score between models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7494351e",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Mean Absolute Error Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "68411901",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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    "xgboost_mae = mean_absolute_error(y_true=y_test, y_pred=xgb_reg.predict(X_test))\n",
    "catboost_mae = mean_absolute_error(y_true=y_test, y_pred=catb_reg.predict(X_test))\n",
    "rf_mae = mean_absolute_error(y_true=y_test, y_pred=rf_reg.predict(X_test))\n",
    "svr_mae = mean_absolute_error(y_true=y_test, y_pred=svr.predict(X_test))\n",
    "score_df = pd.DataFrame({'MAE' : [xgboost_mae, catboost_mae, rf_mae, svr_mae], 'Models' : ['XGBoost', 'CatBoost', 'RF', 'SVR']})\n",
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   "id": "59a8dd9d",
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   },
   "source": [
    "### R2 Score Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "b56bd5bc",
   "metadata": {
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     "metadata": {},
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   ],
   "source": [
    "xgboost_mae = r2_score(y_true=y_test, y_pred=xgb_reg.predict(X_test))\n",
    "catboost_mae = r2_score(y_true=y_test, y_pred=catb_reg.predict(X_test))\n",
    "rf_mae = r2_score(y_true=y_test, y_pred=rf_reg.predict(X_test))\n",
    "svr_mae = r2_score(y_true=y_test, y_pred=svr.predict(X_test))\n",
    "score_df = pd.DataFrame({'R2 Score' : [xgboost_mae, catboost_mae, rf_mae, svr_mae], \n",
    "                         'Models' : ['XGBoost', 'CatBoost', 'RF', 'SVR']})\n",
    "px.histogram(score_df, x='Models', y='R2 Score', color='Models', barmode='overlay', title='Regression Model Score')"
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   "cell_type": "code",
   "execution_count": null,
   "id": "f0e1301d",
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   "outputs": [],
   "source": []
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