├── README.md
└── House_Price_Prediction.ipynb


/README.md:
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1 | # house-price-prediction


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/House_Price_Prediction.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "id": "e2af74a8",
   6 |    "metadata": {},
   7 |    "source": [
   8 |     "# House Price Prediction"
   9 |    ]
  10 |   },
  11 |   {
  12 |    "cell_type": "markdown",
  13 |    "id": "8e1ef12c",
  14 |    "metadata": {},
  15 |    "source": [
  16 |     "### Importing the dependencies"
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "code",
  21 |    "execution_count": 45,
  22 |    "id": "576b658e",
  23 |    "metadata": {},
  24 |    "outputs": [],
  25 |    "source": [
  26 |     "import numpy as np\n",
  27 |     "import pandas as pd\n",
  28 |     "import matplotlib.pyplot as plt\n",
  29 |     "import seaborn as sns\n",
  30 |     "import sklearn.datasets\n",
  31 |     "from sklearn.model_selection import train_test_split\n",
  32 |     "from xgboost import XGBRegressor\n",
  33 |     "from sklearn import metrics"
  34 |    ]
  35 |   },
  36 |   {
  37 |    "cell_type": "markdown",
  38 |    "id": "c35c3667",
  39 |    "metadata": {},
  40 |    "source": [
  41 |     "### Data Collection and Preprocessing"
  42 |    ]
  43 |   },
  44 |   {
  45 |    "cell_type": "code",
  46 |    "execution_count": 46,
  47 |    "id": "a037ed0f",
  48 |    "metadata": {},
  49 |    "outputs": [
  50 |     {
  51 |      "name": "stderr",
  52 |      "output_type": "stream",
  53 |      "text": [
  54 |       "C:\\Users\\TanveerKader\\anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
  55 |       "\n",
  56 |       "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
  57 |       "    the documentation of this function for further details.\n",
  58 |       "\n",
  59 |       "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
  60 |       "    dataset unless the purpose of the code is to study and educate about\n",
  61 |       "    ethical issues in data science and machine learning.\n",
  62 |       "\n",
  63 |       "    In this special case, you can fetch the dataset from the original\n",
  64 |       "    source::\n",
  65 |       "\n",
  66 |       "        import pandas as pd\n",
  67 |       "        import numpy as np\n",
  68 |       "\n",
  69 |       "\n",
  70 |       "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
  71 |       "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
  72 |       "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
  73 |       "        target = raw_df.values[1::2, 2]\n",
  74 |       "\n",
  75 |       "    Alternative datasets include the California housing dataset (i.e.\n",
  76 |       "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
  77 |       "    dataset. You can load the datasets as follows::\n",
  78 |       "\n",
  79 |       "        from sklearn.datasets import fetch_california_housing\n",
  80 |       "        housing = fetch_california_housing()\n",
  81 |       "\n",
  82 |       "    for the California housing dataset and::\n",
  83 |       "\n",
  84 |       "        from sklearn.datasets import fetch_openml\n",
  85 |       "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
  86 |       "\n",
  87 |       "    for the Ames housing dataset.\n",
  88 |       "    \n",
  89 |       "  warnings.warn(msg, category=FutureWarning)\n"
  90 |      ]
  91 |     }
  92 |    ],
  93 |    "source": [
  94 |     "house_price_dataset = sklearn.datasets.load_boston()"
  95 |    ]
  96 |   },
  97 |   {
  98 |    "cell_type": "code",
  99 |    "execution_count": 47,
 100 |    "id": "d40ce017",
 101 |    "metadata": {},
 102 |    "outputs": [
 103 |     {
 104 |      "name": "stdout",
 105 |      "output_type": "stream",
 106 |      "text": [
 107 |       "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
 108 |       "        4.9800e+00],\n",
 109 |       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
 110 |       "        9.1400e+00],\n",
 111 |       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
 112 |       "        4.0300e+00],\n",
 113 |       "       ...,\n",
 114 |       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
 115 |       "        5.6400e+00],\n",
 116 |       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
 117 |       "        6.4800e+00],\n",
 118 |       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
 119 |       "        7.8800e+00]]), 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
 120 |       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
 121 |       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
 122 |       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
 123 |       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
 124 |       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
 125 |       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
 126 |       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
 127 |       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
 128 |       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
 129 |       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
 130 |       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
 131 |       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
 132 |       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
 133 |       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
 134 |       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
 135 |       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
 136 |       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
 137 |       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
 138 |       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
 139 |       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
 140 |       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
 141 |       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
 142 |       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
 143 |       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
 144 |       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
 145 |       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
 146 |       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
 147 |       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
 148 |       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
 149 |       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
 150 |       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
 151 |       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
 152 |       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
 153 |       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
 154 |       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
 155 |       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
 156 |       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
 157 |       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
 158 |       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
 159 |       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
 160 |       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
 161 |       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
 162 |       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
 163 |       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
 164 |       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]), 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
 165 |       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'), 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\", 'filename': 'boston_house_prices.csv', 'data_module': 'sklearn.datasets.data'}\n"
 166 |      ]
 167 |     }
 168 |    ],
 169 |    "source": [
 170 |     "print(house_price_dataset)"
 171 |    ]
 172 |   },
 173 |   {
 174 |    "cell_type": "code",
 175 |    "execution_count": 48,
 176 |    "id": "8ee3c931",
 177 |    "metadata": {},
 178 |    "outputs": [
 179 |     {
 180 |      "data": {
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 197 |        "  <thead>\n",
 198 |        "    <tr style=\"text-align: right;\">\n",
 199 |        "      <th></th>\n",
 200 |        "      <th>CRIM</th>\n",
 201 |        "      <th>ZN</th>\n",
 202 |        "      <th>INDUS</th>\n",
 203 |        "      <th>CHAS</th>\n",
 204 |        "      <th>NOX</th>\n",
 205 |        "      <th>RM</th>\n",
 206 |        "      <th>AGE</th>\n",
 207 |        "      <th>DIS</th>\n",
 208 |        "      <th>RAD</th>\n",
 209 |        "      <th>TAX</th>\n",
 210 |        "      <th>PTRATIO</th>\n",
 211 |        "      <th>B</th>\n",
 212 |        "      <th>LSTAT</th>\n",
 213 |        "    </tr>\n",
 214 |        "  </thead>\n",
 215 |        "  <tbody>\n",
 216 |        "    <tr>\n",
 217 |        "      <th>0</th>\n",
 218 |        "      <td>0.00632</td>\n",
 219 |        "      <td>18.0</td>\n",
 220 |        "      <td>2.31</td>\n",
 221 |        "      <td>0.0</td>\n",
 222 |        "      <td>0.538</td>\n",
 223 |        "      <td>6.575</td>\n",
 224 |        "      <td>65.2</td>\n",
 225 |        "      <td>4.0900</td>\n",
 226 |        "      <td>1.0</td>\n",
 227 |        "      <td>296.0</td>\n",
 228 |        "      <td>15.3</td>\n",
 229 |        "      <td>396.90</td>\n",
 230 |        "      <td>4.98</td>\n",
 231 |        "    </tr>\n",
 232 |        "    <tr>\n",
 233 |        "      <th>1</th>\n",
 234 |        "      <td>0.02731</td>\n",
 235 |        "      <td>0.0</td>\n",
 236 |        "      <td>7.07</td>\n",
 237 |        "      <td>0.0</td>\n",
 238 |        "      <td>0.469</td>\n",
 239 |        "      <td>6.421</td>\n",
 240 |        "      <td>78.9</td>\n",
 241 |        "      <td>4.9671</td>\n",
 242 |        "      <td>2.0</td>\n",
 243 |        "      <td>242.0</td>\n",
 244 |        "      <td>17.8</td>\n",
 245 |        "      <td>396.90</td>\n",
 246 |        "      <td>9.14</td>\n",
 247 |        "    </tr>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>2</th>\n",
 250 |        "      <td>0.02729</td>\n",
 251 |        "      <td>0.0</td>\n",
 252 |        "      <td>7.07</td>\n",
 253 |        "      <td>0.0</td>\n",
 254 |        "      <td>0.469</td>\n",
 255 |        "      <td>7.185</td>\n",
 256 |        "      <td>61.1</td>\n",
 257 |        "      <td>4.9671</td>\n",
 258 |        "      <td>2.0</td>\n",
 259 |        "      <td>242.0</td>\n",
 260 |        "      <td>17.8</td>\n",
 261 |        "      <td>392.83</td>\n",
 262 |        "      <td>4.03</td>\n",
 263 |        "    </tr>\n",
 264 |        "    <tr>\n",
 265 |        "      <th>3</th>\n",
 266 |        "      <td>0.03237</td>\n",
 267 |        "      <td>0.0</td>\n",
 268 |        "      <td>2.18</td>\n",
 269 |        "      <td>0.0</td>\n",
 270 |        "      <td>0.458</td>\n",
 271 |        "      <td>6.998</td>\n",
 272 |        "      <td>45.8</td>\n",
 273 |        "      <td>6.0622</td>\n",
 274 |        "      <td>3.0</td>\n",
 275 |        "      <td>222.0</td>\n",
 276 |        "      <td>18.7</td>\n",
 277 |        "      <td>394.63</td>\n",
 278 |        "      <td>2.94</td>\n",
 279 |        "    </tr>\n",
 280 |        "    <tr>\n",
 281 |        "      <th>4</th>\n",
 282 |        "      <td>0.06905</td>\n",
 283 |        "      <td>0.0</td>\n",
 284 |        "      <td>2.18</td>\n",
 285 |        "      <td>0.0</td>\n",
 286 |        "      <td>0.458</td>\n",
 287 |        "      <td>7.147</td>\n",
 288 |        "      <td>54.2</td>\n",
 289 |        "      <td>6.0622</td>\n",
 290 |        "      <td>3.0</td>\n",
 291 |        "      <td>222.0</td>\n",
 292 |        "      <td>18.7</td>\n",
 293 |        "      <td>396.90</td>\n",
 294 |        "      <td>5.33</td>\n",
 295 |        "    </tr>\n",
 296 |        "  </tbody>\n",
 297 |        "</table>\n",
 298 |        "</div>"
 299 |       ],
 300 |       "text/plain": [
 301 |        "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
 302 |        "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
 303 |        "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
 304 |        "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
 305 |        "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
 306 |        "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
 307 |        "\n",
 308 |        "   PTRATIO       B  LSTAT  \n",
 309 |        "0     15.3  396.90   4.98  \n",
 310 |        "1     17.8  396.90   9.14  \n",
 311 |        "2     17.8  392.83   4.03  \n",
 312 |        "3     18.7  394.63   2.94  \n",
 313 |        "4     18.7  396.90   5.33  "
 314 |       ]
 315 |      },
 316 |      "execution_count": 48,
 317 |      "metadata": {},
 318 |      "output_type": "execute_result"
 319 |     }
 320 |    ],
 321 |    "source": [
 322 |     "# Loading the dataset to Pandas DataFrame\n",
 323 |     "house_price_dataframe = pd.DataFrame(house_price_dataset.data, columns = house_price_dataset.feature_names)\n",
 324 |     "house_price_dataframe.head()"
 325 |    ]
 326 |   },
 327 |   {
 328 |    "cell_type": "code",
 329 |    "execution_count": 49,
 330 |    "id": "defcfe86",
 331 |    "metadata": {},
 332 |    "outputs": [
 333 |     {
 334 |      "data": {
 335 |       "text/html": [
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 349 |        "</style>\n",
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 351 |        "  <thead>\n",
 352 |        "    <tr style=\"text-align: right;\">\n",
 353 |        "      <th></th>\n",
 354 |        "      <th>CRIM</th>\n",
 355 |        "      <th>ZN</th>\n",
 356 |        "      <th>INDUS</th>\n",
 357 |        "      <th>CHAS</th>\n",
 358 |        "      <th>NOX</th>\n",
 359 |        "      <th>RM</th>\n",
 360 |        "      <th>AGE</th>\n",
 361 |        "      <th>DIS</th>\n",
 362 |        "      <th>RAD</th>\n",
 363 |        "      <th>TAX</th>\n",
 364 |        "      <th>PTRATIO</th>\n",
 365 |        "      <th>B</th>\n",
 366 |        "      <th>LSTAT</th>\n",
 367 |        "      <th>price</th>\n",
 368 |        "    </tr>\n",
 369 |        "  </thead>\n",
 370 |        "  <tbody>\n",
 371 |        "    <tr>\n",
 372 |        "      <th>0</th>\n",
 373 |        "      <td>0.00632</td>\n",
 374 |        "      <td>18.0</td>\n",
 375 |        "      <td>2.31</td>\n",
 376 |        "      <td>0.0</td>\n",
 377 |        "      <td>0.538</td>\n",
 378 |        "      <td>6.575</td>\n",
 379 |        "      <td>65.2</td>\n",
 380 |        "      <td>4.0900</td>\n",
 381 |        "      <td>1.0</td>\n",
 382 |        "      <td>296.0</td>\n",
 383 |        "      <td>15.3</td>\n",
 384 |        "      <td>396.90</td>\n",
 385 |        "      <td>4.98</td>\n",
 386 |        "      <td>24.0</td>\n",
 387 |        "    </tr>\n",
 388 |        "    <tr>\n",
 389 |        "      <th>1</th>\n",
 390 |        "      <td>0.02731</td>\n",
 391 |        "      <td>0.0</td>\n",
 392 |        "      <td>7.07</td>\n",
 393 |        "      <td>0.0</td>\n",
 394 |        "      <td>0.469</td>\n",
 395 |        "      <td>6.421</td>\n",
 396 |        "      <td>78.9</td>\n",
 397 |        "      <td>4.9671</td>\n",
 398 |        "      <td>2.0</td>\n",
 399 |        "      <td>242.0</td>\n",
 400 |        "      <td>17.8</td>\n",
 401 |        "      <td>396.90</td>\n",
 402 |        "      <td>9.14</td>\n",
 403 |        "      <td>21.6</td>\n",
 404 |        "    </tr>\n",
 405 |        "    <tr>\n",
 406 |        "      <th>2</th>\n",
 407 |        "      <td>0.02729</td>\n",
 408 |        "      <td>0.0</td>\n",
 409 |        "      <td>7.07</td>\n",
 410 |        "      <td>0.0</td>\n",
 411 |        "      <td>0.469</td>\n",
 412 |        "      <td>7.185</td>\n",
 413 |        "      <td>61.1</td>\n",
 414 |        "      <td>4.9671</td>\n",
 415 |        "      <td>2.0</td>\n",
 416 |        "      <td>242.0</td>\n",
 417 |        "      <td>17.8</td>\n",
 418 |        "      <td>392.83</td>\n",
 419 |        "      <td>4.03</td>\n",
 420 |        "      <td>34.7</td>\n",
 421 |        "    </tr>\n",
 422 |        "    <tr>\n",
 423 |        "      <th>3</th>\n",
 424 |        "      <td>0.03237</td>\n",
 425 |        "      <td>0.0</td>\n",
 426 |        "      <td>2.18</td>\n",
 427 |        "      <td>0.0</td>\n",
 428 |        "      <td>0.458</td>\n",
 429 |        "      <td>6.998</td>\n",
 430 |        "      <td>45.8</td>\n",
 431 |        "      <td>6.0622</td>\n",
 432 |        "      <td>3.0</td>\n",
 433 |        "      <td>222.0</td>\n",
 434 |        "      <td>18.7</td>\n",
 435 |        "      <td>394.63</td>\n",
 436 |        "      <td>2.94</td>\n",
 437 |        "      <td>33.4</td>\n",
 438 |        "    </tr>\n",
 439 |        "    <tr>\n",
 440 |        "      <th>4</th>\n",
 441 |        "      <td>0.06905</td>\n",
 442 |        "      <td>0.0</td>\n",
 443 |        "      <td>2.18</td>\n",
 444 |        "      <td>0.0</td>\n",
 445 |        "      <td>0.458</td>\n",
 446 |        "      <td>7.147</td>\n",
 447 |        "      <td>54.2</td>\n",
 448 |        "      <td>6.0622</td>\n",
 449 |        "      <td>3.0</td>\n",
 450 |        "      <td>222.0</td>\n",
 451 |        "      <td>18.7</td>\n",
 452 |        "      <td>396.90</td>\n",
 453 |        "      <td>5.33</td>\n",
 454 |        "      <td>36.2</td>\n",
 455 |        "    </tr>\n",
 456 |        "  </tbody>\n",
 457 |        "</table>\n",
 458 |        "</div>"
 459 |       ],
 460 |       "text/plain": [
 461 |        "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
 462 |        "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
 463 |        "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
 464 |        "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
 465 |        "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
 466 |        "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
 467 |        "\n",
 468 |        "   PTRATIO       B  LSTAT  price  \n",
 469 |        "0     15.3  396.90   4.98   24.0  \n",
 470 |        "1     17.8  396.90   9.14   21.6  \n",
 471 |        "2     17.8  392.83   4.03   34.7  \n",
 472 |        "3     18.7  394.63   2.94   33.4  \n",
 473 |        "4     18.7  396.90   5.33   36.2  "
 474 |       ]
 475 |      },
 476 |      "execution_count": 49,
 477 |      "metadata": {},
 478 |      "output_type": "execute_result"
 479 |     }
 480 |    ],
 481 |    "source": [
 482 |     "# add the target(price) column to dataframe\n",
 483 |     "house_price_dataframe['price'] = house_price_dataset.target\n",
 484 |     "house_price_dataframe.head()"
 485 |    ]
 486 |   },
 487 |   {
 488 |    "cell_type": "code",
 489 |    "execution_count": 50,
 490 |    "id": "13f58d1d",
 491 |    "metadata": {},
 492 |    "outputs": [
 493 |     {
 494 |      "data": {
 495 |       "text/plain": [
 496 |        "(506, 14)"
 497 |       ]
 498 |      },
 499 |      "execution_count": 50,
 500 |      "metadata": {},
 501 |      "output_type": "execute_result"
 502 |     }
 503 |    ],
 504 |    "source": [
 505 |     "# number of rows and columns in dataframe\n",
 506 |     "house_price_dataframe.shape"
 507 |    ]
 508 |   },
 509 |   {
 510 |    "cell_type": "code",
 511 |    "execution_count": 51,
 512 |    "id": "361f4539",
 513 |    "metadata": {},
 514 |    "outputs": [
 515 |     {
 516 |      "data": {
 517 |       "text/plain": [
 518 |        "CRIM       0\n",
 519 |        "ZN         0\n",
 520 |        "INDUS      0\n",
 521 |        "CHAS       0\n",
 522 |        "NOX        0\n",
 523 |        "RM         0\n",
 524 |        "AGE        0\n",
 525 |        "DIS        0\n",
 526 |        "RAD        0\n",
 527 |        "TAX        0\n",
 528 |        "PTRATIO    0\n",
 529 |        "B          0\n",
 530 |        "LSTAT      0\n",
 531 |        "price      0\n",
 532 |        "dtype: int64"
 533 |       ]
 534 |      },
 535 |      "execution_count": 51,
 536 |      "metadata": {},
 537 |      "output_type": "execute_result"
 538 |     }
 539 |    ],
 540 |    "source": [
 541 |     "# check for missing values \n",
 542 |     "house_price_dataframe.isnull().sum()"
 543 |    ]
 544 |   },
 545 |   {
 546 |    "cell_type": "code",
 547 |    "execution_count": 52,
 548 |    "id": "edf9ef75",
 549 |    "metadata": {},
 550 |    "outputs": [
 551 |     {
 552 |      "data": {
 553 |       "text/html": [
 554 |        "<div>\n",
 555 |        "<style scoped>\n",
 556 |        "    .dataframe tbody tr th:only-of-type {\n",
 557 |        "        vertical-align: middle;\n",
 558 |        "    }\n",
 559 |        "\n",
 560 |        "    .dataframe tbody tr th {\n",
 561 |        "        vertical-align: top;\n",
 562 |        "    }\n",
 563 |        "\n",
 564 |        "    .dataframe thead th {\n",
 565 |        "        text-align: right;\n",
 566 |        "    }\n",
 567 |        "</style>\n",
 568 |        "<table border=\"1\" class=\"dataframe\">\n",
 569 |        "  <thead>\n",
 570 |        "    <tr style=\"text-align: right;\">\n",
 571 |        "      <th></th>\n",
 572 |        "      <th>CRIM</th>\n",
 573 |        "      <th>ZN</th>\n",
 574 |        "      <th>INDUS</th>\n",
 575 |        "      <th>CHAS</th>\n",
 576 |        "      <th>NOX</th>\n",
 577 |        "      <th>RM</th>\n",
 578 |        "      <th>AGE</th>\n",
 579 |        "      <th>DIS</th>\n",
 580 |        "      <th>RAD</th>\n",
 581 |        "      <th>TAX</th>\n",
 582 |        "      <th>PTRATIO</th>\n",
 583 |        "      <th>B</th>\n",
 584 |        "      <th>LSTAT</th>\n",
 585 |        "      <th>price</th>\n",
 586 |        "    </tr>\n",
 587 |        "  </thead>\n",
 588 |        "  <tbody>\n",
 589 |        "    <tr>\n",
 590 |        "      <th>count</th>\n",
 591 |        "      <td>506.000000</td>\n",
 592 |        "      <td>506.000000</td>\n",
 593 |        "      <td>506.000000</td>\n",
 594 |        "      <td>506.000000</td>\n",
 595 |        "      <td>506.000000</td>\n",
 596 |        "      <td>506.000000</td>\n",
 597 |        "      <td>506.000000</td>\n",
 598 |        "      <td>506.000000</td>\n",
 599 |        "      <td>506.000000</td>\n",
 600 |        "      <td>506.000000</td>\n",
 601 |        "      <td>506.000000</td>\n",
 602 |        "      <td>506.000000</td>\n",
 603 |        "      <td>506.000000</td>\n",
 604 |        "      <td>506.000000</td>\n",
 605 |        "    </tr>\n",
 606 |        "    <tr>\n",
 607 |        "      <th>mean</th>\n",
 608 |        "      <td>3.613524</td>\n",
 609 |        "      <td>11.363636</td>\n",
 610 |        "      <td>11.136779</td>\n",
 611 |        "      <td>0.069170</td>\n",
 612 |        "      <td>0.554695</td>\n",
 613 |        "      <td>6.284634</td>\n",
 614 |        "      <td>68.574901</td>\n",
 615 |        "      <td>3.795043</td>\n",
 616 |        "      <td>9.549407</td>\n",
 617 |        "      <td>408.237154</td>\n",
 618 |        "      <td>18.455534</td>\n",
 619 |        "      <td>356.674032</td>\n",
 620 |        "      <td>12.653063</td>\n",
 621 |        "      <td>22.532806</td>\n",
 622 |        "    </tr>\n",
 623 |        "    <tr>\n",
 624 |        "      <th>std</th>\n",
 625 |        "      <td>8.601545</td>\n",
 626 |        "      <td>23.322453</td>\n",
 627 |        "      <td>6.860353</td>\n",
 628 |        "      <td>0.253994</td>\n",
 629 |        "      <td>0.115878</td>\n",
 630 |        "      <td>0.702617</td>\n",
 631 |        "      <td>28.148861</td>\n",
 632 |        "      <td>2.105710</td>\n",
 633 |        "      <td>8.707259</td>\n",
 634 |        "      <td>168.537116</td>\n",
 635 |        "      <td>2.164946</td>\n",
 636 |        "      <td>91.294864</td>\n",
 637 |        "      <td>7.141062</td>\n",
 638 |        "      <td>9.197104</td>\n",
 639 |        "    </tr>\n",
 640 |        "    <tr>\n",
 641 |        "      <th>min</th>\n",
 642 |        "      <td>0.006320</td>\n",
 643 |        "      <td>0.000000</td>\n",
 644 |        "      <td>0.460000</td>\n",
 645 |        "      <td>0.000000</td>\n",
 646 |        "      <td>0.385000</td>\n",
 647 |        "      <td>3.561000</td>\n",
 648 |        "      <td>2.900000</td>\n",
 649 |        "      <td>1.129600</td>\n",
 650 |        "      <td>1.000000</td>\n",
 651 |        "      <td>187.000000</td>\n",
 652 |        "      <td>12.600000</td>\n",
 653 |        "      <td>0.320000</td>\n",
 654 |        "      <td>1.730000</td>\n",
 655 |        "      <td>5.000000</td>\n",
 656 |        "    </tr>\n",
 657 |        "    <tr>\n",
 658 |        "      <th>25%</th>\n",
 659 |        "      <td>0.082045</td>\n",
 660 |        "      <td>0.000000</td>\n",
 661 |        "      <td>5.190000</td>\n",
 662 |        "      <td>0.000000</td>\n",
 663 |        "      <td>0.449000</td>\n",
 664 |        "      <td>5.885500</td>\n",
 665 |        "      <td>45.025000</td>\n",
 666 |        "      <td>2.100175</td>\n",
 667 |        "      <td>4.000000</td>\n",
 668 |        "      <td>279.000000</td>\n",
 669 |        "      <td>17.400000</td>\n",
 670 |        "      <td>375.377500</td>\n",
 671 |        "      <td>6.950000</td>\n",
 672 |        "      <td>17.025000</td>\n",
 673 |        "    </tr>\n",
 674 |        "    <tr>\n",
 675 |        "      <th>50%</th>\n",
 676 |        "      <td>0.256510</td>\n",
 677 |        "      <td>0.000000</td>\n",
 678 |        "      <td>9.690000</td>\n",
 679 |        "      <td>0.000000</td>\n",
 680 |        "      <td>0.538000</td>\n",
 681 |        "      <td>6.208500</td>\n",
 682 |        "      <td>77.500000</td>\n",
 683 |        "      <td>3.207450</td>\n",
 684 |        "      <td>5.000000</td>\n",
 685 |        "      <td>330.000000</td>\n",
 686 |        "      <td>19.050000</td>\n",
 687 |        "      <td>391.440000</td>\n",
 688 |        "      <td>11.360000</td>\n",
 689 |        "      <td>21.200000</td>\n",
 690 |        "    </tr>\n",
 691 |        "    <tr>\n",
 692 |        "      <th>75%</th>\n",
 693 |        "      <td>3.677083</td>\n",
 694 |        "      <td>12.500000</td>\n",
 695 |        "      <td>18.100000</td>\n",
 696 |        "      <td>0.000000</td>\n",
 697 |        "      <td>0.624000</td>\n",
 698 |        "      <td>6.623500</td>\n",
 699 |        "      <td>94.075000</td>\n",
 700 |        "      <td>5.188425</td>\n",
 701 |        "      <td>24.000000</td>\n",
 702 |        "      <td>666.000000</td>\n",
 703 |        "      <td>20.200000</td>\n",
 704 |        "      <td>396.225000</td>\n",
 705 |        "      <td>16.955000</td>\n",
 706 |        "      <td>25.000000</td>\n",
 707 |        "    </tr>\n",
 708 |        "    <tr>\n",
 709 |        "      <th>max</th>\n",
 710 |        "      <td>88.976200</td>\n",
 711 |        "      <td>100.000000</td>\n",
 712 |        "      <td>27.740000</td>\n",
 713 |        "      <td>1.000000</td>\n",
 714 |        "      <td>0.871000</td>\n",
 715 |        "      <td>8.780000</td>\n",
 716 |        "      <td>100.000000</td>\n",
 717 |        "      <td>12.126500</td>\n",
 718 |        "      <td>24.000000</td>\n",
 719 |        "      <td>711.000000</td>\n",
 720 |        "      <td>22.000000</td>\n",
 721 |        "      <td>396.900000</td>\n",
 722 |        "      <td>37.970000</td>\n",
 723 |        "      <td>50.000000</td>\n",
 724 |        "    </tr>\n",
 725 |        "  </tbody>\n",
 726 |        "</table>\n",
 727 |        "</div>"
 728 |       ],
 729 |       "text/plain": [
 730 |        "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
 731 |        "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
 732 |        "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
 733 |        "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
 734 |        "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
 735 |        "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
 736 |        "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
 737 |        "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
 738 |        "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
 739 |        "\n",
 740 |        "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
 741 |        "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
 742 |        "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
 743 |        "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
 744 |        "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
 745 |        "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
 746 |        "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
 747 |        "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
 748 |        "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
 749 |        "\n",
 750 |        "            LSTAT       price  \n",
 751 |        "count  506.000000  506.000000  \n",
 752 |        "mean    12.653063   22.532806  \n",
 753 |        "std      7.141062    9.197104  \n",
 754 |        "min      1.730000    5.000000  \n",
 755 |        "25%      6.950000   17.025000  \n",
 756 |        "50%     11.360000   21.200000  \n",
 757 |        "75%     16.955000   25.000000  \n",
 758 |        "max     37.970000   50.000000  "
 759 |       ]
 760 |      },
 761 |      "execution_count": 52,
 762 |      "metadata": {},
 763 |      "output_type": "execute_result"
 764 |     }
 765 |    ],
 766 |    "source": [
 767 |     "# statistical measures of the dataset\n",
 768 |     "house_price_dataframe.describe()"
 769 |    ]
 770 |   },
 771 |   {
 772 |    "cell_type": "markdown",
 773 |    "id": "da54fb45",
 774 |    "metadata": {},
 775 |    "source": [
 776 |     "#### Correlation between features "
 777 |    ]
 778 |   },
 779 |   {
 780 |    "cell_type": "code",
 781 |    "execution_count": 53,
 782 |    "id": "f82582ca",
 783 |    "metadata": {},
 784 |    "outputs": [],
 785 |    "source": [
 786 |     "correlation = house_price_dataframe.corr()"
 787 |    ]
 788 |   },
 789 |   {
 790 |    "cell_type": "code",
 791 |    "execution_count": 54,
 792 |    "id": "62eec5b4",
 793 |    "metadata": {},
 794 |    "outputs": [
 795 |     {
 796 |      "data": {
 797 |       "text/plain": [
 798 |        "<AxesSubplot:>"
 799 |       ]
 800 |      },
 801 |      "execution_count": 54,
 802 |      "metadata": {},
 803 |      "output_type": "execute_result"
 804 |     },
 805 |     {
 806 |      "data": {
 807 |       "image/png": 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\n",
 808 |       "text/plain": [
 809 |        "<Figure size 1000x1000 with 2 Axes>"
 810 |       ]
 811 |      },
 812 |      "metadata": {},
 813 |      "output_type": "display_data"
 814 |     }
 815 |    ],
 816 |    "source": [
 817 |     "# heatmap for correlation\n",
 818 |     "plt.figure(figsize=(10,10))\n",
 819 |     "sns.heatmap(correlation, cbar = True, square = True, fmt = '.1f', annot = True, annot_kws = {'size':8}, cmap = 'Blues')"
 820 |    ]
 821 |   },
 822 |   {
 823 |    "cell_type": "code",
 824 |    "execution_count": 55,
 825 |    "id": "0b3a96af",
 826 |    "metadata": {},
 827 |    "outputs": [],
 828 |    "source": [
 829 |     "# separating features and target\n",
 830 |     "X = house_price_dataframe.drop(['price'], axis=1)\n",
 831 |     "Y = house_price_dataframe['price']"
 832 |    ]
 833 |   },
 834 |   {
 835 |    "cell_type": "code",
 836 |    "execution_count": 56,
 837 |    "id": "9492415c",
 838 |    "metadata": {},
 839 |    "outputs": [
 840 |     {
 841 |      "name": "stdout",
 842 |      "output_type": "stream",
 843 |      "text": [
 844 |       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
 845 |       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
 846 |       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
 847 |       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
 848 |       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
 849 |       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
 850 |       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
 851 |       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
 852 |       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
 853 |       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
 854 |       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
 855 |       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
 856 |       "\n",
 857 |       "     PTRATIO       B  LSTAT  \n",
 858 |       "0       15.3  396.90   4.98  \n",
 859 |       "1       17.8  396.90   9.14  \n",
 860 |       "2       17.8  392.83   4.03  \n",
 861 |       "3       18.7  394.63   2.94  \n",
 862 |       "4       18.7  396.90   5.33  \n",
 863 |       "..       ...     ...    ...  \n",
 864 |       "501     21.0  391.99   9.67  \n",
 865 |       "502     21.0  396.90   9.08  \n",
 866 |       "503     21.0  396.90   5.64  \n",
 867 |       "504     21.0  393.45   6.48  \n",
 868 |       "505     21.0  396.90   7.88  \n",
 869 |       "\n",
 870 |       "[506 rows x 13 columns]\n",
 871 |       "0      24.0\n",
 872 |       "1      21.6\n",
 873 |       "2      34.7\n",
 874 |       "3      33.4\n",
 875 |       "4      36.2\n",
 876 |       "       ... \n",
 877 |       "501    22.4\n",
 878 |       "502    20.6\n",
 879 |       "503    23.9\n",
 880 |       "504    22.0\n",
 881 |       "505    11.9\n",
 882 |       "Name: price, Length: 506, dtype: float64\n"
 883 |      ]
 884 |     }
 885 |    ],
 886 |    "source": [
 887 |     "print(X)\n",
 888 |     "print(Y)"
 889 |    ]
 890 |   },
 891 |   {
 892 |    "cell_type": "markdown",
 893 |    "id": "a3b65a3a",
 894 |    "metadata": {},
 895 |    "source": [
 896 |     "### Spliting Training and Test data"
 897 |    ]
 898 |   },
 899 |   {
 900 |    "cell_type": "code",
 901 |    "execution_count": 57,
 902 |    "id": "f40bde82",
 903 |    "metadata": {},
 904 |    "outputs": [],
 905 |    "source": [
 906 |     "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)"
 907 |    ]
 908 |   },
 909 |   {
 910 |    "cell_type": "code",
 911 |    "execution_count": 58,
 912 |    "id": "5c6afd34",
 913 |    "metadata": {},
 914 |    "outputs": [
 915 |     {
 916 |      "name": "stdout",
 917 |      "output_type": "stream",
 918 |      "text": [
 919 |       "Total data:  (506, 13)\n",
 920 |       "Train data:  (404, 13)\n",
 921 |       "Test data:  (102, 13)\n"
 922 |      ]
 923 |     }
 924 |    ],
 925 |    "source": [
 926 |     "print(\"Total data: \", X.shape)\n",
 927 |     "print(\"Train data: \", X_train.shape)\n",
 928 |     "print(\"Test data: \", X_test.shape)"
 929 |    ]
 930 |   },
 931 |   {
 932 |    "cell_type": "markdown",
 933 |    "id": "82b890f0",
 934 |    "metadata": {},
 935 |    "source": [
 936 |     "### Model Training using XGBoost Regressor"
 937 |    ]
 938 |   },
 939 |   {
 940 |    "cell_type": "code",
 941 |    "execution_count": 59,
 942 |    "id": "07c92166",
 943 |    "metadata": {},
 944 |    "outputs": [],
 945 |    "source": [
 946 |     "# loading model\n",
 947 |     "model = XGBRegressor()"
 948 |    ]
 949 |   },
 950 |   {
 951 |    "cell_type": "code",
 952 |    "execution_count": 60,
 953 |    "id": "9cf04e86",
 954 |    "metadata": {},
 955 |    "outputs": [
 956 |     {
 957 |      "data": {
 958 |       "text/plain": [
 959 |        "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
 960 |        "             colsample_bylevel=None, colsample_bynode=None,\n",
 961 |        "             colsample_bytree=None, early_stopping_rounds=None,\n",
 962 |        "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
 963 |        "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
 964 |        "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
 965 |        "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
 966 |        "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
 967 |        "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
 968 |        "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
 969 |        "             predictor=None, random_state=None, ...)"
 970 |       ]
 971 |      },
 972 |      "execution_count": 60,
 973 |      "metadata": {},
 974 |      "output_type": "execute_result"
 975 |     }
 976 |    ],
 977 |    "source": [
 978 |     "# training the XGBRegressor model\n",
 979 |     "model.fit(X_train, Y_train)"
 980 |    ]
 981 |   },
 982 |   {
 983 |    "cell_type": "markdown",
 984 |    "id": "8001a791",
 985 |    "metadata": {},
 986 |    "source": [
 987 |     "### Model Evaluation"
 988 |    ]
 989 |   },
 990 |   {
 991 |    "cell_type": "code",
 992 |    "execution_count": 61,
 993 |    "id": "2f59e691",
 994 |    "metadata": {},
 995 |    "outputs": [],
 996 |    "source": [
 997 |     "# Prediction on training data\n",
 998 |     "training_data_prediction = model.predict(X_train)"
 999 |    ]
1000 |   },
1001 |   {
1002 |    "cell_type": "code",
1003 |    "execution_count": 62,
1004 |    "id": "6ea6922a",
1005 |    "metadata": {},
1006 |    "outputs": [
1007 |     {
1008 |      "name": "stdout",
1009 |      "output_type": "stream",
1010 |      "text": [
1011 |       "R squared error:  0.9999948236320982\n",
1012 |       "Mean absolute error:  0.0145848437110976\n"
1013 |      ]
1014 |     }
1015 |    ],
1016 |    "source": [
1017 |     "# R squared error\n",
1018 |     "score_1 = metrics.r2_score(Y_train, training_data_prediction)\n",
1019 |     "\n",
1020 |     "# Mean absolute error\n",
1021 |     "score_2 = metrics.mean_absolute_error(Y_train, training_data_prediction)\n",
1022 |     "\n",
1023 |     "print(\"R squared error: \", score_1)\n",
1024 |     "print(\"Mean absolute error: \", score_2)"
1025 |    ]
1026 |   },
1027 |   {
1028 |    "cell_type": "code",
1029 |    "execution_count": 63,
1030 |    "id": "626e8430",
1031 |    "metadata": {},
1032 |    "outputs": [
1033 |     {
1034 |      "data": {
1035 |       "image/png": 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QQv/RTBUBIMaMGVPmc0+OaNHl87h37574+OOPRaNGjYSLi4uQy+UiJCREvPfee5rPtSIfffSRACACAwNLjEw5dOiQeOGFF0SdOnWEVCoV3t7eIiIiQmzatKnC85ric0lPTxfDhw8XtWrVEs7OzsLHx0e0a9dOTJs2TWu/tLQ00a1bNyGTyYSXl5cYMWKE2LhxY4WjmYR4NKLtm2++EcHBwZrPMDw8XGzevFmzz6VLl0T37t2Fh4eHAKB1Dl2vwd27d8Xw4cNF9erVhZubm+jWrZv4888/9RrN9OWXX5a7X/GIpSdHVz3+3OPf40IIsW3bNhERESHc3d2Fm5ubCAoKEjNnztTa59SpU+Lll18Wvr6+wtnZWSgUCtGlSxfNKEWix0mEMOGsXURERESVjH1miIiIyKYxzBAREZFNY5ghIiIim8YwQ0RERDaNYYaIiIhsGsMMERER2TS7nzRPrVbj2rVr8PDw0HladyIiIrIsIQRyc3MREBAAB4fy217sPsxcu3YNgYGBli6DiIiIDHDlypUKF8G1+zBTvIbHlStXSkwzT0RERNYpJycHgYGBOq3FZfdhpvjWkqenJ8MMERGRjdGliwg7ABMREZFNY5ghIiIim8YwQ0RERDaNYYaIiIhsGsMMERER2TSGGSIiIrJpDDNERERk0xhmiIiIyKYxzBAREZFNs/sZgImIiMj0kv/ORr8F+yEASABsGt0eIU/JLVKLRVtmpkyZAolEovVQKBSa54UQmDJlCgICAuDq6opOnTohNTXVghUTERFR3Q+3ou//BxkAEAD6LtiPuh9utUg9Fr/N1LRpU2RkZGgeycnJmudmzZqF2bNnY/78+UhMTIRCoUC3bt2Qm5trwYqJiIiqrooCiyUCjcXDjJOTExQKhebh4+MD4FGrzLfffovJkyejf//+CA4OxooVK/DgwQOsXr3awlUTERFVPcl/Z5t0P1OxeJg5d+4cAgICoFQqMXDgQFy8eBEAkJ6ejszMTHTv3l2zr1QqRUREBA4ePFjm+QoKCpCTk6P1ICIiIuP1W7DfpPuZikXDTJs2bbBy5Ur8/vvvWLx4MTIzM9GuXTvcunULmZmZAAA/Pz+tY/z8/DTPlSY2NhZyuVzzCAwMNOt7ICIiqipExbvotZ+pWDTM9OrVCwMGDEBISAgiIyOxdeuj+2wrVqzQ7CORSLSOEUKU2Pa4SZMmITs7W/O4cuWKeYonIiKqYsr+62vYfqZi8dtMj3N3d0dISAjOnTunGdX0ZCtMVlZWidaax0mlUnh6emo9iIiIyHibRrc36X6mYlVhpqCgAGfOnIG/vz+USiUUCgXi4+M1zxcWFiIhIQHt2rWzYJVERERVk67zyFT2fDMWDTMffPABEhISkJ6ejiNHjuDFF19ETk4OhgwZAolEgujoaMyYMQPr169HSkoKhg4dCjc3NwwaNMiSZRMREdkdlVrg0IVb2Jh0FYcu3IJKXXrPl0tfRJV7noqeNweLzgD8v//9D6+++ipu3rwJHx8ftG3bFocPH0adOnUAABMmTEBeXh5Gjx6NO3fuoE2bNtixYwc8PDwsWTYREZFdiUvJwNTNacjIztds85fLENM3CD2D/Uvsf+mLKKuaAVgihKjsTseVKicnB3K5HNnZ2ew/Q0RE9IS4lAyMWnWixAik4k68CweHlRpozE2fv99W1WeGiIiIKo9KLTB1c1qpQ6mLt03dnFbmLSdrwTBDRERUxRT3j/km/i+tW0tPEgAysvNxNP125RVnAK6aTUREVIWU1j+mIlm5uu9rCQwzREREVURZ/WMq4ushM0s9psIwQ0REVAWU1z+mLBIACrkMrZVe5irLJNhnhoiIqAo4mn5br1tLxaOZYvoGwdGhshco0A9bZoiIiKoAffu9KMqZZ8baMMwQERFVAbr2exnbuT6ebeCD1kovq2+RKcYwQ0REVAW0VnrBXy5DZnZ+qf1mivvHvNetkc2EmGLsM0NERFQFODpIENM3CMA//WGK2VL/mNIwzBAREVURPYP9sXBwGBRy7VtOCrnMYssWmAJvMxEREVUhPYP90S1IgaPpt5GVmw9fD5lN9Y8pDcMMERFRFePoIEF4fW9Ll2EyvM1ERERENo1hhoiIiGwawwwRERHZNIYZIiIismkMM0RERGTTOJqJiIjICqnUwq6GT5sTwwwREZGViUvJwNTNaVqrXPvb0MKPlY23mYiIiKxIXEoGRq06oRVkACAzOx+jVp1AXEqGhSqzXgwzREREVkKlFpi6Oa3UhSCLt03dnAaVurQ9qi7eZiIiIrKgx/vG3MwtKNEi8zgBICM7H0fTb9vVDL7GYpghIiKykNL6xugiK1e//e0dwwwREZEFFPeNMeSGka+HrOKdqhCGGSIiokpWXt+Y8kgAKOSPhmnTP9gBmIiIqJIdTb+t962l4hlmYvoGcb6ZJ7BlhoiIqBI83tH33PVcvY9XcJ6ZMjHMEBERmZmhHX0/iWqCmh5SzgBcAYYZIiIiMzKko29x35ihzyoZYHTAPjNERERmYkhHX/aN0R9bZoiIiMzEkI6+7BujP4YZIiIiM9F1cruxnRugoV819o0xEMMMERGRmeg6ud2zDWpyeQIjMMwQEREZ4fEh10+2rLRWesFfLkNmdn6p/WY4CZ5pMMwQEREZqLQh1/6P9XlxdJAgpm8QRq06AQmgFWjY0dd0OJqJiIjIAMVDrp/s4JuZnY9Rq04gLiUDANAz2B8LB4dBIde+5aSQy7BwcBg7+poAW2aIiIj0VN6Qa4FHrS5TN6ehW5ACjg4S9Az2R7cgRZm3o8g4DDNERER6qmjItQCQkZ2Po+m3NR17HR0k7ORrJgwzREREFXiyk29mdp5Ox+k6NJuMwzBDRERUjtI6+Xq5u+h0rK5Ds8k4DDNERERlKGtdpTv3C8s9jkOuKxdHMxEREZWisEiNj9anlNnJtywccl35GGaIiIieEJeSgbaxf+B2BS0wAODl7qz1NYdcVz7eZiIiInpMWbeWyvJJn6ZQeMo45NqCGGaIiIj+X3nzx5RF4SnjkGsLY5ghIqIqr3jo9YHzN8udP+Zx7ORrPRhmiIioSitt6LWu2MnXOjDMEBFRlaVv/5hi3u4umP5CMDv5WgmGGSIiqpIM6R8DPBq9dGhSV7g4cUCwtWCYISKiKuPxZQlu5hbodWup+GbSjBdCGGSsDMMMERFVCcb0jQEedfaN6RvEW0tWiGGGiIjsnqF9YwBgbOf6eLaBD+ePsWIMM0REZLdUaoHDF2/hw9+S9Q4yxUOv3+vWiCHGyjHMEBGRXTLmthLXV7ItDDNERGR3jLmtBLB/jK1hmCEiIrti6JDrT6KaoKaHlOsr2SCGGSIisitH02/rPeRaIZdh6LNKBhgbxYHyRERkV7Jy9Z87hn1jbBtbZoiIyK74esh03pd9Y+wDwwwREdmV1kov+MtlyMzOL7PfTHU3Z3z3ahja1vdmi4wd4G0mIiKyK44OEsT0DQLwz22kYpL/f3zRPwTPNqzJIGMnGGaIiMju9Az2x8LBYVDItW85KeQyLBwcxttKdsZqwkxsbCwkEgmio6M124QQmDJlCgICAuDq6opOnTohNTXVckUSEZHN6Bnsj/0Tu+DnkW0xZ2Bz/DyyLfZP7MIgY4esos9MYmIi/vOf/yA0NFRr+6xZszB79mwsX74cTz/9NKZNm4Zu3brh7Nmz8PDwsFC1RERUmR5f6VrfOWAcHSQIr+9t5grJ0iweZu7du4fXXnsNixcvxrRp0zTbhRD49ttvMXnyZPTv3x8AsGLFCvj5+WH16tV46623LFUyERFVktKWJPDnCCR6gsVvM40ZMwZRUVGIjIzU2p6eno7MzEx0795ds00qlSIiIgIHDx6s7DKJiKiSFS9J8OQEeJnZ+Ri16gTiUjIsVBlZG4u2zKxZswbHjx/HsWPHSjyXmZkJAPDz89Pa7ufnh8uXL5d5zoKCAhQUFGi+zsnJMVG1RERUWcpbkkDg0YikqZvT0C1IwRFJZLmWmStXruDdd9/FTz/9BJms7AmOJBLtb1IhRIltj4uNjYVcLtc8AgMDTVYzERFVjoqWJBAAMrLzcTT9duUVRVbLYmHm+PHjyMrKwjPPPAMnJyc4OTkhISEBc+fOhZOTk6ZFpriFplhWVlaJ1prHTZo0CdnZ2ZrHlStXzPo+iIjI9HRdkkCfpQvIflnsNlPXrl2RnJystW3YsGFo3LgxJk6ciHr16kGhUCA+Ph4tWrQAABQWFiIhIQEzZ84s87xSqRRSqdSstRMRkXnpuiSBPksXkP2yWJjx8PBAcHCw1jZ3d3d4e3trtkdHR2PGjBlo2LAhGjZsiBkzZsDNzQ2DBg2yRMlERFRJKlqSoHil69ZKr8oujayQxYdml2fChAnIy8vD6NGjcefOHbRp0wY7duzgHDNERHaueEmCUatOQAJoBRqudE1PkgghylqHyy7k5ORALpcjOzsbnp6eli6HiIig+0R4nGem6tLn77dVt8wQEZH90Seg9Az2R7cghcEzAFPVwJYZIiKqNMUT4T35h6c4mnARSCqmz99vi88ATEREVUNFE+EBjybCU6nt+v/YZAYMM0REVCk4ER6ZC8MMERFVCk6ER+bCDsBERGRypY1W4kR4ZC4MM0REZFJljVb6JKoJJ8Ijs+BtJiIiMpni0UpP9o3JzM7HmNUn0a/Zo5FKTw6s5kR4ZAyGGSIiMgldRittOpWB7waFQSHXvpWkkMs4LJsMxttMRERkkCf7xaiF0Gm0Ug13F+yf2IUT4ZHJMMwQEZHeSusXU93VWadjs3Lz4eggQXh9b3OVR1UMwwwREell2+lrGL36ZIntd/Me6nQ8RyuRqTHMEBGRzradzsDYn0sGGV1wtBKZC8MMERHpJC4lA6NXnzDoWI5WInPiaCYiIqpQ8UglXT3Zf4ajlcic2DJDREQVqmhdpSd9NygMDg4SjlaiSsEwQ0REFdJnvSR/uQxt63szvFClYZghIiIthUVq/HjoEi7ffoA6Xm54PbyuXiOQ2C+GKhvDDBERaUzfmoYf9qdDPDaN7/RtZzCivbLcdZUAwEECzH+1BfvFUKVjB2AiIgIAjFyZiMX7tIMMAKgFsHhfOoJreQIoua5SsfmvhqF3aIB5iyQqBcMMERFhS9JVxKdllbvPzjNZmDewRYl1lfzlMiwaHIbeoWyRIcvgbSYioipOpRaYtCG5wv3UAriem891lcjqMMwQEVVxR9NvIzdfpdO+l28/4LpKZHV4m4mIqIrTZ9h1HS83M1ZCZBi2zBARVREqtSj19pCuw64dJMDr4XXNWySRARhmiIiqgLiUDEzdnKY1i6+/XIaYvkHoFqSAv1xW4Qy/I9or4eLEBn2yPvyuJCKyc3EpGRi16kSJsJKZnY9Rq04gPi0TMX2DyhxyDQDdgnwxOSrIvIUSGYhhhojIjuUVqvDvX0+XOtFd8bapm9PQLUiBhYPD4P/EsGsPmSPmD2yOxf9qZfZaiQzF20xERHZIpRZ4d81JbDmdUe5+AkBGdj6Opt9Gz2B/dAtScNg12RyGGSIiOxOXkoHxa0/hQaFuw62Bf0Y0cdg12SKGGSIiOxKXkoG3V53Q+zh9FpIksjYMM0REdkKlFpi6OU2vYyQAFPJHt5OIbBU7ABMR2Ymj6bcrHF5dmpi+QewXQzbN6DCTk5ODDRs24MyZM6aoh4iIDKTPTL4A4ClzwsLBYegZzAUiybbpHWZefvllzJ8/HwCQl5eHli1b4uWXX0ZoaCh+++03kxdIRES60affiwTAkY8iGWTILugdZvbu3YsOHToAANavXw8hBO7evYu5c+di2rRpJi+QiIi0qdQChy7cwsakqzh04RZU6kczxrRWesFfLit38rtib3ZUwtXF0byFElUSvTsAZ2dnw8vrUUexuLg4DBgwAG5uboiKisK///1vkxdIRET/KG9Zgp7B/ojpG4RRq05AApQ6UZ5EArzZQYlJvTmbL9kPvVtmAgMDcejQIdy/fx9xcXHo3r07AODOnTuQyTi0j4jIXCpaliAuJQM9g/2xcHAYFE/M5Ovm7IgXw2rh7Oe9GGTI7ujdMhMdHY3XXnsN1apVw1NPPYVOnToBeHT7KSQkxNT1ERFVeYVFaqw4mI5v/jhX5rIEEvyzLAFn8qWqRu8wM3r0aLRu3RpXrlxBt27d4ODwqHGnXr167DNDRGRi07em4od9l0oNMY97fFmC8PrenMmXqhSDJs1r2bIlQkNDkZ6ejvr168PJyQlRUVGmro2IqMoqLFIjak4Czt14oNdx+g7PJrIHeveZefDgAUaMGAE3Nzc0bdoUf//9NwDgnXfewRdffGHyAomIqprpW1Px9Mfb9Q4yAJcloKpJ7zAzadIknDp1Cnv27NHq8BsZGYlffvnFpMUREVU1I1cmYvG+S3ofJ8GjUU1cloCqIr1vM23YsAG//PIL2rZtC4nkn85kQUFBuHDhgkmLIyKqKlRqgTnxfyE+LUvvY4t/E3NZAqqq9A4zN27cgK+vb4nt9+/f1wo3RESkm7iUDEzZlIrMnAKDjlc8Ns8MUVWkd5hp1aoVtm7dinHjxgGAJsAsXrwY4eHhpq2OiMiOqdQC83edwzd/nDP4HD8Ob412DWqyRYaqNL3DTGxsLHr27Im0tDQUFRVhzpw5SE1NxaFDh5CQkGCOGomI7M6j1pg0ZOYYPvpoZIe66PC0jwmrIrJNencAbteuHQ4cOIAHDx6gfv362LFjB/z8/HDo0CE888wz5qiRiMiubDudgbdXnTAqyHRt7IPJUU1NWBWR7ZIIISqai8mm5eTkQC6XIzs7G56enpYuh4iqMJVaYO7Oc5i7s/SZfHXVtbEvlgxtZbK6iKyRPn+/9b7NtG3bNjg6OqJHjx5a23///Xeo1Wr06tVL31MSEdm9uJQMTPztNLLziow6z8gOddkiQ/QEvW8zffjhh1CpVCW2CyHw4YcfmqQoIiJ7Epfy6LaSoUHG2VGC/i1q4a9pvRhkiEqhd8vMuXPnEBRUcsXVxo0b4/z58yYpiojIHhQWqbH8QDpm/f6nwed4L7IhxnZpyNFKROXQO8zI5XJcvHgRdevW1dp+/vx5uLu7m6ouIiKb9vmWFCzZf9ng4xWeUkzp15RzxxDpQO8w069fP0RHR2P9+vWoX78+gEdB5v3330e/fv1MXiARka3pN38fTv8vx+Dj34t8GmO7NGBrDJGO9O4z8+WXX8Ld3R2NGzeGUqmEUqlEkyZN4O3tja+++socNRIR2QSVWmD0quMGBxkHCbBgUBjejeRtJSJ9GHSb6eDBg4iPj8epU6fg6uqK0NBQdOzY0Rz1ERHZhLiUDMRsTMH13EKDzzH/1RboHcrbSkT64jwzRERG2nb6GkavPmnw8RIJ8N2rLdA7NMCEVRHZNpPPMzN37ly8+eabkMlkmDt3brn7vvPOO7pXSkRkw1RqgXk7z2HOTsPXVgKA714NY4sMkRF0aplRKpU4duwYvL29oVQqyz6ZRIKLFy+atEBjsWWGiMwhLiUDH65Lxt0HDw0+hz9XuyYqk8lbZtLT00v9NxFRVVQ8CZ4xOH8MkenoNZrp4cOHqFevHtLS0sxVDxGRVSssUuOj9ckGH+/kIMGiwWF4N/JpBhkiE9FrNJOzszMKCgogkfAHkIiqFpVaYP6u81i87wLuFZRc0kUXvYMVmDcojCGGyMT0Hpo9btw4zJw5Ez/88AOcnPQ+nIjI5hjbP+bFsFqY0T8ULk56T+1FRDrQO40cOXIEO3fuxI4dOxASElJiCYN169aZrDgiIkvbfOoaxv1s2LBrB0nx3DEcck1kTnr/N6F69eoYMGAAevTogYCAAMjlcq2HPhYuXIjQ0FB4enrC09MT4eHh2L59u+Z5IQSmTJmCgIAAuLq6olOnTkhNTdW3ZCIig0zfmmpwkAGA+a+GMcgQVQKLTpq3efNmODo6okGDBgCAFStW4Msvv8TJkyfRtGlTzJw5E9OnT8fy5cvx9NNPY9q0adi7dy/Onj0LDw8PnV6DQ7OJSF+FRWr8a+kRHL5426Dja7g5I7Z/CIdcExlBn7/fOocZtVqNr7/+Ghs2bMDDhw8RGRmJTz/9FDKZzCRFF/Py8sKXX36J4cOHIyAgANHR0Zg4cSIAoKCgAH5+fpg5cybeeustnc7HMENEuiosUmPI0sM4dPGOQce7Sx3xZod6HHJNZAL6/P3W+TbTzJkz8eGHH8Ld3R3+/v6YPXu2SWf7ValUWLNmDe7fv4/w8HCkp6cjMzMT3bt31+wjlUoRERGBgwcPlnmegoIC5OTkaD2IiCoSuy0NjT7ebnCQ8XZ3wclPunPINZEF6Bxmli9fjnnz5mHHjh3YuHEjNmzYgJUrV8LYu1TJycmoVq0apFIp3n77baxfvx5BQUHIzMwEAPj5+Wnt7+fnp3muNLGxsVp9eAIDA42qj4jsX+y2NHy/Nx2G/jaTAJj+QjBHKxFZiM4/eZcvX0afPn00X/fo0QNCCFy7ds2oAho1aoSkpCQcPnwYo0aNwpAhQ7Qm5XtyThshRLnz3EyaNAnZ2dmax5UrV4yqj4jsW2GRGov3GT6zeQ03ZywcHMb+MUQWpPPQ7MLCQri6umq+lkgkcHFxQUFBgVEFuLi4aDoAt2zZEomJiZgzZ46mn0xmZib8/f/5JZGVlVWiteZxUqkUUqnUqJqIyP6p1AJH02/jl8S/oTawSeadLg14W4nICug1z8wnn3wCNzc3zdeFhYWYPn261pDs2bNnG1WQEAIFBQVQKpVQKBSIj49HixYtNK+XkJCAmTNnGvUaRFS1bUm6ikkbkpGbb9hMvgAwskNdjO/eyIRVEZGhdA4zHTt2xNmzZ7W2tWvXTmuVbH2XOfjoo4/Qq1cvBAYGIjc3F2vWrMGePXsQFxcHiUSC6OhozJgxAw0bNkTDhg0xY8YMuLm5YdCgQXq9DhFRsZErExGflmXw8RIAb3ZUYlLvINMVRURG0TnM7Nmzx+Qvfv36dbz++uvIyMiAXC5HaGgo4uLi0K1bNwDAhAkTkJeXh9GjR+POnTto06YNduzYofMcM0RExVRqgXd/PmlUkAlXemHFiDbs6EtkZSw6aV5l4DwzRFWbSi0wd+df+E/CBeQVGf7r7i22xhBVKn3+fnOlSCKyW1uSruG9tUl4aGAPXwmAtmyNIbJ6DDNEZJeM6RvTPcgPbZReeD28LkMMkQ1gmCEiuzN9a5rBQcZD5oiFg5/hcGsiG8L/chCRXckrVOEHIybBi30+hEGGyMbo1DJz+vRpnU8YGhpqcDFERIZSqQXm7TyH7/acN3hZgm5BvujTvJZJ6yIi89MpzDRv3hwSiaTCpQSARwtGEhFVpriUDIxfewoPCg37/SMB8EYHJSZHcbQSkS3SKcykp//TZHvy5El88MEH+Pe//43w8HAAwKFDh/D1119j1qxZ5qmSiKgMcSkZeHvVCYOPlzpJkPRpD7i6OJqwKiKqTDqFmTp16mj+/dJLL2Hu3Lno3bu3ZltoaCgCAwPxySef4Pnnnzd5kURETyosUmPFwXTMjv/LqPPMGdiCQYbIxuk9mik5ORlKpbLEdqVSqbXaNRGRucRuS8PifekGLxAJANVdnfDFgFCudk1kB/QezdSkSRNMmzYN+fn5mm0FBQWYNm0amjRpYtLiiIieFLstDd/vNTzINA+U46cRbXD8k+4MMkR2Qu+WmUWLFqFv374IDAxEs2bNAACnTp2CRCLBli1bTF4gERHw6LbS8v3p+H6vYcOu3ZwdcPyT7rylRGSH9A4zrVu3Rnp6OlatWoU///wTQgi88sorGDRoENzd3c1RIxFVcdO3puGH/ekwZiW5r15qziBDZKcMmgHYzc0Nb775pqlrISLSUKkFjqbfxoxtaUi+mmPUud7qqETvUN5SIrJXBs0A/OOPP6J9+/YICAjA5cuXAQDffPMNNm7caNLiiKhqikvJQPuZu/Dq4sNGBZlqUkcsGNSCq10T2Tm9w8zChQsxfvx49OrVC3fu3NFMklejRg18++23pq6PiKqYuJQMjFp1AhnZ+RXvXIbREfXx0xttcCqmB3qHBpiwOiKyRnqHmXnz5mHx4sWYPHkynJz+uUvVsmVLJCcnm7Q4Iqpa8gpViP4lyeDlCIBHt5Qm9GqMZxvU5BpLRFWE3n1m0tPT0aJFixLbpVIp7t+/b5KiiKjqKR5ybSgHCTCyg5K3lIiqIL3DjFKpRFJSktaswACwfft2BAXxlwgR6W/61keT4BlqUq/GGPasEi5OBnUDJCIbp3eY+fe//40xY8YgPz8fQggcPXoUP//8M2JjY/HDDz+Yo0YismNbkq4ZFWRGdlDirYj6JqyIiGyN3mFm2LBhKCoqwoQJE/DgwQMMGjQItWrVwpw5czBw4EBz1EhEdiouJQNj15w0+PhuQb5c6ZqIIBHC8Gmobt68CbVaDV9fX1PWZFI5OTmQy+XIzs6Gp6enpcshov+nUgu0n7nLoFFLHjJHxD4fgj7Na5mhMiKyBvr8/db7BnOXLl1w9+5dAEDNmjU1QSYnJwddunTRv1oiqpKOpt/WO8hInRzw04g2SPq0B4MMEWnofZtpz549KCwsLLE9Pz8f+/btM0lRRGT/snL1b5H55uVmeLZhTTNUQ0S2TOcwc/r0ac2/09LSkJmZqflapVIhLi4OtWrxf0pEpBtfD5le+z9akoAT4BFRSTqHmebNm0MikUAikZR6O8nV1RXz5s0zaXFEZNtUaoHDF2/h0IVbAATC69VE2/recHSQoLXSC/5yGTKz88udJE/q5IBvXm7OtZWIqEw6h5n09HQIIVCvXj0cPXoUPj4+mudcXFzg6+sLR0euSEtEj0LM/F3n8P3ei3hQqNJsn7/7Aqq7OeOL/iHoGeyPmL5BGLXqBCRAqYHmnS4N8G7k05zJl4jKZdRoJlvA0UxElSsuJQMTfj2NnPyicvdbNDgMPYP9EZeSgamb07Q6A/vLZYjpG4SewWyNIaqq9Pn7rXeYiY2NhZ+fH4YPH661fenSpbhx4wYmTpyof8VmxDBDVHk2n7qGcT/rNm+MwlOKAx92haODBCq1wNH028jKzYevhwytlV5sjSGq4sw6NPv7779H48aNS2xv2rQpFi1apO/piMhOxGw6rXOQAYDMnAIcTb8NAHB0kCC8vjeea14L4f/fp4aISFd6D83OzMyEv3/Jpl8fHx9kZGSYpCgish0qtUDbGX/gxr2SUzZUxJDh2URET9K7ZSYwMBAHDhwosf3AgQMICOCwSaKqZMOxK6j/0TaDggyg//BsIqLS6N0y88YbbyA6OhoPHz7UDNHeuXMnJkyYgPfff9/kBRKRdeow8w9cuVNg8PEKTylaK71MWBERVVV6h5kJEybg9u3bGD16tGYmYJlMhokTJ2LSpEkmL5CIrE/LafG4aWBrTLEp/ZqybwwRmYTBQ7Pv3buHM2fOwNXVFQ0bNoRUKjV1bSbB0UxEplNYpEaPb3Yj/ZbhfV08XCT48uUWHHZNROXS5++33i0zxapVq4ZWrVoZejgR2ZiYTclYcfBvo87h6uyApCk92SJDRCalU5jp378/li9fDk9PT/Tv37/cfdetW2eSwojIOhQWqdFsShzyioybX9NT5oTTU3qYqCoion/oFGbkcjkkEonm30RUNUzfmorF+y4ZdQ4HAEc+ioSPp3XeiiYi28flDIioVCNXJiI+LcuocwTWkGHfxK4mqoiIqpJK6TNDRPbpXn4RBv7nEFKu5Rh1nuAAD2x5p6OJqiIiKptOYaZFixaa20wVOXHihFEFEZHl9J23F8lXc40+z4j2dfFJn6YmqIiIqGI6hZnnn39e8+/8/HwsWLAAQUFBCA8PBwAcPnwYqampGD16tFmKJCLzUqkFWs+Ix617D406j9Jbht/f6wwXJ70nFyciMphOYSYmJkbz7zfeeAPvvPMOPv/88xL7XLlyxbTVEZHZ6bPSdXlCanlg8zjeViKiyqd3B2C5XI5jx46hYcOGWtvPnTuHli1bIjs726QFGosdgInKNmzpEez+66bR54ls4osfhnDeKSIyHbN2AHZ1dcX+/ftLhJn9+/dDJuOicUS24pnPd+DWfeNuKzk5AMlTesLVxdFEVRER6U/vMBMdHY1Ro0bh+PHjaNu2LYBHfWaWLl2KTz/91OQFEpFpqdQCzabE4V6h2qjzcNg1EVkLvcPMhx9+iHr16mHOnDlYvXo1AKBJkyZYvnw5Xn75ZZMXSESms+10BkavNn7E4bBn6yCmb7AJKiIiMh4nzSOqIj7fkool+y8ZdY42yhr4cURbjlYiIrMz+6R5d+/exa+//oqLFy/igw8+gJeXF06cOAE/Pz/UqlXLoKKJyDxUaoEXvtuP01eNmwTvjWfr4uO+nDuGiKyP3mHm9OnTiIyMhFwux6VLl/DGG2/Ay8sL69evx+XLl7Fy5Upz1ElEBjDVsOsR7eviY06CR0RWSu+24vHjx2Po0KE4d+6c1uilXr16Ye/evSYtjogMN3zZYZMEmbc6KjmbLxFZNb1bZhITE/H999+X2F6rVi1kZmaapCgiMty9/CI88/nvKFAZdx5fDxfsn9iV/WOIyOrpHWZkMhlyckreez979ix8fHxMUhQRGabfvH1G940BuLYSEdkWvcPMc889h88++wxr164FAEgkEvz999/48MMPMWDAAJMXSEQVKyxSo/X0HbibZ1xzjKMEOPN5L7bGEJFN0fs31ldffYUbN27A19cXeXl5iIiIQIMGDeDh4YHp06ebo0YiKkfstjQ0+ni70UHGQ+aEC7FRDDJEZHP0bpnx9PTE/v37sWvXLpw4cQJqtRphYWGIjIw0R31EVI6YjSlYceiy0ecZEl4HU5/jJHhEZJv0CjNFRUWQyWRISkpCly5d0KVLF3PVRUQV6DBzJ67cyTfqHC6OEqRM7cnWGCKyaXqFGScnJ9SpUwcqlZHDJIjIYCq1QIvPfkdOvnE/h0/VkGEv11YiIjug93/HPv74Y0yaNAm3b982Rz1EVI5tpzPQ4KNtRgUZJwlw6tPuDDJEZDf07jMzd+5cnD9/HgEBAahTpw7c3d21nj9xwvhF7IiopOlb07B4X7pR53jKyxV7J/D2MBHZF4OGZkskEnPUQkSlyCtU4bn5+/BX1n2jzjOiXR180o+dfInI/nDVbCIrNmzZUew+e8Ooc7SqUx0/jQxnJ18isin6/P3W+bfbgwcPMGbMGNSqVQu+vr4YNGgQbt68aXSxRFS64JjtRgeZkR2U+O+oZxlkiMiu6fwbLiYmBsuXL0dUVBQGDhyI+Ph4jBo1ypy1EVVJ9/KLoPxwK+4VqI06z4JBLTA5KshEVRERWS+d+8ysW7cOS5YswcCBAwEAgwcPxrPPPguVSgVHR0ezFUhUlUR9uwepmcb1jfGUOeLkpz3g6MC+bURUNejcMnPlyhV06NBB83Xr1q3h5OSEa9euGfzisbGxaNWqFTw8PODr64vnn38eZ8+e1dpHCIEpU6YgICAArq6u6NSpE1JTUw1+TSJrpFILKD/canSQaapwx+kpPRlkiKhK0TnMqFQquLi4aG1zcnJCUVGRwS+ekJCAMWPG4PDhw4iPj0dRURG6d++O+/f/+YU+a9YszJ49G/Pnz0diYiIUCgW6deuG3Nxcg1+XyFqo1ALfxJ9F/Y+2wdie+JFNfLA1upMpyiIisik6j2ZycHBAr169IJVKNds2b96MLl26aM01s27dOoOLKV7AMiEhAR07doQQAgEBAYiOjsbEiRMBAAUFBfDz88PMmTPx1ltvVXhOjmYiaxWXkoH3fklC3kPj+sY87euOjWM7wNWFt3uJyH7o8/db5z4zQ4YMKbFt8ODB+ldXjuzsbACAl5cXACA9PR2ZmZno3r27Zh+pVIqIiAgcPHiw1DBTUFCAgoICzdc5OTkmrZHIFOJSMvD2KuMnmKzj5Yod4zsZXxARkQ3TOcwsW7bMnHVACIHx48ejffv2CA5+NLFXZmYmAMDPz09rXz8/P1y+XPpKwbGxsZg6dapZayUyRvaDhyYJMqG1PLFpXIeKdyQisnN6zwBsLmPHjsXp06exf//+Es89OeOwEKLMWYgnTZqE8ePHa77OyclBYGCgaYslMlDfefuQfNW41kIJgOQpPVBNZjU/vkREFmUVvw3HjRuHTZs2Ye/evahdu7Zmu0KhAPCohcbf31+zPSsrq0RrTTGpVKrVr4fIWjzz2e+49cDwDvMA0LmRD5YNa22iioiI7INFpwUVQmDs2LFYt24ddu3aBaVSqfW8UqmEQqFAfHy8ZlthYSESEhLQrl27yi6XyCD38ovw9KStRgeZM5/1ZJAhIiqFRVtmxowZg9WrV2Pjxo3w8PDQ9JGRy+VwdXWFRCJBdHQ0ZsyYgYYNG6Jhw4aYMWMG3NzcMGjQIEuWTqSTvnP3IvmacdMIyJwc8Oe0XiaqiIjI/lg0zCxcuBAA0KlTJ63ty5Ytw9ChQwEAEyZMQF5eHkaPHo07d+6gTZs22LFjBzw8PCq5WiLdFRap0Xzq73hg5LDrIH8PbHu3o4mqIiKyT1w1m8jEPt+UiiUHLxl9niHtAjG1X6jxBRER2SCzzDNDRBWLmLULl2/nGX2ekR2UXCSSiEhHDDNEJhI1d69JgsyCQWHoHepf8Y5ERASAYYbIaCq1QP/5e5F67Z5R52le2xO/jW7PRSKJiPTEMENkhG2nMzBuzQmojOjnK3METsb05NpKREQGYpghMoBKLRC95gQ2n8406jxPebli74QuJqqKiKhqYpgh0lNcSgYm/paM7LyHRp1nRLs6+KRfsImqIiKquhhmiPSw7XQGRq82bpFIucwRiR93h4uTRSfgJiKyGwwzRDooLFJjwq+nsCHpmlHn6drYB0uGckkCIiJTYpghqsD0rWlYvC/dqHPUcHXEwUnd2MmXiMgMGGaIyjFi2VHsPHvDqHN0auiF5SPCTVQRERE9iWGGqAy9v01AWqZxc8eE1vJkkCEiMjOGGaLHqNQCR9NvY+TKRNwrUBl1rhHtlfikD5ckICIyN4YZov8Xl5KBKZtSkZlTYNR5+jbzw9cvhXG0EhFRJWGYIQKw7fQ1jF590ujzhNbyxLxXW5qgIiIi0hX/60hV3sakq6YJMrU9sWlcBxNURERE+mDLDFVpb6xIxB9nsow6R013Z+z5dxdUk/HHiYjIEvjbl6oklVrgxYUHcPJKtlHniWzigx+GcBI8IiJLYpihKmdL0jW8t/YkHhqx0jUAzB/YHH2a1zJNUUREZDCGGapS3lhxFH+cMW4SPE+ZI05+2gOODhITVUVERMZgmKEq41H/GOOCTHAtD2wZ19FEFRERkSkwzJDdU6kF5vzxl1EdfZ0dga8HNEO/sNomrIyIiEyBYYbs2saTVzHxt1PILxIGnyMqxB9zX23B20pERFaKYYbsUmGRGhGzdiHDyNl8R3aoi8lRTU1UFRERmQPDDNmd2G1p+H5vulHncHIA5g4MQ+9QfxNVRURE5sIwQ3Zl6uZULDtwyahz9Az2w3eDnuFtJSIiG8EwQ3ahsEiN3nMScP7GA6POM7KDEpOjuNI1EZEtYZghm2eK1hgnBwnmDmzB20pERDaIYYZsWsdZO/H37XyDj3d1dsDIDvXwbuTTvK1ERGSjGGbIJplitNLYzvXxXrdGDDFERDaOYYZsztTNKVh24LJR5wit7YkPejQ2UUVERGRJDDNkUyJm7cLl23lGnYMrXRMR2ReGGbIJKrVA1692GxVkGvu5Y/2YDnB1cTRhZUREZGkMM2T1Np34H6LXnoLaiHN0buyDZUPZGkNEZI8YZsiq9Zu3D6ev5hh1jtDangwyRER2jGGGrFJeoQrPfrETtx88NOo8c19ujn5htUxUFRERWSOGGbI6I1cmIj4ty6hzKL3d8Mf7nTjsmoioCmCYIavyxoqj+OPMDYOPdwDwzSvN8VwLtsYQEVUVDDNkFe7lF+GV7w8gNeOewefw9XDBoUmRbI0hIqpiGGbI4vrM3YuUa7lGnSM4wANb3uloooqIiMiWMMyQRYVO+R05+UVGnWNEu7r4pF9TE1VERES2hmGGLEKlFnh+3l6jgoyjgwTzuNI1EVGVxzBDlS4uJQNjV59AkRGz4ClruuGP8RytREREDDNUiVRqgfm7zuObP/4y6jxD2j2Fqf1CTFQVERHZOoYZqhRxKRmI2ZiK67kFRp1nZAclJkcFmagqIiKyBwwzZHbbTmdg9OoTRp2jmtQRswY0Y/8YIiIqgWGGzGrb6WsYs/qkUed4pk51rH2rHfvHEBFRqRhmyGy2nb6G0UYGGd5WIiKiijDMkMmp1ALzdp7DnJ3nDD5HHS8Z4sd3houTgwkrIyIie8QwQya17XQGJvx2GvcKDJ8/hrP5EhGRPhhmyCRUaoFxq49jW8p1o84TWtsTm8Z2MFFVRERUFTDMkNG2JF3DO7+chFoYfo4ujWpi7qvPoJqM35JERKQf/uUgo4xcmYj4tCyjzvFWRyUm9WYnXyIiMgx7V5LBpm9NY5AhIiKLY8sMGaSwSI0f9qcbdKwEQP8WAYgd0IyjlYiIyGgMM2SQHw9dgjCwj8x3g8I4ky8REZkMwwzpRKUWOJp+G1m5+fD1kOHSrft6n6O6mzO+6B+CnsEMMkREZDoMM1Shbacz8PHGFNy+X6jZ5qHnqKPewX6YN+gZLklAREQmxzBD5Zq+NQ2L95XsG5Obr/ukeF0b+2DB4JamLIuIiEiDYYbKNH1rKhbvu2TUOSKb+OKHIa1MUxAREVEpGGaohOK1lXQNMhIAT/YFdnYAvnm5Ofo0r2Xq8oiIiLQwzJCWLUlX8eH6ZNwrUOl8zFcvNcPt+4VIvHQLbi5OGBBWG+0a1GT/GCIiqhQMMwQAyCtUodvsPfjf3Xy9jw2o7ooBz9TGyI71zFAZERFR+RhmyKglCbzdXdBa6WXiioiIiHRn0elX9+7di759+yIgIAASiQQbNmzQel4IgSlTpiAgIACurq7o1KkTUlNTLVOsnTJ2baXPnwvm7SQiIrIoi4aZ+/fvo1mzZpg/f36pz8+aNQuzZ8/G/PnzkZiYCIVCgW7duiE3N7eSK7VPeYUqo4LMyA5KzuRLREQWZ9HbTL169UKvXr1KfU4IgW+//RaTJ09G//79AQArVqyAn58fVq9ejbfeeqsyS7UrxbP5frfrnMHnGNmhLiZHcYFIIiKyPKvtM5Oeno7MzEx0795ds00qlSIiIgIHDx5kmDFA8ZDrH/Zf1Gu00uO83F0w7blgtsgQEZHVsNowk5mZCQDw8/PT2u7n54fLly+XeVxBQQEKCgo0X+fk5JinQBsTl5KB8WtP4UGhYSEGAMZ1qY/oyEbsI0NERFbFon1mdCGRaP/hFEKU2Pa42NhYyOVyzSMwMNDcJVq9uJQMvL3qhFFBpluQL97v3phBhoiIrI7VhhmFQgHgnxaaYllZWSVaax43adIkZGdnax5Xrlwxa53WTqUWmLIpzahzjOygxOJ/cUkCIiKyTlZ7m0mpVEKhUCA+Ph4tWrQAABQWFiIhIQEzZ84s8zipVAqpVFpZZVq9o+m3kZmj/0R4APC0nzu2jOsIFyerzbxERESWDTP37t3D+fPnNV+np6cjKSkJXl5eeOqppxAdHY0ZM2agYcOGaNiwIWbMmAE3NzcMGjTIglXblqxc/YJMSC1PNA+sjo96B8HVxdFMVREREZmORcPMsWPH0LlzZ83X48ePBwAMGTIEy5cvx4QJE5CXl4fRo0fjzp07aNOmDXbs2AEPDw9LlWz1ioddZ+Xmw9dDhpruurdSebu7YMOY9uwXQ0RENkUihHhywWO7kpOTA7lcjuzsbHh6elq6HLPadjoDH29Mwe37hZptCk8Z8h+qcDfvYYXHLxgUxiHXRERkFfT5+221fWZId4VFavxr6REcvni7xHO69pd5qyNn8yUiItvEMGPjPt+ShiX70yvcr4abM9RCIDuvSGu7u9QRXw4IRe/QAHOVSEREZFYMMzas3/x9OP0/3SYFvPPgIX56ow0A4NCFWwAEwuvVRNv63uwjQ0RENo1hxgap1ALvrD6mc5ApdvNeAZ5rXgvPNqhppsqIiIgqH8OMjYlLycCUTWkGzR3j6yEzQ0VERESWxTBjQ+JSMjBq1QkYMvzM290FrZVeJq+JiIjI0ji1q41QqQWmbk4zKMgAwOfPBbNvDBER2SW2zFixxyfAu5lbgIxsw5Yl4LBrIiKyZwwzViouJQNTN6cZHGAAQOokwTcvN+ewayIismsMM1bImL4xxer5uCH+vU68tURERHaPYcbK5BWq8O9fTxkVZEa0r4NP+gSbrCYiIiJrxjBjJcpbkkBXL4bVwoz+oXBxYr9uIiKqOhhmrEDstjT8Z2+6wa0x/nIZYvoGoWcwO/kSEVHVwzBjYbHb0vD93orXVnrSJ1FNUNNDCl8PGVorvdg3hoiIqiyGGQsqLFJj8T79gowEgEIuw9BnlQwwRERE4KR5FvXjoUtQG3BvKaZvEIMMERHR/2PLTCV6fBI8Xw8ZLt26r9fxHjJHfPliM/aNISIiegzDTCUpbRI8D5nuH7+DBDj6UTe4ujiaozwiIiKbxTBTCcqaBC83v0jnc4zsoGSQISIiKgX7zJiZsQtEAo/WVprUO8hkNREREdkTtsyY2dH02zqtrySRAOKxxCMB0FbphRUj2nASPCIionIwzJhZVq5uC0V+9WIz3H1QiMu3H6COlxteD6/LEENERKQDhhkTe3LEUs1qUp2OC6juigHP1DZzdURERPaHYcaEShuxpPCUorqbM7IfPCy130zxJHitlV6VVicREZE9YZgxkbJGLF3PKdBskwBazxdPe8dJ8IiIiAzHThkmUN6IJYFHoaW6mzP8PGVazynkMiwcHMZJ8IiIiIzAlhkDPd435mZuQbkjlgSAuw8e4qcRYXBwkGj603CBSCIiIuMxzBigtL4xurh5vwDPNa9lpqqIiIiqJoYZPZXVN0YXvh6yinciIiIivTDM6MHQ2Xw5YomIiMh82AFYD7rO5vs4jlgiIiIyL7bM6EHX2Xwfp5DLENM3iCOWiIiIzIRhRg+69nn5JKoJanpIOWKJiIioEjDM6KG10gv+chkys/PLnc136LNKBhgiIqJKwj4zenB0kCCmbxCAf/rCFGPfGCIiIstgmNFTz2B/LBwcBoWcs/kSERFZA95mMkDPYH90C1JorY7NvjFERESWwTBjIEcHCcLre1u6DCIioiqPt5mIiIjIpjHMEBERkU1jmCEiIiKbxjBDRERENo1hhoiIiGwawwwRERHZNIYZIiIismkMM0RERGTTGGaIiIjIptn9DMBCPFrfOicnx8KVEBERka6K/24X/x0vj92HmdzcXABAYGCghSshIiIifeXm5kIul5e7j0ToEnlsmFqtxrVr1+Dh4QGJhAtBliYnJweBgYG4cuUKPD09LV1OlcfrYV14PawLr4d1Mef1EEIgNzcXAQEBcHAov1eM3bfMODg4oHbt2pYuwyZ4enryl4MV4fWwLrwe1oXXw7qY63pU1CJTjB2AiYiIyKYxzBAREZFNY5ghSKVSxMTEQCqVWroUAq+HteH1sC68HtbFWq6H3XcAJiIiIvvGlhkiIiKyaQwzREREZNMYZoiIiMimMcwQERGRTWOYqSL27t2Lvn37IiAgABKJBBs2bNB6XgiBKVOmICAgAK6urujUqRNSU1MtU2wVEBsbi1atWsHDwwO+vr54/vnncfbsWa19eE0qz8KFCxEaGqqZ+Cs8PBzbt2/XPM9rYVmxsbGQSCSIjo7WbOM1qVxTpkyBRCLReigUCs3zlr4eDDNVxP3799GsWTPMnz+/1OdnzZqF2bNnY/78+UhMTIRCoUC3bt00a1uRaSUkJGDMmDE4fPgw4uPjUVRUhO7du+P+/fuafXhNKk/t2rXxxRdf4NixYzh27Bi6dOmC5557TvPLmNfCchITE/Gf//wHoaGhWtt5TSpf06ZNkZGRoXkkJydrnrP49RBU5QAQ69ev13ytVquFQqEQX3zxhWZbfn6+kMvlYtGiRRaosOrJysoSAERCQoIQgtfEGtSoUUP88MMPvBYWlJubKxo2bCji4+NFRESEePfdd4UQ/PmwhJiYGNGsWbNSn7OG68GWGUJ6ejoyMzPRvXt3zTapVIqIiAgcPHjQgpVVHdnZ2QAALy8vALwmlqRSqbBmzRrcv38f4eHhvBYWNGbMGERFRSEyMlJrO6+JZZw7dw4BAQFQKpUYOHAgLl68CMA6rofdLzRJFcvMzAQA+Pn5aW338/PD5cuXLVFSlSKEwPjx49G+fXsEBwcD4DWxhOTkZISHhyM/Px/VqlXD+vXrERQUpPllzGtRudasWYPjx4/j2LFjJZ7jz0fla9OmDVauXImnn34a169fx7Rp09CuXTukpqZaxfVgmCENiUSi9bUQosQ2Mr2xY8fi9OnT2L9/f4nneE0qT6NGjZCUlIS7d+/it99+w5AhQ5CQkKB5ntei8ly5cgXvvvsuduzYAZlMVuZ+vCaVp1evXpp/h4SEIDw8HPXr18eKFSvQtm1bAJa9HrzNRJoe6cXpulhWVlaJpE2mNW7cOGzatAm7d+9G7dq1Ndt5TSqfi4sLGjRogJYtWyI2NhbNmjXDnDlzeC0s4Pjx48jKysIzzzwDJycnODk5ISEhAXPnzoWTk5Pmc+c1sRx3d3eEhITg3LlzVvEzwjBDUCqVUCgUiI+P12wrLCxEQkIC2rVrZ8HK7JcQAmPHjsW6deuwa9cuKJVKred5TSxPCIGCggJeCwvo2rUrkpOTkZSUpHm0bNkSr732GpKSklCvXj1eEwsrKCjAmTNn4O/vbx0/I5XSzZgsLjc3V5w8eVKcPHlSABCzZ88WJ0+eFJcvXxZCCPHFF18IuVwu1q1bJ5KTk8Wrr74q/P39RU5OjoUrt0+jRo0Scrlc7NmzR2RkZGgeDx480OzDa1J5Jk2aJPbu3SvS09PF6dOnxUcffSQcHBzEjh07hBC8Ftbg8dFMQvCaVLb3339f7NmzR1y8eFEcPnxY9OnTR3h4eIhLly4JISx/PRhmqojdu3cLACUeQ4YMEUI8GloXExMjFAqFkEqlomPHjiI5OdmyRdux0q4FALFs2TLNPrwmlWf48OGiTp06wsXFRfj4+IiuXbtqgowQvBbW4Mkww2tSuV555RXh7+8vnJ2dRUBAgOjfv79ITU3VPG/p6yERQojKaQMiIiIiMj32mSEiIiKbxjBDRERENo1hhoiIiGwawwwRERHZNIYZIiIismkMM0RERGTTGGaIiIjIpjHMEJFNkUgk2LBhg8nP26lTJ0RHR5v8vERkfgwzRFSqgwcPwtHRET179tT72Lp16+Lbb781fVE6GDp0KCQSCSQSCZydnVGvXj188MEHuH//frnHrVu3Dp9//nklVUlEpsQwQ0SlWrp0KcaNG4f9+/fj77//tnQ5eunZsycyMjJw8eJFTJs2DQsWLMAHH3xQ6r4PHz4EAHh5ecHDw6MyyyQiE2GYIaIS7t+/j7Vr12LUqFHo06cPli9fXmKfTZs2oWXLlpDJZKhZsyb69+8P4NHtmsuXL+O9997TtJAAwJQpU9C8eXOtc3z77beoW7eu5uvExER069YNNWvWhFwuR0REBE6cOKF3/VKpFAqFAoGBgRg0aBBee+01za2p4jqWLl2KevXqQSqVQghR4jZTQUEBJkyYgMDAQEilUjRs2BBLlizRPJ+WlobevXujWrVq8PPzw+uvv46bN29qnv/1118REhICV1dXeHt7IzIyssLWISIyDMMMEZXwyy+/oFGjRmjUqBEGDx6MZcuW4fFl3LZu3Yr+/fsjKioKJ0+exM6dO9GyZUsAj27X1K5dG5999hkyMjKQkZGh8+vm5uZiyJAh2LdvHw4fPoyGDRuid+/eyM3NNer9uLq6alpgAOD8+fNYu3YtfvvtNyQlJZV6zL/+9S+sWbMGc+fOxZkzZ7Bo0SJUq1YNAJCRkYGIiAg0b94cx44dQ1xcHK5fv46XX35Z8/yrr76K4cOH48yZM9izZw/69+8PLoVHZB5Oli6AiKzPkiVLMHjwYACPbtncu3cPO3fuRGRkJABg+vTpGDhwIKZOnao5plmzZgAe3a5xdHSEh4cHFAqFXq/bpUsXra+///571KhRAwkJCejTp49B7+Xo0aNYvXo1unbtqtlWWFiIH3/8ET4+PqUe89dff2Ht2rWIj4/XvOd69eppnl+4cCHCwsIwY8YMzbalS5ciMDAQf/31F+7du4eioiL0798fderUAQCEhIQYVD8RVYwtM0Sk5ezZszh69CgGDhwIAHBycsIrr7yCpUuXavZJSkrSCgemkpWVhbfffhtPP/005HI55HI57t27p3efnS1btqBatWqQyWQIDw9Hx44dMW/ePM3zderUKTPIAI/en6OjIyIiIkp9/vjx49i9ezeqVaumeTRu3BgAcOHCBTRr1gxdu3ZFSEgIXnrpJSxevBh37tzR6z0Qke7YMkNEWpYsWYKioiLUqlVLs00IAWdnZ9y5cwc1atSAq6ur3ud1cHAocZvl8Vs/wKORSDdu3MC3336LOnXqQCqVIjw8HIWFhXq9VufOnbFw4UI4OzsjICAAzs7OWs+7u7uXe3xF70+tVqNv376YOXNmief8/f3h6OiI+Ph4HDx4EDt27MC8efMwefJkHDlyBEqlUq/3QkQVY8sMEWkUFRVh5cqV+Prrr5GUlKR5nDp1CnXq1MFPP/0EAAgNDcXOnTvLPI+LiwtUKpXWNh8fH2RmZmoFmif7q+zbtw/vvPMOevfujaZNm0IqlWp1qtWVu7s7GjRogDp16pQIMroICQmBWq1GQkJCqc+HhYUhNTUVdevWRYMGDbQexUFJIpHg2WefxdSpU3Hy5Em4uLhg/fr1etdCRBVjmCEijS1btuDOnTsYMWIEgoODtR4vvviiZjRPTEwMfv75Z8TExODMmTNITk7GrFmzNOepW7cu9u7di6tXr2rCSKdOnXDjxg3MmjULFy5cwHfffYft27drvX6DBg3w448/4syZMzhy5Ahee+01g1qBjFW3bl0MGTIEw4cPx4YNG5Ceno49e/Zg7dq1AIAxY8bg9u3bePXVV3H06FFcvHgRO3bswPDhw6FSqXDkyBHMmDEDx44dw99//41169bhxo0baNKkSaW/F6KqgGGGiDSWLFmCyMhIyOXyEs8NGDAASUlJOHHiBDp16oT//ve/2LRpE5o3b44uXbrgyJEjmn0/++wzXLp0CfXr19f0TWnSpAkWLFiA7777Ds2aNcPRo0dLzP2ydOlS3LlzBy1atMDrr7+Od955B76+vuZ902VYuHAhXnzxRYwePRqNGzfGyJEjNUOrAwICcODAAahUKvTo0QPBwcF49913IZfL4eDgAE9PT+zduxe9e/fG008/jY8//hhff/01evXqZZH3QmTvJIJjBYmIiMiGsWWGiIiIbBrDDBEREdk0hhkiIiKyaQwzREREZNMYZoiIiMimMcwQERGRTWOYISIiIpvGMENEREQ2jWGGiIiIbBrDDBEREdk0hhkiIiKyaQwzREREZNP+D/aSGxo5Lrk/AAAAAElFTkSuQmCC\n",
1036 |       "text/plain": [
1037 |        "<Figure size 640x480 with 1 Axes>"
1038 |       ]
1039 |      },
1040 |      "metadata": {},
1041 |      "output_type": "display_data"
1042 |     }
1043 |    ],
1044 |    "source": [
1045 |     "# visualising actual prices and predicted prices\n",
1046 |     "plt.scatter(Y_train, training_data_prediction)\n",
1047 |     "plt.xlabel(\"Actual Prices\")\n",
1048 |     "plt.ylabel(\"Predicted Prices\")\n",
1049 |     "plt.title(\"Actual Price vs Predicted Price\")\n",
1050 |     "plt.show()"
1051 |    ]
1052 |   },
1053 |   {
1054 |    "cell_type": "code",
1055 |    "execution_count": 64,
1056 |    "id": "d3276ffa",
1057 |    "metadata": {},
1058 |    "outputs": [],
1059 |    "source": [
1060 |     "# Prediction on testing data\n",
1061 |     "test_data_prediction = model.predict(X_test)"
1062 |    ]
1063 |   },
1064 |   {
1065 |    "cell_type": "code",
1066 |    "execution_count": 65,
1067 |    "id": "4788b336",
1068 |    "metadata": {
1069 |     "scrolled": true
1070 |    },
1071 |    "outputs": [
1072 |     {
1073 |      "name": "stdout",
1074 |      "output_type": "stream",
1075 |      "text": [
1076 |       "R squared error:  0.8711660369151691\n",
1077 |       "Mean absolute error:  2.2834744154238233\n"
1078 |      ]
1079 |     }
1080 |    ],
1081 |    "source": [
1082 |     "# R squared error\n",
1083 |     "score_1 = metrics.r2_score(Y_test, test_data_prediction)\n",
1084 |     "\n",
1085 |     "# Mean absolute error\n",
1086 |     "score_2 = metrics.mean_absolute_error(Y_test, test_data_prediction)\n",
1087 |     "\n",
1088 |     "print(\"R squared error: \", score_1)\n",
1089 |     "print(\"Mean absolute error: \", score_2)"
1090 |    ]
1091 |   },
1092 |   {
1093 |    "cell_type": "code",
1094 |    "execution_count": 66,
1095 |    "id": "d29926ed",
1096 |    "metadata": {},
1097 |    "outputs": [
1098 |     {
1099 |      "data": {
1100 |       "image/png": 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Sn3zyic3dTCtWrBAtW7YUer1eNG3aVCQmJorly5db7fQQQv5uJkcAiIkTJ9p9rOyOFimfx59//ileeukl0bJlS1G9enVhMBhEmzZtxJQpUyyfqyMvvviiACAaNmxYbmdKamqq+Pvf/y4aN24s9Hq9CA0NFbGxseLLL790+LpKfC6ZmZli7Nixon79+sLPz0/UrVtXdOvWTcyZM8fqvIyMDBEfHy/8/f1FSEiIeOyxx8QXX3zhcDeTECU72t566y0RHR1t+QxjYmLEpk2bLOecPn1a9OnTRwQFBQkAVq8h9Rpcu3ZNjB07VtSqVUsEBgaK+Ph48csvv8jazfTvf/+7wvPMO5bK7q4q/Vjp33EhhPjmm29EbGysqFGjhggMDBRRUVEiKSnJ6pxDhw6Jhx9+WNSrV0/4+fkJo9EoevfubdmlSFSaTggFq3YRERERuRlzZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESaxmCGiIiINI3BDBEREWma1xfNKy4uxvnz5xEUFCS5rDsRERF5lhAC169fR0REBHx8Kp578fpg5vz582jYsKGnh0FEREROOHv2rMMmuF4fzJh7eJw9e7ZcmXkiIiJSp9zcXDRs2FBSLy6vD2bMS0vBwcEMZoiIiDRGSooIE4CJiIhI0zwazMyaNQs6nc7qy2g0Wh4XQmDWrFmIiIhAQEAAevXqhfT0dA+OmIiIiNTG4zMzrVu3RlZWluXryJEjlsfmzZuH+fPnY/Hixdi3bx+MRiPi4+Nx/fp1D46YiIiI1MTjwUy1atVgNBotX3Xr1gVQMiuzYMECzJgxA4MHD0Z0dDRWr16NGzduYO3atR4eNREREamFx4OZEydOICIiApGRkRg6dCh+++03AEBmZiZMJhP69OljOVev1yM2Nha7d++2+3oFBQXIzc21+iIiIiLv5dFgpkuXLvjggw/w7bffYtmyZTCZTOjWrRuys7NhMpkAAGFhYVbPCQsLszxmS2JiIgwGg+WLNWaIiIi8m0eDmX79+mHIkCFo06YN4uLi8PXXXwMAVq9ebTmn7JYsIUSF27SmT5+OnJwcy9fZs2ddM3giIiJSBY8vM5VWo0YNtGnTBidOnLDsaio7C3Px4sVyszWl6fV6S00Z1pYhIiLyfqoKZgoKCnDs2DGEh4cjMjISRqMRW7dutTx+69YtpKSkoFu3bh4cJREREamJRysAP/fccxg0aBAaNWqEixcvYs6cOcjNzcWoUaOg0+mQkJCAuXPnonnz5mjevDnmzp2LwMBADB8+3JPDJiIiG4qKBfZmXsHF6/moF+SPuyND4OvDBr/keh4NZv744w8MGzYMly9fRt26ddG1a1fs2bMHjRs3BgBMnToVN2/exIQJE3D16lV06dIFW7ZskdSngYiI3Cf5aBZmb8pAVk6+5Vi4wR8zB0Whb3S4B0dGVYFOCCE8PQhXys3NhcFgQE5ODvNniIhcIPloFsavOYCyNxPznMySkR0Z0JBscu7fqsqZISIibSkqFpi9KaNcIAPAcmz2pgwUFXv1/5vJwxjMEBGR0/ZmXrFaWipLAMjKycfezCvuGxRVOR7NmSEiIm27eN1+IOPMeeRe3pK0zWCGiIicVi/IX9HzyH28KWmby0xEROS0uyNDEG7wh73/y+tQcoO8OzLEncMiB8xJ22WXCE05+Ri/5gCSj2Z5aGTOYTBDRERO8/XRYeagKAAoF9CYv585KEqTSxfeyhuTthnMEBFRpfSNDseSkR1hNFgvJRkN/tyWrULemLTNnBkiIqq0vtHhiI8yekUyqbfzxqRtBjNERKQIXx8dYpqFenoY5IA3Jm1zmYmIiKgK8cakbQYzREREVYg3Jm0zmCEiIqpivC1pmzkzREREVVBlk7bVVD2YwQwREVEV5WzSttqqB3OZiYiIiCRTY/VgBjNEREQkiVqrBzOYISIiIknUWj2YwQwRERFJotbqwQxmiIiISBK1Vg9mMENERESSqLV6MIMZIiIikkSt1YMZzBAREZFkaqwezKJ5REREJEtlqwcrjcEMERERyeZs9WBX4DITERERaRqDGSIiItI0BjNERESkaQxmiIiISNMYzBAREZGmMZghIiIiTWMwQ0RERJrGYIaIiIg0jcEMERERaRqDGSIiItI0BjNERESkaQxmiIiISNMYzBAREZGmMZghIiIiTWMwQ0RERJrGYIaIiIg0jcEMERERaRqDGSIiItI0BjNERESkadU8PQAiIiKSp6hYYG/mFVy8no96Qf64OzIEvj46Tw/LYxjMEBERaUjy0SzM3pSBrJx8y7Fwgz9mDopC3+hwD47Mc7jMREREpBHJR7Mwfs0Bq0AGAEw5+Ri/5gCSj2Z5aGSexWCGiIhIA4qKBWZvyoCw8Zj52OxNGSgqtnWGd2MwQ0REpAF7M6+Um5EpTQDIysnH3swr7huUSjCYISIi0oCL1+0HMs6c500YzBAREWlAvSB/Rc/zJgxmiIiINODuyBCEG/xhbwO2DiW7mu6ODHHnsFSBwQwREZEG+ProMHNQFACUC2jM388cFFUl680wmCEiItKIvtHhWDKyI4wG66Uko8EfS0Z2rLJ1Zlg0j4iISEP6RocjPsrICsClMJghIiLSGF8fHWKahXp6GKrBZSYiIiLSNAYzREREpGkMZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESaxmCGiIiINI3BDBEREWkagxkiIiLSNAYzREREpGkMZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESapppgJjExETqdDgkJCZZjQgjMmjULERERCAgIQK9evZCenu65QRIREZHqqCKY2bdvH/7zn/+gbdu2VsfnzZuH+fPnY/Hixdi3bx+MRiPi4+Nx/fp1D42UiIiI1Mbjwcyff/6JESNGYNmyZahdu7bluBACCxYswIwZMzB48GBER0dj9erVuHHjBtauXevBERMREZGaeDyYmThxIgYMGIC4uDir45mZmTCZTOjTp4/lmF6vR2xsLHbv3m339QoKCpCbm2v1RURERN6rmid/+Lp16/Dzzz9j//795R4zmUwAgLCwMKvjYWFhOHPmjN3XTExMxOzZs5UdKBEREamWx2Zmzp49i8mTJ+Ojjz6Cv7+/3fN0Op3V90KIcsdKmz59OnJycixfZ8+eVWzMREREpD4em5n5+eefcfHiRdx1112WY0VFRdi5cycWL16M48ePAyiZoQkPD7ecc/HixXKzNaXp9Xro9XrXDZyIiIhUxWMzM/fddx+OHDmCtLQ0y1enTp0wYsQIpKWloWnTpjAajdi6davlObdu3UJKSgq6devmqWETERGRynhsZiYoKAjR0dFWx2rUqIHQ0FDL8YSEBMydOxfNmzdH8+bNMXfuXAQGBmL48OGeGDIRERGpkEcTgB2ZOnUqbt68iQkTJuDq1avo0qULtmzZgqCgIE8PjYiIiFRCJ4QQnh6EK+Xm5sJgMCAnJwfBwcGeHg4RERFJIOf+7fE6M0RERESVwWCGiIiINI3BDBEREWkagxkiIiLSNAYzREREpGkMZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESaxmCGiIiINI3BDBEREWmaqhtNEhERqVFRscDezCu4eD0f9YL8cXdkCHx9dJ4eVpXFYIaIiEiG5KNZmL0pA1k5+ZZj4QZ/zBwUhb7R4R4cWdXFZSYiIiKJko9mYfyaA1aBDACYcvIxfs0BJB/N8tDIqjYGM0RERBIUFQvM3pQBYeMx87HZmzJQVGzrDHIlBjNEREQS7M28Um5GpjQBICsnH29tPY7UU9kMatyIwQwREZEEF6/bD2RKW7z9FIYt24MeSdu47OQmDGaIiIgkqBfkL+t85tG4D4MZIiIiCe6ODEG4wR9SN2Azj8Z9GMwQERFJ4Oujw8xBUQAgK6DJysnH3swrLhsXMZghIiKSrG90OJaM7AijQd6Sk9R8G3IOi+YRERHJ0Dc6HPFRRuzNvIJdJy9j8faTDp8jN9+G5OHMDBERkUy+PjrENAvFlPgWFebR6FBSHfjuyBB3Dq/KYTBDREQASorCpZ7Kxhdp5yx1Umwdo79UlEdj/n7moCj2bXIxLjMREZHNfkO1Av0AANduFFqOsQdReeY8mrKfn5GfldvohBBeHWbn5ubCYDAgJycHwcHBnh4OEZHHle34fDXvFiauPWCzTH9Z5vmFJSM78iZdBjtpK0vO/ZszM0REVYitGRgfHSQFMkDJeTqU1E6JjzLyZl2KOY+G3I85M0REVYS9js9y02BYO4XUhsEMEVEVUFHHZ2exdgqpBYMZIqIqwFHHZ2ewdgqpBXNmiIiqACVnUXQo2anD2imkFpyZISKqApScRRFg7RRSF87MEBGpiKu295o7Ppty8u3mzfjopCUD1w70Q3yUsdJjIlIKgxkiIpWwtW1aqSJ15kq149ccgA7WW7HNodLiYR2QlZOP174+VuFrXb1RiL2ZV7gNmVSDy0xERCpgb9u0KScf49ccQPLRrEr/DHsdn40GfywZ2RH920agTpBe0mtxJxOpCWdmiIg8rKJt00oXqSvd8dnWUpbU3BruZCI1qXQwk5ubi23btqFly5Zo1aqVEmMiIqpSHG2bLl2kTomlnYoq1TrKreFOJlIj2ctMDz/8MBYvXgwAuHnzJjp16oSHH34Ybdu2xeeff674AImIvJ3UJRt3LO2wCzRpkexgZufOnejZsycAYMOGDRBC4Nq1a1i0aBHmzJmj+ACJiLyd2pZ2HOXWsMEkqY3sZaacnByEhJRMLyYnJ2PIkCEIDAzEgAED8Pzzzys+QCIib6fGpR1HuTVEaiJ7ZqZhw4ZITU1FXl4ekpOT0adPHwDA1atX4e/PhDAiIrnUurRjzq15oH19xDQLZSBDqiU7mElISMCIESPQoEEDhIeHo1evXgBKlp/atGmj9PiIiKoELu0QOU8nhJDdRHX//v04e/Ys4uPjUbNmTQDA119/jVq1aqF79+6KD7IycnNzYTAYkJOTg+DgYE8Ph4ioQkpXAHZVRWEiV5Nz/3YqmAGAW7duITMzE82aNUO1auotV8NghoiqKldWFCZyNTn3b9nLTDdu3MBjjz2GwMBAtG7dGr///jsA4Omnn8Ybb7zh3IiJiFSqqFgg9VQ2vkg7h9RT2SiS0rxIBdxRUZhILWQHM9OnT8ehQ4ewY8cOq4TfuLg4fPLJJ4oOjojIk5KPZqFH0jYMW7YHk9elYdiyPeiRtE31gYCjisJASUVhrQRmRI7IDmY2btyIxYsXo0ePHtDp/lp3jYqKwqlTpxQdHBGRM5SYTdHyzIacisJE3kB2ssulS5dQr169csfz8vKsghsiIk9QIk/Enb2SXEFqpeClKScBgEnBpHmyZ2Y6d+6Mr7/+2vK9OYBZtmwZYmJilBsZEZFMSs2maH1mQ2ql4JRfL2tm6YyoIrJnZhITE9G3b19kZGTg9u3bWLhwIdLT05GamoqUlBRXjJGIyCElZ1PU1CvJGY4qCpeV9b9gj/VsSKtkz8x069YNu3btwo0bN9CsWTNs2bIFYWFhSE1NxV133eWKMRIROaTkbIraeiXJZa4oLDdTiEnBpFVOFYhp06YNVq9erfRYiIicpuRsihp7JcnVNzocY7s3wYpdpyWdXzrYi2kW6tKxESlN9szMN998g2+//bbc8W+//RabN29WZFBERHIpOZui1l5JcsVHGWU/R61LZ0QVkR3MvPDCCygqKip3XAiBF154QZFBEcml1cJmpBzzbIq98EKHkl1NUmdTvKFXkqPPxBa1Lp0RVUT2MtOJEycQFRVV7vidd96JkydPKjIoIjlYsp2Av2ZTxq85AB1gtTzk7GxK3+hwxEcZNdvbqKLPpCwtLJ0R2SN7ZsZgMOC3334rd/zkyZOoUaOGIoMikkrLhc1Iea6YTfH10SGmWSgeaF8fMc1CNRPImNn7TErT0tIZkS2yG02OGzcOe/bswYYNG9CsWTMAJYHMkCFD0LlzZ7z//vsuGaiz2GjSexUVC/RI2mZ3B4v5f5o/TuvNf6CrGHaKLs/8mXyXYcKGtHO4kldoeYwzmaRGLu2anZOTg759+2L//v1o0KABAOCPP/5Az549sX79etSqVcvpgbsCgxnvlXoqG8OW7XF43sdPdOXuDKJSGOyRFsi5f8vOmTEYDNi9eze2bt2KQ4cOISAgAG3btsU999zj9ICJnKH1wmZEnmJeOiPyFk7VmdHpdOjTpw/69Omj9HiIJNN6YTMiIlKGpGBm0aJFGDduHPz9/bFo0aIKz3366acVGRiRI95Q2Izch0srRN5LUs5MZGQk9u/fj9DQUERGRtp/MZ3O5k4nT2LOjHcz72YCbG/F1Uo9EHItbt93HQaJ5CouTQDWGgYz3o83KqqIOeAt+w+d0gFvVbyp8+8euZLLgpnCwkK0bNkSX331lc3CeWrEYKZqqIo3EnLMXdv3q+JN3V1BIlVdcu7fsorm+fn5oaCgADodbxKkLlovbEauoWQnbXuqYuHGomKB2ZsybOaqmY+xAze5k+wKwE899RSSkpJw+/ZtV4yHiEgxrt6+r9abuqt7lbkjSCSSQ/bW7J9++gnff/89tmzZgjZt2pRrYbB+/XrFBkdEVBmu3r4v56burrou7ljyYo0nUhvZwUytWrUwZMgQV4yFiEhRrt6+r7abur08FvOSl1J5LKzxRGojO5hZuXKlK8ZBRKQ4V3TSLk1NN3VHS146lCx5xUcZK51TxhpPpDaSc2aKi4vx73//G927d8fdd9+NF198Efn5lfvfxpIlS9C2bVsEBwcjODgYMTEx2Lx5s+VxIQRmzZqFiIgIBAQEoFevXkhPT6/UzySiqsVe1+iwYD0S4pqj4Hax03kl5pu6vdBAh5Ilnopu6krlt7gzj8UcJAIo997ZgZs8QfLMTFJSEl566SXcd999CAgIwPz583H58mX85z//cfqHN2jQAG+88QbuuOMOAMDq1avxwAMP4ODBg2jdujXmzZuH+fPnY9WqVWjRogXmzJmD+Ph4HD9+HEFBQU7/XCKqWvpGhyM+ymjZvn/68g18vPd3vPXdCcs5zuSVVHbmR8n8FncveZmDxLLjN3r5lnRSJ8l1Zlq2bInJkydjwoQJAIDk5GQ8+OCDuHnzpqJbtUNCQvDvf/8bY8eORUREBBISEjBt2jQAQEFBAcLCwpCUlIQnn3xS0uuxzgyR9riybpAr6qM4E5QoPQ5PdZFnjSdyFZd0zT5z5gwGDhxo+f7++++HEALnz59H/fr1nR/t/xQVFeGzzz5DXl4eYmJikJmZCZPJZNXMUq/XIzY2Frt377YbzBQUFKCgoMDyfW5ubqXHRkTu48rdOK7KKyk78+Popu6KcXgqj4UduEkNJOfM3Lp1CwEBAZbvdTodqlevbhU4OOPIkSOoWbMm9Ho9/vWvf2HDhg2IioqCyWQCAISFhVmdHxYWZnnMlsTERBgMBstXw4YNKzU+InIfVxegUzKvpGyuCwDJhRtdkd/CPBaqymTtZnr55ZcRGBho+f7WrVt4/fXXYTAYLMfmz58vawAtW7ZEWloarl27hs8//xyjRo1CSkqK5fGyS1hCiAqXtaZPn45nnnnG8n1ubi4DGiINcMduHKXySio7e+Sq/BbmsVBVJTmYueeee3D8+HGrY926dbPqku1M7kz16tUtCcCdOnXCvn37sHDhQkuejMlkQnj4X38BL168WG62pjS9Xg+9Xi97HETkWe4oQKfEVmolarm4cku33CUvIm8gOZjZsWOHC4fxFyEECgoKEBkZCaPRiK1bt6JDhw4ASmaCUlJSkJSU5JaxEJH7uGM3TmXzSpSaPXJ1fgvzWKiqkd2bSUkvvvgifvjhB5w+fRpHjhzBjBkzsGPHDowYMQI6nQ4JCQmYO3cuNmzYgKNHj2L06NEIDAzE8OHDPTlsInKBOjWkzahKPc+WyuaVKJXrwvwWImXJrgCspAsXLuDRRx9FVlYWDAYD2rZti+TkZMTHxwMApk6dips3b2LChAm4evUqunTpgi1btrDGDJE3knrfruT9vTJ5JUrOHjG/hUg5Hg1mli9fXuHjOp0Os2bNwqxZs9wzICLymMt/StsZKfW8ijibV6J0rgvzW4iU4dFghojIzN19jpzJK3FFrgvzW4gqz6M5M0REZkr0OXI15roQqZOkmZnDhw9LfsG2bds6PRgiqrpc3eFaKcx1IVIfSb2ZfHx8oNPpHBasA0raEqgJezMRaYsr2xkoiT2JiFxL8d5MmZmZlj8fPHgQzz33HJ5//nnExMQAAFJTU/Hmm29i3rx5lRg2EZF2kmKZ60KkHpKCmcaNG1v+/I9//AOLFi1C//79Lcfatm2Lhg0b4uWXX8aDDz6o+CCJqGpxd6DAWRYibZO9m+nIkSOIjIwsdzwyMhIZGRmKDIqIyF20sqxFRPbJ3s3UqlUrzJkzB/n5f/3FLygowJw5c9CqVStFB0dE5Equ7tJNRO4he2Zm6dKlGDRoEBo2bIh27doBAA4dOgSdToevvvpK8QESEbmCO7p0E5F7yA5m7r77bmRmZmLNmjX45ZdfIITAI488guHDh6NGjRquGCMRkeLc0aWbiNzDqQrAgYGBGDdunNJjISJyG6l9ljb/b6mJScFE6uVUBeAPP/wQPXr0QEREBM6cOQMAeOutt/DFF18oOjgi0paiYoHUU9n4Iu0cUk9lo6jYYRkrj5HaFuGD1DMYtmwPeiRtYw4NkUrJDmaWLFmCZ555Bv369cPVq1ctRfJq166NBQsWKD0+ItKI5KNZ6JG0DcOW7cHkdWmqDwActU8oi0nBROolO5h5++23sWzZMsyYMQPVqv21StWpUyccOXJE0cERkTZocVdQRX2WbDHPMc3elKHqGSeiqkh2MJOZmYkOHTqUO67X65GXl6fIoIhIGe5Y9nG0KwhQbwBg7rNkNEhbciqdFExE6iE7ATgyMhJpaWlWVYEBYPPmzYiKilJsYERUOe4qBqf1XUGl2ydsPpqFD1LPOHyO1ORhInIP2cHM888/j4kTJyI/Px9CCOzduxcff/wxEhMT8f7777tijEQkk3nZp+xciHnZZ8nIjooFNFJv7GoOAEq3T5ASzEhNHiYi95AdzIwZMwa3b9/G1KlTcePGDQwfPhz169fHwoULMXToUFeMkYhkcHcxOKk3di0EAOakYFNOvs3PTwfAaCjp3URE6uHU1uwnnngCZ86cwcWLF2EymXD27Fk89thjSo+NiJwgZ9lHCY52BelQsrylhQCgoqRg8/czB0Wx3gyRysgOZnr37o1r164BAOrUqYN69eoBAHJzc9G7d29FB0dE8rl72cfRriABYGjnRor8LHewlxRsNPgrujxHRMqRvcy0Y8cO3Lp1q9zx/Px8/PDDD4oMioic54llH3MAUDbh2Oyt737Fun2/a6YTdemk4IvX81EvyJ8VgIlUTHIwc/jwYcufMzIyYDKZLN8XFRUhOTkZ9evXV3Z0RCSbp/I+zAHA4m0n8NZ3J8o97orkY1cqnRRMROomOZhp3749dDoddDqdzeWkgIAAvP3224oOjojkMy/7jF9zADrAKqBxR97Hun1nbR5nJ2oichXJwUxmZiaEEGjatCn27t2LunXrWh6rXr066tWrB19fX5cMkojksbfsY3RBnZnStF5zhoi0SXIwYy6SV1xc7LLBEJFyPJH34Q01Z4hIe2QnACcmJiIsLAxjx461Or5ixQpcunQJ06ZNU2xwRFQ5lcn7KCoWsgMhb6o5Q0TaITuYee+997B27dpyx1u3bo2hQ4cymCHyAs62QmDROSLyBNl1ZkwmE8LDy/9jVrduXWRlqa8zLhHJU5kO2Cw6R0SeIDuYadiwIXbt2lXu+K5duxAREaHIoIhIGXK7ZivRAZtF54jI3WQvMz3++ONISEhAYWGhZYv2999/j6lTp+LZZ59VfIBE5BxnloqU2o3EonNE5E6yg5mpU6fiypUrmDBhgqUSsL+/P6ZNm4bp06crPkAiks/ZrtlK7kZi0TkichfZwYxOp0NSUhJefvllHDt2DAEBAWjevDn0er0rxkdEMlWmazZ3IxGRFjnVNRsAatasic6dOyM6OpqBDJGKVKZrtjd1wCaiqkPSzMzgwYOxatUqBAcHY/DgwRWeu379ekUGRkTOqcxSUUWtEPC/77kbiYjURlIwYzAYoNPpLH8mIvWq7FKReTfSC+uP4NqNQqvHagX6VXp8SnGmqB8ReSdJwczKlStt/pmI1EepwnU5ZQIZ8zE1dL52tqgfEXknp3NmiEidKlu4rjK1ZuTWtXFGZYr6EZF3kjQz06FDB8sykyMHDhyo1ICIqPIq0zXb2Voz7pgtqcxOLSLyXpKCmQcffNDy5/z8fLz77ruIiopCTEwMAGDPnj1IT0/HhAkTXDJIIpLP2cJ1ziQQO1vXRi6livoRkXeRFMzMnDnT8ufHH38cTz/9NF577bVy55w9e1bZ0RFRpThTuE5uArE7Z0uULOpHRN5Dds7MZ599hn/+85/ljo8cORKff/65IoMiImU4k8PiqNYMAITWqI67GtcGULm6NnKxqB8R2SK7AnBAQAB+/PFHNG/e3Or4jz/+CH9//gNCpBbO5rA4qjUDANl5txD77+2YOSgKBbeLJY1HidkSpXZqEZF3kT0zk5CQgPHjx2PSpElYs2YN1qxZg0mTJmHixImYMmWKK8ZIRDJVdsePvc7Xtl7r9OUbksakxGxJ6Z1a9rCoH1HVoxNCyN47+emnn2LhwoU4duwYAKBVq1aYPHkyHn74YcUHWFm5ubkwGAzIyclBcHCwp4dD5DSpReKKigV6JG2zu/Rjnr34cVpvhzf9W7eL0fG1LfizoKjC1xJC4EJuQYWzJVJ+nlSJ32Rg2Q+ZKL1q5qMDnugZien9Kw52iEgb5Ny/ZS8zAcDDDz+sysCFyFvJWTJScsfPkh2n7AYypV9rSlxzLPjuRLllKSl1beRKPpqF/+zMLN9qQQD/2ZmJDo1qs3AeURXjVNG8a9eu4f3338eLL76IK1dKkvoOHDiAc+fOKTo4IpK/ZKTUjp+iYoGVuzIlvVaTOjVsLksZDf6KVgt2tHNKAHhh/RHsOnHZJQX7iEidZM/MHD58GHFxcTAYDDh9+jQef/xxhISEYMOGDThz5gw++OADV4yTqEpyZtuzUjt+9mZewbWb5Vsa2HutmGahTtW1kcPRrBMAXLtRiBHLf2J7A6IqRPbMzDPPPIPRo0fjxIkTVruX+vXrh507dyo6OKKqzpltz462VutQskTlaMeP1BmeWgF+ltcy17V5oH19xDQLVTwRV86OKLY3IKo6ZAcz+/btw5NPPlnueP369WEymRQZFBGVcGbJyLzjx94ii4C0HBapMzxjujdx2+4hOTuiHPWRIiLvITuY8ff3R25ubrnjx48fR926dRUZFBGV8GSROCnF82oH+mFS7+YVnOH+MZWmZME+IlIv2cHMAw88gFdffRWFhSVr6TqdDr///jteeOEFDBkyRPEBElVlziwZmfNsKiIlSbai7tvmY4mD27i1poujMdnD9gZE3k12MPN///d/uHTpEurVq4ebN28iNjYWd9xxB4KCgvD666+7YoxEVVZFN297257lJMn2SNpWYU6JveJ54QrvUpJDSkG/stjegMi7OVU0DwC2bduGAwcOoLi4GB07dkRcXJzSY1MEi+aRN5BSZ6aoWGDPqWys2v0bth67JOl1zSGQo8BEasE+dyoqFtjzWzYmfnTA7q4rVxTsIyL3kHP/lhXM3L59G/7+/khLS0N0dHSlB+oODGbIG5hv3LtPXsa5azcRUSsA3e+og65NS3YMJR/Nwgvrj+DaDWlbqUvT+g3fXIcHsF2wz1MzSERUOS6rAFytWjU0btwYRUX2K4ISqZ0aZxkqYmtWBgDe3XEK4QZ//K1dON7bKa24nS1yKgKrkXnZqexnZGSdGaIqQ3bRvJdeegnTp0/HmjVrEBLCzrSkLc52kvYU86yDvenTrJz8SgUypWk5SbZvdLjLC/YRkXrJDmYWLVqEkydPIiIiAo0bN0aNGjWsHj9w4IBigyNSkr3AwFxczdXLEXJnhCqq/usKcpNk1TbDZS7YR0RVj+xg5oEHHoBOx//tkLY40xZASc7MCEnZlaQEc86Mo4rApWlthouIvJvsYGbWrFkuGAaRaynZSVouqTNCZWc6TDk3FR2HLc50tfb0DBcRUVmSg5kbN27g+eefx8aNG1FYWIi4uDgsWrQIderUceX4iBShVCdpuaTOCBUXA699bT3TEVKjuqJjqR3oB301H5hyCyzH5CbJenqGi4jIFsnBzMyZM7Fq1SqMGDEC/v7++PjjjzF+/Hh89tlnrhwfkSI81RZA6ozQhLXlc82u5t1SdCyJg9tUOknWkzNcRET2SA5m1q9fj+XLl2Po0KEAgJEjR6J79+4oKiqCr6+vywZIpARzWwBTTr7NWQVn8kakqMxMj1KJvzodMK5npGX2pTJBhqdmuIiIKiK5ncHZs2fRs2dPy/d33303qlWrhvPnz7tkYERKcqYtgBKUmumpHVjx/zt0KFlGskUI4L2dmVj43YlKd4/2ZONLIiJ7JAczRUVFqF7deg2/WrVquH37tuKDInIFez19jAr0GSoqFkg9lY0v0s4h9VS2JWiQ2+XZnlcGRWNKXAubj5lf21GY8tZ3v6L7G99X2IvJEWcaXxIRuZrkdgY+Pj7o168f9Hq95dimTZvQu3dvq1oz69evV36UlcB2BlWPo/onStdHcbRNuaJy+1LnST5+oitimoXa/VlDOzfCW9/9Kum1dLBf4l/KZ8P2AUTkDi7pzTRmzBhJP3zlypWSznMXBjNVi7vrn9jbplz2xm5vXC8PaIXXvj7mMJendN8kWwHHV4fPY/K6NEljtteLSc5nxzozRORqLms0qUUMZqoOqYGFUoqKBXokbbO7u6ds0GBv1sPeTIfZlLgWmNT7jgpnj1JPZWPYsj2yxm+e7QGc++zUVgGYiLyLnPu35JwZIjVzVP8EKKl/UtkE2NLkbFMG/iq3P7BtBADgq8PnkXoqG/FRRpu5PGZScl2cyc0x7zhy9rMzv58H2tdHTLNQBjJE5DEMZsgryA0slODMNuXko1nokbQNw5btweR1aRi2bA96JG0DAPw4rTemxDW3+Rqm3AL8a80BuwFN6d1aUpl3HHnisyMiUpJHg5nExER07twZQUFBqFevHh588EEcP37c6hwhBGbNmoWIiAgEBASgV69eSE9P99CISa3cUf+k7I6lOjX1jp+Ev4IG81JO2cDB3Abg26MmrNx9usLXemH9EbuzS5bdWsEVb4suu+OItWOISOtk92ZSUkpKCiZOnIjOnTvj9u3bmDFjBvr06YOMjAzLDql58+Zh/vz5WLVqFVq0aIE5c+YgPj4ex48fR1BQkCeHTyri6vonthJejcF61Ar0Q86NQoeF+KS0AXhp4xFcu1FY4Tiu3SjEnlPZ6N7cdhuRvtHhiI8yYvG2E3jruxM2xwRY19Rh7Rgi0jqPzswkJydj9OjRaN26Ndq1a4eVK1fi999/x88//wygZFZmwYIFmDFjBgYPHozo6GisXr0aN27cwNq1az05dFIZV9Y/sTejciG3ANf+F8g4KsQnZSnnioNAxiz1t8sVPu7ro8PkuBZYOrIjwiXU1GHtGCLSOo/OzJSVk5MDAAgJKflHMzMzEyaTCX369LGco9frERsbi927d+PJJ5/0yDhJfcw5I+PXHChXv6UyFX6lzKgYAv3gX80XptxSszZltikru0Qj7T2YZ2kc7Thy1WdHROQuqglmhBB45pln0KNHD0RHRwMATCYTACAsLMzq3LCwMJw5c8bm6xQUFKCg4K+uwLm5uS4aMamNOWek3HJQJeqfSJlRuXajEB891hE+Pjq7QYOSSzTm7dSlt0bXqaEHdMDlPwusfr55x5EjrvjsiIjcRTXBzKRJk3D48GH8+OOP5R7T6az/RyiEKHfMLDExEbNnz3bJGEn9pM5GSCV1RuVyXgEeaF/f7uN3R4bAGKyHKbfA7jlASVPIiio/1Q70Q9emtisBl+ZMATulPzsiIndRxdbsp556Cl9++SW2b9+OBg0aWI4bjUYAf83QmF28eLHcbI3Z9OnTkZOTY/k6e/as6wZOqqRk/RNnkmNt9Wny9dFh2N2NHL6OoxKWiYPbYGuGyWYOT2nmHVJy+zCxdgwRaZFHZ2aEEHjqqaewYcMG7NixA5GRkVaPR0ZGwmg0YuvWrejQoQMA4NatW0hJSUFSUpLN19Tr9Vb9o4gqw5wca6/dAGCdHFtRmf8mdWrYeQVrj3Vvgq+PmKxycEJq+GHOA9GIjzKiR9I2hz2dzPk8szdlID7KyKCEiLyaR2dmJk6ciDVr1mDt2rUICgqCyWSCyWTCzZs3AZQsLyUkJGDu3LnYsGEDjh49itGjRyMwMBDDhw/35NCpiihdjM5eOHCzsAhbM0wO68icvpwn6WdG1ArAS/1bIaTGX13qr+QV4rWvj2HxtpMVzsiUxmJ3RFRVeLQ3k728l5UrV2L06NEASmZvZs+ejffeew9Xr15Fly5d8M4771iShB1hbyZSQvLRLLyw3nYdGPMOoFqBfnbrxOgAhAXrAehwIdf+LE9F5HTZLm3h0PYV5vMQEakRG02WwmCGlFBULND9je8dJvA6MiWuBRZ89ysA5wITZ5RuKGkPm0YSkdrIuX+rZjcTkZrtzbxS6UAGAJrUCbS5BdoVSlcgrkhFeT7ckk1EWqCK3UxEaqdU0bt6Qf7oGx2OH6f1xssDWlXqtRzNmwgAQztXvIPKUZ6P3N1QRESewGCGSAIlit6V3vXk66NDnSDnd91NiWsOo8HxmN767lf0SNpmMyhxVN0YKNkNZa+xJRGRWnCZiUgCKVu0HXl5QFSlqwKbl44m9W6OSb2bW/JcQgKqY+OhP/D5gfPlnmOeZSnbk0lKdWPzbigpVYSJiDyFMzNEEkjZou1I7VJbrQHHDR7LKtsnyVzgTl/NB89/fthmIAPYn2WRunSmbF8pIiLlMZghksjcv6js8k6tAD9Jzy8bFMgNkGx1vDbnvJQusGeLrZozzlQ3JiJSIy4zkVdTesuxrf5FxUJgxPs/OXyuraDAXoPHcIM/Xh4Qhdo1qluNHQBST2VbmkvO+tJ2zos9pQMqR0tnUndDERF5GoMZ8lqu2nJcthN1UbGoVFAgtcGjo+aSUpQOqMwzQ+PXHChXkK/skhYRkZpxmYm8kju3HFe0XCQ1KHDU4NHe+5FKB+vdVGb2ls5sLWkREakVKwCT1ykqFuiRtM3ujd88U/LjtN6Kzjq4aibI0fuRQgdUGJywAjARqQ0rAFOV5qktx1KXiwB5wYOj9+OIMViPWX9rXWFAVXbpjIhISxjMkNfx5JZjKUGBoxmcsoGOKeem0+OZEtcCk3rfwVkWIvJqDGbI66h5y7E596Xs2m7W/3J5xt0TiS8PZVkFOiFl6tNIwd5KRFSVMJghr6PWLccVtQ8ASpa/3tuZWe741bxbFb6uDkBYsB5vPtwel/8sYM4LEVU5DGZIM6Tmmah1y7GzuS8VZeib38Gsv7VG9zvqODUuIiKtYzBDmiB3p5C9YnRGDy6/VCb3xaxGdV/k3SqyfO/J90NEpBYMZkj17OWZ2GugaCZnd5E7XHGwXCSFOZCpFeCHMd0jmdxLRAQGM6RyFeWZCJQss8zelIH4KKPdJSe1bDkOqalX7LVybhZiwXe/oqWxJmdliKjKYwVgUjU5NWPUzhis3O4pe52wiYiqIgYzpGqerBmjNPMuK6VoKZAjInIlBjOkamquGWNLUbFA6qlsfJF2Dqmnsq1mTcy7rHSw3cNJB+DJeyJlBzxaCOSIiFyJOTOkamqtGWOLlB1XUnZZTe3bCnszr2DXyctYvP2kw5+rlkCOiMhT2GiSVM+8mwmwXTNGDd2d7e24sjdGKTVzzA0mHQVySjfMJCJSAzn3by4zkVtUtPziiHk2w1hm+cVo8FdFIONoxxVQPlHXvMvqgfb1EdMstMLif4DtZSnAM8X/iIjUhstM5HJyC97ZoraaMaW5sku3Gov/ERGpDYMZcilnC97ZoqaaMaW5eseVmgM5IiI1YDBDLlPZgndKjcHVQYA7dlypNZAjIlIDBjPkMq5cfpFCieUtKbS044qIyBsxAZhcxpMF78zLW2WDKfPyVvLRrHLPcTZJmYm6RESexZkZchlPFbxzZnmrsrM4TNQlIvIcBjPkMp5afpG7vKVUkjITdYmIPIPLTOQyUpZfXh5QUu3Wmfoz9shZ3nKmRkxFpNSPISIiZXFmhlyqouWXv7ULx2tfH1M8QVfO8pank5SJiKjyGMyQy9lafrmadwsT1ypTf6YsOctbXx0+L+k12cyRiEi9GMyQW5Suk2LuOeQoQTdI74fLeQWyc0/My1vj1xyADrb7OZl3F2mtKzcREZXHYIbcTurSzojlP1mOmZefpCbYSt1dxBoxRETax2CG3M6ZJRtTTj7+teYAagX64dqNQsvxinJspOwukjOLQ0RE6sTdTOR2zizZmIOM0oEMUHERPEDa7iK1d+UmIqKKcWaG3M7R0o4cSvV4Yo0YIiLt4swMuV1F9WecUXr7dGXHxRoxRETaw2CGPMLe0k5lcPs0EVHVxGUm8piySzt1aurx7KdpuJBb4NTyE7dPExFVTQxmyKNK158BgFl/a21zZ1FFuH2aiKhq4zITqYq95afagX4A7Pd44vZpIqKqizMzpDr2dhZtzTA5LIJHRERVj04IUfk2xSqWm5sLg8GAnJwcBAcHe3o4VElFxYLbp4mIqgA592/OzJCmlM2xISIiYs4MERERaRpnZkhVuIxERERyMZgh1Ug+mlUuwbeiRpJEREQAl5lIJZKPZmH8mgNWgQzguJEkERERgxnyuKJigdmbMmwWyTMfm70pA0XFXr3xjoiInMRgRsOKigVST2Xji7RzSD2Vrdmb/d7MK+VmZEpTqpEkERF5J+bMaJQ35ZdIbRBpymUjSSIiKo8zMxrkbfklUhtEvvZVuubeGxERuR6DGY3xxvySuyNDEG7wL9d3qawreYWaDNaIiMi1GMxojDfml/j66DBzUBSA8o0kbdFasEZERK7FYEZjpOaXSD1PLczdsmvXqF7heVoM1oiIyLUYzGiM1PwSqeepSd/ocLw8oJWkc7UWrBERketwN5PGmPNLTDn5NvNmdACMhpI2AEpxZ4sBoyFA0nlaDNaIiMg1GMxojDm/ZPyaA9ABVgGNObyYOShKsWBD6S3gjgIjTwRrRESkbTohhFdnUubm5sJgMCAnJwfBwcGeHo5i3FFnxrwFvOwviDmIGtu9CeKjjJJnaqSO2fxzAdvB2pKRHVVRS4dNMYmIXEfO/ZvBjIa58mZaVCzQI2lbhTunzKQEURUFRkD5AEXtRQHVPj4iIq1jMFOKNwczrpR6KhvDlu2RdK6jGRNHgZF56ejHab2tgjG1znzIDcyIiEg+Ofdv7mYim+TsFhL/+5r1ZbrN+i/O1sbx9dEhplkoHmhfHzHNQlURyHhj0UIiIq1jMEM2ObNbyJRbgMXbTpY77k21cbyxaCERkdYxmCGbpLYYKOut734t127Am2rjeFNgRkTkLRjMkE1yWwyUVnaZxVFgpENJ8qwWtlt7U2BGROQtGMyQXeYWA0aDvBtz2WWWigIjV9TGcSVvCsyIiLwFgxmqUN/ocPw4rTc+fqIrHuveRPLzyi6z2AuMjAZ/Te3+8abAjIjIW3g0mNm5cycGDRqEiIgI6HQ6bNy40epxIQRmzZqFiIgIBAQEoFevXkhPT/fMYKsw866ilwe1xpS45pKeY2uZpXRgtHBoe3z8RFf8OK23ZgIZM28JzIiIvIVH2xnk5eWhXbt2GDNmDIYMGVLu8Xnz5mH+/PlYtWoVWrRogTlz5iA+Ph7Hjx9HUFCQB0ZMk3o3x8d7z8KUW3HNGHvLLObASOv6RocjPsqoyjo4RERVjWqK5ul0OmzYsAEPPvgggJJZmYiICCQkJGDatGkAgIKCAoSFhSEpKQlPPvmkpNdl0TzlaaXdABERaZdXFM3LzMyEyWRCnz59LMf0ej1iY2Oxe/duu88rKChAbm6u1Rcpi8ssRESkJqrtmm0ymQAAYWFhVsfDwsJw5swZu89LTEzE7NmzXTo24jILERGph2qDGTOdzvrmKIQod6y06dOn45lnnrF8n5ubi4YNG7psfFWZt+S/EBGRtqk2mDEajQBKZmjCw/9atrh48WK52ZrS9Ho99Hq9y8enBmptxEhEROROqg1mIiMjYTQasXXrVnTo0AEAcOvWLaSkpCApKcnDo/O85KNZmL0pw6pPULjBHzMHRTFnhYiIqhSPBjN//vknTp78qzFhZmYm0tLSEBISgkaNGiEhIQFz585F8+bN0bx5c8ydOxeBgYEYPny4B0ftHhXNuph3E5XdhmbKycf4NQeYhEtERFWKR4OZ/fv3495777V8b851GTVqFFatWoWpU6fi5s2bmDBhAq5evYouXbpgy5YtXl9jpqJZl/goI2ZvyigXyAAl26R1KOmNFB9l5JITERFVCaqpM+MqWqszY2/WxRyWJMQ1x1vfnXD4OpPubYbud9RlHg0REWmSnPs3gxkVKSoW6JG0zWpGpjQdAEOAH67dLJT8msyjISIiLfKKonlV0d7MK3YDGaBkGUlOIAP8lUeTfDSrkqMjIiJSJwYzKlK207Q9tQL8ynVstsc87TZ7UwaKir16Eo6IiKooBjMqYqvTtC1jukcCgKyAJisnH3szrzg3MCIiIhVjMKMiV/MKUFGurg4lOTCTet9hszeSI1JnfoiIiLREtUXzqprko1mYuPagzS3Xpc0cFAVfH51Vb6RdJy9j8faTDp4pfeaHiIhISzgzowJFxcJu7RgzHx3wznDrYnjm3khT4lsg3OBvd9nJPKNzd2SIksMmIiJSBQYzKuBoFxMAFAugdo3qNh/z9dFh5qAoAOXzaMzfm2d0iIiIvA2DGRWQmstS0Xl9o8Nt5tEYDf5sb0BERF6NOTMqIDWXxdF5pfNo2EmbiIiqCgYzKnB3ZAjCDf4w5eTbzJvRoWSGRUrOizmPhoiIqKrgMpMKqC3npahYIPVUNr5IO4fUU9kstkdERKrGmRknFRULRZdzzDkvZbtlG93cW6mijt3MuyEiIjVio0knuPKGr3SQJIejjt1MJCYiIndh1+xSlA5mvPWGL6Vjt9Hgjx+n9WZCMRERuRy7ZrtIRcXttN7QUUrHbvZ3IiIiNWIwI4M33/CVqHVDRETkCQxmZPDmG75StW6IiIjcjcGMDN58wzfXumF/JyIi0hoGMzJ48w1fbbVuiIiIpGIwI4O33/DZ34mIiLSIW7Od4O2F5TxZ64aIiAhgnRkrrghmAN7wiYiIXEnO/ZvtDJzEho5ERETqwJwZIiIi0jQGM0RERKRpDGaIiIhI0xjMEBERkaYxmCEiIiJNYzBDREREmsZghoiIiDSNwQwRERFpGoMZIiIi0jSvrwBs7taQm5vr4ZEQERGRVOb7tpSuS14fzFy/fh0A0LBhQw+PhIiIiOS6fv06DAZDhed4faPJ4uJinD9/HkFBQdDp2AjSltzcXDRs2BBnz55VtBknOYfXQ114PdSF10NdXHk9hBC4fv06IiIi4ONTcVaM18/M+Pj4oEGDBp4ehiYEBwfzHwcV4fVQF14PdeH1UBdXXQ9HMzJmTAAmIiIiTWMwQ0RERJrGYIag1+sxc+ZM6PV6Tw+FwOuhNrwe6sLroS5quR5enwBMRERE3o0zM0RERKRpDGaIiIhI0xjMEBERkaYxmCEiIiJNYzBTRezcuRODBg1CREQEdDodNm7caPW4EAKzZs1CREQEAgIC0KtXL6Snp3tmsFVAYmIiOnfujKCgINSrVw8PPvggjh8/bnUOr4n7LFmyBG3btrUU/oqJicHmzZstj/NaeFZiYiJ0Oh0SEhIsx3hN3GvWrFnQ6XRWX0aj0fK4p68Hg5kqIi8vD+3atcPixYttPj5v3jzMnz8fixcvxr59+2A0GhEfH2/pbUXKSklJwcSJE7Fnzx5s3boVt2/fRp8+fZCXl2c5h9fEfRo0aIA33ngD+/fvx/79+9G7d2888MADln+MeS08Z9++ffjPf/6Dtm3bWh3nNXG/1q1bIysry/J15MgRy2Mevx6CqhwAYsOGDZbvi4uLhdFoFG+88YblWH5+vjAYDGLp0qUeGGHVc/HiRQFApKSkCCF4TdSgdu3a4v333+e18KDr16+L5s2bi61bt4rY2FgxefJkIQT/fnjCzJkzRbt27Ww+pobrwZkZQmZmJkwmE/r06WM5ptfrERsbi927d3twZFVHTk4OACAkJAQAr4knFRUVYd26dcjLy0NMTAyvhQdNnDgRAwYMQFxcnNVxXhPPOHHiBCIiIhAZGYmhQ4fit99+A6CO6+H1jSbJMZPJBAAICwuzOh4WFoYzZ854YkhVihACzzzzDHr06IHo6GgAvCaecOTIEcTExCA/Px81a9bEhg0bEBUVZfnHmNfCvdatW4eff/4Z+/fvL/cY/364X5cuXfDBBx+gRYsWuHDhAubMmYNu3bohPT1dFdeDwQxZ6HQ6q++FEOWOkfImTZqEw4cP48cffyz3GK+J+7Rs2RJpaWm4du0aPv/8c4waNQopKSmWx3kt3Ofs2bOYPHkytmzZAn9/f7vn8Zq4T79+/Sx/btOmDWJiYtCsWTOsXr0aXbt2BeDZ68FlJrJkpJuja7OLFy+Wi7RJWU899RS+/PJLbN++HQ0aNLAc5zVxv+rVq+OOO+5Ap06dkJiYiHbt2mHhwoW8Fh7w888/4+LFi7jrrrtQrVo1VKtWDSkpKVi0aBGqVatm+dx5TTynRo0aaNOmDU6cOKGKvyMMZgiRkZEwGo3YunWr5ditW7eQkpKCbt26eXBk3ksIgUmTJmH9+vXYtm0bIiMjrR7nNfE8IQQKCgp4LTzgvvvuw5EjR5CWlmb56tSpE0aMGIG0tDQ0bdqU18TDCgoKcOzYMYSHh6vj74hb0ozJ465fvy4OHjwoDh48KACI+fPni4MHD4ozZ84IIYR44403hMFgEOvXrxdHjhwRw4YNE+Hh4SI3N9fDI/dO48ePFwaDQezYsUNkZWVZvm7cuGE5h9fEfaZPny527twpMjMzxeHDh8WLL74ofHx8xJYtW4QQvBZqUHo3kxC8Ju727LPPih07dojffvtN7NmzRwwcOFAEBQWJ06dPCyE8fz0YzFQR27dvFwDKfY0aNUoIUbK1bubMmcJoNAq9Xi/uuececeTIEc8O2ovZuhYAxMqVKy3n8Jq4z9ixY0Xjxo1F9erVRd26dcV9991nCWSE4LVQg7LBDK+Jez3yyCMiPDxc+Pn5iYiICDF48GCRnp5uedzT10MnhBDumQMiIiIiUh5zZoiIiEjTGMwQERGRpjGYISIiIk1jMENERESaxmCGiIiINI3BDBEREWkagxkiIiLSNAYzRKQpOp0OGzduVPx1e/XqhYSEBMVfl4hcj8EMEdm0e/du+Pr6om/fvrKf26RJEyxYsED5QUkwevRo6HQ66HQ6+Pn5oWnTpnjuueeQl5dX4fPWr1+P1157zU2jJCIlMZghIptWrFiBp556Cj/++CN+//13Tw9Hlr59+yIrKwu//fYb5syZg3fffRfPPfeczXMLCwsBACEhIQgKCnLnMIlIIQxmiKicvLw8fPrppxg/fjwGDhyIVatWlTvnyy+/RKdOneDv7486depg8ODBAEqWa86cOYMpU6ZYZkgAYNasWWjfvr3VayxYsABNmjSxfL9v3z7Ex8ejTp06MBgMiI2NxYEDB2SPX6/Xw2g0omHDhhg+fDhGjBhhWZoyj2PFihVo2rQp9Ho9hBDllpkKCgowdepUNGzYEHq9Hs2bN8fy5cstj2dkZKB///6oWbMmwsLC8Oijj+Ly5cuWx//73/+iTZs2CAgIQGhoKOLi4hzODhGRcxjMEFE5n3zyCVq2bImWLVti5MiRWLlyJUq3cfv6668xePBgDBgwAAcPHsT333+PTp06AShZrmnQoAFeffVVZGVlISsrS/LPvX79OkaNGoUffvgBe/bsQfPmzdG/f39cv369Uu8nICDAMgMDACdPnsSnn36Kzz//HGlpaTaf889//hPr1q3DokWLcOzYMSxduhQ1a9YEAGRlZSE2Nhbt27fH/v37kZycjAsXLuDhhx+2PD5s2DCMHTsWx44dw44dOzB48GCwFR6Ra1Tz9ACISH2WL1+OkSNHAihZsvnzzz/x/fffIy4uDgDw+uuvY+jQoZg9e7blOe3atQNQslzj6+uLoKAgGI1GWT+3d+/eVt+/9957qF27NlJSUjBw4ECn3svevXuxdu1a3HfffZZjt27dwocffoi6devafM6vv/6KTz/9FFu3brW856ZNm1oeX7JkCTp27Ii5c+dajq1YsQINGzbEr7/+ij///BO3b9/G4MGD0bhxYwBAmzZtnBo/ETnGmRkisnL8+HHs3bsXQ4cOBQBUq1YNjzzyCFasWGE5Jy0tzSo4UMrFixfxr3/9Cy1atIDBYIDBYMCff/4pO2fnq6++Qs2aNeHv74+YmBjcc889ePvtty2PN27c2G4gA5S8P19fX8TGxtp8/Oeff8b27dtRs2ZNy9edd94JADh16hTatWuH++67D23atME//vEPLFu2DFevXpX1HohIOs7MEJGV5cuX4/bt26hfv77lmBACfn5+uHr1KmrXro2AgADZr+vj41NumaX00g9QshPp0qVLWLBgARo3bgy9Xo+YmBjcunVL1s+69957sWTJEvj5+SEiIgJ+fn5Wj9eoUaPC5zt6f8XFxRg0aBCSkpLKPRYeHg5fX19s3boVu3fvxpYtW/D2229jxowZ+OmnnxAZGSnrvRCRY5yZISKL27dv44MPPsCbb76JtLQ0y9ehQ4fQuHFjfPTRRwCAtm3b4vvvv7f7OtWrV0dRUZHVsbp168JkMlkFNGXzVX744Qc8/fTT6N+/P1q3bg29Xm+VVCtVjRo1cMcdd6Bx48blAhkp2rRpg+LiYqSkpNh8vGPHjkhPT0eTJk1wxx13WH2ZAyWdTofu3btj9uzZOHjwIKpXr44NGzbIHgsROcZghogsvvrqK1y9ehWPPfYYoqOjrb4eeughy26emTNn4uOPP8bMmTNx7NgxHDlyBPPmzbO8TpMmTbBz506cO3fOEoz06tULly5dwrx583Dq1Cm888472Lx5s9XPv+OOO/Dhhx/i2LFj+OmnnzBixAinZoEqq0mTJhg1ahTGjh2LjRs3IjMzEzt27MCnn34KAJg4cSKuXLmCYcOGYe/evfjtt9+wZcsWjB07FkVFRfjpp58wd+5c7N+/H7///jvWr1+PS5cuoVWrVm5/L0RVAYMZIrJYvnw54uLiYDAYyj02ZMgQpKWl4cCBA+jVqxc+++wzfPnll2jfvj169+6Nn376yXLuq6++itOnT6NZs2aW3JRWrVrh3XffxTvvvIN27dph79695Wq/rFixAlevXkWHDh3w6KOP4umnn0a9evVc+6btWLJkCR566CFMmDABd955J5544gnL1uqIiAjs2rULRUVFuP/++xEdHY3JkyfDYDDAx8cHwcHB2LlzJ/r3748WLVrgpZdewptvvol+/fp55L0QeTud4F5BIiIi0jDOzBAREZGmMZghIiIiTWMwQ0RERJrGYIaIiIg0jcEMERERaRqDGSIiItI0BjNERESkaQxmiIiISNMYzBAREZGmMZghIiIiTWMwQ0RERJrGYIaIiIg07f8BzbzpweiW9s8AAAAASUVORK5CYII=\n",
1101 |       "text/plain": [
1102 |        "<Figure size 640x480 with 1 Axes>"
1103 |       ]
1104 |      },
1105 |      "metadata": {},
1106 |      "output_type": "display_data"
1107 |     }
1108 |    ],
1109 |    "source": [
1110 |     "# visualising actual prices and predicted prices\n",
1111 |     "plt.scatter(Y_test, test_data_prediction)\n",
1112 |     "plt.xlabel(\"Actual Prices\")\n",
1113 |     "plt.ylabel(\"Predicted Prices\")\n",
1114 |     "plt.title(\"Actual Price vs Predicted Price\")\n",
1115 |     "plt.show()"
1116 |    ]
1117 |   },
1118 |   {
1119 |    "cell_type": "code",
1120 |    "execution_count": null,
1121 |    "id": "0735ec2b",
1122 |    "metadata": {},
1123 |    "outputs": [],
1124 |    "source": []
1125 |   }
1126 |  ],
1127 |  "metadata": {
1128 |   "kernelspec": {
1129 |    "display_name": "Python 3 (ipykernel)",
1130 |    "language": "python",
1131 |    "name": "python3"
1132 |   },
1133 |   "language_info": {
1134 |    "codemirror_mode": {
1135 |     "name": "ipython",
1136 |     "version": 3
1137 |    },
1138 |    "file_extension": ".py",
1139 |    "mimetype": "text/x-python",
1140 |    "name": "python",
1141 |    "nbconvert_exporter": "python",
1142 |    "pygments_lexer": "ipython3",
1143 |    "version": "3.9.13"
1144 |   }
1145 |  },
1146 |  "nbformat": 4,
1147 |  "nbformat_minor": 5
1148 | }
1149 | 


--------------------------------------------------------------------------------