Linear Regression📈.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Build the linear regression model using scikit learn in boston data to predict 'Price' based on other dependent variable.**\n",
    " \n",
    " ✨Here is the code to load the data: \n",
    " \n",
    "import numpy as np \n",
    " \n",
    "import pandas as pd \n",
    " \n",
    "import scipy.stats as stats \n",
    " \n",
    "import matplotlib.pyplot as plt \n",
    " \n",
    "import sklearn \n",
    " \n",
    "from sklearn.datasets import load_boston \n",
    " \n",
    "boston = load_boston() \n",
    " \n",
    "bos = pd.DataFrame(boston.data) \n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import our libraries\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import scipy.stats as stats \n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn \n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Boston Housing Dataset\n",
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Show type of Boston =  <class 'sklearn.utils.Bunch'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let print the value of the boston dataset to understand what it contains.\n",
    "print(\"Show type of Boston = \", type(boston))\n",
    "boston.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston 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 blacks 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",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \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",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data shape\n",
    "boston.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       PRICE  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.08829</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.012</td>\n",
       "      <td>66.6</td>\n",
       "      <td>5.5605</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>395.60</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.14455</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.172</td>\n",
       "      <td>96.1</td>\n",
       "      <td>5.9505</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.21124</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>5.631</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.0821</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.63</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.17004</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.004</td>\n",
       "      <td>85.9</td>\n",
       "      <td>6.5921</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.71</td>\n",
       "      <td>17.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0     1     2    3      4      5      6       7    8      9    10  \\\n",
       "0  0.00632  18.0  2.31  0.0  0.538  6.575   65.2  4.0900  1.0  296.0  15.3   \n",
       "1  0.02731   0.0  7.07  0.0  0.469  6.421   78.9  4.9671  2.0  242.0  17.8   \n",
       "2  0.02729   0.0  7.07  0.0  0.469  7.185   61.1  4.9671  2.0  242.0  17.8   \n",
       "3  0.03237   0.0  2.18  0.0  0.458  6.998   45.8  6.0622  3.0  222.0  18.7   \n",
       "4  0.06905   0.0  2.18  0.0  0.458  7.147   54.2  6.0622  3.0  222.0  18.7   \n",
       "5  0.02985   0.0  2.18  0.0  0.458  6.430   58.7  6.0622  3.0  222.0  18.7   \n",
       "6  0.08829  12.5  7.87  0.0  0.524  6.012   66.6  5.5605  5.0  311.0  15.2   \n",
       "7  0.14455  12.5  7.87  0.0  0.524  6.172   96.1  5.9505  5.0  311.0  15.2   \n",
       "8  0.21124  12.5  7.87  0.0  0.524  5.631  100.0  6.0821  5.0  311.0  15.2   \n",
       "9  0.17004  12.5  7.87  0.0  0.524  6.004   85.9  6.5921  5.0  311.0  15.2   \n",
       "\n",
       "       11     12  \n",
       "0  396.90   4.98  \n",
       "1  396.90   9.14  \n",
       "2  392.83   4.03  \n",
       "3  394.63   2.94  \n",
       "4  396.90   5.33  \n",
       "5  394.12   5.21  \n",
       "6  395.60  12.43  \n",
       "7  396.90  19.15  \n",
       "8  386.63  29.93  \n",
       "9  386.71  17.10  "
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let convert the Boston Data into dataframe\n",
    "bos = pd.DataFrame(boston.data)\n",
    "\n",
    "# Let display our 10 first dataset\n",
    "bos.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.02985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.430</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.08829</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.012</td>\n",
       "      <td>66.6</td>\n",
       "      <td>5.5605</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>395.60</td>\n",
       "      <td>12.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.14455</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.172</td>\n",
       "      <td>96.1</td>\n",
       "      <td>5.9505</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>19.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.21124</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>5.631</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.0821</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.63</td>\n",
       "      <td>29.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.17004</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.524</td>\n",
       "      <td>6.004</td>\n",
       "      <td>85.9</td>\n",
       "      <td>6.5921</td>\n",
       "      <td>5.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.71</td>\n",
       "      <td>17.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575   65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421   78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185   61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998   45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147   54.2  6.0622  3.0  222.0   \n",
       "5  0.02985   0.0   2.18   0.0  0.458  6.430   58.7  6.0622  3.0  222.0   \n",
       "6  0.08829  12.5   7.87   0.0  0.524  6.012   66.6  5.5605  5.0  311.0   \n",
       "7  0.14455  12.5   7.87   0.0  0.524  6.172   96.1  5.9505  5.0  311.0   \n",
       "8  0.21124  12.5   7.87   0.0  0.524  5.631  100.0  6.0821  5.0  311.0   \n",
       "9  0.17004  12.5   7.87   0.0  0.524  6.004   85.9  6.5921  5.0  311.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  \n",
       "5     18.7  394.12   5.21  \n",
       "6     15.2  395.60  12.43  \n",
       "7     15.2  396.90  19.15  \n",
       "8     15.2  386.63  29.93  \n",
       "9     15.2  386.71  17.10  "
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let Add the feature to the dataframe\n",
    "bos.columns = boston.feature_names\n",
    "bos.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let Add our target variable to Dataframe\n",
    "bos['PRICE'] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numner of features= 14\n"
     ]
    }
   ],
   "source": [
    "print(\"Numner of features=\", len(bos.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       PRICE  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.describe(include ='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       float64\n",
       "ZN         float64\n",
       "INDUS      float64\n",
       "CHAS       float64\n",
       "NOX        float64\n",
       "RM         float64\n",
       "AGE        float64\n",
       "DIS        float64\n",
       "RAD        float64\n",
       "TAX        float64\n",
       "PTRATIO    float64\n",
       "B          float64\n",
       "LSTAT      float64\n",
       "PRICE      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data types\n",
    "bos.dtypes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       504\n",
       "ZN          26\n",
       "INDUS       76\n",
       "CHAS         2\n",
       "NOX         81\n",
       "RM         446\n",
       "AGE        356\n",
       "DIS        412\n",
       "RAD          9\n",
       "TAX         66\n",
       "PTRATIO     46\n",
       "B          357\n",
       "LSTAT      455\n",
       "PRICE      229\n",
       "dtype: int64"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let identify the unique numerical values in the dataset\n",
    "bos.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "PRICE      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now let check for missing values / Data preprocessing\n",
    "pd.isnull(bos).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT, PRICE]\n",
       "Index: []"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos[bos.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.613524</td>\n",
       "      <td>8.601545</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.25651</td>\n",
       "      <td>3.677083</td>\n",
       "      <td>88.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>9.69000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>27.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>506.0</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>6.20850</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>8.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>506.0</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>77.50000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>3.20745</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>12.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>506.0</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>506.0</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>506.0</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>19.05000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>506.0</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>391.44000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>396.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>506.0</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>11.36000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRICE</th>\n",
       "      <td>506.0</td>\n",
       "      <td>22.532806</td>\n",
       "      <td>9.197104</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>21.20000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std        min         25%        50%  \\\n",
       "CRIM     506.0    3.613524    8.601545    0.00632    0.082045    0.25651   \n",
       "ZN       506.0   11.363636   23.322453    0.00000    0.000000    0.00000   \n",
       "INDUS    506.0   11.136779    6.860353    0.46000    5.190000    9.69000   \n",
       "CHAS     506.0    0.069170    0.253994    0.00000    0.000000    0.00000   \n",
       "NOX      506.0    0.554695    0.115878    0.38500    0.449000    0.53800   \n",
       "RM       506.0    6.284634    0.702617    3.56100    5.885500    6.20850   \n",
       "AGE      506.0   68.574901   28.148861    2.90000   45.025000   77.50000   \n",
       "DIS      506.0    3.795043    2.105710    1.12960    2.100175    3.20745   \n",
       "RAD      506.0    9.549407    8.707259    1.00000    4.000000    5.00000   \n",
       "TAX      506.0  408.237154  168.537116  187.00000  279.000000  330.00000   \n",
       "PTRATIO  506.0   18.455534    2.164946   12.60000   17.400000   19.05000   \n",
       "B        506.0  356.674032   91.294864    0.32000  375.377500  391.44000   \n",
       "LSTAT    506.0   12.653063    7.141062    1.73000    6.950000   11.36000   \n",
       "PRICE    506.0   22.532806    9.197104    5.00000   17.025000   21.20000   \n",
       "\n",
       "                75%       max  \n",
       "CRIM       3.677083   88.9762  \n",
       "ZN        12.500000  100.0000  \n",
       "INDUS     18.100000   27.7400  \n",
       "CHAS       0.000000    1.0000  \n",
       "NOX        0.624000    0.8710  \n",
       "RM         6.623500    8.7800  \n",
       "AGE       94.075000  100.0000  \n",
       "DIS        5.188425   12.1265  \n",
       "RAD       24.000000   24.0000  \n",
       "TAX      666.000000  711.0000  \n",
       "PTRATIO   20.200000   22.0000  \n",
       "B        396.225000  396.9000  \n",
       "LSTAT     16.955000   37.9700  \n",
       "PRICE     25.000000   50.0000  "
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bos.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let apply some visualizations to understand the relationship of the target variable with other features. EDA\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "sns.distplot(bos['PRICE'], hist=True, rug=False, color='b', bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 14)"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let check our features are correlated\n",
    "feature_cor = bos.corr()\n",
    "feature_cor.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_cor = bos.corr().round(2)\n",
    "sns.heatmap(data=feature_cor, fmt='.1f', annot_kws={'size':12}, annot=True, cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RM         0.70\n",
       "PTRATIO    0.51\n",
       "LSTAT      0.74\n",
       "PRICE      1.00\n",
       "Name: PRICE, dtype: float64"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlation with output variable\n",
    "cor_target = abs(feature_cor[\"PRICE\"])\n",
    "#Selecting highly correlated features\n",
    "relevant_features = cor_target[cor_target>0.5]\n",
    "relevant_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            LSTAT   PTRATIO\n",
      "LSTAT    1.000000  0.374044\n",
      "PTRATIO  0.374044  1.000000\n",
      "             RM     LSTAT\n",
      "RM     1.000000 -0.613808\n",
      "LSTAT -0.613808  1.000000\n"
     ]
    }
   ],
   "source": [
    "print(bos[[\"LSTAT\",\"PTRATIO\"]].corr())\n",
    "print(bos[[\"RM\",\"LSTAT\"]].corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let split target with other variable\n",
    "X = bos.drop(['PRICE'], axis = 1)\n",
    "y = bos['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25, random_state  = 355)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "227    31.6\n",
       "382    11.3\n",
       "23     14.5\n",
       "348    24.5\n",
       "157    41.3\n",
       "       ... \n",
       "291    37.3\n",
       "501    22.4\n",
       "346    17.2\n",
       "130    19.2\n",
       "254    21.9\n",
       "Name: PRICE, Length: 379, dtype: float64"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let Train our model using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "linearReg = LinearRegression()\n",
    "linearReg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.244920872475433"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value of y intercept\n",
    "linearReg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attribute</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-0.103237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>0.0504499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>-0.0169408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>3.76344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-17.1714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>4.42606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>-0.0139559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-1.51537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAD</td>\n",
       "      <td>0.263113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-0.0108409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-0.85327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>0.00899731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-0.406334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attribute Coefficients\n",
       "0       CRIM    -0.103237\n",
       "1         ZN    0.0504499\n",
       "2      INDUS   -0.0169408\n",
       "3       CHAS      3.76344\n",
       "4        NOX     -17.1714\n",
       "5         RM      4.42606\n",
       "6        AGE   -0.0139559\n",
       "7        DIS     -1.51537\n",
       "8        RAD     0.263113\n",
       "9        TAX   -0.0108409\n",
       "10   PTRATIO     -0.85327\n",
       "11         B   0.00899731\n",
       "12     LSTAT    -0.406334"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting the coefficient values to a dataframe\n",
    "coef = pd.DataFrame([X_train.columns,linearReg.coef_]).T\n",
    "coef = coef.rename(columns={0: 'Attribute', 1: 'Coefficients'})\n",
    "coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 423,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "linearReg = LinearRegression()\n",
    "linearReg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting Train data with the model\n",
    "y_pred = linearReg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model performance for training set\n",
      "--------------------------------------\n",
      "RMSE is 4.720391098867943\n",
      "R2 score is 0.7306475313474942\n",
      "\n",
      "\n",
      "The model performance for testing set\n",
      "--------------------------------------\n",
      "RMSE is 4.709397726218259\n",
      "R2 score is 0.7519030329262357\n"
     ]
    }
   ],
   "source": [
    "# Model evaluation for training and testing set for R2_score and RMSE model evaluation.\n",
    "\n",
    "y_train_predict = linearReg.predict(X_train)\n",
    "rmse = (np.sqrt(mean_squared_error(y_train, y_train_predict)))\n",
    "r2 = r2_score(y_train, y_train_predict)\n",
    "\n",
    "print(\"The model performance for training set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))\n",
    "print(\"\\n\")\n",
    "\n",
    "# model evaluation for testing set\n",
    "\n",
    "y_test_predict = linearReg.predict(X_test)\n",
    "# root mean square error of the model\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_test_predict)))\n",
    "\n",
    "# r-squared score of the model\n",
    "r2 = r2_score(y_test, y_test_predict)\n",
    "\n",
    "print(\"The model performance for testing set\")\n",
    "print(\"--------------------------------------\")\n",
    "print('RMSE is {}'.format(rmse))\n",
    "print('R2 score is {}'.format(r2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "features = ['LSTAT', 'RM']\n",
    "target = bos['PRICE']\n",
    "\n",
    "for i, col in enumerate(features):\n",
    "    plt.subplot(1, len(features) , i+1)\n",
    "    x = bos[col]\n",
    "    y = target\n",
    "    plt.scatter(x, y, marker='o')\n",
    "    plt.title(col)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('PRICE')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Differences between current prices and predicted values\n",
    "# Notice that it should have been a straight line\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAH/CAYAAAC/wqBOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABTCklEQVR4nO3df3wU9b3v8fcmG5JIgEgaTK3KbYMVTYvt1VMbocZ6FI0QoJS22l6Fco61x9YfvZZTsZ7jEUul2vuw/nhwzqmt1lLRqjdCQaFaUHwIqbTWigbRmodgqQZiQAOUBJLs/YO762YzMzuzO7Mzs/N6Ph4+HmbZZL87M7vzns985juxRCKREAAAABBhJX4PAAAAAPAboRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGECk7d+7UySefrJkzZ6b+mzFjhh599NG8//bll1+u1tZWSdLMmTPV09Nj+tx9+/bp0ksvdfwaa9eu1SWXXJLzGL2ya9cuXXTRRYb/tmjRIt111105/+277rpLixYtyvn3AcCOuN8DAIBCq6io0MqVK1M/79q1S9OnT9cnPvEJTZw40ZXXSP/7Rt5//329/PLLrrxWEBxzzDF66KGH/B4GAOSMUAwg8o455hiNHz9e27dv19atW/Xoo4/q4MGDqqqq0rJly/TII4/owQcf1ODgoKqrq/Vv//Zvqq+v165du3Tddddp9+7dOvbYY9Xd3Z36myeddJLa2to0duxY/fd//7cee+wxxeNxjR8/XkuWLNHChQvV29urmTNnqrW1Vdu3b9fixYv13nvvaWBgQJdcconmzJkjSbrjjju0atUqVVdXa/z48Ybv4dprr1VDQ4Pmz58vSVq+fLk2b96sxYsXa+HChdqxY4dKSkrU0NCgRYsWqaRk6InCc845R5MmTdJrr72m//2//7cmTZqkRYsW6Z133tHhw4c1bdo0ffOb31R/f79uvvlm/elPf1JZWZmOO+443XLLLdq7d69aWlr04osvav/+/fr+97+vbdu2ady4cSotLdVpp52Wep077rhDn/zkJ4f9/F//9V9at26dent7dfDgQX3ve9/TeeedN2Scy5cv10MPPaSysjKVl5dr0aJFmjBhgjsbAoBIIxQDiLwXX3xRb731lk499VS1tbXpjTfe0Pr161VVVaXNmzdrxYoVeuCBB1RZWannnntO3/72t7VmzRotWrRIp556qq655hrt2LFDs2bNGva3161bp9bWVj388MMaM2aMbrnlFv3qV7/SLbfcopaWFq1cuVL9/f266qqrdOutt6qhoUH79u3TV77yFU2YMEHvvvuunnzySa1YsUIVFRX61re+ZfgevvSlL2nx4sWpUPzYY4/pO9/5jp566ikdOHBAK1eu1MDAgG688Ub99a9/NQzXJ554on7yk59Iki699FLNmzdP55xzjvr6+nTZZZfphBNO0Lhx47R582Y98cQTisViuu222/Taa69p3Lhxqb9z5513qqKiQmvXrtXevXv1hS98IRWKzfztb3/Tpk2btGzZMlVUVOjxxx/XnXfeOSQUDwwM6Ic//KHWr1+vcePGacWKFXrhhRcIxQBcQSgGEDnJCq10JGgdffTRuu222/ThD39Y0pEqb1VVlSTpmWee0Y4dO4b0y/b09Oi9997Tpk2b9L3vfU+SNH78eJ1xxhnDXqutrU0XXHCBxowZI0lauHChpCO9zUnbt2/XW2+9peuvv37IGLdu3aqOjg6dd955qfF88Ytf1LJly4a9zhlnnKG+vj69/PLLqqys1J49e9TY2KidO3fq9ttv1yWXXKIzzzxTc+fONa02n3766ZKkv//97/rDH/6g999/X3fccUfqsW3btmnKlCkqLS3Vl770JU2ZMkXnn3++Jk2aNOT9tLW16frrr1csFtPYsWOHVXuNfOQjH9Gtt96qVatWaceOHXrppZd04MCBIc8pLS3VBRdcoIsuukhnn322pkyZoqampqx/GwDsIBQDiJzMnuJMRx11VOr/BwcHNXPmTC1YsCD18+7duzVmzBjFYjElEonUc+Px4V+ppaWlisViqZ97enqGXYA3MDCgUaNGDRnTu+++q1GjRunWW28d8hqlpaWGY47FYpozZ45WrlypsrIyzZkzR7FYTMcff7yeeuopPf/88/r973+vr3/961q0aJHOOecc0/c9ODioRCKhhx56SJWVlZKkPXv2qLy8XCNHjtTKlSv1pz/9Sb///e91zTXX6J/+6Z+GhVOrMaf/26FDhyRJ7e3tuuKKKzRv3jxNnjxZ//AP/6Cbbrpp2Bh//OMf6/XXX9emTZv005/+VCtXrkwFdwDIB7NPAICFKVOm6PHHH9fu3bslSQ8++KDmzp0rSfrc5z6nX//615Kkt99+W88///yw3z/zzDP11FNPaf/+/ZKOzKTwi1/8QvF4XAMDA0okEvroRz86JKi/8847mj59ul555RWdddZZWrt2rXp6ejQ4OGgZ5r/whS9o/fr1+u1vf6vZs2dLOtKDu3DhQk2ZMkULFizQlClTtHXrVsv3XFVVpU996lO67777JB0J8hdffLHWrVunp59+WvPmzdOnP/1pXXnllZo1a5ZeeeWVIb//uc99To8++qgGBwf1/vvva926dal/Gzt2bOr5zz//vLq6uiRJf/jDH/SJT3xCX//61/WZz3xG69at08DAwJC/u2fPHjU1Nam6ulrz5s3TNddcU1QXKwLwF5ViALAwZcoUXXbZZZo/f75isZiqqqp09913KxaL6cYbb9TChQvV3Nysuro6w5krmpqa9MYbb+jiiy+WJE2YMEE333yzKisrNWnSJE2bNk0PPPCAli5dqsWLF+tnP/uZ+vv7dfXVV6f6cF977TV98Ytf1OjRozVx4kTt3bvXcKy1tbU65ZRT1N/fr2OOOUaSNGvWLG3evFkXXnihKisr9eEPf9jWlG4//vGPdfPNN6ulpUWHDh3S9OnTNWPGDA0MDOjZZ5/V9OnTddRRR2nMmDG6+eabh/zulVdeqRtvvFHNzc0aO3asPv7xj6f+7bvf/a7+4z/+Q7/+9a/V0NCghoYGSdL06dP15JNPqrm5WYODg/r85z+v999/P3UwIR0J1P/yL/+iefPmqaKiQqWlpfrBD36Q9b0AgB2xRPp5LAAAACCCaJ8AAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHlFM0/x3r0HNDjI7HKSVFNTpe7u/dmfiKLDuo821n90se6ji3VvX0lJTEcfPdL034smFA8OJgjFaVgW0cW6jzbWf3Sx7qOLde8O2icAAAAQeYRiAAAARB6hGAAAAJFHKAYAAEDkEYoBAAAQeYRiAAAARB6hGAAAAJFHKAYAAEDkEYoBAAAQeYRiAAAARB6hGAAAAJFHKAYAAEDkEYoBAAAQeYRiAAAARF7c7wEAAKKtrb1TrRs61N3Tp5rR5ZrdVK/Ghjq/hwUgYgjFAADftLV36v4123Sof1CS1N3Tp/vXbJMkgjGAgqJ9AgDgm9YNHalAnHSof1CtGzp8GhGAqCIUAwB8093T5+hxAPAKoRgA4Jua0eWOHgcArxCKAQC+md1UrxHxobuiEfESzW6q92lEAKKKC+0AAL5JXkzH7BMA/EYoBgD4qrGhjhAMwHe0TwAAACDyCMUAAACIPEIxAAAAIo9QDAAAgMgjFAMAACDyCMUAAACIPEIxAAAAIo9QDAAAgMgjFAMAACDyCMUAAACIPEIxAAAAIi/u9wAAAPBaW3unWjd0qLunTzWjyzW7qV6NDXV+DwtAgBCKAQBFra29U/ev2aZD/YOSpO6ePt2/ZpskEYwBpNA+AQAoaq0bOlKBOOlQ/6BaN3T4NCIAQUQoBgAUte6ePkePA4gmQjEAoKjVjC539DiAaCIUAwCK2uymeo2ID93djYiXaHZTvU8jAhBEXGgHAChqyYvpmH0CgBVCMQCg6DU21BGCAViifQIAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAESeL6H47rvv1rRp0zRt2jTdeuutkqRNmzappaVFU6dO1e233+7HsAAAABBRBQ/FmzZt0nPPPafHHntMK1asUHt7u1avXq3rr79eS5cu1RNPPKFXXnlFGzZsKPTQAAAAEFEFD8W1tbW67rrrNGLECJWVlam+vl7bt2/X+PHjdfzxxysej6ulpUVr164t9NAAAAAQUfFCv+CJJ56Y+v/t27drzZo1+l//63+ptrY29fi4ceO0a9cuR3+3pqbKtTEWg9raUX4PAT5h3Ucb6z+6WPfRxbp3R8FDcdJf/vIXXX755frXf/1XlZaWavv27al/SyQSisVijv5ed/d+DQ4mXB5lONXWjlJX1z6/hwEfsO6jjfUfXaz76GLd21dSErMsovpyod0LL7ygefPm6dprr9UXvvAF1dXVqaurK/XvXV1dGjdunB9DAwAAQAQVPBS/8847+ta3vqUf//jHmjZtmiTp1FNP1ZtvvqkdO3ZoYGBAq1ev1llnnVXooQEAACCiCt4+8fOf/1x9fX1asmRJ6rGLLrpIS5Ys0ZVXXqm+vj41NTXpggsuKPTQAAAAEFGxRCJRFI249BR/gP6i6GLdRxvrP7pY99HFurcvkD3FAAAAQJAQigEAABB5hGIAAABEHqEYAAAAkUcoBgAAQOQRigEAABB5hGIAAABEHqEYAAAAkUcoBgAAQOQRigEAABB5hGIAAABEHqEYAAAAkRf3ewAAACAc2to71bqhQ909faoZXa7ZTfVqbKjze1iAKwjFAAAgq7b2Tt2/ZpsO9Q9Kkrp7+nT/mm2SRDBGUaB9AgAAZNW6oSMViJMO9Q+qdUOHTyMC3EUoBgAAWXX39Dl6HAgbQjEAAMiqZnS5o8eBsCEUAwCArGY31WtEfGhsGBEv0eymep9GBLiLC+0AAEBWyYvpmH0CxYpQDAAAbGlsqCMEo2jRPgEAAIDIIxQDAAAg8gjFAAAAiDxCMQAAACKPUAwAAIDIIxQDAAAg8gjFAAAAiDxCMQAAACKPUAwAAIDIIxQDAAAg8gjFAAAAiDxCMQAAACIv7vcAAAAA0rW1d6p1Q4e6e/pUM7pcs5vq1dhQ5/ewUOQIxQAAIDDa2jt1/5ptOtQ/KEnq7unT/Wu2SRLBGJ6ifQIAAARG64aOVCBOOtQ/qNYNHT6NCFFBKAYAAIHR3dPn6HHALbRPwFX0gQEA8lEzutwwANeMLvdhNIgSKsVwTbIPLPllluwDa2vv9HlkAICwmN1UrxHxofFkRLxEs5vqfRoRooJQDNfQBwYAyFdjQ53mNk9MVYZrRpdrbvNEzjrCc7RPwDX0gQEA3NDYUEcIRsFRKYZrzPq96AMDAABBRyiGa+gDAwAAYUX7BFyTPNXF7BMAACBsCMVwFX1gAAAgjGifAAAAQORRKQYCgJueAADgL0Ix4LPkTU+Sczwnb3oiiWAMAECB0D4B+IybngAA4D9CMeAzbnoCAID/aJ8AfFYzutwwAHPTEwDwBtdxwAiVYsBn3PQEAAoneR1HshiRvI6jrb3T55HBb4RiwGeNDXWa2zwxVRmuGV2uuc0TqVoAgAe4jgNmaJ8AAoCbngBAYXAdB8xQKQYAAJFhdr0G13GAUAwAACKD6zhghvYJAAAQGclWNWafQCZCMQAAiBSu44AR2icAAAAQeYRiAAAARB6hGAAAAJFHKAYAAEDkEYoBAAAQeYRiAAAARB6hGAAAAJFHKAYAAEDkEYoBAAAQeYRiAAAARB63eUZWbe2d3CMeAAAUNUIxLLW1d+r+Ndt0qH9QktTd06f712yTJIIxAIQIBQ7AGu0TsNS6oSMViJMO9Q+qdUOHTyMCADiVLHB09/RJ+qDA0dbe6fPIgOAgFMNS8gvU7uMAgOChwAFkRyiGpZrR5Y4eBwAEDwUOIDtCMSzNbqrXiPjQzWREvESzm+p9GhEAwCkKHEB2hGJYamyo09zmiakvzprR5ZrbPJGLMwAgRChwANkx+wSyamyoIwQDQIglv8OZfQIwRygGACACKHDkhyntih+hGAAAwAJz9kcDPcUAAAAWmNIuGqgUAwCAopZv6wNT2kUDlWIAAFC03LibH1PaRQOhGAAAFC03Wh+Y0i4aaJ8AAABFy43WB6a0iwZCMQAAKFo1o8sNA7DT1gemtCt+hGIABcVcnwAKaXZT/ZDp1CRaH2CMUAygYJjrE0Ch0foAuwjFAArG6oIXdlAAvELrA+zwbfaJ/fv3a/r06dq5c6ckadOmTWppadHUqVN1++23+zUsAB5irk8AcEdbe6cWLN2oGdeu1IKlGx1NMQdjvlSKX3rpJd1www3avn27JKm3t1fXX3+9li1bpg9/+MO6/PLLtWHDBjU1NfkxvKzoiQRy49YFLwAQZbSiecOXSvHDDz+sG2+8UePGjZMkbdmyRePHj9fxxx+veDyulpYWrV271o+hZeXGJOBAVDHXJwDkj9tOe8OXSvHixYuH/Lx7927V1tamfh43bpx27drl6G/W1FS5MrZsVjzXZrghrnjuTc04+8SCjMGO2tpRfg8BPgnyup9x9iiNHlWhX655Ve/uPagPHV2pS5tP1tmnHe/30IpGkNc/vMW6j449Fq1o/7RkPd+tOQrEhXaDg4OKxWKpnxOJxJCf7eju3q/BwYTbQxuma+9B08e7uvZ5/vp21NaOCsxYUFhhWPcNJ1TrR5c3Dnks6GMOizCsf9rPvOH2umc9BdtYk1Y0SUroSCa56+E/q2dfL+stTUlJzLKIGojbPNfV1amrqyv1c1dXV6q1Imi4/zkA5Ib2s3BgPQWfUStaJtopnAtEKD711FP15ptvaseOHRoYGNDq1at11lln+T0sQ/REAkBu6IMMB9ZT8DU21Glu80TVjC6X1Xl1ZvZxJhDtE+Xl5VqyZImuvPJK9fX1qampSRdccIHfwzLEJOAAkBum5AsH1lM4JOderq0dpXk3rWVmHxf4GorXr1+f+v/Gxkb95je/8XE09jEJOAA4x5R84cB6Ch9uZe2OQFSKASDKonJREzvucGA9hQ9nsd1BKEZRi0rYQHhFaRJ+dtzhwHoKJ85i549QjKIVpbCB8LK6qKkYt1N23OHAeipuFIyMBWL2CcALXEGNMOCiJgCFZDTl3j2rtmrZb7f5PDL/EYpRtAgbCAPmPgdQSEYFI0l6+sW3Iz8XNaEYRYuwgTBg7nMAhWRVGIr6mVRCMYoWYQNhkD4Jv3TkoG1u80T6+wB4wqowFPUzqVxoh6LFFdQICy5qKi5cxIQgm91Ur3tWbTX8t6ifSSUUo6gRNgAUErPeBAcHJ8YaG+r0xs739PSLbw95nDOptE8AAOAaZr0JBqMZFu5fsy3yF5IlXXL+RF3WcgptWxmoFAMA4BJmvQmGqM3/nQvOpA5HKAYAwCU1o8sNA3DUezULjYOT4WgnyY5QDACAS2Y31Q/pKZbo1fQDBydDudHrHoVQTSgGisgzL/xVv1jdXtRfWkCQMetNMHBwMlS+7SRRuYCUUAwUibb2Tv1y7WvqOzwgqXi+tKJQnUBxoVfTfxycDJVvO0lUerQJxUCRaN3QkQrESWH/0opKdQKA+zg4+UC+7SRR6dFmSjagSBTjlxbTWwFA/vK9w6tZeC62Hm1CMVAkivFLqxiDPgAUWr63k883VIcF7RNAkZjdVD+kp1gK/5cWV5Dnhj5sAJnyaSeJSo82oRgoEo0NdRo9qqKoZp/gCnLn6MMG4IUo9Gg7DsW7du3SX//6V51++ulejAdAHs4+7Xg1nFDt9zBcE5XqhJuicpU4EDacwQk+W6F4+fLleuGFF/T9739fs2fPVlVVlaZOnaprr73W6/EBiLgoVCfcRB82EDycwQkHWxfaPfroo1q4cKHWrl2rf/zHf9Tjjz+ujRs3ej02AIBDxXjBJRB2zKQTDrYqxbFYTB/60IfU1tam5uZmxeNxDQ4OZv9FoEhxGgxBRR82EDycwQkHW5XiESNG6J577tHmzZs1efJkLV++XJWVlV6PDQik5Gmw5JdZ8jRYW3unzyMD8p96CYD7OIMTDrYqxT/4wQ9077336kc/+pHGjBmjF154QT/4wQ+8HhsQSFzIhKCjDxsIFs7ghIOtUFxfX6/Fixenfv4//+f/eDYgIOg4DQYAxc1Ji5yd5zKTzlBBbUG0DMWf/vSnFYvFTP/9T3/6k+sDAoKOG0oAQPFyMlOEk+dyBueIIM/EYRmKV69eXahxAKHBaTAAKF5OWuRop3MuyMvMMhR/5CMfSf3/1q1b9fe//12JREIDAwN666239OUvf9nzAQJBw2kwAGEV1NPWQeKkRS6q7XT5bEdBXma2eopvuOEGrVu3Tn19fRo3bpzeeustnXbaaYRiRBanwQCETZBPWweJkxa5KLbT5bsdBXmZ2ZqSbdOmTVq3bp3OO+88/fSnP9V9992niooKr8cGAABcwg0k7JndVK8R8aHxyKxFzslzi0W+21GQl5mtSnFtba2OOuoofexjH9Prr7+uc889lynZAA9wahOAV4J82jpInLTIRbGdLt/tKMjLzFYoLisr0x/+8AfV19fr2Wef1RlnnKG///3vXo8NiBRObQLwUpBPWweNkxa5qLXTubEdBXWZ2Wqf+O53v6uHHnpITU1N2rZtmz772c9qxowZXo8NiBRObQLwUpBPWyM8ink7slUp/tSnPqVPfepTkqSHH35Y+/bt06hRo7wcFxA5nNoE4KUgn7ZGeBTzdmT7Ns9GbrjhBlcHA0QZpzaLB73hCKqgnrZGuBTrdmQrFFdXV6f+//Dhw3r66af1mc98xqsxAZHETUGyC0PYpDccAMLJVij+9re/PeTnyy67TP/yL//iyYAQTWEIO14r5lNSbghL2Azy3ZqKEd8dANxiKxRnqqqq0u7du90eCyIqLGGnEPw8JeVHuHDymmEJm/SGFw7fHdHFwRC84LinOJFIqL29XR/72Mc8GxSiJSxhp5j5ES6cvmZYwia94YXDd0c0cTAErzjuKZakGTNmMCUbXBOWsFPM/AgXdl4zvRpUEpMGE8P/TtDCJr3hhcN3Rzi4XdXlYAheyamnGHATlTX/+REusr1mZjXIKBAHMWzSG144fHcEnxdV3WI6GKINJFgsQ/E555yjWCxm+u/r1q1zfUCInjBX1orlC82PcJHtNY2qQZJSFeMgL+9kb3hy+7hn1Va1bugI7HjDyui7Q5Im1df4NCJk8qKqG8SDoVz2BbSBBI9lKL7zzjslScuXL1dZWZm+8pWvqLS0VK2trTp8+HBBBojiF9bKWjF9oflxYJLtNc2qPoMJ6d7rzvFsXG4ppu0jqBob6vTGzvf09ItvD3l848udmnBcNcs5ALyo6gatkJLrZ502kOCxDMWf+MQnJEl/+ctf9Mgjj6QeX7hwoebMmePtyBApYZwIvJi+0Pw4MMn2ml5UgwpZ2S+m7SPItnR0D3ss/fboYTvYLjZefI6DVkjJ9bNeTG0gxcJWT3FPT4/27NmjsWPHSpJ27dql/fv3ezowoNCcBqZi+0LL5cAk35Bp9ZpuV4MKXbkttu0jKWgtQ1bLmUq9/7yq6gapkJLrZz2IbSBRZysUz507Vy0tLZoyZYoSiYQ2btyoBQsWeD02oGByCUxR/0LzOmS6XQ1yWs3JN/wV4/YRxJYQs+VcEhOV+jR+HcwErarrhVw/60FrA4HNUPzVr35V//N//k+1tbVJkv75n/9ZH//4xz0dGFBIuZz+ivoXWiHaA9ysBjmp5rgR/uxsH0GrumYThJaQzGU2qb5GG1/uHLacjS7SlMJfqc+F3wczQarqeiHXfUEUDhjCxjIUt7W1qbGxUU8++aQk6SMf+Ygkafv27dq+fbumTp3q/QiBAsjl9FfUv9DC1h6QrZqTbU5kp+Ev2/bhd1DJhZ1p9Lz8PBgts40vd2ryJ+u0paN7yOsmx5EpzJX6XAXhYKaYZX7Wk2cpkn3tVsu42A8YwsYyFD/++ONqbGzUsmXLhv1bLBYjFKNo5Hr6K8pfaGFrD7Cq5tiZE1lyHvitto8wBhWrdV6IkG+2zLZ0dOu2KyYPe36Uz+Skc3oAG7YzGEGQXD5hO9DFUJahOHl758xQfPjwYZWVlXk3KqDAot4KkYuwLTOryu2CpRtNT7dnWrB0oyshIWyVdsl6nRci5DtZZlE/k5POyQFsGM9gBEUYD3QxlK2e4j/+8Y/avHmz/vmf/1lf+9rX9Prrr+uWW27RhRde6PX4gIJgB+pcGJeZWeXWSRB1KySErdIuWa/ze1ZtNfwdN0O+02UW5TM56ZwcwBLschfGA10MZSsU33bbbbr66qv1u9/9TtXV1Xr88cd1zTXXEIpRVNiBOufXMnP79K7VDAZGrRRuhAS/K+25LkOzdV6IkO/3MgsrJwewbgS7qLZfhPFAF0PZCsUDAwM688wzdcMNN+jcc8/Vcccdp8FBe6caAa9E9Ys36rw4vWsWtuY2T/SsAppLpd2tbT7bMszldQoRWMN4diIo7B7A5hvsotx+wUFb+NkKxYODg9qyZYueeeYZXX755Xr99de5zTN8FeUv3qjz4vSuVdjychYDJ5V2N7d5q2Uo5XaxUKECK2d0vJVvsIty+wUHbeFnKxR/85vf1LXXXqs5c+bo+OOP1znnnKPvf//7Xo8NMBXlL96gKXTF3qu+PbOwFZTqj5vbvNUyzOd1CKzhl2+wi3pfLZ+BcLMViqdOnTpk+rWnnnpKpaWlng0KyCbqX7xB4WXF3ixsF7pvz2lI8Oogwc1t3moZ8tlCPsGOvlqEWYmdJ3V1dekb3/iGzj//fL377rv6xje+od27d3s9NsCU2RcsX7yFle00fK6SYTu5c02G7bb2Ts1uqteI+NCvLqPKbVt7pxYs3aj5S9ZrwdKNamvvzHk8jQ11uu2Kybr3unN02xWTLQOx2bjz5eY2b7UM+WwhH3Y/n0AQ2aoU33TTTTr33HP1q1/9SmPGjNHEiRN1ww036Kc//anX4wMMBeWUdrooXvjnVVXRKmwnb9Jgtaz96jn3sq3HzW0+W/U7aJ+tdNk+Z1H8HAYJfbUIM1uh+G9/+5u+/OUva/ny5SorK9OCBQvU0tLi9dgAU0H74nU7hIVlx+7VqdJsYTvb6V2/es69bD1we5s3W4ZB+2ylszNrBhfg+o++WoSVrVAci8WGTMG2f/9+pmSD74L0xetmCAtTwPaqYp9v2PYinNpZjkHqp8xnvQfps5Uu2+eMC3AB5MP2hXbf/e53tW/fPj300EN65JFH1Nzc7PXYgNBwM4QFOWBn8qqqmG/Ydjuc2l2OXrb1OFmXxVoxzfY5i+JFgsmDnz09fRoboKq+l8JyJg3hY3tKthUrVmhwcFCbNm3SV77yFX35y1/2emxAaLgZwoIasM3YqSo63YnlG7bzCadGY7W7HL1sPXCyLou1YprtcxakSr1d+QS8Yj34sRLF94zCsRWKJWnWrFmaNWtW6ueNGzdq8uTJXowJCB03K4RBDdi5ynUnlk/YzjWcmo01M2Amdff0qa29c1gw9mLn7GRdBmG9eyHb5yyIF+BayTfgmR38LH/qtaKtpBbrAR+CwTIUv/LKK/rBD36g6upq/fCHP9TYsWP19ttv64c//KGeffZZbdmypVDjBALNzQphUAN2rrzaiWULFLmEU7OxlsSkwYTx7+R7e2S7nKzLIKx3L2T7nAX5IkEj+X42zA5yDvQO6EDvQOo5xVRJLdYDPgSDZSi+6aab1NzcrLffflv/+Z//qTPOOEPXXXedTj31VK1cubJQYwRCwa0KYVADdq78mLYt1/VgNqbBhBQvjal/YHgyzvf2yHY5WZdBWO9eyfY5C+pFgkby/WxY3WwlXTFVUov1gA/BYBmK9+3bp/nz52tgYEDnn3++1qxZo5tuuknTpk0r1PiASApiwM6VX9O25cJsrFWVcR3o7bccy4O/e93T07pO1mUQ1juyy/ezYXTwY6ZYKqnFfMAH/1mG4srKSklSaWmp+vr69NOf/lSnnHJKQQYGwB1+V86CMm2bndYGs7EmEgklTNonpCOhef9B49Cc7Dt2I6A6WZd+r3c3FPssA/l+NtIPfpKzT/QdHjDcFoulksoBH7xkGYoTaXuBo48+mkAMwLEgTNtm94Ims7Hes2qr5VgSFol5ZEXpsNe+Z9VWLX/qNX31vJMCtzMPShCNwiwDbnw2kgc/tbWj1NW1b9hyk4qvkloMB3wIJstQPDg4qPfffz/1hZ/+/5JUXV3t6eAAFAcvdmJOAoWT/mOjsSZfw8jIitLURU1G+gdkeHr7QO9A4EJekIJoVGYZcPuzQSUVyJ1lKH799df12c9+NhWEzzjjjNS/xWIxvfrqq96ODgiAoFTOMJzdQJFv//Hspnrd98Srwy60K41JXz3vJMvQ3HfYPDAHLeQFKYgyy0DuqKQCubEMxdu2bSvUOIBAClLlDLkz6z8uiWnYPMNGkv/+4O9eT/VrjqwoHdL+kK3FwkyQQl6QgiizDBQWB/+Ag5t3AFEUpMoZcmd2lf5gQrYPcqyqb40NdVr+1GuGbRQjK0p1uD9hOkOAVTC3G1Ta2juHBfbPnHyMtnR0Owo5QQqizDJQOBz8A0eU+D0AIMiCVDlD7hob6jS3eaJKYsP/LX2e4Xx89byTNCI+9Ct1RLxEXz3vJM1tnqiqSuMaRDKYt7V3Dnl82W+36Z5VW1PbWjKoZD6vrb1T9z3x6pAZBw70DujpF9/O+ruZZjfVG74HP4Jocp2l38J5bvNEQpoHrA7+w6StvVMLlm7U/CXrtWDpxqzbO5CJSjFgIUiVM5izU1FtbKgzbXFw4yDHzt3W2to79fPVW4fdHS/z7MOy327T0y++Pew1jM5StG7oMLypiJ3fdfoeCo3e2MIohoN/qt1wA6EYsMAp3OBzsjP0Ym7jdHbutpYtmLe1dxoG4sznmf1sxc5zCaL+8auvtxgO/ml1gxsIxYCFoFXOMJyTnaGdg5z0YJLOrcpTtgCS7ZR1ZlCxe6vfzN/NJ4BxUZb7/Kx0FsPBfzFUu+E/QjGQBZWzYHOyM8x2kGN044N0blSezC766+7p04KlG7PuxDODitl0cZnSQ04+Acyt8FaoYB2WAO9npbMYDv6LodoN/xGKAbiukEHE6c7Q6iDHKJhkyrfylHxto9kqsv3tz3/6WMNeaWn4dHFWs0/kE8DcCG+Fqor6VX3NZfv3u9KZy8F/kA44iqHaDf8RigG4qtBBZFJ9jWEP7qT6Gsd/y04AyQzbuQaDw/3ZL45L9/lPH6tLzp9o+G+Zt/pNjmtLR7fh8/MJYFa/u2DpRlvv326wzjd0+VF9zXX7L0Sl080QG7QL24qh2g3/EYoBOGa1cy10EDELfptf3WUaIs1k68816j/OJRjYqUgnZd4kxI5s47J6n8t+u81yuVn9rt33byeUuxG6/Ki+5rr9e13pdDvEBvHCNi9b3YJUFYd3CMUAHMm2c7UbeNzawZi93oHeAVt3q0tn1u8rHQmDk+pr1LqhQ/es2qqa0eXqOzyQUzBwEsqcVpSl7IFldlO96SwYT7/4tiYcV52aQi5zPVkto8zXMWOnKupG6PKjzzTXIO51pdPtEFvIAw6/A2nQquLwDqEYgCGzHVG2nWtVZXzIjSSSkkHE7g7G7o7QqnLpdIdvFUyMxm0mWzBwMmNELsElW2CxmhpO+mAGDKP1NLd5ouY2TzScoSPb6yeZVUUn1ddkvdjQSejyo880nyDuZaXT7RBbqAOOIATSIFbF4Q1CMVBgflc97LDaEVntXNvaO3Wwd3ggjpfGUkHEzg4m8+YVVjtCq6pnLjt8s2DipOUhWzDIVm3N5PR9WIXuZN9vtjYIq/V02xWT1dhQZxpgYzFp/pL1ljdSkYYefEyqr9HGlzuzLhOjZWv2mfKjzzSoF3y5HWIL9T6z3W2vEOvW74sgUTiEYqCAglD1sMNqR2S1c23d0CGjmcHKy0pS7y/bDsbs5hVmlZnGhrohMy9kjsktdneAdoKBWVgzq75WVcZTAdTOzt8qdCe3ucmfrDO9ScjIilJbQcDsIsdEYuhrpb/npMyDjwVLN2YNxEbLNttnqtBTKgb1gi+3Q2yh3me2/vVCfJcy3Vt0BCoUr1q1Sv/5n/+p/v5+zZ07V1/72tf8HhLgqrCchrPaEV3WcorpztWsYps+9Vg+N68wG9fF537c86qV2bhHVpSqYkTcMhikVzKrKuNKJBI60DugmtHluqzllCHPz3wf8dKYDvb2p0K/nZ1/Y0Od3tj5njb8+e1ht5SWjmxzWzq69flPH2sYavsOD9pqg9n4cqfh62e+lp3t207LidGyDeJnKohzm3sRYgvxPs0+dyUxFWy9B7X6D/cFJhTv2rVLt99+u1pbWzVixAhddNFFOuOMMzRhwgS/hwa4Jiyn4ayCq9XO1azSmV5RybaDsVoWVnMPm43JLWbjzjYzRGYlMz1oGlU1M99H76H+YfMZZ9v5JwOrUSBOf+1Lzp+oP2zbPSz89g8klEgkNCJeMix4JKe6c9JOYneqO7Nt57YrJhv+Tlt7Z2g+U0l+tk8FMaxnY/a5M9v2vFjvQa3+w32BCcWbNm3SZz/7WVVXV0uSzj//fK1du1bf/va3/R0Y4KKwnIbLFlzNdq525gzOtoOx6nW1qszY2eHnE0hy3TFmC4+ZATfzfcxfst7w96x2/nYDa1t7p2E1WDpS3TeqJG98uVMTjqt2FD7sbN9Oq3Ft7Z26d7X5xYJB+0xJ4WmfSmf2mSlUuHfaauTVeg/jAQWcC0wo3r17t2pra1M/jxs3Tlu2bLH9+zU1VV4MK7Rqa0f5PQQYmDe9QXc/8pL6Dn9Q+SsvK9W86Q2urTM3/s6Ms0dp9KgK/XLNq3p370F96OhKXdp8ss4+7XjL33tl+17Tx9PHNePsUZpx9onDnvfMC381DXMXNo43/J3037Ua7zMv/FW/XPtaatl39/Tpl2tf0+hRFVnfl51xm732HhvhcU9Pn+l6qz26Ul17Dxo+bvQ7tbWjbL2mJK147k2VlMQ0aFJSNuvtXvHcm6bjMnLG/7+xiBW721xyWVu9ttufKbeseK7N8JT/iufetNy27XL7/Zp9Zv727gGt++POvD5LThh97kaPqvD8uzRMovievRCYUDw4OKhYLJb6OZFIDPk5m+7u/aZf7FGTflcrBEvDCdW69IKThlU9Gk6odmWdubnuG06o1o8ubxzyWLa/bRZUuvYe1G+e+YtlZSlzxomkqsq4Lj7342psqDN9/cwKXNfeg7rr4T+rZ19v6jV+sbp9yA5UkvoOD+gXq9vVcEK15fuykqxYJi8w7Np7UD958E+p1x5rY/q1saPLTd/brCkfNaygzpryUXV17RtSsas9ulKzpnzU1msmx5qLrr0HDXvLzTzf3qk5Tdm3y2zbXOZ6NnPpBSe59plyk9XnI9+xevG9b/aZWfv7HcNac9z4LDnh9XdpmLDPt6+kJGZZRA1MKK6rq9Mf//jH1M9dXV0aN26cjyMCvFHMp+HMWh+qKuOWp43NZpyQjlR/cmlRyGxL8Kr3dPlTrw2bcWMgceTx5I0yrIJceotAtjsFZps7uWvvwdTMEnamN8uVWW+53WWc66l3u20hQf18mV28WFUZmF3xEGbr06z+VOg+7mL+LoU/AvNJPPPMM3XXXXdpz549qqys1JNPPqmbb77Z72EBcMCsLzSRSFiGViczThgFKjthzKt+7syL4DIfzwyPmbNPWN0cxOgivKS29k79fPXWYQElObNE+g02ystKh1X8clUak2lvudm8xenLOJ++WjuhK6gBUzpyBtTJ436zmvnBKBgHsY8bcCIw3x7HHHOMvvOd7+jSSy/V4cOHNWfOHE2aNMnvYQFwwKyqme3mGnZnnDALVNmmD5P8nVbJTkXLydRiyeWQrWJ32xWTU891S2VF3PS92FnG+Uyhlu1OgPHSmC4+9+N23oYvsh1A+Sn9YDMZeqsq4yqNaciZkNKYFI/H1Hd46Mbn9WcpDDc9QvgFJhRLUktLi1paWvweBoA8GAXAbFeKW4Wd9Fv/GlWoDvUPqiweGzZNU+ZO2qtpldw6JZ7tToHp47TTRpAMwk6mTrPDbLYKyd4yzqfFwmx2E+nIfNHZpsfzW1Bnn8k82Ex+xvYf7Fe8NKaRZSU60DugkRWl6js8OCwQp/f9F2J8YZi1A+EUqFAMoDiZ9dUmp2oz+/djayqHhCCzyuiB3gFd1nJK1sDrRQ/ixed+XPc98ar608ppVhVLs4qX2SlpScMCgJ02gmT11e0+z2wBLtsyznYQ0dbeOewOhd09ffrZqq0qLTW/+LrvsDf909k4qWAG9SYQVgdO/QMJjRkZ113XNGnB0o060Dt8e7LT9+/2+Py+QQuKE6EYCLHMHfK86Q0Fu/o7m8yx1X9ktF7d8d6Q5yTnvDWqMFpVBTMlL/zyYwfppAJtVfGymjwnMwBkayNIMquu2zGyolSH+4f3gvcdHhhWuXbCqq/WanaJhDTkwCNT/0BCD/7u9YJuA04rmIW8CYST+YWzbUvZ2pySj3vV4hC2G7QgvAjFQEgZ7ZDvfuQlXXqBN6eQnezwjMZmtANLD3tGF23ZEYRKm91AblXxyhZ00/8t24wW6YwCcbw0pvL/f0q8qjKug739Q/pGk3fqk47MopHe87r/YH9ep66t+mrzbfVIVpezbatuhbdcKpiFOHgzC+tv7HxvyKwk2Xryk7K1OdWMLldbe+eQMybdPX2674lXJeXf4hDUthMUnxK/BwAgN0Y75L7DA5YzOeQquZNNrxjdv2ab2to7bY/NTC5VoJL/fxa9ZnS55jZPDM0pVKv3OrupXiPi5l/J6QGgsaFOkz9ZJwdTuQ9ZZl+/8GTddU2T7r3uHN159VmaP/0UjawoTT13RFlJ6nUqRgyvnSSDXy7MgkxJzJ3KX7Zt1em2bCWoFUyzsL7hz28bPp68pbeR9INOo200+e8P/u71YZX8ZPU+X1avC7iJSjEQUoXcITutiLlxC2Cryuk/TT8lNEE4nVXFK/l+MvtppeEBoK29Uxtf7pSTmbyytVAc7v/gCfsP9uve1VsNx5KU6ylzsyq3G/deGllRmnVbdbM/NagVTKfzC2f25CdbbjLXp1X7h9kMM+nbT64V+kK2nSDaCMVASBVyh+w0gNvtebWq9piFp89/+tjQ7gyzXWiVPLWeLTzk02Zg1Pdq9PcGEtYzTVRVxg1P09+zaqvuWbXV8mJHSYZzLOejNCZ99byTLKf/M5tHOfnvTgX1wrlc5he229aRa/uH2bay/KnXbM0awo06UAiEYiCkjHbI5WWlnuyQnQZws7Aw+ZN12tLRbava43d1yM2LhtL/1siKUo0oO9LDmessGfmeDbB7tz8rB3v79eDvXjcN51YXnVlVFiXz8Gb2vORyzMbufNh2+b2NmrH6/GXe6dCtED+yotSwXzzZlmN2IHegd4Dp1RAYhGIgpIx2yF7NPuG0IuZWWPCrOuTmvKiZf+tA74BGxEt0WUvuLSBWlfjMgw8z6e0PuchWSZaGh2+jG0QYsVtBHkxI9153TupnuxdnZsonGAaxgmn1+ZtwXLUnIf6r552ke1dvHXajj+QFm1bbItOrISgIxUBA5FKZzNwh19aOUlfXPtfHlkvIDWJYsMvNvlMv5lg1ay0ZdVSZLvrHEx3detmLCzPTpYdvoxtE5COzuuu0lz1I1V23mX3+vPpcZvuOcDK7CuAXQjEQAGG4Y1OYQ65TbvadenFBpFkAmXH2icMOirJV+fMNI/HSmOX8wSWxDw74jE6fJyvGTudTjsWk3kP9mr9kfer92+1lrxldrtuumGz/xWCL1XdEtmkE/b44EZAIxUAgcMcm7xlV4iXjypabFzF6dUGkkwujJA3paY7FYrpn1Va1bugw7QW1q7ysRGNGxi1nPMi841/mv2feotuOROKDOY+TB5GTP1mnZ//8tiwyuis9tF7dpKKYOZldBfALoRgIgKDOd1osjCrx9z3xqhKDiVSASq/OuzmrQLZbXOfKKJjNOHuU6b/NbqofdiMON7avA70DuuuaptTrGs0qka2SnM8NO5IO9Q9qS0e35k8/Zcj7LC+LqSxeanlhoxNhOKsTVHZnVwH8QigGAiCo8516qZA7RqNKvFFQS1bnk6fW7d4mN1tv9Rs73xt2y+rMW1w7YRbMRo+qUM++3mH/du/qrYqVWLc5JGW7u5nR85OyzSqRKVuF2Ontvrt7+oZVxqsqR7i6bXFWJ3+FasUifMMpQjEQAEGd79Qrha62OamIJp9rtOPOddxbOrqHPWY3SBnt2M2C2S/XvKrBgUHDOYctewrSlJeVqrys1PYyS2TcQcRuX68kzW2eaDpfcUlMqYOTbDNppL+219sWZ3XCgYo+ckEoBgIgqPOdesUs1C1/6jVPloGToGZVnc+1SmgVpBYs3Wj6Ps127GbV1Xf3HlS+kzp09/TpspZTLF8nXWY/crYLqpKSN4wwqyynB2U7fzN5EGm2jn6++sjr5Ls9mVXS0yvmQeTVvNtB/a6ioo9cBPtTDIRIvjuKYpzdwWyZmIXEA70Dwy6ekvIPMkahKl4aG9JTLGWvzudaJbQK5Vbv02zHbjZTw4eOrtTgwGBeVcv0W06n9+bGJMPAnXkQkXmAV14WU9/h4b+Z7Km20zpkdNA4qb7G8EYwViHbaDk7+dy2tXfqYK9xa8nB3n61tXcG8jPspGqabXmEpQJLRR+5IBQDLgjLjqKQrJaJ3cqtW5Uds0q80WNWr5Vr73e2SqfZ+7Sa0SGzH3dEvESXNp88rKdYOnITBbs9xekHBYf7P3i+0W9mHkRkBqrLWk5R64YO9R0e/j6SLSV2W4fsHjRabVtGNxNx8rlt3dBh2oUykFBgq5B2q6Z2lkdYKrBRvE4D+SMUAy4Iy46ikKyWid1T7NLQmz94UYl38jeMLvqy0/udGcqNmO3AzR5PtgqkL4+zTzs+NU/xBxeaxZVIJHSgd8DWXMBW4UcafmvlbAHT6jbQma/nxun4bNtW+vI020Yf/N3rjs5wGP3tILG7zdn5HgtLBTZq12nAHYRiwAVh2VEUktUyMQpCfYcHDHs1C3HxlB1t7Z3a+PLwWyJP/mT2Cqad2xsbVbCsduxWldP0qa8y7yI3Il6iEWUlpss6yapKnX5r5aQHf/e6o1aP5E09kmO1WoZODoiSj5tdwGfnPe4/2J9aPk7OcCS31aD129qtmtr5HgtLBTbbwVYQ1xP8RygGXBCWHUUhZVsmmUEoM8BJ2S+eKmQl3qxyajSzRDo7tzc2q2DlW0U1W25l8Zhh+0X6GJxs023tnabTuJndnMOsx9fobzs9IEo+nq1S6LSNx2p6uBHxEk2qr/H94M2I3aqpnXUepgqs2cFWEA6yEUwlfg8AKAazm+o1Ij704xTUHUWhOF0mjQ11mts8MbUDrhldrrnNEy1PWxeyEp/rGKzaEKSh79NIY0Odbrtisu697hzddsVkRzttqwsazZZ1kpP117qhw3QMyb+dfL/pkmHTitUBkRWr7SnJ6D2a6e7pMz0AKokdmV5uS0d3TmP1mp1lIdlb53b/VpDluk2h+FEpBlwQtSnV7MhlmWTe8SrbrYgLWYnP9WyA0zYEN1mNOVvLgpP1Z3VgkG1WiFz7dLt7+jR/yXrLceXyHnsP9Ztua1brMttY/WbnQkW76zzsM+UEeT3BX4RiwCVh31F4IZdlYnRqM14aU2lMjqZPc5vd08aZvYp+Bvp8T3XnO+PDiHgs9fu5HlRka3HI99S30zYeo7FUVcZTYzB7D2FhZ50Xoh/Xy9eg3Q1maJ8AEChmt2SurIj7esrWzmnjZKBK7nC7e/rUd3hQpRmtA4UK9IU61T27qX7Ye5SOVFDb2jtTzzFqVeg91J96jtnfztbiYHbqu629U1f+ZIPmL1mv+UvW66o7nrV8Lcl6mZm1FyQSCdPZLoqtjcpoG79n1VYt+635QYEbr3H/mm1Z151dtLvBDJViAIFiNSPAnVefVZAxpFep0qc0y1axMpqBoX8goarKeOrWyYVurSnEGYzGhjo9+LvXh11s1z+QSF0MmRxD5vMO9A5YVnrtTGcnDd9u2to7de/qrUPOLuw/2K/7nnjV9LXMXjMZuM3aC8xaQySFrt82G7Me+adffFsTjqt25b16fWEt7W4wQygGECh+n9rMPH2eHuCy3QnMbAaGQgZ6v5i99/R12dhQp9YNHcOemy3wpIfqBUs3Wm4f6Qc0RtKDelLmqfpJ9TXa+HJn1ptYJAOzVYuM2XsK65RgVgcmboXWQvT8ujkNIIoHoRhAoPg95ZNZJSzJLMBlm4HBC5k77nnTG9RwQrUnr5WNm3PhWrHaPoz6gbO9llEPu9G0a+ktGrn0vGeefTjY2596fpimBMt2y3IvX8OvA+MwrR/kh1AMRFRQKyF+n9q0s2M3ek62GRjcXt5GO+67H3lJl15wki/r0c25cK1YbR8Llm60dZdE6YMbh2Q7CEqXrAw7bZGxOvuQFJY7YFq1i+QTWjMPGvy8sDYI86LDH4RiIIKCXgmx2wfrRbC3c0MHo52/2e+NrCiVNLy6mO/yNtpx9x0eMNxxF+IAyCisTqqv0fKnXkuFqPKyUiUSwwOo08Bjtn04qVQml5OT37HaNvYf7NfF537csA/ZbvAOw5RgjQ11emPneznd7tyM0UFDvDSmkWUltnr53caUbdFFKAYiqBgqIU6CvZNQaFTxTGe28zerlH71vJM8Wd52d9yFPABKD6tGF7r1HR7ed1tVGdfF537clbHYvUOd9MFysvs72aZlG1lRarqc7Y4pLFOCXXL+RE04rtq1Ay2z6vuYkXHddU2TG0N2xO/2DfiHKdmACCqGSojdu1I5nd4pc0quqsp4qtprNaVZoe/IZ7aDznzcr7t3tW7oGBKIzZSXlboWzp3coS65nCbV1wz7txHxEn3+08carkuj50tS/8Cg6XI2upuf0WuGaUqwfO60mClo30dM2RZdVIqBCCqGSojdHWkuVdpcpzEz+z0vlrdRZbq8rHTYjjuXwOFGu4XTiq0bMls4qirjOnCwX5nZPF4aS/V5b3x5+MHR5E/W6ZLzJxq+htmtnvsOGx8B2Hl/QerpNxOlm2n4fV0D/EMoBiLIqxke8tlxOv3dQs124AYvlrfRjtto9gmngcOtdgu7bQluB5/M6duMLmorLyuxvDAvM/hmm+bNSvL9ma2D266Y7PhvFprVNiHlHx79nnHGCHcojSZCMRBBXlRC8glTufxuoWY7yDZuO8vQyfK28zczn3NZyylqbKhTbe0odXXtG/Jcp4HDrf7n2U31w3qKMyUrtl4xC7HJOYXtHDDZneatqjKuQ4cHTZdz0EKfE2bbxPKnXtPh/kTeB1BUZhEUhGIgotyuhOQTpnJtcUj+rtWO1MuquJMgb2d52/mbVs+ZcfYow9eV7AcOtyrryb+//KnXDG9sIUmJQRtNx3nIdkCU7d/b2jv189VbZWeY/zBxXNaLz8Ia+rIdXKTL9QLSoFZmgzp1JbxBKAbginzCVK6/a2dH6lUVyosZJez8TavnzDj7RMO/6yRwuFlZz3YnuoGEe3dBM5LtgMjOjUDs5vaNL3dqwnHVpu0QRnfCS388yJzM6iGF64JdK0GfuhLuIxQDcEU+YcqNIGZV0fGiCuVFr7Kdv+l1j7RXlXU/eruzHRC5dSMQKfsBUZgDltk2MaKsxLBnO0wX7Fophqkr4QyhGIAr8glT+QYxPwKHVfUsebc0t/5mesjw+kp9ryrrfs0wkO2AyI0bgaT/zvwl601bJ8z6coMesMy2CSncvdLZBOEiXRQWoRiAK/IJU/kGMT8qOla3u831de0cHBTiSn0vKutBnGHAilmIL4lJR1XEDSukSZkHZW3tnZZ9ubkeRBWS1TZRrD23QZsqDt4jFANwTT5hKp/f9evUvFkozvV17RwcuFnJLeRFRGGbYcDszoaDCSmRSCheGlO/xdQa6TdISZ++zEiYT8fn+rkNwwVsYTuQQ/4IxQBCz6+Kjheva/fiwXwDhJctJ2aBJ6gzDBjJDPHpDvQOqDR2ZBq2bBVjo7MYRs/LFIbQmKuw9FeH7UAO+SMUAwg9vyo6Zq87qb4mNdtCUHekXrWchCXw2JEM8WYzZ5SXlerOq88y/Hcp9xuYFNMyNBKmC9jCdCCH/Nm7STwABFhjQ53mNk8cMv/s3OaJnu/MjF538ifrtPHlD3pIk4GmrX347YT95FXLiVXgCatsy2p2U71GxIfuTpMHZdnOGhgdvBXjMkzHBWwIKirFAIqCXxWdzNc1msoriFUwr1pOijHwWC2rZJvDof5BlcSO9Bxnnh0wuyOe2VmEsC/DbK0fXMCGoCIUA4CLwhJocmk5sdPnWoyBx6pNJv3x5I0+JtXXmM6FPLKiVLFYzLIX2e1lWMj+ZDutH1zAhqCifQIAXGQWXIIWCp22nCTDTra2EKtWgrAyW1ZbOroNK8BPv/j2kOXS2FCn266YrMtaTtHh/kQqEBdiGdpdb26x0/rhV7sTkA2VYgBwUZiqYE5aTuxeHFWsV+wbLSuzKfkk42nW/FiGD/7u9YK289g9U8IFbAgiQjEAuKhYQ2FY2kIKyWp2CaPHnTzXrWn3zNo0vFpvxdg+g+ggFAPISTHPo5qvQlTBjJb/jLNHefZ6dsNOsU8nls7qroZGIbDQgdFqtgqvXjNMZ0qATPQUA3Cs0H2KhdDW3qkFSzdq/pL1WrB0Y6Dfi9nyf+aFv3r2mnb7XIt9OrF0jQ11+vynjx32uFkILHS/tVU12KvXpF8YYUalGIBjYZp8346wVTfNlv8v17yqH13e6Mlr2m0LKUSbhZOzFF6f0bjk/ImacFy1rdcodGuNWWV6ZEWpp9s1/cIIK0IxAMeKrb80bCHfbDm/u/egp69rJ+x43SLg5ACmUAc7TkJgIQOjWSvDV887qSCvD4QN7RMAHAvLtGN2FTrk59uqYbacP3R0pRvDy4vXLQJO2jOi1MphhFYGwBkqxQAcK7aLaQp5AZQb1Uuz5X9p88muj9cpr1sEvJrtoVjRygDYRygG4FixTTtWyJDvRquG2fI/+7Tj1dW1z/UxO+VlEHNyAMP0YACcIBQDyEkxVaAKGfLdql4W0/J3wskBTLGd0Ugq9HR8QFQQigFAhQuZVC/z4+QAptjOaEjm7TejR1Wo4YRqfwcHhByhGAAcyHeKr1yrl9ws5QNBne0hH3bXrx/T8QFRQSgGAJvcuEgul+pl2OZRhjNO1q9f0/EBUUAoBgCb3JrP2Gn1MmzzKMMZJ+vXrP0mCNPxAWFHKAYQOn61Evg1xRdTixU3J+s3yNPxAWHHzTsAhEryVHMyMCRPNTu9AUYu/LppSbHdLAVDOVm/ZjfkOPu04z0dIxAFVIoBhEq+rQT5VJn9muKrWKcWwxFO129YLh4EwoZQDCBU8mklyPeCNb+m+HLrdZnBIpiKceo4IIwIxQBCJZ95fs2qzPes2qrWDR22gohfVbp8X5cZLIKN6i/gP3qKAYTK7KZ6jYgP/eqy20pgVU0uZG+yH6zaTgAAhGIAIWN2oZGdKlu2anIxh0RmsAAAa7RPAAidXE81G13QlMnPkOhlzy+3lwYAa1SKAURGZpXZiF8h0eup5vJpOwGAKCAUA4iUxoY63XbFZF3WckqgQqLXPb/5tJ0AQBTQPgEgkoI2DVYhen6Z4QAAzBGKAURWkEIiPb8A4C/aJwAgAOj5BQB/USkGgAAIWjsHAEQNoRgAAiJI7RwAEDW0TwAAACDyCMUAAACIPEIxAAAAIo9QDAAAgMgjFAMAACDyCMUAAACIPEIxAAAAIo9QDAAAgMgjFAMAACDyuKMdAGCYtvZObjkNIFJ8qxT/5Cc/0V133ZX6uaenR9/4xjfU3Nysr33ta+rq6vJraAAQaW3tnbp/zTZ19/RJkrp7+nT/mm1qa+/0eWQA4J2Ch+J9+/bp+uuv13333Tfk8Z/85Cc6/fTTtWbNGn3pS1/S4sWLCz00AICk1g0dOtQ/OOSxQ/2Dat3Q4dOIAMB7BQ/F69at0//4H/9DX//614c8/swzz6ilpUWSNH36dD377LM6fPhwoYcHAJGXrBDbfRwAikHBQ/GsWbP0jW98Q6WlpUMe3717t2prayVJ8XhcVVVV2rNnT6GHBwCRVzO63NHjAFAMPLvQbs2aNbrllluGPPaxj31Mv/jFL2z9fiKRUEmJ/cxeU1PlZHhFr7Z2lN9DgE9Y99HmxvqfN71Bdz/ykvoOD6QeKy8r1bzpDWxfAca6iS7WvTs8C8XNzc1qbm62/fxx48bp3XffVV1dnfr7+3XgwAFVV1fb/v3u7v0aHEzkMNLiU1s7Sl1d+/weBnzAuo82t9Z/wwnVuvSCk4bNPtFwQnUotq8ozpzBZz+6WPf2lZTELIuogZmSrampSStWrNA3v/lNPfHEEzr99NNVVlbm97AAIJIaG+pCGSSTM2ckLxRMzpwhKZTvB0DhBObmHVdffbX+/Oc/a9q0aVq+fLn+/d//3e8hAQBChpkzAOTKt0rxlVdeOeTn6upq/dd//ZdPowEAFANmzgCQq8C0TwBAMYpif6ufakaXGwZgZs4AkE1g2icAoNhwZ7jCm91UrxHxobu2EfESzW6q92lEAMKCUAwAHqG/tfAaG+o0t3liqjJcM7pcc5snUp0HkBXtEwDgEfpb/RHWmTMA+ItKMQB4hDvDAUB4EIoBwCP0twJAeNA+AQAeSZ7CZ/YJAAg+QjEAeIj+VgAIB9onAAAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAQAAEHlxvwcAAAAQNW3tnWrd0KHunj7VjC7X7KZ6NTbU+T2sSCMUAwAAFFBbe6fuX7NNh/oHJUndPX26f802SSIY+4j2CQAAgAJq3dCRCsRJh/oH1bqhw6cRQSIUAwAAFFR3T5+jx1EYhGIAAIACqhld7uhxFAahGAAAoIBmN9VrRHxoBBsRL9HspnqfRgSJC+0AAAAKKnkxHbNPBAuhGAAAoMAaG+oIwQFD+wQAAAAij1AMAACAyCMUAwAAIPIIxQAAAIg8QjEAAAAij1AMAACAyCMUAwAAIPIIxQAAAIg8QjEAAAAij1AMAACAyCMUAwAAIPIIxQAAAIg8QjEAAAAij1AMAACAyIv7PQC3lJTE/B5CoLA8oot1H22s/+hi3UcX696ebMsplkgkEgUaCwAAABBItE8AAAAg8gjFAAAAiDxCMQAAACKPUAwAAIDIIxQDAAAg8gjFAAAAiDxCMQAAACKPUAwAAIDIIxQDAAAg8gjFRWTVqlW68MILNXXqVD3wwAN+DwcFsH//fk2fPl07d+6UJG3atEktLS2aOnWqbr/9dp9HBy/dfffdmjZtmqZNm6Zbb71VEus/Ku644w5deOGFmjZtmu677z5JrPuo+dGPfqTrrrtOEuveTYTiIrFr1y7dfvvtWr58uVasWKFf//rXeuONN/weFjz00ksv6eKLL9b27dslSb29vbr++uu1dOlSPfHEE3rllVe0YcMGfwcJT2zatEnPPfecHnvsMa1YsULt7e1avXo16z8CNm/erN///vf6zW9+o//7f/+vli1bpm3btrHuI6StrU2PPfaYJL733UYoLhKbNm3SZz/7WVVXV+uoo47S+eefr7Vr1/o9LHjo4Ycf1o033qhx48ZJkrZs2aLx48fr+OOPVzweV0tLC9tAkaqtrdV1112nESNGqKysTPX19dq+fTvrPwI+85nP6Je//KXi8bi6u7s1MDCgnp4e1n1EvPfee7r99tv1zW9+UxLf+24jFBeJ3bt3q7a2NvXzuHHjtGvXLh9HBK8tXrxYp59+eupntoHoOPHEE/WpT31KkrR9+3atWbNGsViM9R8RZWVluvPOOzVt2jQ1Njby2Y+Qf//3f9d3vvMdjR49WhLf+24jFBeJwcFBxWKx1M+JRGLIzyh+bAPR85e//EXz58/Xv/7rv+r4449n/UfIVVddpba2Nr3zzjvavn076z4CHnnkEX34wx9WY2Nj6jG+990V93sAcEddXZ3++Mc/pn7u6upKnVZHNNTV1amrqyv1M9tAcXvhhRd01VVX6frrr9e0adO0efNm1n8EdHR06NChQzr55JNVWVmpqVOnau3atSotLU09h3VfnJ544gl1dXVp5syZev/99/X3v/9df/vb31j3LqJSXCTOPPNMtbW1ac+ePTp48KCefPJJnXXWWX4PCwV06qmn6s0339SOHTs0MDCg1atXsw0UqXfeeUff+ta39OMf/1jTpk2TxPqPip07d+qGG27QoUOHdOjQIa1bt04XXXQR6z4C7rvvPq1evVorV67UVVddpXPOOUc/+9nPWPcuolJcJI455hh95zvf0aWXXqrDhw9rzpw5mjRpkt/DQgGVl5dryZIluvLKK9XX16empiZdcMEFfg8LHvj5z3+uvr4+LVmyJPXYRRddxPqPgKamJm3ZskWzZs1SaWmppk6dqmnTpmns2LGs+wjie99dsUQikfB7EAAAAICfaJ8AAABA5BGKAQAAEHmEYgAAAEQeoRgAAACRRygGAABA5BGKAcAHO3fu1Mknn6yZM2em/psxY4YeffTRvP7u5ZdfrtbWVknSzJkz1dPTY/rcffv26dJLL3X8GmvXrtUll1yS8xgBIIiYpxgAfFJRUaGVK1emft61a5emT5+uT3ziE5o4cWLefz/9bxt5//339fLLL+f9OgBQDAjFABAQxxxzjMaPH6+NGzdq0aJFOnjwoKqqqrRs2TI98sgjevDBBzU4OKjq6mr927/9m+rr67Vr1y5dd9112r17t4499lh1d3en/t5JJ52ktrY2jR07Vv/93/+txx57TPF4XOPHj9eSJUu0cOFC9fb2aubMmWptbdX27du1ePFivffeexoYGNAll1yiOXPmSJLuuOMOrVq1StXV1Ro/frxfiwgAPEMoBoCAePHFF/XWW2+pt7dXb7zxhtavX6+qqipt3rxZK1as0AMPPKDKyko999xz+va3v601a9Zo0aJFOvXUU3XNNddox44dmjVr1rC/u27dOrW2turhhx/WmDFjdMstt+hXv/qVbrnlFrW0tGjlypXq7+/XVVddpVtvvVUNDQ3at2+fvvKVr2jChAl699139eSTT2rFihWqqKjQt771rcIvHADwGKEYAHySrNJK0sDAgI4++mjddttt6u7u1kknnaSqqipJ0jPPPKMdO3booosuSv1uT0+P3nvvPW3atEnf+973JEnjx4/XGWecMex12tradMEFF2jMmDGSpIULF0o60tectH37dr311lu6/vrrh4xv69at6ujo0HnnnZcazxe/+EUtW7bMzUUBAL4jFAOATzJ7ipNaW1t11FFHpX4eHBzUzJkztWDBgtTPu3fv1pgxYxSLxZRIJFLPjceHf62XlpYqFoulfu7p6Rl2Ad7AwIBGjRo1ZDzvvvuuRo0apVtvvXXIa5SWlubwbgEg2Jh9AgACbsqUKXr88ce1e/duSdKDDz6ouXPnSpI+97nP6de//rUk6e2339bzzz8/7PfPPPNMPfXUU9q/f78k6a677tIvfvELxeNxDQwMKJFI6KMf/eiQkP7OO+9o+vTpeuWVV3TWWWdp7dq16unp0eDgYNYL+AAgjKgUA0DATZkyRZdddpnmz5+vWCymqqoq3X333YrFYrrxxhu1cOFCNTc3q66uznDWiqamJr3xxhu6+OKLJUkTJkzQzTffrMrKSk2aNEnTpk3TAw88oKVLl2rx4sX62c9+pv7+fl199dU67bTTJEmvvfaavvjFL2r06NGaOHGi9u7dW9BlAABeiyXSz4kBAAAAEUT7BAAAACKPUAwAAIDIIxQDAAAg8gjFAAAAiDxCMQAAACKPUAwAAIDIIxQDAAAg8gjFAAAAiLz/B2ZYDShoCkE+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let check for residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Normality of errors\n",
    "sns.distplot(y_train-y_pred)\n",
    "plt.title(\"Histogram of Residuals\")\n",
    "plt.xlabel(\"Residuals\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}