15. Boston House Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from numpy import arange\n",
    "from matplotlib import pyplot\n",
    "import pandas as pd\n",
    "from pandas import set_option\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load Dataset\n",
    "names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
    "dataset = pd.read_csv('datasets/housing.csv', delim_whitespace=True, names = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Descriptive Statistics\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       float64\n",
       "ZN         float64\n",
       "INDUS      float64\n",
       "CHAS         int64\n",
       "NOX        float64\n",
       "RM         float64\n",
       "AGE        float64\n",
       "DIS        float64\n",
       "RAD          int64\n",
       "TAX        float64\n",
       "PTRATIO    float64\n",
       "B          float64\n",
       "LSTAT      float64\n",
       "MEDV       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.32e-03</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>6.58</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.09</td>\n",
       "      <td>1</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.73e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>6.42</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.97</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.73e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>7.18</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.97</td>\n",
       "      <td>2</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.24e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>7.00</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.06</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.91e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>7.15</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.06</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.99e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>6.43</td>\n",
       "      <td>58.7</td>\n",
       "      <td>6.06</td>\n",
       "      <td>3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.12</td>\n",
       "      <td>5.21</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.83e-02</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.01</td>\n",
       "      <td>66.6</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>395.60</td>\n",
       "      <td>12.43</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.45e-01</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.17</td>\n",
       "      <td>96.1</td>\n",
       "      <td>5.95</td>\n",
       "      <td>5</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>19.15</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.11e-01</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>5.63</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.63</td>\n",
       "      <td>29.93</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.70e-01</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.00</td>\n",
       "      <td>85.9</td>\n",
       "      <td>6.59</td>\n",
       "      <td>5</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>386.71</td>\n",
       "      <td>17.10</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.25e-01</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.38</td>\n",
       "      <td>94.3</td>\n",
       "      <td>6.35</td>\n",
       "      <td>5</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>392.52</td>\n",
       "      <td>20.45</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.17e-01</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.01</td>\n",
       "      <td>82.9</td>\n",
       "      <td>6.23</td>\n",
       "      <td>5</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>13.27</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9.38e-02</td>\n",
       "      <td>12.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>5.89</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5.45</td>\n",
       "      <td>5</td>\n",
       "      <td>311.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>390.50</td>\n",
       "      <td>15.71</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.30e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>5.95</td>\n",
       "      <td>61.8</td>\n",
       "      <td>4.71</td>\n",
       "      <td>4</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>8.26</td>\n",
       "      <td>20.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6.38e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>6.10</td>\n",
       "      <td>84.5</td>\n",
       "      <td>4.46</td>\n",
       "      <td>4</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>380.02</td>\n",
       "      <td>10.26</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.27e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>5.83</td>\n",
       "      <td>56.5</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>395.62</td>\n",
       "      <td>8.47</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.05e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>5.93</td>\n",
       "      <td>29.3</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>386.85</td>\n",
       "      <td>6.58</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.84e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>5.99</td>\n",
       "      <td>81.7</td>\n",
       "      <td>4.26</td>\n",
       "      <td>4</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>386.75</td>\n",
       "      <td>14.67</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8.03e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>5.46</td>\n",
       "      <td>36.6</td>\n",
       "      <td>3.80</td>\n",
       "      <td>4</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>288.99</td>\n",
       "      <td>11.69</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.26e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>5.73</td>\n",
       "      <td>69.5</td>\n",
       "      <td>3.80</td>\n",
       "      <td>4</td>\n",
       "      <td>307.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>390.95</td>\n",
       "      <td>11.28</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS   NOX    RM    AGE   DIS  RAD    TAX  PTRATIO  \\\n",
       "0   6.32e-03  18.0   2.31     0  0.54  6.58   65.2  4.09    1  296.0     15.3   \n",
       "1   2.73e-02   0.0   7.07     0  0.47  6.42   78.9  4.97    2  242.0     17.8   \n",
       "2   2.73e-02   0.0   7.07     0  0.47  7.18   61.1  4.97    2  242.0     17.8   \n",
       "3   3.24e-02   0.0   2.18     0  0.46  7.00   45.8  6.06    3  222.0     18.7   \n",
       "4   6.91e-02   0.0   2.18     0  0.46  7.15   54.2  6.06    3  222.0     18.7   \n",
       "5   2.99e-02   0.0   2.18     0  0.46  6.43   58.7  6.06    3  222.0     18.7   \n",
       "6   8.83e-02  12.5   7.87     0  0.52  6.01   66.6  5.56    5  311.0     15.2   \n",
       "7   1.45e-01  12.5   7.87     0  0.52  6.17   96.1  5.95    5  311.0     15.2   \n",
       "8   2.11e-01  12.5   7.87     0  0.52  5.63  100.0  6.08    5  311.0     15.2   \n",
       "9   1.70e-01  12.5   7.87     0  0.52  6.00   85.9  6.59    5  311.0     15.2   \n",
       "10  2.25e-01  12.5   7.87     0  0.52  6.38   94.3  6.35    5  311.0     15.2   \n",
       "11  1.17e-01  12.5   7.87     0  0.52  6.01   82.9  6.23    5  311.0     15.2   \n",
       "12  9.38e-02  12.5   7.87     0  0.52  5.89   39.0  5.45    5  311.0     15.2   \n",
       "13  6.30e-01   0.0   8.14     0  0.54  5.95   61.8  4.71    4  307.0     21.0   \n",
       "14  6.38e-01   0.0   8.14     0  0.54  6.10   84.5  4.46    4  307.0     21.0   \n",
       "15  6.27e-01   0.0   8.14     0  0.54  5.83   56.5  4.50    4  307.0     21.0   \n",
       "16  1.05e+00   0.0   8.14     0  0.54  5.93   29.3  4.50    4  307.0     21.0   \n",
       "17  7.84e-01   0.0   8.14     0  0.54  5.99   81.7  4.26    4  307.0     21.0   \n",
       "18  8.03e-01   0.0   8.14     0  0.54  5.46   36.6  3.80    4  307.0     21.0   \n",
       "19  7.26e-01   0.0   8.14     0  0.54  5.73   69.5  3.80    4  307.0     21.0   \n",
       "\n",
       "         B  LSTAT  MEDV  \n",
       "0   396.90   4.98  24.0  \n",
       "1   396.90   9.14  21.6  \n",
       "2   392.83   4.03  34.7  \n",
       "3   394.63   2.94  33.4  \n",
       "4   396.90   5.33  36.2  \n",
       "5   394.12   5.21  28.7  \n",
       "6   395.60  12.43  22.9  \n",
       "7   396.90  19.15  27.1  \n",
       "8   386.63  29.93  16.5  \n",
       "9   386.71  17.10  18.9  \n",
       "10  392.52  20.45  15.0  \n",
       "11  396.90  13.27  18.9  \n",
       "12  390.50  15.71  21.7  \n",
       "13  396.90   8.26  20.4  \n",
       "14  380.02  10.26  18.2  \n",
       "15  395.62   8.47  19.9  \n",
       "16  386.85   6.58  23.1  \n",
       "17  386.75  14.67  17.5  \n",
       "18  288.99  11.69  20.2  \n",
       "19  390.95  11.28  18.2  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          CRIM     ZN  INDUS     CHAS    NOX     RM    AGE    DIS    RAD  \\\n",
      "count  5.1e+02  506.0  506.0  5.1e+02  506.0  506.0  506.0  506.0  506.0   \n",
      "mean   3.6e+00   11.4   11.1  6.9e-02    0.6    6.3   68.6    3.8    9.5   \n",
      "std    8.6e+00   23.3    6.9  2.5e-01    0.1    0.7   28.1    2.1    8.7   \n",
      "min    6.3e-03    0.0    0.5  0.0e+00    0.4    3.6    2.9    1.1    1.0   \n",
      "25%    8.2e-02    0.0    5.2  0.0e+00    0.4    5.9   45.0    2.1    4.0   \n",
      "50%    2.6e-01    0.0    9.7  0.0e+00    0.5    6.2   77.5    3.2    5.0   \n",
      "75%    3.7e+00   12.5   18.1  0.0e+00    0.6    6.6   94.1    5.2   24.0   \n",
      "max    8.9e+01  100.0   27.7  1.0e+00    0.9    8.8  100.0   12.1   24.0   \n",
      "\n",
      "         TAX  PTRATIO      B  LSTAT   MEDV  \n",
      "count  506.0    506.0  506.0  506.0  506.0  \n",
      "mean   408.2     18.5  356.7   12.7   22.5  \n",
      "std    168.5      2.2   91.3    7.1    9.2  \n",
      "min    187.0     12.6    0.3    1.7    5.0  \n",
      "25%    279.0     17.4  375.4    6.9   17.0  \n",
      "50%    330.0     19.1  391.4   11.4   21.2  \n",
      "75%    666.0     20.2  396.2   17.0   25.0  \n",
      "max    711.0     22.0  396.9   38.0   50.0  \n"
     ]
    }
   ],
   "source": [
    "set_option('precision', 1)\n",
    "print(dataset.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         CRIM    ZN  INDUS      CHAS   NOX    RM   AGE   DIS       RAD   TAX  \\\n",
      "CRIM     1.00 -0.20   0.41 -5.59e-02  0.42 -0.22  0.35 -0.38  6.26e-01  0.58   \n",
      "ZN      -0.20  1.00  -0.53 -4.27e-02 -0.52  0.31 -0.57  0.66 -3.12e-01 -0.31   \n",
      "INDUS    0.41 -0.53   1.00  6.29e-02  0.76 -0.39  0.64 -0.71  5.95e-01  0.72   \n",
      "CHAS    -0.06 -0.04   0.06  1.00e+00  0.09  0.09  0.09 -0.10 -7.37e-03 -0.04   \n",
      "NOX      0.42 -0.52   0.76  9.12e-02  1.00 -0.30  0.73 -0.77  6.11e-01  0.67   \n",
      "RM      -0.22  0.31  -0.39  9.13e-02 -0.30  1.00 -0.24  0.21 -2.10e-01 -0.29   \n",
      "AGE      0.35 -0.57   0.64  8.65e-02  0.73 -0.24  1.00 -0.75  4.56e-01  0.51   \n",
      "DIS     -0.38  0.66  -0.71 -9.92e-02 -0.77  0.21 -0.75  1.00 -4.95e-01 -0.53   \n",
      "RAD      0.63 -0.31   0.60 -7.37e-03  0.61 -0.21  0.46 -0.49  1.00e+00  0.91   \n",
      "TAX      0.58 -0.31   0.72 -3.56e-02  0.67 -0.29  0.51 -0.53  9.10e-01  1.00   \n",
      "PTRATIO  0.29 -0.39   0.38 -1.22e-01  0.19 -0.36  0.26 -0.23  4.65e-01  0.46   \n",
      "B       -0.39  0.18  -0.36  4.88e-02 -0.38  0.13 -0.27  0.29 -4.44e-01 -0.44   \n",
      "LSTAT    0.46 -0.41   0.60 -5.39e-02  0.59 -0.61  0.60 -0.50  4.89e-01  0.54   \n",
      "MEDV    -0.39  0.36  -0.48  1.75e-01 -0.43  0.70 -0.38  0.25 -3.82e-01 -0.47   \n",
      "\n",
      "         PTRATIO     B  LSTAT  MEDV  \n",
      "CRIM        0.29 -0.39   0.46 -0.39  \n",
      "ZN         -0.39  0.18  -0.41  0.36  \n",
      "INDUS       0.38 -0.36   0.60 -0.48  \n",
      "CHAS       -0.12  0.05  -0.05  0.18  \n",
      "NOX         0.19 -0.38   0.59 -0.43  \n",
      "RM         -0.36  0.13  -0.61  0.70  \n",
      "AGE         0.26 -0.27   0.60 -0.38  \n",
      "DIS        -0.23  0.29  -0.50  0.25  \n",
      "RAD         0.46 -0.44   0.49 -0.38  \n",
      "TAX         0.46 -0.44   0.54 -0.47  \n",
      "PTRATIO     1.00 -0.18   0.37 -0.51  \n",
      "B          -0.18  1.00  -0.37  0.33  \n",
      "LSTAT       0.37 -0.37   1.00 -0.74  \n",
      "MEDV       -0.51  0.33  -0.74  1.00  \n"
     ]
    }
   ],
   "source": [
    "set_option('precision', 2)\n",
    "print(dataset.corr(method='pearson'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Unimodel Data Visualizations\n",
    "dataset.hist(sharex = False, sharey=False, xlabelsize=1, ylabelsize=1)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>CHAS</th>\n",
       "      <th>RM</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6.58</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>6.42</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7.18</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>7.15</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHAS    RM    TAX  PTRATIO       B  LSTAT  MEDV\n",
       "0     0  6.58  296.0     15.3  396.90   4.98  24.0\n",
       "1     0  6.42  242.0     17.8  396.90   9.14  21.6\n",
       "2     0  7.18  242.0     17.8  392.83   4.03  34.7\n",
       "3     0  7.00  222.0     18.7  394.63   2.94  33.4\n",
       "4     0  7.15  222.0     18.7  396.90   5.33  36.2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = dataset['MEDV']\n",
    "dataset = dataset.drop(['CRIM','ZN','INDUS','NOX','AGE','DIS','RAD'], axis = 1)\n",
    "features = dataset.drop('MEDV', axis = 1)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#density \n",
    "dataset.plot(kind='density', subplots=True, layout=(4,4), sharex=False, legend=False, fontsize=1)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#boxplot \n",
    "dataset.plot(kind='box', subplots=True, layout=(4,4), sharex=False, legend=False, fontsize=8)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Multimodel Data Visualizations\n",
    "#Scatter Plot\n",
    "scatter_matrix(dataset)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "fig = pyplot.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(dataset.corr(), vmin = -1, vmax = 1, interpolation='none')\n",
    "fig.colorbar(cax)\n",
    "ticks = numpy.arange(0,14,1)\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_yticklabels(names)\n",
    "ax.set_yticklabels(names)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 12 is out of bounds for axis 1 with size 7",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-2fe36ef474cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mvalidation_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.20\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mseed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 12 is out of bounds for axis 1 with size 7"
     ]
    }
   ],
   "source": [
    "# Split-out validation dataset\n",
    "array = dataset.values\n",
    "X = array[:,0:12]\n",
    "Y = array[:,12]\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "lab_enc = LabelEncoder()\n",
    "Y = lab_enc.fit_transform(Y)\n",
    "\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split-out validation dataset\n",
    "array = dataset.values\n",
    "X = array[:,0:6]\n",
    "Y = array[:,6]\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X, Y, test_size=validation_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_folds = 10\n",
    "seed = 7\n",
    "scoring = 'neg_mean_squared_error'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spot Check \n",
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LASSO', Lasso()))\n",
    "models.append(('EN', ElasticNet()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('CART', DecisionTreeRegressor()))\n",
    "models.append(('SVR', SVR()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:530: FutureWarning: From version 0.22, errors during fit will result in a cross validation score of NaN by default. Use error_score='raise' if you want an exception raised or error_score=np.nan to adopt the behavior from version 0.22.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: 'continuous'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-6aa85a3a5098>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mkfold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_folds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mcv_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkfold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv_results\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mnames\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[0;32m    389\u001b[0m                                 \u001b[0mfit_params\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    390\u001b[0m                                 \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 391\u001b[1;33m                                 error_score=error_score)\n\u001b[0m\u001b[0;32m    392\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'test_score'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[1;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    230\u001b[0m             \u001b[0mreturn_times\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_estimator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreturn_estimator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m             error_score=error_score)\n\u001b[1;32m--> 232\u001b[1;33m         for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[0;32m    233\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m     \u001b[0mzipped_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    919\u001b[0m             \u001b[1;31m# remaining jobs.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    920\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 921\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    922\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    923\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    757\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    758\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 759\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    760\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    761\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 716\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    717\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    180\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 182\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    183\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    547\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    548\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 549\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    551\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 225\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 225\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[0;32m    514\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1531\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order=\"C\",\n\u001b[0;32m   1532\u001b[0m                          accept_large_sparse=solver != 'liblinear')\n\u001b[1;32m-> 1533\u001b[1;33m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1534\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1535\u001b[0m         \u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\utils\\multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m    167\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[0;32m    168\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[1;32m--> 169\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Unknown label type: %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Unknown label type: 'continuous'"
     ]
    }
   ],
   "source": [
    "# evaluate each model in turn \n",
    "results = [] \n",
    "names = [] \n",
    "for name, model in models: \n",
    "    kfold = KFold(n_splits=num_folds, random_state=seed) \n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) \n",
    "    results.append(cv_results) \n",
    "    names.append(name) \n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std()) \n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compare Algorithms\n",
    "fig = pyplot.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: -1186.921585 (345.588314)\n",
      "LASSO: -772.781809 (221.919191)\n",
      "EN: -834.002665 (256.351617)\n",
      "KNN: -1444.782332 (481.195886)\n",
      "CART: -1035.691646 (370.013507)\n",
      "SVR: -3479.981103 (628.386643)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Standardization\n",
    "pipeline = []\n",
    "pipeline.append(('ScaledLR', Pipeline([('Scaler', StandardScaler()), ('LR', LinearRegression())])))\n",
    "pipeline.append(('ScaledLasso', Pipeline([('Scaler', StandardScaler()), ('LASSO', Lasso())])))\n",
    "pipeline.append(('ScaledEN', Pipeline([('Scaler', StandardScaler()), ('EN', ElasticNet())])))\n",
    "pipeline.append(('ScaledKNN', Pipeline([('Scaler', StandardScaler()), ('KNN', KNeighborsRegressor())])))\n",
    "pipeline.append(('ScaledCART', Pipeline([('Scaler', StandardScaler()), ('CART', DecisionTreeRegressor())])))\n",
    "pipeline.append(('ScaledSVR', Pipeline([('Scaler', StandardScaler()), ('SVR', SVR())])))\n",
    "\n",
    "results = []\n",
    "names = []\n",
    "\n",
    "for name, model in models: \n",
    "    kfold = KFold(n_splits=num_folds, random_state=seed) \n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring) \n",
    "    results.append(cv_results) \n",
    "    names.append(name) \n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std()) \n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
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