Linear_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression to predict House prices\n",
    "Data Used: BOSTON HOUSING DATA\n",
    "link: https://archive.ics.uci.edu/ml/machine-learning-databases/housing/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear Regression to "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import read_csv\n",
    "import sklearn as skl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename='https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nAttribute Information:\\n\\n    1. CrimeRate      per capita crime rate by town\\n    2. Zone        proportion of residential land zoned for lots over \\n                 25,000 sq.ft.\\n    3. Indus     proportion of non-retail business acres per town\\n    4. Chas      Charles River dummy variable (= 1 if tract bounds \\n                 river; 0 otherwise)\\n    5. NOx       nitric oxides concentration (parts per 10 million)\\n    6. Rooms        average number of rooms per dwelling\\n    7. Age       proportion of owner-occupied units built prior to 1940\\n    8. Dis       weighted distances to five Boston employment centres\\n    9. Rad       index of accessibility to radial highways\\n    10. Tax      full-value property-tax rate per $10,000\\n    11. PT_Ratio  pupil-teacher ratio by town\\n    12. Blacks        1000(Bk - 0.63)^2 where Bk is the proportion of blacks \\n                 by town\\n    13. Lstat    % lower status of the population\\n    14. MedVal    Median value of owner-occupied homes in $1000's\\n    \""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names=['CrimeRate','Zone','Indus','Chas''NOx','Rooms','Age','Dis','Rad','Tax','PT_Ratio','Blacks','Lstat','MedVal']\n",
    "'''\n",
    "Attribute Information:\n",
    "\n",
    "    1. CrimeRate      per capita crime rate by town\n",
    "    2. Zone        proportion of residential land zoned for lots over \n",
    "                 25,000 sq.ft.\n",
    "    3. Indus     proportion of non-retail business acres per town\n",
    "    4. Chas      Charles River dummy variable (= 1 if tract bounds \n",
    "                 river; 0 otherwise)\n",
    "    5. NOx       nitric oxides concentration (parts per 10 million)\n",
    "    6. Rooms        average number of rooms per dwelling\n",
    "    7. Age       proportion of owner-occupied units built prior to 1940\n",
    "    8. Dis       weighted distances to five Boston employment centres\n",
    "    9. Rad       index of accessibility to radial highways\n",
    "    10. Tax      full-value property-tax rate per $10,000\n",
    "    11. PT_Ratio  pupil-teacher ratio by town\n",
    "    12. Blacks        1000(Bk - 0.63)^2 where Bk is the proportion of blacks \n",
    "                 by town\n",
    "    13. Lstat    % lower status of the population\n",
    "    14. MedVal    Median value of owner-occupied homes in $1000's\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe=read_csv(filename,delim_whitespace=True,names=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          CrimeRate   Zone  Indus  ChasNOx  Rooms    Age     Dis  Rad    Tax  \\\n",
      "0.00632        18.0   2.31      0    0.538  6.575   65.2  4.0900    1  296.0   \n",
      "0.02731         0.0   7.07      0    0.469  6.421   78.9  4.9671    2  242.0   \n",
      "0.02729         0.0   7.07      0    0.469  7.185   61.1  4.9671    2  242.0   \n",
      "0.03237         0.0   2.18      0    0.458  6.998   45.8  6.0622    3  222.0   \n",
      "0.06905         0.0   2.18      0    0.458  7.147   54.2  6.0622    3  222.0   \n",
      "0.02985         0.0   2.18      0    0.458  6.430   58.7  6.0622    3  222.0   \n",
      "0.08829        12.5   7.87      0    0.524  6.012   66.6  5.5605    5  311.0   \n",
      "0.14455        12.5   7.87      0    0.524  6.172   96.1  5.9505    5  311.0   \n",
      "0.21124        12.5   7.87      0    0.524  5.631  100.0  6.0821    5  311.0   \n",
      "0.17004        12.5   7.87      0    0.524  6.004   85.9  6.5921    5  311.0   \n",
      "0.22489        12.5   7.87      0    0.524  6.377   94.3  6.3467    5  311.0   \n",
      "0.11747        12.5   7.87      0    0.524  6.009   82.9  6.2267    5  311.0   \n",
      "0.09378        12.5   7.87      0    0.524  5.889   39.0  5.4509    5  311.0   \n",
      "0.62976         0.0   8.14      0    0.538  5.949   61.8  4.7075    4  307.0   \n",
      "0.63796         0.0   8.14      0    0.538  6.096   84.5  4.4619    4  307.0   \n",
      "0.62739         0.0   8.14      0    0.538  5.834   56.5  4.4986    4  307.0   \n",
      "1.05393         0.0   8.14      0    0.538  5.935   29.3  4.4986    4  307.0   \n",
      "0.78420         0.0   8.14      0    0.538  5.990   81.7  4.2579    4  307.0   \n",
      "0.80271         0.0   8.14      0    0.538  5.456   36.6  3.7965    4  307.0   \n",
      "0.72580         0.0   8.14      0    0.538  5.727   69.5  3.7965    4  307.0   \n",
      "1.25179         0.0   8.14      0    0.538  5.570   98.1  3.7979    4  307.0   \n",
      "0.85204         0.0   8.14      0    0.538  5.965   89.2  4.0123    4  307.0   \n",
      "1.23247         0.0   8.14      0    0.538  6.142   91.7  3.9769    4  307.0   \n",
      "0.98843         0.0   8.14      0    0.538  5.813  100.0  4.0952    4  307.0   \n",
      "0.75026         0.0   8.14      0    0.538  5.924   94.1  4.3996    4  307.0   \n",
      "0.84054         0.0   8.14      0    0.538  5.599   85.7  4.4546    4  307.0   \n",
      "0.67191         0.0   8.14      0    0.538  5.813   90.3  4.6820    4  307.0   \n",
      "0.95577         0.0   8.14      0    0.538  6.047   88.8  4.4534    4  307.0   \n",
      "0.77299         0.0   8.14      0    0.538  6.495   94.4  4.4547    4  307.0   \n",
      "1.00245         0.0   8.14      0    0.538  6.674   87.3  4.2390    4  307.0   \n",
      "...             ...    ...    ...      ...    ...    ...     ...  ...    ...   \n",
      "4.87141         0.0  18.10      0    0.614  6.484   93.6  2.3053   24  666.0   \n",
      "15.02340        0.0  18.10      0    0.614  5.304   97.3  2.1007   24  666.0   \n",
      "10.23300        0.0  18.10      0    0.614  6.185   96.7  2.1705   24  666.0   \n",
      "14.33370        0.0  18.10      0    0.614  6.229   88.0  1.9512   24  666.0   \n",
      "5.82401         0.0  18.10      0    0.532  6.242   64.7  3.4242   24  666.0   \n",
      "5.70818         0.0  18.10      0    0.532  6.750   74.9  3.3317   24  666.0   \n",
      "5.73116         0.0  18.10      0    0.532  7.061   77.0  3.4106   24  666.0   \n",
      "2.81838         0.0  18.10      0    0.532  5.762   40.3  4.0983   24  666.0   \n",
      "2.37857         0.0  18.10      0    0.583  5.871   41.9  3.7240   24  666.0   \n",
      "3.67367         0.0  18.10      0    0.583  6.312   51.9  3.9917   24  666.0   \n",
      "5.69175         0.0  18.10      0    0.583  6.114   79.8  3.5459   24  666.0   \n",
      "4.83567         0.0  18.10      0    0.583  5.905   53.2  3.1523   24  666.0   \n",
      "0.15086         0.0  27.74      0    0.609  5.454   92.7  1.8209    4  711.0   \n",
      "0.18337         0.0  27.74      0    0.609  5.414   98.3  1.7554    4  711.0   \n",
      "0.20746         0.0  27.74      0    0.609  5.093   98.0  1.8226    4  711.0   \n",
      "0.10574         0.0  27.74      0    0.609  5.983   98.8  1.8681    4  711.0   \n",
      "0.11132         0.0  27.74      0    0.609  5.983   83.5  2.1099    4  711.0   \n",
      "0.17331         0.0   9.69      0    0.585  5.707   54.0  2.3817    6  391.0   \n",
      "0.27957         0.0   9.69      0    0.585  5.926   42.6  2.3817    6  391.0   \n",
      "0.17899         0.0   9.69      0    0.585  5.670   28.8  2.7986    6  391.0   \n",
      "0.28960         0.0   9.69      0    0.585  5.390   72.9  2.7986    6  391.0   \n",
      "0.26838         0.0   9.69      0    0.585  5.794   70.6  2.8927    6  391.0   \n",
      "0.23912         0.0   9.69      0    0.585  6.019   65.3  2.4091    6  391.0   \n",
      "0.17783         0.0   9.69      0    0.585  5.569   73.5  2.3999    6  391.0   \n",
      "0.22438         0.0   9.69      0    0.585  6.027   79.7  2.4982    6  391.0   \n",
      "0.06263         0.0  11.93      0    0.573  6.593   69.1  2.4786    1  273.0   \n",
      "0.04527         0.0  11.93      0    0.573  6.120   76.7  2.2875    1  273.0   \n",
      "0.06076         0.0  11.93      0    0.573  6.976   91.0  2.1675    1  273.0   \n",
      "0.10959         0.0  11.93      0    0.573  6.794   89.3  2.3889    1  273.0   \n",
      "0.04741         0.0  11.93      0    0.573  6.030   80.8  2.5050    1  273.0   \n",
      "\n",
      "          PT_Ratio  Blacks  Lstat  MedVal  \n",
      "0.00632       15.3  396.90   4.98    24.0  \n",
      "0.02731       17.8  396.90   9.14    21.6  \n",
      "0.02729       17.8  392.83   4.03    34.7  \n",
      "0.03237       18.7  394.63   2.94    33.4  \n",
      "0.06905       18.7  396.90   5.33    36.2  \n",
      "0.02985       18.7  394.12   5.21    28.7  \n",
      "0.08829       15.2  395.60  12.43    22.9  \n",
      "0.14455       15.2  396.90  19.15    27.1  \n",
      "0.21124       15.2  386.63  29.93    16.5  \n",
      "0.17004       15.2  386.71  17.10    18.9  \n",
      "0.22489       15.2  392.52  20.45    15.0  \n",
      "0.11747       15.2  396.90  13.27    18.9  \n",
      "0.09378       15.2  390.50  15.71    21.7  \n",
      "0.62976       21.0  396.90   8.26    20.4  \n",
      "0.63796       21.0  380.02  10.26    18.2  \n",
      "0.62739       21.0  395.62   8.47    19.9  \n",
      "1.05393       21.0  386.85   6.58    23.1  \n",
      "0.78420       21.0  386.75  14.67    17.5  \n",
      "0.80271       21.0  288.99  11.69    20.2  \n",
      "0.72580       21.0  390.95  11.28    18.2  \n",
      "1.25179       21.0  376.57  21.02    13.6  \n",
      "0.85204       21.0  392.53  13.83    19.6  \n",
      "1.23247       21.0  396.90  18.72    15.2  \n",
      "0.98843       21.0  394.54  19.88    14.5  \n",
      "0.75026       21.0  394.33  16.30    15.6  \n",
      "0.84054       21.0  303.42  16.51    13.9  \n",
      "0.67191       21.0  376.88  14.81    16.6  \n",
      "0.95577       21.0  306.38  17.28    14.8  \n",
      "0.77299       21.0  387.94  12.80    18.4  \n",
      "1.00245       21.0  380.23  11.98    21.0  \n",
      "...            ...     ...    ...     ...  \n",
      "4.87141       20.2  396.21  18.68    16.7  \n",
      "15.02340      20.2  349.48  24.91    12.0  \n",
      "10.23300      20.2  379.70  18.03    14.6  \n",
      "14.33370      20.2  383.32  13.11    21.4  \n",
      "5.82401       20.2  396.90  10.74    23.0  \n",
      "5.70818       20.2  393.07   7.74    23.7  \n",
      "5.73116       20.2  395.28   7.01    25.0  \n",
      "2.81838       20.2  392.92  10.42    21.8  \n",
      "2.37857       20.2  370.73  13.34    20.6  \n",
      "3.67367       20.2  388.62  10.58    21.2  \n",
      "5.69175       20.2  392.68  14.98    19.1  \n",
      "4.83567       20.2  388.22  11.45    20.6  \n",
      "0.15086       20.1  395.09  18.06    15.2  \n",
      "0.18337       20.1  344.05  23.97     7.0  \n",
      "0.20746       20.1  318.43  29.68     8.1  \n",
      "0.10574       20.1  390.11  18.07    13.6  \n",
      "0.11132       20.1  396.90  13.35    20.1  \n",
      "0.17331       19.2  396.90  12.01    21.8  \n",
      "0.27957       19.2  396.90  13.59    24.5  \n",
      "0.17899       19.2  393.29  17.60    23.1  \n",
      "0.28960       19.2  396.90  21.14    19.7  \n",
      "0.26838       19.2  396.90  14.10    18.3  \n",
      "0.23912       19.2  396.90  12.92    21.2  \n",
      "0.17783       19.2  395.77  15.10    17.5  \n",
      "0.22438       19.2  396.90  14.33    16.8  \n",
      "0.06263       21.0  391.99   9.67    22.4  \n",
      "0.04527       21.0  396.90   9.08    20.6  \n",
      "0.06076       21.0  396.90   5.64    23.9  \n",
      "0.10959       21.0  393.45   6.48    22.0  \n",
      "0.04741       21.0  396.90   7.88    11.9  \n",
      "\n",
      "[506 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "print(dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CrimeRate</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Indus</th>\n",
       "      <th>ChasNOx</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Age</th>\n",
       "      <th>Dis</th>\n",
       "      <th>Rad</th>\n",
       "      <th>Tax</th>\n",
       "      <th>PT_Ratio</th>\n",
       "      <th>Blacks</th>\n",
       "      <th>Lstat</th>\n",
       "      <th>MedVal</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>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>0.000000</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>0.000000</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>0.000000</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>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>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": [
       "        CrimeRate        Zone       Indus     ChasNOx       Rooms         Age  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    11.363636   11.136779    0.069170    0.554695    6.284634   68.574901   \n",
       "std     23.322453    6.860353    0.253994    0.115878    0.702617   28.148861   \n",
       "min      0.000000    0.460000    0.000000    0.385000    3.561000    2.900000   \n",
       "25%      0.000000    5.190000    0.000000    0.449000    5.885500   45.025000   \n",
       "50%      0.000000    9.690000    0.000000    0.538000    6.208500   77.500000   \n",
       "75%     12.500000   18.100000    0.000000    0.624000    6.623500   94.075000   \n",
       "max    100.000000   27.740000    1.000000    0.871000    8.780000  100.000000   \n",
       "\n",
       "              Dis         Rad         Tax    PT_Ratio      Blacks       Lstat  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.795043    9.549407  408.237154   18.455534  356.674032   12.653063   \n",
       "std      2.105710    8.707259  168.537116    2.164946   91.294864    7.141062   \n",
       "min      1.129600    1.000000  187.000000   12.600000    0.320000    1.730000   \n",
       "25%      2.100175    4.000000  279.000000   17.400000  375.377500    6.950000   \n",
       "50%      3.207450    5.000000  330.000000   19.050000  391.440000   11.360000   \n",
       "75%      5.188425   24.000000  666.000000   20.200000  396.225000   16.955000   \n",
       "max     12.126500   24.000000  711.000000   22.000000  396.900000   37.970000   \n",
       "\n",
       "           MedVal  \n",
       "count  506.000000  \n",
       "mean    22.532806  \n",
       "std      9.197104  \n",
       "min      5.000000  \n",
       "25%     17.025000  \n",
       "50%     21.200000  \n",
       "75%     25.000000  \n",
       "max     50.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array=dataframe.values\n",
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 18.     2.31   0.   ...  15.3  396.9    4.98]\n",
      " [  0.     7.07   0.   ...  17.8  396.9    9.14]\n",
      " [  0.     7.07   0.   ...  17.8  392.83   4.03]\n",
      " ...\n",
      " [  0.    11.93   0.   ...  21.   396.9    5.64]\n",
      " [  0.    11.93   0.   ...  21.   393.45   6.48]\n",
      " [  0.    11.93   0.   ...  21.   396.9    7.88]]\n"
     ]
    }
   ],
   "source": [
    "X=array[:,0:12]\n",
    "Y=array[:,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'sklearn' has no attribute 'model_selection'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-f01a33f713dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mkfold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\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[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'sklearn' has no attribute 'model_selection'"
     ]
    }
   ],
   "source": [
    "kfold=skl.model_selection.KFold(n_splits=10,random_state=7)\n",
    "model=skl.linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring='neg_mean_squared_error'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'sklearn' has no attribute 'model_selection'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-f7e781286278>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\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\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\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      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'sklearn' has no attribute 'model_selection'"
     ]
    }
   ],
   "source": [
    "results=skl.model_selection.cross_val_score(model,X,Y,cv=kfold,scoring=scoring)\n",
    "print(results.mean())\n",
    "print(results)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'sklearn' has no attribute 'cross_validation'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-8b7ea1412ecf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mskl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcross_validation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'sklearn' has no attribute 'cross_validation'"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=skl.cross_validation.train_test_split(X,Y,test_size=0.2,random_state=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.27463798e-02,  1.50819601e-02,  3.39302964e+00, -1.59607064e+01,\n",
       "        3.88905575e+00, -1.04807992e-02, -1.45889429e+00,  2.28396832e-01,\n",
       "       -1.32108421e-02, -8.96188511e-01,  8.83643220e-03, -4.91193102e-01])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.489378387911259"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fe8af12f98>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fe8ae99160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test[0]\n",
    "plt.plot(a)\n",
    "plt.plot(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  25.705931134898844\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fe8b039860>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fe8afbd828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(\"MSE: \",np.mean((a-y_test)**2))\n",
    "mse=(a-y_test)**2\n",
    "plt.plot(mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}