supervised learning.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Supervised Learning\n",
    "\n",
    "In the world of Machine learning we come across multiple kind of data o work on , as an example let's talk about a data that's given to us are salary data of employees , and in the given data it's clearly mentioned that which column denotes the salary of the employees over there. So we can consider the salary to be labelled. Also in the data other points like employee designation , department , these points are also labelled. So this labelled data if we come across , the kind of machine learning algorithm we will apply over here can be identified as supervised learning."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Boston House Price Prediction - Supervised learning example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the dataset\n",
    "boston = pd.read_csv('BostonHousing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>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</td>\n",
       "      <td>296</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>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>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</td>\n",
       "      <td>242</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>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>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</td>\n",
       "      <td>242</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>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>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</td>\n",
       "      <td>222</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>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>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</td>\n",
       "      <td>222</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": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \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  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the data\n",
    "boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   crim     506 non-null    float64\n",
      " 1   zn       506 non-null    float64\n",
      " 2   indus    506 non-null    float64\n",
      " 3   chas     506 non-null    int64  \n",
      " 4   nox      506 non-null    float64\n",
      " 5   rm       506 non-null    float64\n",
      " 6   age      506 non-null    float64\n",
      " 7   dis      506 non-null    float64\n",
      " 8   rad      506 non-null    int64  \n",
      " 9   tax      506 non-null    int64  \n",
      " 10  ptratio  506 non-null    float64\n",
      " 11  b        506 non-null    float64\n",
      " 12  lstat    506 non-null    float64\n",
      " 13  medv     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "boston.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
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       "      <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>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",
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      "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        medv  \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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
       "       'ptratio', 'b', 'lstat', 'medv'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in here our target variable for prediction is 'medv' or median value of the household\n",
    "# we will do x,y seggregation now , in the x only features without target variable , means all data apart from medv column will be present\n",
    "# in the y data only the medv column data will be present\n",
    "x = boston.drop(\"medv\",axis =1)\n",
    "y = boston[['medv']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  <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</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</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</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</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</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</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</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</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</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        b  lstat  \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  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   medv\n",
       "0  24.0\n",
       "1  21.6\n",
       "2  34.7\n",
       "3  33.4\n",
       "4  36.2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='medv'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing my data on age and median value comparison\n",
    "sns.regplot(x = boston['age'],y = boston['medv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test splitting of data with 80 , 20 ratio\n",
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data shape for x (404, 13)\n",
      "testing data shape for x (102, 13)\n",
      "training data shape for y (404, 1)\n",
      "testing data shape for y (102, 1)\n"
     ]
    }
   ],
   "source": [
    "print(f'training data shape for x {xtrain.shape}')\n",
    "print(f'testing data shape for x {xtest.shape}')\n",
    "print(f'training data shape for y {ytrain.shape}')\n",
    "print(f'testing data shape for y {ytest.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing linear regression model and implementing the same\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting the model\n",
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a preidtor variable as ypred , which will predict the xtest data\n",
    "ypred = model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.795970262424333\n"
     ]
    }
   ],
   "source": [
    "# checking our model based on two parameters\n",
    "# firstly with mean squared error , this will compare ypred data with ytest data\n",
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(ytest,ypred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7351748338719721\n"
     ]
    }
   ],
   "source": [
    "# Now checking our prediction with r2 score method , r2 score near to 1 ensures better model and prediction\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's check the model score\n",
    "model.score(xtest,ypred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Anaconda\\envs\\github1\\lib\\site-packages\\sklearn\\base.py:409: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[30.1359213]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's try to predict a datapoint\n",
    "model.predict([[0.00632,18.0,2.31,0,0.538,6.575,65.2,4.0900,1,296,15.3,396.90,4.98]])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of supervised machine learning , the part of code which is left unclear will be cleared upon upcoming notes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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