Explaination and Example for P value with code/P - value using python code  (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing important libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork/labs/boston_housing.csv'\n",
    "boston_df = pd.read_csv(boston_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading top 5 records of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0     CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  \\\n",
       "0           0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0   \n",
       "1           1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0   \n",
       "2           2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0   \n",
       "3           3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0   \n",
       "4           4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0   \n",
       "\n",
       "     TAX  PTRATIO  LSTAT  MEDV  \n",
       "0  296.0     15.3   4.98  24.0  \n",
       "1  242.0     17.8   9.14  21.6  \n",
       "2  242.0     17.8   4.03  34.7  \n",
       "3  222.0     18.7   2.94  33.4  \n",
       "4  222.0     18.7   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deleting Unwanted Unnamed column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>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>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO  LSTAT  MEDV  \n",
       "0       15.3   4.98  24.0  \n",
       "1       17.8   9.14  21.6  \n",
       "2       17.8   4.03  34.7  \n",
       "3       18.7   2.94  33.4  \n",
       "4       18.7   5.33  36.2  \n",
       "..       ...    ...   ...  \n",
       "501     21.0   9.67  22.4  \n",
       "502     21.0   9.08  20.6  \n",
       "503     21.0   5.64  23.9  \n",
       "504     21.0   6.48  22.0  \n",
       "505     21.0   7.88  11.9  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1 = boston_df.drop(['Unnamed: 0'], axis = 1)\n",
    "boston_df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking no of rows and columns of dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 13 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    float64\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    float64\n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  LSTAT    506 non-null    float64\n",
      " 12  MEDV     506 non-null    float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 51.5 KB\n"
     ]
    }
   ],
   "source": [
    "boston_df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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>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",
       "    </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>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>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>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>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>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</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>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>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   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       LSTAT  \\\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   12.653063   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946    7.141062   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    1.730000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000    6.950000   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000   11.360000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000   16.955000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000   37.970000   \n",
       "\n",
       "             MEDV  \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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation between all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.388305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZN</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.360445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.483725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHAS</th>\n",
       "      <td>-0.055892</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.175260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOX</th>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.427321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RM</th>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.695360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.376955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIS</th>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.249929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAD</th>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>-0.381626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAX</th>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>-0.468536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTRATIO</th>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTAT</th>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.737663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEDV</th>\n",
       "      <td>-0.388305</td>\n",
       "      <td>0.360445</td>\n",
       "      <td>-0.483725</td>\n",
       "      <td>0.175260</td>\n",
       "      <td>-0.427321</td>\n",
       "      <td>0.695360</td>\n",
       "      <td>-0.376955</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>-0.381626</td>\n",
       "      <td>-0.468536</td>\n",
       "      <td>-0.507787</td>\n",
       "      <td>-0.737663</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "ZN      -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "INDUS    0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "CHAS    -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "NOX      0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "RM      -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "AGE      0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "DIS     -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "RAD      0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "TAX      0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "PTRATIO  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "LSTAT    0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "MEDV    -0.388305  0.360445 -0.483725  0.175260 -0.427321  0.695360 -0.376955   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO     LSTAT      MEDV  \n",
       "CRIM    -0.379670  0.625505  0.582764  0.289946  0.455621 -0.388305  \n",
       "ZN       0.664408 -0.311948 -0.314563 -0.391679 -0.412995  0.360445  \n",
       "INDUS   -0.708027  0.595129  0.720760  0.383248  0.603800 -0.483725  \n",
       "CHAS    -0.099176 -0.007368 -0.035587 -0.121515 -0.053929  0.175260  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933  0.590879 -0.427321  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501 -0.613808  0.695360  \n",
       "AGE     -0.747881  0.456022  0.506456  0.261515  0.602339 -0.376955  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471 -0.496996  0.249929  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741  0.488676 -0.381626  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853  0.543993 -0.468536  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000  0.374044 -0.507787  \n",
       "LSTAT   -0.496996  0.488676  0.543993  0.374044  1.000000 -0.737663  \n",
       "MEDV     0.249929 -0.381626 -0.468536 -0.507787 -0.737663  1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = boston_df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.695360\n",
       "ZN         0.360445\n",
       "DIS        0.249929\n",
       "CHAS       0.175260\n",
       "AGE       -0.376955\n",
       "RAD       -0.381626\n",
       "CRIM      -0.388305\n",
       "NOX       -0.427321\n",
       "TAX       -0.468536\n",
       "INDUS     -0.483725\n",
       "PTRATIO   -0.507787\n",
       "LSTAT     -0.737663\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that RM is (positively) strongly correlated with MEDV and then LSTAT is (negatively) strongly correlated with MEDV."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmap showing correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b0aa250d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_heatmap = boston_df1.corr()\n",
    "f , ax = plt.subplots(figsize=(20,12))\n",
    "sb.heatmap(boston_df1.corr(),vmax=0.8, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    471\n",
       "1.0     35\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1[\"CHAS\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0    132\n",
       "5.0     115\n",
       "4.0     110\n",
       "3.0      38\n",
       "6.0      26\n",
       "8.0      24\n",
       "2.0      24\n",
       "1.0      20\n",
       "7.0      17\n",
       "Name: RAD, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1[\"RAD\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization through different plots :- Histograms and scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b05aab350>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u63t7lD8DPgdsHWdxA+kz5t8HrqyqewGqaq6Pu8+YC3hWkgDPZBQK28db5uypqusZjWFXZv37a76HwnS3zVi2B33mkt0dz7mM/tOYy2Ycc5JlwOuBD42xriH1+T0/HzgsyXVJbkrylrFVN4w+Y/4g8MuMLnq9FXh7VT0xnvImYta/v/ap6xQGMONtM3r2mUt6jyfJqxiFwisGrWh4fcb8fuCCqnp89E/knNdnzIuAlwKnAAcDNyS5sar+c+jiBtJnzKcCNwOvBp4HbEjy71X18NDFTcisf3/N91Doc9uM+XZrjV7jSfIi4KPA6VX10JhqG0qfMa8CrugCYTFwRpLtVXXVeEqcdX3/tn9YVY8AjyS5HjgemKuh0GfM5wCX1GiH++Yk9wAvBDaOp8Sxm/Xvr/m++6jPbTPWA2/pjuK/DPjfqnpg3IXOohnHnORo4ErgzXP4v8apZhxzVR1TVSuqagXwWeBtczgQoN/f9tXAK5MsSnIIozsO3znmOmdTnzHfy2jLiCRHAi8AvjfWKsdr1r+/5vWWQu3ithlJ/qR7/0OMzkQ5A9gM/JTRfxpzVs8xvwv4ReDS7j/n7TWH7zDZc8zzSp8xV9WdSf4FuAV4AvhoVU17auNc0PP3/NfAx5PcymjXygVVNWdvqZ3k08DJwOIkW4B3AwfAcN9f3uZCktTM991HkqTdYChIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEnN/wNoqo5DCBwkFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boston_df1['CHAS'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b04750550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b04716610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b046cdc90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b04690350>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b046459d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b045f1b90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b045bc790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b04570d50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b04570d90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b04533550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b0452d190>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05c83450>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b0476d310>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b045706d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b06119150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b060ce7d0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attr = [\"MEDV\",\"RM\",\"ZN\",\"LSTAT\"]\n",
    "scatter_matrix(boston_df1[attr],figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b05e9d850>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "boston_df1.plot(kind= \"scatter\",x=\"RM\",y=\"MEDV\",alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05e5e6d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05e17390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05dcca10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05d8e0d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05d43750>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05cf8dd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05cba510>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05cf1ad0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b05cf1b10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b0442f2d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b0441aed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b043dc590>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f9b04390c10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b043542d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b0430a950>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f9b042b5b10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "boston_df.hist(bins=50,figsize=(20,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discretize the age variable into three groups of 35 years and younger, between 35 and 70 years and 70 years and older"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>AGE_bins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "      <td>(35, 70]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "      <td>(70, 101]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "      <td>(35, 70]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "      <td>(35, 70]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "      <td>(35, 70]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "      <td>(35, 70]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "      <td>(70, 101]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "      <td>(70, 101]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>(70, 101]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "      <td>(70, 101]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO  LSTAT  MEDV   AGE_bins  \n",
       "0       15.3   4.98  24.0   (35, 70]  \n",
       "1       17.8   9.14  21.6  (70, 101]  \n",
       "2       17.8   4.03  34.7   (35, 70]  \n",
       "3       18.7   2.94  33.4   (35, 70]  \n",
       "4       18.7   5.33  36.2   (35, 70]  \n",
       "..       ...    ...   ...        ...  \n",
       "501     21.0   9.67  22.4   (35, 70]  \n",
       "502     21.0   9.08  20.6  (70, 101]  \n",
       "503     21.0   5.64  23.9  (70, 101]  \n",
       "504     21.0   6.48  22.0  (70, 101]  \n",
       "505     21.0   7.88  11.9  (70, 101]  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1['AGE_bins']=pd.cut(x=boston_df1['AGE'], bins=[0,35,70,101])\n",
    "boston_df1\n"
   ]
  },
  {
   "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>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>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>AGE_bins</th>\n",
       "      <th>AGE_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "      <td>(35, 70]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "      <td>(70, 101]</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "      <td>(35, 70]</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "      <td>(35, 70]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "      <td>(35, 70]</td>\n",
       "      <td>2</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "      <td>(35, 70]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "      <td>(70, 101]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "      <td>(70, 101]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>(70, 101]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "      <td>(70, 101]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO  LSTAT  MEDV   AGE_bins AGE_group  \n",
       "0       15.3   4.98  24.0   (35, 70]         2  \n",
       "1       17.8   9.14  21.6  (70, 101]         3  \n",
       "2       17.8   4.03  34.7   (35, 70]         2  \n",
       "3       18.7   2.94  33.4   (35, 70]         2  \n",
       "4       18.7   5.33  36.2   (35, 70]         2  \n",
       "..       ...    ...   ...        ...       ...  \n",
       "501     21.0   9.67  22.4   (35, 70]         2  \n",
       "502     21.0   9.08  20.6  (70, 101]         3  \n",
       "503     21.0   5.64  23.9  (70, 101]         3  \n",
       "504     21.0   6.48  22.0  (70, 101]         3  \n",
       "505     21.0   7.88  11.9  (70, 101]         3  \n",
       "\n",
       "[506 rows x 15 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df1['AGE_group']=pd.cut(x=boston_df1['AGE'], bins=[0,35,70,101],labels=[1,2,3])\n",
    "boston_df1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter plot to show the relationship between Nitric oxide concentrations and the proportion of non-retail business acres per town"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "plt.scatter(boston_df1.NOX,boston_df1.INDUS,c=\"red\")\n",
    "plt.ylabel('NOX')\n",
    "plt.xlabel('INDUS')\n",
    "plt.title(\"NOX vs INDUS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplot for the MEDV variable vs the AGE variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9afe91d4d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.boxplot(boston_df1.AGE_group,boston_df1.MEDV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9aff420e50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.boxplot(boston_df1.MEDV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating P - Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T-test between median value of houses bounded by the Charles river (independent samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.996437466090509, pvalue=7.390623170519905e-05)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(boston_df1[boston_df1['CHAS'] == 0]['MEDV'],boston_df1[boston_df1['CHAS'] == 1]['MEDV'],equal_var= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.113291312794837, pvalue=0.003567170098137517)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(boston_df1[boston_df1['CHAS'] == 0]['MEDV'],boston_df1[boston_df1['CHAS'] == 1]['MEDV'],equal_var= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANOVA Test between Median values of houses (MEDV) for each proportion of owner occupied units built prior to 1940 (AGE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'medv' : boston_df1['MEDV'], 'age_group' : boston_df1['AGE_group']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df1 = df1.drop_duplicates(subset=\"age_group\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>22.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>20.6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>23.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>11.9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     medv age_group\n",
       "0    24.0         2\n",
       "1    21.6         3\n",
       "2    34.7         2\n",
       "3    33.4         2\n",
       "4    36.2         2\n",
       "..    ...       ...\n",
       "501  22.4         2\n",
       "502  20.6         3\n",
       "503  23.9         3\n",
       "504  22.0         3\n",
       "505  11.9         3\n",
       "\n",
       "[506 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fvalue,pvalue = stats.f_oneway(df1['age_group'],df1['medv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2410.6937451934, 8.513426061695347e-270)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fvalue,pvalue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the impact of an additional weighted distance to the five Boston employment centres on the median value of owner occupied homes? (Regression analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from OLS method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = boston_df1[['RM','LSTAT']]\n",
    "Y = boston_df1[['MEDV']]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2, random_state = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>MEDV</td>       <th>  R-squared:         </th> <td>   0.639</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.637</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   444.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Feb 2021</td> <th>  Prob (F-statistic):</th> <td>7.01e-112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:30:15</td>     <th>  Log-Likelihood:    </th> <td> -1582.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3172.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   503</td>      <th>  BIC:               </th> <td>   3184.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -1.3583</td> <td>    3.173</td> <td>   -0.428</td> <td> 0.669</td> <td>   -7.592</td> <td>    4.875</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RM</th>    <td>    5.0948</td> <td>    0.444</td> <td>   11.463</td> <td> 0.000</td> <td>    4.222</td> <td>    5.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LSTAT</th> <td>   -0.6424</td> <td>    0.044</td> <td>  -14.689</td> <td> 0.000</td> <td>   -0.728</td> <td>   -0.556</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>145.712</td> <th>  Durbin-Watson:     </th> <td>   0.834</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 457.690</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.343</td>  <th>  Prob(JB):          </th> <td>4.11e-100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.807</td>  <th>  Cond. No.          </th> <td>    202.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   MEDV   R-squared:                       0.639\n",
       "Model:                            OLS   Adj. R-squared:                  0.637\n",
       "Method:                 Least Squares   F-statistic:                     444.3\n",
       "Date:                Mon, 08 Feb 2021   Prob (F-statistic):          7.01e-112\n",
       "Time:                        10:30:15   Log-Likelihood:                -1582.8\n",
       "No. Observations:                 506   AIC:                             3172.\n",
       "Df Residuals:                     503   BIC:                             3184.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -1.3583      3.173     -0.428      0.669      -7.592       4.875\n",
       "RM             5.0948      0.444     11.463      0.000       4.222       5.968\n",
       "LSTAT         -0.6424      0.044    -14.689      0.000      -0.728      -0.556\n",
       "==============================================================================\n",
       "Omnibus:                      145.712   Durbin-Watson:                   0.834\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              457.690\n",
       "Skew:                           1.343   Prob(JB):                    4.11e-100\n",
       "Kurtosis:                       6.807   Cond. No.                         202.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y,X).fit()\n",
    "predictions = model.predict(X)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>MEDV</td>       <th>  R-squared:         </th> <td>   0.647</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.645</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   306.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 08 Feb 2021</td> <th>  Prob (F-statistic):</th> <td>5.22e-113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:30:15</td>     <th>  Log-Likelihood:    </th> <td> -1577.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3162.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   502</td>      <th>  BIC:               </th> <td>   3179.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    2.8083</td> <td>    3.368</td> <td>    0.834</td> <td> 0.405</td> <td>   -3.809</td> <td>    9.425</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RM</th>    <td>    4.8734</td> <td>    0.445</td> <td>   10.962</td> <td> 0.000</td> <td>    4.000</td> <td>    5.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LSTAT</th> <td>   -0.7233</td> <td>    0.049</td> <td>  -14.662</td> <td> 0.000</td> <td>   -0.820</td> <td>   -0.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>DIS</th>   <td>   -0.4613</td> <td>    0.135</td> <td>   -3.418</td> <td> 0.001</td> <td>   -0.726</td> <td>   -0.196</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>117.960</td> <th>  Durbin-Watson:     </th> <td>   0.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 319.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.135</td>  <th>  Prob(JB):          </th> <td>4.13e-70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.163</td>  <th>  Cond. No.          </th> <td>    220.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   MEDV   R-squared:                       0.647\n",
       "Model:                            OLS   Adj. R-squared:                  0.645\n",
       "Method:                 Least Squares   F-statistic:                     306.4\n",
       "Date:                Mon, 08 Feb 2021   Prob (F-statistic):          5.22e-113\n",
       "Time:                        10:30:15   Log-Likelihood:                -1577.0\n",
       "No. Observations:                 506   AIC:                             3162.\n",
       "Df Residuals:                     502   BIC:                             3179.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.8083      3.368      0.834      0.405      -3.809       9.425\n",
       "RM             4.8734      0.445     10.962      0.000       4.000       5.747\n",
       "LSTAT         -0.7233      0.049    -14.662      0.000      -0.820      -0.626\n",
       "DIS           -0.4613      0.135     -3.418      0.001      -0.726      -0.196\n",
       "==============================================================================\n",
       "Omnibus:                      117.960   Durbin-Watson:                   0.888\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              319.526\n",
       "Skew:                           1.135   Prob(JB):                     4.13e-70\n",
       "Kurtosis:                       6.163   Cond. No.                         220.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = boston_df1[['RM','LSTAT','DIS']]\n",
    "Y = boston_df1[['MEDV']]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size = 0.2, random_state = 30)\n",
    "import statsmodels.api as sm\n",
    "X = sm.add_constant(X)\n",
    "model = sm.OLS(Y,X).fit()\n",
    "predictions = model.predict(X)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table displays all the statistics regarding the independent variables. But right now, we are only interested in looking at the column with the p-values in it. We can clearly see that the “DIS” , “RM” and “LSTAT” have a p-value less than 0.50! \n",
    "\n",
    "But the question is, what does this p-value mean in a regression model? For that, let’s understand what’s the hypothesis for which these p-values are calculated:\n",
    "\n",
    "**Null Hypothesis: The independent variable has no significant effect over the target variable**\n",
    "\n",
    "**Alternate Hypothesis: The independent variables have a significant effect on the target variable**\n",
    "\n",
    "Now, the above results show that “DIS” , “RM” and “LSTAT”  have a significant effect over the “MEDV”. So we have to consider all the variables in our model."
   ]
  }
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