Aryan_Chaurasia.ipynb

{
 "metadata": {
  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
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   "name": "python392jvsc74a57bd045b893f9af261bf65cff03da3edb098238d6e40582ce795f29522f7c3287b35c",
   "display_name": "Python 3.9.2 64-bit"
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   "interpreter": {
    "hash": "45b893f9af261bf65cff03da3edb098238d6e40582ce795f29522f7c3287b35c"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn"
   ]
  },
  {
   "source": [
    "# Loading data to train model\n",
    "from sklearn.datasets import load_boston\n",
    "df = load_boston()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 33,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "# Retrieving Dataset Keys from Directory\n",
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      ".. _boston_dataset:\n\nBoston house prices dataset\n---------------------------\n\n**Data Set Characteristics:**  \n\n    :Number of Instances: 506 \n\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n\n    :Attribute Information (in order):\n        - CRIM     per capita crime rate by town\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n        - INDUS    proportion of non-retail business acres per town\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n        - NOX      nitric oxides concentration (parts per 10 million)\n        - RM       average number of rooms per dwelling\n        - AGE      proportion of owner-occupied units built prior to 1940\n        - DIS      weighted distances to five Boston employment centres\n        - RAD      index of accessibility to radial highways\n        - TAX      full-value property-tax rate per $10,000\n        - PTRATIO  pupil-teacher ratio by town\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n        - LSTAT    % lower status of the population\n        - MEDV     Median value of owner-occupied homes in $1000's\n\n    :Missing Attribute Values: None\n\n    :Creator: Harrison, D. and Rubinfeld, D.L.\n\nThis is a copy of UCI ML housing dataset.\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n\n\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\nprices and the demand for clean air', J. Environ. Economics & Management,\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\npages 244-261 of the latter.\n\nThe Boston house-price data has been used in many machine learning papers that address regression\nproblems.   \n     \n.. topic:: References\n\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n\n"
     ]
    }
   ],
   "source": [
    "# Retrieving Dataset Description\n",
    "print(df.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n 'B' 'LSTAT']\nC:\\Users\\KulterRyan\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\datasets\\data\\boston_house_prices.csv\n"
     ]
    }
   ],
   "source": [
    "# Retrieving Dataset Features\n",
    "print(df.feature_names)\n",
    "\n",
    "# Retrieving File name\n",
    "print(df.filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n 22.  11.9]\n"
     ]
    }
   ],
   "source": [
    "# Retrieving Target from Dataset\n",
    "print(df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00]\n [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00]\n [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]\n ...\n [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00]\n [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00]\n [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]\n"
     ]
    }
   ],
   "source": [
    "# Retreiving Data from List\n",
    "print(df.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "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",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0.0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1.0</td>\n      <td>296.0</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "# Converting datasets into Pandas Dataframe\n",
    "pd_dataframe = pd.DataFrame(df.data, columns=df.feature_names)\n",
    "pd_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "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",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  \n",
       "3     18.7  394.63   2.94  33.4  \n",
       "4     18.7  396.90   5.33  36.2  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n      <th>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>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>0.0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n      <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>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>0.0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n      <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>396.90</td>\n      <td>5.33</td>\n      <td>36.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "# Adding new columns to Dataframe\n",
    "# Adding Target Value Column\n",
    "pd_dataframe['MEDV'] = df.target\n",
    "pd_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      CRIM     ZN  INDUS   CHAS    NOX     RM    AGE    DIS    RAD    TAX  \\\n",
       "0    False  False  False  False  False  False  False  False  False  False   \n",
       "1    False  False  False  False  False  False  False  False  False  False   \n",
       "2    False  False  False  False  False  False  False  False  False  False   \n",
       "3    False  False  False  False  False  False  False  False  False  False   \n",
       "4    False  False  False  False  False  False  False  False  False  False   \n",
       "..     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "501  False  False  False  False  False  False  False  False  False  False   \n",
       "502  False  False  False  False  False  False  False  False  False  False   \n",
       "503  False  False  False  False  False  False  False  False  False  False   \n",
       "504  False  False  False  False  False  False  False  False  False  False   \n",
       "505  False  False  False  False  False  False  False  False  False  False   \n",
       "\n",
       "     PTRATIO      B  LSTAT   MEDV  \n",
       "0      False  False  False  False  \n",
       "1      False  False  False  False  \n",
       "2      False  False  False  False  \n",
       "3      False  False  False  False  \n",
       "4      False  False  False  False  \n",
       "..       ...    ...    ...    ...  \n",
       "501    False  False  False  False  \n",
       "502    False  False  False  False  \n",
       "503    False  False  False  False  \n",
       "504    False  False  False  False  \n",
       "505    False  False  False  False  \n",
       "\n",
       "[506 rows x 14 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n      <th>MEDV</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</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>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>502</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>503</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>504</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>505</th>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n<p>506 rows × 14 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "# Checking null values in dataset\n",
    "pd_dataframe.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "# Getting sum of null values\n",
    "pd_dataframe.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(430, 13)\n(76, 13)\n(430,)\n(76,)\n"
     ]
    }
   ],
   "source": [
    "# Training model using datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = pd_dataframe.drop('MEDV', axis=1)\n",
    "Y = pd_dataframe['MEDV']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.15, random_state=5)\n",
    "\n",
    "# test size = amount of data to put inside test dataset\n",
    "# random state = for selecting the same data points on every run\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Linear Regression Model and Training it using Training dataset\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "# Fitting Model on Training Dataset\n",
    "li_m = LinearRegression()\n",
    "li_m.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The Model Performance for Training Set\nRMSE is 4.710901797319796\n\n\nThe Model Performance for Testing Set\nRMSE is 4.687543527902969 \n\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "Y_train_predict = li_m.predict(X_train)\n",
    "rmse = (np.sqrt(mean_squared_error(Y_train, Y_train_predict)))\n",
    "\n",
    "print(\"The Model Performance for Training Set\")\n",
    "print(\"RMSE is {}\".format(rmse))\n",
    "print(\"\\n\")\n",
    "\n",
    "# Testing Test\n",
    "Y_test_predict = li_m.predict(X_test)\n",
    "rmse = (np.sqrt(mean_squared_error(Y_test, Y_test_predict)))\n",
    "\n",
    "print(\"The Model Performance for Testing Set\")\n",
    "print(\"RMSE is {}\".format(rmse), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Predicted')"
      ]
     },
     "metadata": {},
     "execution_count": 49
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# CREATING MATPLOTLIB CURVES\n",
    "import matplotlib.pyplot as  mtplot\n",
    "\n",
    "mtplot.figure(figsize=(5,5))\n",
    "mtplot.scatter(Y_test, Y_test_predict)\n",
    "mtplot.plot([min(Y_test_predict), max(Y_test_predict)], [min(Y_train_predict),max(Y_train_predict)])\n",
    "mtplot.xlabel('Actual')\n",
    "mtplot.ylabel('Predicted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}