boston_d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import plotly.express as px\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning:\n",
      "\n",
      "Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "boston_df = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston 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",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \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": [
    "##check the description along the datasets\n",
    "print(boston_df.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": [
    "##dependant feature price (target)\n",
    "print(boston_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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",
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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>392.83</td>\n",
       "      <td>4.03</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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
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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>391.99</td>\n",
       "      <td>9.67</td>\n",
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       "    <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>396.90</td>\n",
       "      <td>9.08</td>\n",
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       "    <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>396.90</td>\n",
       "      <td>5.64</td>\n",
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       "    <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>393.45</td>\n",
       "      <td>6.48</td>\n",
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       "    <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>396.90</td>\n",
       "      <td>7.88</td>\n",
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       "<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       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  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##prepare a dataframe \n",
    "dataset = pd.DataFrame(boston_df.data, columns=boston_df.feature_names)\n",
    "dataset ## but the data set contain all independant feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>price</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",
       "    <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>391.99</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>396.90</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>396.90</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>393.45</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>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</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       B  LSTAT  price  \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  \n",
       "..       ...     ...    ...    ...  \n",
       "501     21.0  391.99   9.67   22.4  \n",
       "502     21.0  396.90   9.08   20.6  \n",
       "503     21.0  396.90   5.64   23.9  \n",
       "504     21.0  393.45   6.48   22.0  \n",
       "505     21.0  396.90   7.88   11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## add dependant feature to dataset\n",
    "dataset['price'] = boston_df.target\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    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  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  price    506 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </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%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       price  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe() ##summarization of datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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",
       "B          0\n",
       "LSTAT      0\n",
       "price      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check a missing value\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>price</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",
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       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</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>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.360445</td>\n",
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       "    <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>-0.356977</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>0.048788</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>-0.380051</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>0.128069</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>-0.273534</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>0.291512</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>-0.444413</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>-0.441808</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>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>0.333461</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>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.737663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</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>0.333461</td>\n",
       "      <td>-0.737663</td>\n",
       "      <td>1.000000</td>\n",
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      "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",
       "B       -0.385064  0.175520 -0.356977  0.048788 -0.380051  0.128069 -0.273534   \n",
       "LSTAT    0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "price   -0.388305  0.360445 -0.483725  0.175260 -0.427321  0.695360 -0.376955   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT     price  \n",
       "CRIM    -0.379670  0.625505  0.582764  0.289946 -0.385064  0.455621 -0.388305  \n",
       "ZN       0.664408 -0.311948 -0.314563 -0.391679  0.175520 -0.412995  0.360445  \n",
       "INDUS   -0.708027  0.595129  0.720760  0.383248 -0.356977  0.603800 -0.483725  \n",
       "CHAS    -0.099176 -0.007368 -0.035587 -0.121515  0.048788 -0.053929  0.175260  \n",
       "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.590879 -0.427321  \n",
       "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.613808  0.695360  \n",
       "AGE     -0.747881  0.456022  0.506456  0.261515 -0.273534  0.602339 -0.376955  \n",
       "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.496996  0.249929  \n",
       "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.488676 -0.381626  \n",
       "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.543993 -0.468536  \n",
       "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.374044 -0.507787  \n",
       "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.366087  0.333461  \n",
       "LSTAT   -0.496996  0.488676  0.543993  0.374044 -0.366087  1.000000 -0.737663  \n",
       "price    0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.737663  1.000000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##corr\n",
    "correlation = dataset.corr()\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.histogram(correlation)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span>Attribute Information (in order):</span><br>\n",
    "        - CRIM     per capita crime rate by town<br>\n",
    "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.<br>\n",
    "        - INDUS    proportion of non-retail business acres per town<br>\n",
    "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)<br>\n",
    "        - NOX      nitric oxides concentration (parts per 10 million)<br>\n",
    "        - RM       average number of rooms per dwelling<br>\n",
    "        - AGE      proportion of owner-occupied units built prior to 1940<br>\n",
    "        - DIS      weighted distances to five Boston employment centres<br>\n",
    "        - RAD      index of accessibility to radial highways<br>\n",
    "        - TAX      full-value property-tax rate per $10,000<br>\n",
    "        - PTRATIO  pupil-teacher ratio by town<br>\n",
    "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town<br>\n",
    "        - LSTAT    % lower status of the population<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Analysis of data sets\n",
    "plt.scatter(dataset['CRIM'],dataset['price'])\n",
    "plt.xlabel('CRIM')\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dataset['RM'],dataset['price'])\n",
    "plt.xlabel('RM')\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RM', ylabel='price'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## build a regression model \n",
    "sns.regplot(data = dataset , x='RM',y='price')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delcare Dependant and Independant Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = dataset.drop('price',axis=1) ## dependant variable\n",
    "y = dataset['price']             ## independant variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "##split the data set\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split (x,y,test_size=0.4,random_state=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Standaradize the data set := (we use GDR so that we know to come for global minima this change occur in independant \n",
    "#                               feature so that tranformation of independent feature has to be done)\n",
    "from sklearn.preprocessing import  StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = scaler.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(scaler,open('scaling.pkl','wb'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.14494421,  1.16951961, -0.2397181 ,  0.60553074, -1.9623752 ,\n",
       "        2.3287912 ,  0.1641263 , -3.34332189,  3.0590731 , -2.60543572,\n",
       "       -2.14209273,  0.79201114, -3.71174798])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## linear regression := contain intercept and coefficient model\n",
    "\n",
    "regression.coef_ ## which is equal to independant feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.503300330033003"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'copy_X': True,\n",
       " 'fit_intercept': True,\n",
       " 'n_jobs': None,\n",
       " 'normalize': 'deprecated',\n",
       " 'positive': False}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## model parameter for execution\n",
    "regression.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.68334898, 22.83896263, 24.73324629, 21.39208491, 35.01754563,\n",
       "       14.44553732, 19.35043828, 18.89202765, 27.53249437, 17.53075284,\n",
       "       14.15207937, 20.01818049, 36.42025294, 24.11418883, 16.43188002,\n",
       "       19.98679352, 28.37632534, 11.32640638, 15.45067555, 22.34783829,\n",
       "       22.59448495, 13.88758616, 19.54753202, 32.11297115, 22.5259976 ,\n",
       "       27.51149939, 23.21915748,  7.22262636, 26.43005404, 19.39858407,\n",
       "       21.41592129,  8.29713881, 27.74140122, 24.91083845, 36.03573784,\n",
       "       25.34901301, 40.19233492,  2.35955345, 31.94618401, 32.57051862,\n",
       "       20.25115904, 22.66324242, 17.09135162, 31.51192621, 18.67488208,\n",
       "       18.07878398, 28.85562762, 28.32277589, 18.26792631, 22.46771371,\n",
       "       43.05954614, 16.01712136, 27.92913451, 34.98698008, 16.53957894,\n",
       "       31.5692083 ,  5.88414004, 13.43927835, 18.94671418, 19.85634949,\n",
       "       14.0950188 , 18.00273071, 37.26157459, 36.06190079, 17.88068463,\n",
       "       12.9002725 , 38.76595685, 36.25268472, 15.50163971, 19.42990724,\n",
       "       34.05558221, 19.35282373, 23.56155816, 14.69870668, 10.34809294,\n",
       "       27.40739065, 35.69814284, 14.32179164, 35.5859012 , 12.86687068,\n",
       "       12.80445457, 16.41219389, 32.37269444, 32.37256053, 24.85334355,\n",
       "       35.22177345, 24.40718045, 13.8118151 , 18.28056844,  6.90764387,\n",
       "       21.6123603 , 10.51004052, 22.74404964, 13.0379207 , 31.9171312 ,\n",
       "       37.35084977, 21.94330985, 16.65605546, 30.43829195, 20.48869513,\n",
       "       23.42429835, 29.90618983, 19.61172358, 26.72293048, 26.2687608 ,\n",
       "       22.34635567, 22.10253483, 21.66329436, 14.34573612, 16.03126786,\n",
       "       23.60380211, 10.80642037,  8.80548412, 23.49939848, 25.79013401,\n",
       "       30.19530864, 15.25079055, 13.0747812 , 15.53863818, 38.02717709,\n",
       "       22.97640359, 21.49965368, 27.06701505,  2.53566872, 26.62604401,\n",
       "       33.48865635, 19.38577726, 32.60475483, 29.88125678, 15.09879136,\n",
       "       24.05025018, 19.61464917, 15.28170088, 19.69530539, 20.56112579,\n",
       "       19.03013687, 18.08685981, 28.10670497, 29.77016192, 21.53180416,\n",
       "       34.4983924 , 26.22646234, 22.5047568 , 19.53374958, 14.77018577,\n",
       "       21.88400272, 20.77650013, 13.72549043, 16.04754174, 22.38209092,\n",
       "       32.24732723, 13.72372125, 29.87373263, 28.44391285, 33.85309908,\n",
       "        6.62591179, 16.39043509, 17.26876774, 21.92087007, 11.65159432,\n",
       "       24.03682226, 21.25709586, 21.65100517, 20.42221101, 24.74832967,\n",
       "       20.5700673 , 21.07770408, 33.4591617 , 25.24571548, 24.31162114,\n",
       "       20.95771209, 25.58589835, 29.83137861, 20.7690183 , 12.03672614,\n",
       "       25.28472575, 39.26270585, 35.00290657, 20.64321373, 18.89806632,\n",
       "       17.95837453, 16.12437221, 20.76315971, 26.83412923, 34.12833678,\n",
       "       27.10566643, 16.71026047, 13.82591833, 11.97782958, 13.60422277,\n",
       "       11.54415213, 28.91676871, 22.92542843, 18.30583285, 23.81943363,\n",
       "       30.37915668, 24.34615982, 19.84152394, 23.39782816, 24.42855043,\n",
       "       27.31841056, 31.31033601, 32.36072461])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##for prediction\n",
    "\n",
    "y_test_pred = regression.predict(x_test)\n",
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_test_pred')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_test_pred)\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_test_pred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48      4.716651\n",
      "64     10.161037\n",
      "473     5.066754\n",
      "485    -0.192085\n",
      "228    11.682454\n",
      "         ...    \n",
      "116    -2.197828\n",
      "221    -2.728550\n",
      "271    -2.118411\n",
      "299    -2.310336\n",
      "222    -4.860725\n",
      "Name: price, Length: 203, dtype: float64 "
     ]
    }
   ],
   "source": [
    "## calculate the error (residuals)\n",
    "error = y_test - y_test_pred\n",
    "print(error,end=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a77eef5eb0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##plot error\n",
    "sns.displot(error,kind='kde')\n",
    "# sns.displot(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a77efb3f70>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### scatter plot with respect to prediction and residuals (error)\n",
    "plt.scatter(y_test_pred,error) ## condition : = if(uniform distribution) ==model get well fit and accuracy get high \n",
    "                                                #else  == not well fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### determine the performance of matrics\n",
    "from sklearn.metrics import mean_absolute_error , r2_score ,classification_report , confusion_matrix ,mean_squared_error   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.056049624783746\n",
      "4.588687135203679\n",
      "--\n",
      "3.3470413559431096\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_test,y_test_pred))\n",
    "print(np.sqrt(mean_squared_error(y_test,y_test_pred)))\n",
    "print('--')\n",
    "print(mean_absolute_error(y_test,y_test_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### R square and adjusted R square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7489653059462293"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for R2 square = 1-SSR/SST       \n",
    "# R2 --> coefficient of determination   SSR --> sum of square of residual  \n",
    "# SST -->total sum of square\n",
    "score = r2_score(y_test,y_test_pred)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7316983693181922"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Adjusted R2 = 1- [(1-R2)*(n-1)/(n-k-1)]\n",
    "# where r2 = r2 of the model   n = no. of observation   k = no.of predicator variable\n",
    "1-(1-score)*(len(y_test)-1)/(len(y_test)-x_test.shape[1]-1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### New data prediction ( method -- 02 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.320e-03, 1.800e+01, 2.310e+00, 0.000e+00, 5.380e-01, 6.575e+00,\n",
       "        6.520e+01, 4.090e+00, 1.000e+00, 2.960e+02, 1.530e+01, 3.969e+02,\n",
       "        4.980e+00]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.data[0].reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##transformation of datasets\n",
    "scaler.fit_transform(boston_df.data[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.50330033])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.predict(scaler.fit_transform(boston_df.data[0].reshape(1,-1)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Pickle (model for deployement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(regression,open('regmodel.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_load = pickle.load(open('regmodel.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.50330033])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_load.predict(scaler.fit_transform(boston_df.data[0].reshape(1,-1))) ##model predicton has done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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   "file_extension": ".py",
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