End to end project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0512c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme()\n",
    "sns.set(rc={'figure.figsize': (12, 7)})\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "043bed1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68c77525",
   "metadata": {},
   "outputs": [],
   "source": [
    "boston=load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd0a51da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6e79b18",
   "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": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c68eb341",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(boston.data, columns=boston.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "66d15449",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
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       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>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",
       "    </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",
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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",
       "   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  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a1fe5e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']=boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "796da523",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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",
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       "      <th>PTRATIO</th>\n",
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       "      <th>target</th>\n",
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       "  <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",
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       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.388305</td>\n",
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       "    <tr>\n",
       "      <th>ZN</th>\n",
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "      <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>target</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
       "CRIM     1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "ZN      -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "INDUS    0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "CHAS    -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "NOX      0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "RM      -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "AGE      0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "DIS     -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "RAD      0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "TAX      0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "PTRATIO  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "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",
       "target  -0.388305  0.360445 -0.483725  0.175260 -0.427321  0.695360 -0.376955   \n",
       "\n",
       "              DIS       RAD       TAX   PTRATIO         B     LSTAT    target  \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",
       "target   0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.737663  1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da766a0c",
   "metadata": {},
   "source": [
    "# check multi-collinearity and relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d4b47456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(), annot=True, cmap = \"YlGnBu\")\n",
    "\n",
    "\n",
    "# check for multi-collinearity between independent variables, if above 0.8 or .9 we can dorop any one col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57b72e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  > 0.7\n",
    "# nox indus\n",
    "# age nox\n",
    "# tax rad .9\n",
    "# tax indus\n",
    "\n",
    "# >-0.7\n",
    "# dis nox\n",
    "# dis age\n",
    "# dis indus\n",
    "\n",
    "# <0.7\n",
    "# target rm\n",
    "\n",
    "# <0.7\n",
    "# target lstat\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e790ff",
   "metadata": {},
   "source": [
    "# ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "935b2ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('target', axis=1)\n",
    "y=df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "136da061",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c0f718b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.3, random_state=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d7c76a82",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "75eaafe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sd=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f49268db",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(sd, open('scaling.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d8b93d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Xtrain = sd.fit_transform(Xtrain)\n",
    "Xtest = sd.fit_transform(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb8f7737",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fb4e2c64",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8bdfffc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4bc128ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.57477822  1.53891131  0.57966473  0.52327069 -2.61571068  2.30712032\n",
      "  0.58203021 -3.24197468  3.03389615 -2.37665943 -2.0847086   0.95347108\n",
      " -4.21904768]\n",
      "22.598870056497184\n"
     ]
    }
   ],
   "source": [
    "# how much is the variation in the target w r t input variations\n",
    "# slope -  coeff\n",
    "\n",
    "print(lm.coef_)\n",
    "print(lm.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9cee79f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'copy_X': True,\n",
       " 'fit_intercept': True,\n",
       " 'n_jobs': None,\n",
       " 'normalize': False,\n",
       " 'positive': False}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what are the parameters used while building the model\n",
    "\n",
    "lm.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0cedc4d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict with test\n",
    "\n",
    "ytest_pred = lm.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "84bfdeba",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_error = ytest - ytest_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "69e59b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target', ylabel='Count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# expected is normal dstribution  \n",
    "\n",
    "sns.histplot(test_error, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "67fcd00a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y test pred')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# expected is linear relationship \n",
    "\n",
    "sns.scatterplot(x=ytest, y=ytest_pred)\n",
    "plt.xlabel('y test')\n",
    "plt.ylabel('y test pred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "094b302b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y test error')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# expected is uniform distribution- scattered all over \n",
    "sns.scatterplot(x=ytest, y=test_error)\n",
    "plt.xlabel('y test')\n",
    "plt.ylabel('y test error')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a8eff7",
   "metadata": {},
   "source": [
    "# Permormence metrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f3df3e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "# from sklearn.metrics import a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "61d3dfab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.727436748688041\n",
      "27.46473809683593\n",
      "0.6758983950483787\n",
      "5.2406810718489565\n"
     ]
    }
   ],
   "source": [
    "print(mean_absolute_error(ytest, ytest_pred))\n",
    "print(mean_squared_error(ytest, ytest_pred))\n",
    "print(r2_score(ytest, ytest_pred))\n",
    "print(np.sqrt(mean_squared_error(ytest, ytest_pred)))\n",
    "\n",
    "r2 = r2_score(ytest, ytest_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0534c586",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expected is adj_r2 is lower than r2\n",
    "\n",
    "adj_r2 =1- ((1-r2)*(len(ytest)-1)/(len(ytest)-Xtest.shape[1]-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "26b3ee0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6453670844369941"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4633ea08",
   "metadata": {},
   "source": [
    "# Single data prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3c560d22",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "row = boston.data[0].reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "614c4cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# StandardScaler\n",
    "row_sd = sd.fit_transform(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "71fa1d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.59887006])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.predict(row_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1dff2c12",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.target[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8027050e",
   "metadata": {},
   "source": [
    "# Pickle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4b1b0783",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "753d059e",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './pickle files/linear_reg.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-d0b8d65373cc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# output file is in seriable format so it can be deployed anywhere\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mr'./pickle files/linear_reg.pkl'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './pickle files/linear_reg.pkl'"
     ]
    }
   ],
   "source": [
    "# output file is in seriable format so it can be deployed anywhere\n",
    "\n",
    "pickle.dump(lm, open(r'./pickle files/linear_reg.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1213c397",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_model=pickle.load(open('./pickle files/linear_reg.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22b0a326",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([22.59887006])"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_model.predict(row_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0440074",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}