Day-3/Day3_18Nov2020.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Today Concepts **\n",
    "\n",
    "1.Multi Linear Regression (applying linear Regression with multiple features)\n",
    "\n",
    "2.Polynomial Regression(polynomial features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi Linear Regression\n",
    "-- Applying linear regression algorithm with multiple featutres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prediction of the house price of boston dataset\n",
    "#### 1.get the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 blacks 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": 11,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "print(boston['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(boston['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>78.9</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
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       "      <td>0.06905</td>\n",
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       "      <td>0.458</td>\n",
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       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
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       "      <td>0.06263</td>\n",
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       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
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       "      <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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       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
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       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
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       "      <td>91.0</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
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       "      <td>0.10959</td>\n",
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       "           0     1      2    3      4      5     6       7    8      9    10  \\\n",
       "0    0.00632  18.0   2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
       "1    0.02731   0.0   7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
       "2    0.02729   0.0   7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
       "3    0.03237   0.0   2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
       "4    0.06905   0.0   2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
       "..       ...   ...    ...  ...    ...    ...   ...     ...  ...    ...   ...   \n",
       "501  0.06263   0.0  11.93  0.0  0.573  6.593  69.1  2.4786  1.0  273.0  21.0   \n",
       "502  0.04527   0.0  11.93  0.0  0.573  6.120  76.7  2.2875  1.0  273.0  21.0   \n",
       "503  0.06076   0.0  11.93  0.0  0.573  6.976  91.0  2.1675  1.0  273.0  21.0   \n",
       "504  0.10959   0.0  11.93  0.0  0.573  6.794  89.3  2.3889  1.0  273.0  21.0   \n",
       "505  0.04741   0.0  11.93  0.0  0.573  6.030  80.8  2.5050  1.0  273.0  21.0   \n",
       "\n",
       "         11    12  \n",
       "0    396.90  4.98  \n",
       "1    396.90  9.14  \n",
       "2    392.83  4.03  \n",
       "3    394.63  2.94  \n",
       "4    396.90  5.33  \n",
       "..      ...   ...  \n",
       "501  391.99  9.67  \n",
       "502  396.90  9.08  \n",
       "503  396.90  5.64  \n",
       "504  393.45  6.48  \n",
       "505  396.90  7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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  {
   "cell_type": "code",
   "execution_count": 18,
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   "outputs": [
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     "execution_count": 18,
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   "source": [
    "df.sample(5)"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = boston['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
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     "execution_count": 20,
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    "df.head(3)"
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = boston['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</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",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "   PTRATIO       B  LSTAT  target  \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  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.Pre processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "target     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## is there any missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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  target   506 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i am taking RM column randomly as my feature\n",
    "and we check the performence of the model .if performence not good we can improve the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['RM']]\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "it is better to separate the data for training data and testing data\n",
    "\n",
    "we can say 70% data for training  and 30% data for testing\n",
    "\n",
    "we have 506 rows available\n",
    "\n",
    "in that how many rows for training and how many rows for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "354.2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "70*506/100 # training rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "506-354 # testing rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,train_size = 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354, 1)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152, 1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score 50.23735877738189\n"
     ]
    }
   ],
   "source": [
    "print('training score',model.score(X_train,y_train)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing score 42.71486097786569\n"
     ]
    }
   ],
   "source": [
    "print('testing score',model.score(X_test,y_test)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**improve the model**\n",
    "above score very low so want improve the model\n",
    "\n",
    "1.by giving more examples\n",
    "\n",
    "2.by taking different features\n",
    "\n",
    "3.by parameter tuning\n",
    "\n",
    "**improving score by different feature selection**\n",
    "\n",
    "df.corr() gives co relation between the features\n",
    "\n",
    "if corelation values near to 1 then we can say positive linearity\n",
    "\n",
    "if corealtion values near to -1 then we can -ve linearity\n",
    "\n",
    "if corelation 0 then we can say no relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CRIM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INDUS</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</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>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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RM -->0.69\n",
    "LSTAT -->-0.73\n",
    "\n",
    "We can take RM&LSTAT as features and again check performance.\n",
    "if not uptomark we can apply improve the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['RM','LSTAT']]\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(354, 2)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score 68.31857031226657\n",
      "test score 48.71669296438117\n"
     ]
    }
   ],
   "source": [
    "print('train score',model.score(X_train,y_train)*100)\n",
    "print('test score',model.score(X_test,y_test)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying polynomial features  to linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**underfitting** model not able to recognize the pattern(student not able to learn from the training data)\n",
    "\n",
    "**overfitting** model capturing all the points but failed in testing.\n",
    "\n",
    "**best fit** model recognizing the pattern and giving good performence in training and testing\n",
    "\n",
    "\n",
    "**prediction of salay of employee with his experience**\n",
    "without polynomial features and with polynomial feature and observe the scores.\n",
    "\n",
    "#### 1.get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "experience = [0,1,2,3,4,5,6,7,8]\n",
    "salary = [5000,6000,7000,8000,15000,25000,40000,55000,70000]\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.DataFrame({'experience':experience,'salary':salary})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>experience</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>55000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  salary\n",
       "0           0    5000\n",
       "1           1    6000\n",
       "2           2    7000\n",
       "3           3    8000\n",
       "4           4   15000\n",
       "5           5   25000\n",
       "6           6   40000\n",
       "7           7   55000\n",
       "8           8   70000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 2)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "experience    0\n",
       "salary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 2.pre-processing\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   experience  9 non-null      int64\n",
      " 1   salary      9 non-null      int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 272.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X =df[['experience']]\n",
    "y=df['salary']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7 rows data for training and 2 rows data for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train =X.head(7)\n",
    "X_test = X.tail(2)\n",
    "y_train=y.head(7)\n",
    "y_test = y.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 1)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score 79.92498597868762\n",
      "test score -888.7811791383222\n"
     ]
    }
   ],
   "source": [
    "print('train score',model.score(X_train,y_train)*100)\n",
    "print('test score',model.score(X_test,y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(df['experience'],df['salary'],c='blue',label='true values')\n",
    "plt.plot(df['experience'],model.predict(X),c='red',label='predicted line')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_poly_train = poly.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_poly_test = poly.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_poly_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score 98.77079828005235\n"
     ]
    }
   ],
   "source": [
    "print('train score',model.score(X_poly_train,y_train)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test score 86.75736961451238\n"
     ]
    }
   ],
   "source": [
    "print('test score',model.score(X_poly_test,y_test)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.scatter(df['experience'],df['salary'],c='green',label='true values')\n",
    "plt.plot(df['experience'],model.predict(poly.transform(X)),c='red',label='predictedline')\n",
    "plt.xlabel('experience')\n",
    "plt.ylabel('salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}