week3day1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 13-09-2021\n",
    "# Ml only works on numbers. If it contains string value we can convert the string into numbers by 2 methods\n",
    "## 1. Creating a dummies and 2.One-Hot-Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>town</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>2600</td>\n",
       "      <td>585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>2800</td>\n",
       "      <td>615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               town  area   price\n",
       "0   monroe township  2600  550000\n",
       "1   monroe township  3000  565000\n",
       "2   monroe township  3200  610000\n",
       "3   monroe township  3600  680000\n",
       "4   monroe township  4000  725000\n",
       "5      west windsor  2600  585000\n",
       "6      west windsor  2800  615000\n",
       "7      west windsor  3300  650000\n",
       "8      west windsor  3600  710000\n",
       "9       robinsville  2600  575000\n",
       "10      robinsville  2900  600000\n",
       "11      robinsville  3100  620000\n",
       "12      robinsville  3600  695000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('homeprices2.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a dummies ,where dummies is a part of pandas module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>monroe township</th>\n",
       "      <th>robinsville</th>\n",
       "      <th>west windsor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    monroe township  robinsville  west windsor\n",
       "0                 1            0             0\n",
       "1                 1            0             0\n",
       "2                 1            0             0\n",
       "3                 1            0             0\n",
       "4                 1            0             0\n",
       "5                 0            0             1\n",
       "6                 0            0             1\n",
       "7                 0            0             1\n",
       "8                 0            0             1\n",
       "9                 0            1             0\n",
       "10                0            1             0\n",
       "11                0            1             0\n",
       "12                0            1             0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies=pd.get_dummies(df.town)\n",
    "dummies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  contcat: it is function used to Concatinate a dummies with a dataframe homesales2.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>town</th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>monroe township</th>\n",
       "      <th>robinsville</th>\n",
       "      <th>west windsor</th>\n",
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       "      <th>0</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
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       "      <th>1</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>monroe township</td>\n",
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       "      <td>610000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>monroe township</td>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>2600</td>\n",
       "      <td>585000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>2800</td>\n",
       "      <td>615000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "               town  area   price  monroe township  robinsville  west windsor\n",
       "0   monroe township  2600  550000                1            0             0\n",
       "1   monroe township  3000  565000                1            0             0\n",
       "2   monroe township  3200  610000                1            0             0\n",
       "3   monroe township  3600  680000                1            0             0\n",
       "4   monroe township  4000  725000                1            0             0\n",
       "5      west windsor  2600  585000                0            0             1\n",
       "6      west windsor  2800  615000                0            0             1\n",
       "7      west windsor  3300  650000                0            0             1\n",
       "8      west windsor  3600  710000                0            0             1\n",
       "9       robinsville  2600  575000                0            1             0\n",
       "10      robinsville  2900  600000                0            1             0\n",
       "11      robinsville  3100  620000                0            1             0\n",
       "12      robinsville  3600  695000                0            1             0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged=pd.concat([df,dummies], axis='columns')\n",
    "merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Droping the Town colums from the csv file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>price</th>\n",
       "      <th>monroe township</th>\n",
       "      <th>robinsville</th>\n",
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       "      <th>3</th>\n",
       "      <td>3600</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2600</td>\n",
       "      <td>585000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2800</td>\n",
       "      <td>615000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "    area   price  monroe township  robinsville  west windsor\n",
       "0   2600  550000                1            0             0\n",
       "1   3000  565000                1            0             0\n",
       "2   3200  610000                1            0             0\n",
       "3   3600  680000                1            0             0\n",
       "4   4000  725000                1            0             0\n",
       "5   2600  585000                0            0             1\n",
       "6   2800  615000                0            0             1\n",
       "7   3300  650000                0            0             1\n",
       "8   3600  710000                0            0             1\n",
       "9   2600  575000                0            1             0\n",
       "10  2900  600000                0            1             0\n",
       "11  3100  620000                0            1             0\n",
       "12  3600  695000                0            1             0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final=merged.drop(['town'], axis='columns')\n",
    "final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we can drop any one column among the 3 column,where ased on 2 column we can predict the another column.This fuction is called Multicolinear activity.\n",
    "## Here we droped 'west windsor' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>area</th>\n",
       "      <th>price</th>\n",
       "      <th>monroe township</th>\n",
       "      <th>robinsville</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>4000</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2600</td>\n",
       "      <td>585000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2800</td>\n",
       "      <td>615000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area   price  monroe township  robinsville\n",
       "0   2600  550000                1            0\n",
       "1   3000  565000                1            0\n",
       "2   3200  610000                1            0\n",
       "3   3600  680000                1            0\n",
       "4   4000  725000                1            0\n",
       "5   2600  585000                0            0\n",
       "6   2800  615000                0            0\n",
       "7   3300  650000                0            0\n",
       "8   3600  710000                0            0\n",
       "9   2600  575000                0            1\n",
       "10  2900  600000                0            1\n",
       "11  3100  620000                0            1\n",
       "12  3600  695000                0            1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = final.drop (['west windsor'], axis='columns')\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>monroe township</th>\n",
       "      <th>robinsville</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2600</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2900</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  monroe township  robinsville\n",
       "0   2600                1            0\n",
       "1   3000                1            0\n",
       "2   3200                1            0\n",
       "3   3600                1            0\n",
       "4   4000                1            0\n",
       "5   2600                0            0\n",
       "6   2800                0            0\n",
       "7   3300                0            0\n",
       "8   3600                0            0\n",
       "9   2600                0            1\n",
       "10  2900                0            1\n",
       "11  3100                0            1\n",
       "12  3600                0            1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=final.drop('price', axis='columns')\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     550000\n",
       "1     565000\n",
       "2     610000\n",
       "3     680000\n",
       "4     725000\n",
       "5     585000\n",
       "6     615000\n",
       "7     650000\n",
       "8     710000\n",
       "9     575000\n",
       "10    600000\n",
       "11    620000\n",
       "12    695000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=final.price\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([681241.66845839])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[3400,0,0]]) #3400sqr ft home at west windsor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([590775.63964739])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[2800,0,1]]) #2800sqr ft home at robinsville"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9573929037221873"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>monroe township</td>\n",
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       "    </tr>\n",
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       "      <td>monroe township</td>\n",
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       "    </tr>\n",
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       "      <td>585000</td>\n",
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       "    <tr>\n",
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       "      <td>west windsor</td>\n",
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       "      <td>615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>west windsor</td>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>robinsville</td>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               town  area   price\n",
       "0   monroe township  2600  550000\n",
       "1   monroe township  3000  565000\n",
       "2   monroe township  3200  610000\n",
       "3   monroe township  3600  680000\n",
       "4   monroe township  4000  725000\n",
       "5      west windsor  2600  585000\n",
       "6      west windsor  2800  615000\n",
       "7      west windsor  3300  650000\n",
       "8      west windsor  3600  710000\n",
       "9       robinsville  2600  575000\n",
       "10      robinsville  2900  600000\n",
       "11      robinsville  3100  620000\n",
       "12      robinsville  3600  695000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfle=df\n",
    "dfle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>5</th>\n",
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       "      <td>585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>2800</td>\n",
       "      <td>615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>3300</td>\n",
       "      <td>650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>3600</td>\n",
       "      <td>710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2600</td>\n",
       "      <td>575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2900</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>3100</td>\n",
       "      <td>620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>3600</td>\n",
       "      <td>695000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    town  area   price\n",
       "0      0  2600  550000\n",
       "1      0  3000  565000\n",
       "2      0  3200  610000\n",
       "3      0  3600  680000\n",
       "4      0  4000  725000\n",
       "5      2  2600  585000\n",
       "6      2  2800  615000\n",
       "7      2  3300  650000\n",
       "8      2  3600  710000\n",
       "9      1  2600  575000\n",
       "10     1  2900  600000\n",
       "11     1  3100  620000\n",
       "12     1  3600  695000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfle.town=le.fit_transform(dfle.town)\n",
    "dfle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0, 2600],\n",
       "       [   0, 3000],\n",
       "       [   0, 3200],\n",
       "       [   0, 3600],\n",
       "       [   0, 4000],\n",
       "       [   2, 2600],\n",
       "       [   2, 2800],\n",
       "       [   2, 3300],\n",
       "       [   2, 3600],\n",
       "       [   1, 2600],\n",
       "       [   1, 2900],\n",
       "       [   1, 3100],\n",
       "       [   1, 3600]], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=dfle[['town','area']].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([550000, 565000, 610000, 680000, 725000, 585000, 615000, 650000,\n",
       "       710000, 575000, 600000, 620000, 695000], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=dfle.price.values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "ct=ColumnTransformer([('town', OneHotEncoder(), [0])], remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0e+00, 0.0e+00, 0.0e+00, 2.6e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 3.0e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 3.2e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 3.6e+03],\n",
       "       [1.0e+00, 0.0e+00, 0.0e+00, 4.0e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 2.6e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 2.8e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 3.3e+03],\n",
       "       [0.0e+00, 0.0e+00, 1.0e+00, 3.6e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 2.6e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 2.9e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 3.1e+03],\n",
       "       [0.0e+00, 1.0e+00, 0.0e+00, 3.6e+03]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=ct.fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0e+00, 0.0e+00, 2.6e+03],\n",
       "       [0.0e+00, 0.0e+00, 3.0e+03],\n",
       "       [0.0e+00, 0.0e+00, 3.2e+03],\n",
       "       [0.0e+00, 0.0e+00, 3.6e+03],\n",
       "       [0.0e+00, 0.0e+00, 4.0e+03],\n",
       "       [0.0e+00, 1.0e+00, 2.6e+03],\n",
       "       [0.0e+00, 1.0e+00, 2.8e+03],\n",
       "       [0.0e+00, 1.0e+00, 3.3e+03],\n",
       "       [0.0e+00, 1.0e+00, 3.6e+03],\n",
       "       [1.0e+00, 0.0e+00, 2.6e+03],\n",
       "       [1.0e+00, 0.0e+00, 2.9e+03],\n",
       "       [1.0e+00, 0.0e+00, 3.1e+03],\n",
       "       [1.0e+00, 0.0e+00, 3.6e+03]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=X[:, 1:]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([681241.6684584])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[0,1,3400]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([641227.69296925])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[0,0,3400]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Mileage</th>\n",
       "      <th>Age(yrs)</th>\n",
       "      <th>Sell Price($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>6</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>3</td>\n",
       "      <td>34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>5</td>\n",
       "      <td>26100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>2</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "      <td>31500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "      <td>26750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "      <td>32000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>6</td>\n",
       "      <td>19300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>91000</td>\n",
       "      <td>8</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67000</td>\n",
       "      <td>6</td>\n",
       "      <td>22000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>7</td>\n",
       "      <td>18700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79000</td>\n",
       "      <td>7</td>\n",
       "      <td>19500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "      <td>26000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58780</td>\n",
       "      <td>4</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>82450</td>\n",
       "      <td>7</td>\n",
       "      <td>19400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25400</td>\n",
       "      <td>3</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>28000</td>\n",
       "      <td>2</td>\n",
       "      <td>35500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>69000</td>\n",
       "      <td>5</td>\n",
       "      <td>19700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>87600</td>\n",
       "      <td>8</td>\n",
       "      <td>12800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "      <td>28200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Age(yrs)  Sell Price($)\n",
       "0     69000         6          18000\n",
       "1     35000         3          34000\n",
       "2     57000         5          26100\n",
       "3     22500         2          40000\n",
       "4     46000         4          31500\n",
       "5     59000         5          26750\n",
       "6     52000         5          32000\n",
       "7     72000         6          19300\n",
       "8     91000         8          12000\n",
       "9     67000         6          22000\n",
       "10    83000         7          18700\n",
       "11    79000         7          19500\n",
       "12    59000         5          26000\n",
       "13    58780         4          27500\n",
       "14    82450         7          19400\n",
       "15    25400         3          35000\n",
       "16    28000         2          35500\n",
       "17    69000         5          19700\n",
       "18    87600         8          12800\n",
       "19    52000         5          28200"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('carprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254c538c2e0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(df['Mileage'], df['Sell Price($)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x254c5473ee0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(df['Age(yrs)'], df['Sell Price($)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['Mileage','Age(yrs)']]\n",
    "y=df['Sell Price($)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y, test_size=0.3,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Mileage</th>\n",
       "      <th>Age(yrs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58780</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>82450</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>91000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>69000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>28000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25400</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Age(yrs)\n",
       "13    58780         4\n",
       "2     57000         5\n",
       "14    82450         7\n",
       "8     91000         8\n",
       "17    69000         5\n",
       "16    28000         2\n",
       "19    52000         5\n",
       "12    59000         5\n",
       "11    79000         7\n",
       "1     35000         3\n",
       "0     69000         6\n",
       "15    25400         3\n",
       "4     46000         4\n",
       "9     67000         6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Mileage</th>\n",
       "      <th>Age(yrs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
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       "      <th>5</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
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       "      <th>3</th>\n",
       "      <td>22500</td>\n",
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       "      <th>18</th>\n",
       "      <td>87600</td>\n",
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      ],
      "text/plain": [
       "    Mileage  Age(yrs)\n",
       "7     72000         6\n",
       "10    83000         7\n",
       "5     59000         5\n",
       "6     52000         5\n",
       "3     22500         2\n",
       "18    87600         8"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13    27500\n",
       "2     26100\n",
       "14    19400\n",
       "8     12000\n",
       "17    19700\n",
       "16    35500\n",
       "19    28200\n",
       "12    26000\n",
       "11    19500\n",
       "1     34000\n",
       "0     18000\n",
       "15    35000\n",
       "4     31500\n",
       "9     22000\n",
       "Name: Sell Price($), dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7     19300\n",
       "10    18700\n",
       "5     26750\n",
       "6     32000\n",
       "3     40000\n",
       "18    12800\n",
       "Name: Sell Price($), dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "clf=LinearRegression()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20668.52722622, 16762.33242213, 25160.18381011, 27209.30003936,\n",
       "       37903.32633702, 14729.61531335])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Age(yrs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>87600</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Age(yrs)\n",
       "7     72000         6\n",
       "10    83000         7\n",
       "5     59000         5\n",
       "6     52000         5\n",
       "3     22500         2\n",
       "18    87600         8"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212422483776329"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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