linear regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "498ec120",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd151a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('hire.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02dbddb9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>ID</th>\n",
       "      <th>model</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>lounge</td>\n",
       "      <td>51</td>\n",
       "      <td>882</td>\n",
       "      <td>25000</td>\n",
       "      <td>1</td>\n",
       "      <td>44.907242</td>\n",
       "      <td>8.611560</td>\n",
       "      <td>8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>pop</td>\n",
       "      <td>51</td>\n",
       "      <td>1186</td>\n",
       "      <td>32500</td>\n",
       "      <td>1</td>\n",
       "      <td>45.666359</td>\n",
       "      <td>12.241890</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>sport</td>\n",
       "      <td>74</td>\n",
       "      <td>4658</td>\n",
       "      <td>142228</td>\n",
       "      <td>1</td>\n",
       "      <td>45.503300</td>\n",
       "      <td>11.417840</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>lounge</td>\n",
       "      <td>51</td>\n",
       "      <td>2739</td>\n",
       "      <td>160000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.633171</td>\n",
       "      <td>17.634609</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>pop</td>\n",
       "      <td>73</td>\n",
       "      <td>3074</td>\n",
       "      <td>106880</td>\n",
       "      <td>1</td>\n",
       "      <td>41.903221</td>\n",
       "      <td>12.495650</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>1534</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>3712</td>\n",
       "      <td>115280</td>\n",
       "      <td>1</td>\n",
       "      <td>45.069679</td>\n",
       "      <td>7.704920</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>1535</td>\n",
       "      <td>lounge</td>\n",
       "      <td>74</td>\n",
       "      <td>3835</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>45.845692</td>\n",
       "      <td>8.666870</td>\n",
       "      <td>4600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>1536</td>\n",
       "      <td>pop</td>\n",
       "      <td>51</td>\n",
       "      <td>2223</td>\n",
       "      <td>60457</td>\n",
       "      <td>1</td>\n",
       "      <td>45.481541</td>\n",
       "      <td>9.413480</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>1537</td>\n",
       "      <td>lounge</td>\n",
       "      <td>51</td>\n",
       "      <td>2557</td>\n",
       "      <td>80750</td>\n",
       "      <td>1</td>\n",
       "      <td>45.000702</td>\n",
       "      <td>7.682270</td>\n",
       "      <td>5990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>1538</td>\n",
       "      <td>pop</td>\n",
       "      <td>51</td>\n",
       "      <td>1766</td>\n",
       "      <td>54276</td>\n",
       "      <td>1</td>\n",
       "      <td>40.323410</td>\n",
       "      <td>17.568270</td>\n",
       "      <td>7900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1538 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID   model  engine_power  age_in_days      km  previous_owners  \\\n",
       "0        1  lounge            51          882   25000                1   \n",
       "1        2     pop            51         1186   32500                1   \n",
       "2        3   sport            74         4658  142228                1   \n",
       "3        4  lounge            51         2739  160000                1   \n",
       "4        5     pop            73         3074  106880                1   \n",
       "...    ...     ...           ...          ...     ...              ...   \n",
       "1533  1534   sport            51         3712  115280                1   \n",
       "1534  1535  lounge            74         3835  112000                1   \n",
       "1535  1536     pop            51         2223   60457                1   \n",
       "1536  1537  lounge            51         2557   80750                1   \n",
       "1537  1538     pop            51         1766   54276                1   \n",
       "\n",
       "            lat        lon  price  \n",
       "0     44.907242   8.611560   8900  \n",
       "1     45.666359  12.241890   8800  \n",
       "2     45.503300  11.417840   4200  \n",
       "3     40.633171  17.634609   6000  \n",
       "4     41.903221  12.495650   5700  \n",
       "...         ...        ...    ...  \n",
       "1533  45.069679   7.704920   5200  \n",
       "1534  45.845692   8.666870   4600  \n",
       "1535  45.481541   9.413480   7500  \n",
       "1536  45.000702   7.682270   5990  \n",
       "1537  40.323410  17.568270   7900  \n",
       "\n",
       "[1538 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"lr.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45110dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ID   model  engine_power  age_in_days      km  previous_owners        lat        lon  price\n",
      "0        1  lounge            51          882   25000                1  44.907242   8.611560   8900\n",
      "1        2     pop            51         1186   32500                1  45.666359  12.241890   8800\n",
      "2        3   sport            74         4658  142228                1  45.503300  11.417840   4200\n",
      "3        4  lounge            51         2739  160000                1  40.633171  17.634609   6000\n",
      "4        5     pop            73         3074  106880                1  41.903221  12.495650   5700\n",
      "5        6     pop            74         3623   70225                1  45.000702   7.682270   7900\n",
      "6        7  lounge            51          731   11600                1  44.907242   8.611560  10750\n",
      "7        8  lounge            51         1521   49076                1  41.903221  12.495650   9190\n",
      "8        9   sport            73         4049   76000                1  45.548000  11.549470   5600\n",
      "9       10   sport            51         3653   89000                1  45.438301  10.991700   6000\n",
      "10      11     pop            51          790   43286                1  40.871429  14.438960   8950\n",
      "11      12  lounge            51          366   17500                1  45.069679   7.704920  10990\n",
      "12      13  lounge            51          456   18450                1  45.426571  11.788130   9700\n",
      "13      14     pop            51         3835  120000                1  40.531590  17.436159   4800\n",
      "14      15  lounge            51         1035   40500                1  40.911362  14.211200   9300\n",
      "15      16  lounge            51         1096   28200                1  45.697208   9.845970   9500\n",
      "16      17  lounge            73         4200  110000                1  41.082352  14.254250   5250\n",
      "17      18     pop            51         2223   96848                1  43.782372  11.254990   7990\n",
      "18      19  lounge            51         2861   31000                1  45.069679   7.704920   7300\n",
      "19      20  lounge            51          425   20030                1  45.354389  11.869260  10500\n",
      "20      21  lounge            51          397   19037                1  45.707249  11.477600  10500\n",
      "21      22  lounge            51         1886  110000                1  40.835812  14.504410   6990\n",
      "22      23  lounge            51         1035    8000                1  44.506088  12.044170  10600\n",
      "23      24  lounge            51          790   27595                1  45.688259   8.731450  10200\n",
      "24      25  lounge            51         1583   14900                1  45.069679   7.704920   9990\n",
      "25      26  lounge            51          366    9218                1  45.438110  12.318150  10800\n",
      "26      27     pop            51         3592  124000                1  40.966179  17.116480   6800\n",
      "27      28   sport            51         3531  100000                1  40.976452  14.172280   4950\n",
      "28      29  lounge            51          762   28900                1  45.131672   8.449170  10640\n",
      "29      30  lounge            51          670    4000                1  41.349751  13.353320   9500\n",
      "30      31  lounge            62         2769   59216                1  43.782372  11.254990   6990\n",
      "31      32  lounge            51         4169   99477                2  40.550564  14.225625   5900\n",
      "32      33  lounge            51          821   21730                2  41.903221  12.495650  10500\n",
      "33      34   sport            51         3927  140000                2  40.755932  14.690190   5200\n",
      "34      35  lounge            51          640   32033                2  44.283878  11.888140   9790\n",
      "35      36     pop            51         3653  138116                2  40.633171  17.634609   5000\n",
      "36      37     pop            51          852   17000                1  45.505161   8.939100   8990\n",
      "37      38     pop            51         3013   58527                1  45.688259   8.731450   7200\n",
      "38      39   sport            51          790   43100                1  45.334080  11.376870   9950\n",
      "39      40     pop            51         1858   13373                1  41.903221  12.495650   9000\n",
      "40      41   sport            51         4139  119000                1  45.349319   7.742600   4890\n",
      "41      42     pop            51          609   28500                1  45.746021   9.049970  10900\n",
      "42      43     pop            51         1096   83000                1  41.959721  12.798056   7900\n",
      "43      44  lounge            73         4049   98000                1  38.218128  15.240240   5999\n",
      "44      45  lounge            51          456   12693                1  45.393600  10.482240  10900\n",
      "45      46  lounge            51          762   14586                1  42.341969  12.358490  10500\n",
      "46      47  lounge            51          821   27640                1  41.903221  12.495650  10400\n",
      "47      48  lounge            51         2039   49000                1  41.903221  12.495650   7500\n",
      "48      49   sport            51         3684  160000                1  45.405472  10.278290   4900\n",
      "49      50   sport            51         4596  107000                1  40.845901  14.369270   4300\n",
      "50      51  lounge            51         2192   66000                1  40.840141  14.252260   6999\n",
      "51      52   sport            51          790   35000                1  45.334080  11.376870  10200\n",
      "52      53     pop            51         1186   24651                1  41.903221  12.495650   9300\n",
      "53      54   sport            62         3288   71900                1  41.062401  14.273880   5990\n",
      "54      55  lounge            51          701   11200                1  46.176498   9.903670  10600\n",
      "55      56  lounge            51         3288  124050                1  43.869980  10.339830   5500\n",
      "56      57     pop            51         1066   69395                1  45.231411   9.592890   7450\n",
      "57      58  lounge            51          456   17961                1  45.393600  10.482240  10900\n",
      "58      59  lounge            51          670   18669                1  45.000702   7.682270  10990\n",
      "59      60     pop            51         1431   22700                1  41.903221  12.495650   8900\n",
      "60      61  lounge            51         4169   56000                1  45.467960   9.181780   5900\n",
      "61      62  lounge            51          701   22669                1  45.069679   7.704920   9500\n",
      "62      63     pop            73         2557   50000                1  42.157879  11.902280   8250\n",
      "63      64  lounge            51         2343   39743                1  45.695229   9.669510   8800\n",
      "64      65  lounge            51          456   27466                1  45.393600  10.482240   9800\n",
      "65      66  lounge            51          548   22609                1  45.695831   9.180390   9900\n",
      "66      67     pop            51         1461   94485                1  44.508839  11.469080   7450\n",
      "67      68   sport            51         4261  188000                1  41.062401  14.273880   4490\n",
      "68      69  lounge            51         2343   94000                1  45.438110  12.318150   7400\n",
      "69      70     pop            51         2192   60786                1  43.938122  10.912580   7900\n",
      "70      71     pop            51         1096   41950                1  41.669510  12.502230  10700\n",
      "71      72  lounge            51          456   16971                1  45.393600  10.482240  10900\n",
      "72      73  lounge            51         4108   45300                1  41.299561  13.032590   7400\n",
      "73      74  lounge            51         4108   45300                1  41.299561  13.032590   7400\n",
      "74      75  lounge            51         3227   75359                1  45.069679   7.704920   7800\n",
      "75      76  lounge            51          366   12585                1  45.069679   7.704920   9900\n",
      "76      77     pop            62         2101  103000                1  45.797859   8.644440   7900\n",
      "77      78   sport            51         2739   77149                3  44.754890   8.031900   7800\n",
      "78      79  lounge            51          456   13587                3  45.393600  10.482240  10900\n",
      "79      80  lounge            51         1797   55595                3  45.766979  11.738400   7800\n",
      "80      81  lounge            51          397   17250                3  45.069679   7.704920  10050\n",
      "81      82   sport            51         4292  145000                3  41.062401  14.273880   4799\n",
      "82      83  lounge            51          790   20849                1  41.903221  12.495650   8100\n",
      "83      84  lounge            51         3988  107375                1  41.376560  14.076300   5800\n",
      "84      85     pop            63         2009  100000                1  45.318180   9.105690   7500\n",
      "85      86  lounge            51          456   17841                1  45.393600  10.482240  10900\n",
      "86      87  lounge            51          762   44778                1  45.571220   9.159140   9390\n",
      "87      88     pop            51         3258   78681                1  45.679279   9.237190   7490\n",
      "88      89     pop            51         4474  123000                1  40.633171  17.634609   4900\n",
      "89      90  lounge            51          397   17912                1  41.832649  12.752600   9970\n",
      "90      91  lounge            51         1066   69916                1  41.802990  12.598930   8980\n",
      "91      92  lounge            51         2647   97700                1  44.508839  11.469080   7450\n",
      "92      93     pop            51          366   11500                1  45.707249  11.477600   9500\n",
      "93      94  lounge            51          397   17250                1  45.467960   9.181780  10050\n",
      "94      95  lounge            51          366   12372                1  38.914440  16.584360  10465\n",
      "95      96   sport            51         4292  165600                1  44.715408  11.308300   5950\n",
      "96      97     pop            51         1066   28000                1  41.769051  12.662810   8500\n",
      "97      98   sport            51         2009   86000                2  40.633171  17.634609   7800\n",
      "98      99  lounge            51          456   18592                2  45.393600  10.482240  10900\n",
      "99     100     pop            51          731   41558                2  45.571220   9.159140   8790\n",
      "100    101     pop            51         4018   96000                2  44.294300   9.674440   5700\n",
      "101    102  lounge            51          701   19000                1  41.264111  13.744990  10000\n",
      "102    103  lounge            51          397   18183                1  41.832649  12.752600   9970\n",
      "103    104  lounge            51         1066   69916                1  41.802990  12.598930   8980\n",
      "104    105     pop            51         2647   75500                1  44.508839  11.469080   7500\n",
      "105    106     pop            51         1827   39500                1  45.673401   9.926890   9800\n",
      "106    107  lounge            51          397   17250                1  41.903221  12.495650  10050\n",
      "107    108     pop            51         3835  128000                1  45.764648   8.994500   4900\n",
      "108    109     pop            51          366   12929                1  45.556549   9.534470   9400\n",
      "109    110  lounge            51         3562  113254                2  44.305820   8.483640   6100\n",
      "110    111  lounge            51          366   18000                1  41.264111  13.744990  10700\n",
      "111    112  lounge            51          397   16643                1  41.832649  12.752600   9970\n",
      "112    113  lounge            51         2892   92300                1  43.882069  11.100350   7500\n",
      "113    114  lounge            51          640   34873                1  40.858898  14.357190   9500\n",
      "114    115   sport            51         4018  102000                1  41.357849  13.424630   6500\n",
      "115    116  lounge            51          397   16135                1  45.357769   9.612210  10650\n",
      "116    117  lounge            51         3865   95350                1  41.903221  12.495650   5500\n",
      "117    118  lounge            51          425   19909                1  45.720718   7.321430  10950\n",
      "118    119  lounge            51          425   16518                1  44.399971   8.274500  11000\n",
      "119    120     pop            62         3135  113000                1  45.438110  12.318150   5800\n",
      "120    121     pop            51         1096   50000                1  40.921669  14.196270   9500\n",
      "121    122     pop            62         3013  120000                1  45.069679   7.704920   4900\n",
      "122    123  lounge            51         3227  105000                1  45.684879   9.717910   6500\n",
      "123    124  lounge            51         2831  104000                1  45.964931  12.644530   6000\n",
      "124    125     pop            51         2831   56779                4  41.903221  12.495650   7500\n",
      "125    126  lounge            51          397   17590                1  41.832649  12.752600   9970\n",
      "126    127     pop            51         1127    7772                1  41.069851  15.058990   9000\n",
      "127    128  lounge            51         2861   85000                1  45.609531  10.208570   7700\n",
      "128    129  lounge            51         3623   75900                1  43.782372  11.254990   6300\n",
      "129    130  lounge            51         1127   21400                1  44.332531   7.545920   9500\n",
      "130    131  lounge            51         4292  112400                1  45.706100  11.807370   5000\n",
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      "323    324  lounge            51          425   16783                1  41.903221  12.495650   9100\n",
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      "331    332  lounge            51         1155   40700                1  42.143860  12.540160   8750\n",
      "332    333     pop            51         1521   37500                1  45.532001   9.281530   8290\n",
      "333    334  lounge            51          456   26526                1  45.465801   8.886410   9980\n",
      "334    335  lounge            51          425   12275                1  41.903221  12.495650   9100\n",
      "335    336  lounge            51          701   37083                1  40.465370  17.250540   9000\n",
      "336    337  lounge            51         2223   60000                1  45.317059   8.858700   7700\n",
      "337    338     pop            51         2343   95000                1  45.334080  11.376870   7900\n",
      "338    339  lounge            51         3684   71000                1  40.928501  14.203670   6000\n",
      "339    340  lounge            51         1096   55851                1  41.903221  12.495650   7100\n",
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      "346    347  lounge            51          762   29000                2  45.764648   8.994500  10500\n",
      "347    348     pop            51         1155   29000                2  43.782372  11.254990  10900\n",
      "348    349  lounge            51          821   21877                2  46.176498   9.903670  10800\n",
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      "572    573     pop            51          366   11236                1  45.438110  12.318150   9800\n",
      "573    574     pop            51         2343   97000                1  40.928501  14.203670   5999\n",
      "574    575  lounge            51          640   39038                1  41.996349  12.723440   9800\n",
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      "576    577  lounge            51         2192   78000                1  45.598598   9.164040   7990\n",
      "577    578     pop            51         1127   36495                1  41.903221  12.495650   9480\n",
      "578    579     pop            51         2861   80000                1  40.854710  14.379210   7500\n",
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      "583    584     pop            51         2374   73000                1  45.438110  12.318150   8900\n",
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      "585    586  lounge            51          640   40438                1  41.996349  12.723440   9800\n",
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      "590    591  lounge            51          731   33665                1  41.903221  12.495650   9980\n",
      "591    592     pop            51         1186   19000                1  41.114262  16.485710   8450\n",
      "592    593  lounge            51         1858   55476                1  43.879860  10.782810   8990\n",
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      "594    595  lounge            51         1066   59961                1  41.903221  12.495650   7480\n",
      "595    596     pop            51         2678   75000                1  45.438110  12.318150   6800\n",
      "596    597     pop            51         3347   85500                1  43.782372  11.254990   5699\n",
      "597    598  lounge            51         2739   85366                1  45.695229   9.669510   7400\n",
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      "599    600     pop            51         4322  120000                1  40.734840  14.573420   4399\n",
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      "601    602  lounge            51          790   15716                1  45.133259   7.632730  10790\n",
      "602    603     pop            51         2039   57039                1  40.748241  14.528350   7590\n",
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      "610    611     pop            51          517   11000                1  40.923141  14.310100  10500\n",
      "611    612     pop            51         2313   75000                1  45.628132   9.045460   7400\n",
      "612    613     pop            51         1431   38800                1  44.907242   8.611560   7800\n",
      "613    614  lounge            51         1917   54250                1  41.630291  15.923290   6900\n",
      "614    615     pop            51          701   41000                1  44.752609   8.155280   9500\n",
      "615    616  lounge            51          670   28000                1  45.056599   9.701760  10200\n",
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      "618    619  lounge            51          821   33224                1  45.598598   9.164040   9890\n",
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      "626    627  lounge            62         3074   61000                1  45.518150  10.350670   8450\n",
      "627    628     pop            51         2343   39900                1  45.512569  10.329010   7900\n",
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      "629    630  lounge            62         3347   70340                1  40.748241  14.528350   6990\n",
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      "631    632  lounge            51          609   21000                1  44.715408  11.308300  10900\n",
      "632    633  lounge            51          821   25922                1  45.598598   9.164040   9890\n",
      "633    634     pop            51          821    6700                1  42.453369  14.140380   8900\n",
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      "637    638     pop            51         2374   54198                1  45.312000   9.497010   7800\n",
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      "641    642     pop            62         1492   79000                1  43.898949  11.002060   8000\n",
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      "647    648     pop            51          670   22000                1  45.056599   9.701760  10490\n",
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      "662    663  lounge            51         1492   23016                1  45.131672   8.449170  10000\n",
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      "667    668     pop            51         2769   72784                1  40.797272  14.543610   9890\n",
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      "677    678     pop            51          701   39700                1  41.903221  12.495650   8800\n",
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      "688    689     pop            51          670   17000                1  45.438110  12.318150  10499\n",
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      "699    700     pop            51         2313  106905                1  45.320011  10.578590   6990\n",
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      "707    708  lounge            51         3197   51083                1  45.571220   9.159140   5880\n",
      "708    709     pop            51          790   25000                1  42.463909  14.213540   9900\n",
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      "757    758  lounge            51         4018  102841                1  38.222939  15.380090   6000\n",
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      "1427  1428     pop            51         1155   28000                1  45.764648   8.994500  10900\n",
      "1428  1429  lounge            51         1461  103519                1  43.841980  10.515310   7500\n",
      "1429  1430  lounge            51         2192  100000                1  43.817020  10.682850   8600\n",
      "1430  1431  lounge            51         1066   69916                1  41.903221  12.495650   8980\n",
      "1431  1432  lounge            51          670   34819                1  41.107880  14.208810   9400\n",
      "1432  1433  lounge            51          701   42095                1  41.107880  14.208810   8900\n",
      "1433  1434  lounge            51         1096   16876                1  43.067532  12.551550  10500\n",
      "1434  1435     pop            51         1155   47000                1  41.903221  12.495650   7900\n",
      "1435  1436  lounge            51         3562   88000                1  41.591141  13.246220   5000\n",
      "1436  1437  lounge            51         1797   61000                1  40.858898  14.357190   6990\n",
      "1437  1438  lounge            51          762   15613                1  41.903221  12.495650   9900\n",
      "1438  1439     pop            51          790   44687                1  41.107880  14.208810   8800\n",
      "1439  1440  lounge            51         1461  103519                1  43.879860  10.782810   7500\n",
      "1440  1441  lounge            51         4474  143900                1  44.643131  10.934060   5400\n",
      "1441  1442  lounge            51         1066   69916                1  45.571220   9.159140   8980\n",
      "1442  1443  lounge            51          670   32835                1  41.107880  14.208810   9400\n",
      "1443  1444  lounge            51          701   39024                1  41.107880  14.208810   9400\n",
      "1444  1445  lounge            51          397   16300                1  45.069679   7.704920  10200\n",
      "1445  1446  lounge            51          943   15748                1  43.067532  12.551550  10700\n",
      "1446  1447  lounge            51          790   46038                1  45.068890   9.266180  10900\n",
      "1447  1448  lounge            51         3319  122000                1  44.907242   8.611560   5500\n",
      "1448  1449  lounge            51         3684  105000                1  44.643131  10.934060   6200\n",
      "1449  1450  lounge            51         3105   99999                1  40.947170  14.372540   6500\n",
      "1450  1451     pop            51          397   15000                1  41.903221  12.495650   9970\n",
      "1451  1452   sport            62         3166   89000                1  45.724380  11.763490   5500\n",
      "1452  1453  lounge            51         1247   75000                1  41.683800  12.774520   8000\n",
      "1453  1454     pop            51         1186   42900                1  39.214539   9.110490   9900\n",
      "1454  1455  lounge            51         1066   69916                1  41.903221  12.495650   8980\n",
      "1455  1456  lounge            51          701   33942                1  41.107880  14.208810   9400\n",
      "1456  1457  lounge            51          701   37093                1  41.107880  14.208810   9400\n",
      "1457  1458  lounge            51          397   16300                1  41.903221  12.495650  10200\n",
      "1458  1459  lounge            51          425   17539                1  40.992161  14.213930  10500\n",
      "1459  1460  lounge            51          762   16700                1  40.401070  15.592870   9990\n",
      "1460  1461  lounge            51          762    9500                1  44.541870  10.781420  10900\n",
      "1461  1462  lounge            51          790   24086                1  45.438789  10.764240  10350\n",
      "1462  1463     pop            62         3470   90000                1  40.858898  14.357190   5199\n",
      "1463  1464  lounge            51         1797   48500                1  45.501339   9.025720   7500\n",
      "1464  1465  lounge            51         2039   50000                1  45.438301  10.991700   8000\n",
      "1465  1466   sport            51         3288  160012                4  45.141140   9.108080   3900\n",
      "1466  1467  lounge            51         1492   58000                4  44.988739   9.010500   8890\n",
      "1467  1468  lounge            51         1066   69916                4  45.571220   9.159140   8980\n",
      "1468  1469  lounge            51          701   35752                4  41.107880  14.208810   9400\n",
      "1469  1470  lounge            51          640   34992                4  41.107880  14.208810   9400\n",
      "1470  1471  lounge            51          397   16300                4  45.467960   9.181780  10200\n",
      "1471  1472     pop            51         2527   82000                1  41.769051  12.662810   3950\n",
      "1472  1473     pop            51          731   17000                1  45.069679   7.704920   9500\n",
      "1473  1474  lounge            51         1858   48900                1  45.960571   8.449370  10900\n",
      "1474  1475  lounge            51          731   24235                1  45.438789  10.764240  10350\n",
      "1475  1476     pop            51         2496   80000                1  41.770081  12.227110   8500\n",
      "1476  1477  lounge            51          731   16500                1  40.667141  16.604450  10500\n",
      "1477  1478  lounge            51         1917  126426                1  41.769051  12.662810   6500\n",
      "1478  1479  lounge            51          701   58231                1  45.022388   7.586020   7900\n",
      "1479  1480  lounge            51         1674   89000                1  41.141232  14.395780   7600\n",
      "1480  1481  lounge            51         1066   69916                1  40.752209  14.453410   8979\n",
      "1481  1482  lounge            51          670   36458                1  41.107880  14.208810   9400\n",
      "1482  1483  lounge            51          670   40639                1  41.107880  14.208810   8900\n",
      "1483  1484  lounge            51          974   40000                1  45.282421   9.931300   9950\n",
      "1484  1485  lounge            51          731   12000                1  45.022388   7.586020   9980\n",
      "1485  1486  lounge            51          366   15000                1  45.541481   8.071920  10900\n",
      "1486  1487  lounge            51          731   17228                1  42.664959  13.917870   9900\n",
      "1487  1488  lounge            73         4596  220000                2  40.976452  14.172280   3850\n",
      "1488  1489     pop            51         2527  108000                1  37.511360  15.067520   6800\n",
      "1489  1490     pop            51          366   22148                1  45.707249  11.477600   9500\n",
      "1490  1491  lounge            51         1066   69916                1  40.752209  14.453410   8980\n",
      "1491  1492  lounge            51         2922  100000                1  44.537769  10.816360   6999\n",
      "1492  1493  lounge            51          609   27370                1  41.107880  14.208810   9400\n",
      "1493  1494     pop            51         4414   69000                1  45.056599   9.701760   5290\n",
      "1494  1495   sport            51         2739   77149                3  44.754890   8.031900   7800\n",
      "1495  1496     pop            62         3347   80000                3  44.283878  11.888140   7900\n",
      "1496  1497     pop            51         1461   91055                3  44.508839  11.469080   7450\n",
      "1497  1498  lounge            51          397   15840                3  38.122070  13.361120  10700\n",
      "1498  1499   sport            51         1400   60000                1  45.802021   9.187790  10800\n",
      "1499  1500     pop            51         1066   53100                1  38.122070  13.361120   8900\n",
      "1500  1501     pop            51         1917   52008                1  45.438301  10.991700   9900\n",
      "1501  1502   sport            51         3774  161900                1  44.906120   7.677230   4500\n",
      "1502  1503  lounge            51          670   32473                1  41.107880  14.208810   9400\n",
      "1503  1504     pop            51         1066   82200                1  37.230339  14.523400   7990\n",
      "1504  1505   sport            51         4322   95000                1  40.928501  14.203670   4500\n",
      "1505  1506     pop            51         1886   11000                1  44.813751   7.245400  10500\n",
      "1506  1507     pop            51         1431   87770                1  44.508839  11.469080   7450\n",
      "1507  1508  lounge            51          701   17324                1  45.556549   9.534470   9950\n",
      "1508  1509  lounge            51          701   43379                1  45.584091   9.270960   9850\n",
      "1509  1510     pop            51         2982   89000                1  45.609531  10.208570   6400\n",
      "1510  1511     pop            51         4139  136000                1  41.114262  16.485710   4100\n",
      "1511  1512  lounge            51         2982  127000                1  45.512051  10.427010   5450\n",
      "1512  1513  lounge            62         3439   79000                1  44.884979   7.332200   6500\n",
      "1513  1514     pop            51         1066   52700                1  45.354389  11.869260   9000\n",
      "1514  1515     pop            51         1066   80800                1  37.230339  14.523400   7990\n",
      "1515  1516  lounge            51         1917  124999                1  45.564491  10.115610   6999\n",
      "1516  1517     pop            73         3865   80500                1  40.704109  17.340059   8500\n",
      "1517  1518     pop            51          366   16100                1  44.692520  10.103960  10900\n",
      "1518  1519  lounge            51          397   16053                1  38.122070  13.361120  10500\n",
      "1519  1520  lounge            51          670   30000                1  45.764648   8.994500  10800\n",
      "1520  1521  lounge            51         1035   15000                1  41.903221  12.495650  10990\n",
      "1521  1522  lounge            51         3774   85000                1  44.294300   9.674440   4000\n",
      "1522  1523  lounge            51          366   14618                1  45.707249  11.477600  10500\n",
      "1523  1524     pop            51         2251   79800                1  45.512051  10.427010   6450\n",
      "1524  1525     pop            51         2192   53300                1  40.609531  14.980930   7900\n",
      "1525  1526  lounge            51          790   41870                1  45.707249  11.477600   9500\n",
      "1526  1527  lounge            51         1705   23600                1  38.122070  13.361120   9300\n",
      "1527  1528     pop            51          517    3000                1  40.748241  14.528350   9999\n",
      "1528  1529  lounge            51         2861  126000                1  43.841980  10.515310   5500\n",
      "1529  1530  lounge            51          731   22551                1  38.122070  13.361120   9900\n",
      "1530  1531  lounge            51          670   29000                1  45.764648   8.994500  10800\n",
      "1531  1532   sport            73         4505  127000                1  45.528511   9.593230   4750\n",
      "1532  1533     pop            51         1917   52008                1  45.548000  11.549470   9900\n",
      "1533  1534   sport            51         3712  115280                1  45.069679   7.704920   5200\n",
      "1534  1535  lounge            74         3835  112000                1  45.845692   8.666870   4600\n",
      "1535  1536     pop            51         2223   60457                1  45.481541   9.413480   7500\n",
      "1536  1537  lounge            51         2557   80750                1  45.000702   7.682270   5990\n",
      "1537  1538     pop            51         1766   54276                1  40.323410  17.568270   7900\n"
     ]
    }
   ],
   "source": [
    "print(df.to_string( ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e9c750c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1538, 9)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "70e38b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "null= df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b2994826",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                 0\n",
       "model              0\n",
       "engine_power       0\n",
       "age_in_days        0\n",
       "km                 0\n",
       "previous_owners    0\n",
       "lat                0\n",
       "lon                0\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "344b0bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "na= df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a5538d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                 0\n",
       "model              0\n",
       "engine_power       0\n",
       "age_in_days        0\n",
       "km                 0\n",
       "previous_owners    0\n",
       "lat                0\n",
       "lon                0\n",
       "price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "na.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "81d4ba80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                 1538\n",
       "model              1538\n",
       "engine_power       1538\n",
       "age_in_days        1538\n",
       "km                 1538\n",
       "previous_owners    1538\n",
       "lat                1538\n",
       "lon                1538\n",
       "price              1538\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3d470861",
   "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>ID</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034059</td>\n",
       "      <td>-0.060753</td>\n",
       "      <td>-0.006537</td>\n",
       "      <td>0.007803</td>\n",
       "      <td>-0.058207</td>\n",
       "      <td>0.058941</td>\n",
       "      <td>0.028516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_power</th>\n",
       "      <td>-0.034059</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.319190</td>\n",
       "      <td>0.285495</td>\n",
       "      <td>-0.005030</td>\n",
       "      <td>0.005721</td>\n",
       "      <td>-0.005032</td>\n",
       "      <td>-0.277235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>-0.060753</td>\n",
       "      <td>0.319190</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833890</td>\n",
       "      <td>0.075775</td>\n",
       "      <td>0.062982</td>\n",
       "      <td>-0.042667</td>\n",
       "      <td>-0.893328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>-0.006537</td>\n",
       "      <td>0.285495</td>\n",
       "      <td>0.833890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097539</td>\n",
       "      <td>0.035519</td>\n",
       "      <td>0.004839</td>\n",
       "      <td>-0.859373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_owners</th>\n",
       "      <td>0.007803</td>\n",
       "      <td>-0.005030</td>\n",
       "      <td>0.075775</td>\n",
       "      <td>0.097539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>-0.026836</td>\n",
       "      <td>-0.076274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>-0.058207</td>\n",
       "      <td>0.005721</td>\n",
       "      <td>0.062982</td>\n",
       "      <td>0.035519</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.766646</td>\n",
       "      <td>-0.011733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>0.058941</td>\n",
       "      <td>-0.005032</td>\n",
       "      <td>-0.042667</td>\n",
       "      <td>0.004839</td>\n",
       "      <td>-0.026836</td>\n",
       "      <td>-0.766646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.028516</td>\n",
       "      <td>-0.277235</td>\n",
       "      <td>-0.893328</td>\n",
       "      <td>-0.859373</td>\n",
       "      <td>-0.076274</td>\n",
       "      <td>-0.011733</td>\n",
       "      <td>-0.003541</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       ID  engine_power  age_in_days        km  \\\n",
       "ID               1.000000     -0.034059    -0.060753 -0.006537   \n",
       "engine_power    -0.034059      1.000000     0.319190  0.285495   \n",
       "age_in_days     -0.060753      0.319190     1.000000  0.833890   \n",
       "km              -0.006537      0.285495     0.833890  1.000000   \n",
       "previous_owners  0.007803     -0.005030     0.075775  0.097539   \n",
       "lat             -0.058207      0.005721     0.062982  0.035519   \n",
       "lon              0.058941     -0.005032    -0.042667  0.004839   \n",
       "price            0.028516     -0.277235    -0.893328 -0.859373   \n",
       "\n",
       "                 previous_owners       lat       lon     price  \n",
       "ID                      0.007803 -0.058207  0.058941  0.028516  \n",
       "engine_power           -0.005030  0.005721 -0.005032 -0.277235  \n",
       "age_in_days             0.075775  0.062982 -0.042667 -0.893328  \n",
       "km                      0.097539  0.035519  0.004839 -0.859373  \n",
       "previous_owners         1.000000  0.001697 -0.026836 -0.076274  \n",
       "lat                     0.001697  1.000000 -0.766646 -0.011733  \n",
       "lon                    -0.026836 -0.766646  1.000000 -0.003541  \n",
       "price                  -0.076274 -0.011733 -0.003541  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "541500ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df.drop(columns=['ID','model','lat','lon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "53f93d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>engine_power</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>882</td>\n",
       "      <td>25000</td>\n",
       "      <td>1</td>\n",
       "      <td>8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>1186</td>\n",
       "      <td>32500</td>\n",
       "      <td>1</td>\n",
       "      <td>8800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74</td>\n",
       "      <td>4658</td>\n",
       "      <td>142228</td>\n",
       "      <td>1</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>2739</td>\n",
       "      <td>160000</td>\n",
       "      <td>1</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73</td>\n",
       "      <td>3074</td>\n",
       "      <td>106880</td>\n",
       "      <td>1</td>\n",
       "      <td>5700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>51</td>\n",
       "      <td>3712</td>\n",
       "      <td>115280</td>\n",
       "      <td>1</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>74</td>\n",
       "      <td>3835</td>\n",
       "      <td>112000</td>\n",
       "      <td>1</td>\n",
       "      <td>4600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1535</th>\n",
       "      <td>51</td>\n",
       "      <td>2223</td>\n",
       "      <td>60457</td>\n",
       "      <td>1</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>51</td>\n",
       "      <td>2557</td>\n",
       "      <td>80750</td>\n",
       "      <td>1</td>\n",
       "      <td>5990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>51</td>\n",
       "      <td>1766</td>\n",
       "      <td>54276</td>\n",
       "      <td>1</td>\n",
       "      <td>7900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1538 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      engine_power  age_in_days      km  previous_owners  price\n",
       "0               51          882   25000                1   8900\n",
       "1               51         1186   32500                1   8800\n",
       "2               74         4658  142228                1   4200\n",
       "3               51         2739  160000                1   6000\n",
       "4               73         3074  106880                1   5700\n",
       "...            ...          ...     ...              ...    ...\n",
       "1533            51         3712  115280                1   5200\n",
       "1534            74         3835  112000                1   4600\n",
       "1535            51         2223   60457                1   7500\n",
       "1536            51         2557   80750                1   5990\n",
       "1537            51         1766   54276                1   7900\n",
       "\n",
       "[1538 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cf07cb75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>engine_power</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>engine_power</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.319190</td>\n",
       "      <td>0.285495</td>\n",
       "      <td>-0.005030</td>\n",
       "      <td>-0.277235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_in_days</th>\n",
       "      <td>0.319190</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.833890</td>\n",
       "      <td>0.075775</td>\n",
       "      <td>-0.893328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>km</th>\n",
       "      <td>0.285495</td>\n",
       "      <td>0.833890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097539</td>\n",
       "      <td>-0.859373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous_owners</th>\n",
       "      <td>-0.005030</td>\n",
       "      <td>0.075775</td>\n",
       "      <td>0.097539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.277235</td>\n",
       "      <td>-0.893328</td>\n",
       "      <td>-0.859373</td>\n",
       "      <td>-0.076274</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 engine_power  age_in_days        km  previous_owners  \\\n",
       "engine_power         1.000000     0.319190  0.285495        -0.005030   \n",
       "age_in_days          0.319190     1.000000  0.833890         0.075775   \n",
       "km                   0.285495     0.833890  1.000000         0.097539   \n",
       "previous_owners     -0.005030     0.075775  0.097539         1.000000   \n",
       "price               -0.277235    -0.893328 -0.859373        -0.076274   \n",
       "\n",
       "                    price  \n",
       "engine_power    -0.277235  \n",
       "age_in_days     -0.893328  \n",
       "km              -0.859373  \n",
       "previous_owners -0.076274  \n",
       "price            1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e6fd9547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['lounge', 'pop', 'sport'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['model'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2b4c97a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(86, 9)\n",
      "(1094, 9)\n",
      "(358, 9)\n"
     ]
    }
   ],
   "source": [
    "dfsport = df.loc[df['model'] == 'sport']\n",
    "dflounge = df.loc[df['model'] == 'lounge']\n",
    "dfpop = df.loc[df['model'] == 'pop']\n",
    "print(dfsport.shape)\n",
    "print(dflounge.shape)\n",
    "print(dfpop.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "467d8ffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0], dtype=int64),)\n"
     ]
    }
   ],
   "source": [
    "print(np.where(dfsport['age_in_days'].max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "124ab4c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4658\n",
      "190000\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print(dfsport['age_in_days'].max())\n",
    "print(dfsport['km'].max())\n",
    "print(dfsport['previous_owners'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d6397961",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "517\n",
      "6200\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(dfsport['age_in_days'].min())\n",
    "print(dfsport['km'].min())\n",
    "print(dfsport['previous_owners'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "768d8a04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3310.3720930232557\n",
      "103382.80232558139\n",
      "1.2674418604651163\n"
     ]
    }
   ],
   "source": [
    "print(dfsport['age_in_days'].mean())\n",
    "print(dfsport['km'].mean())\n",
    "print(dfsport['previous_owners'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8e869523",
   "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>ID</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>724.406977</td>\n",
       "      <td>55.465116</td>\n",
       "      <td>3310.372093</td>\n",
       "      <td>103382.802326</td>\n",
       "      <td>1.267442</td>\n",
       "      <td>43.799563</td>\n",
       "      <td>11.436682</td>\n",
       "      <td>6275.406977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>489.008610</td>\n",
       "      <td>8.210034</td>\n",
       "      <td>1165.134994</td>\n",
       "      <td>43041.072473</td>\n",
       "      <td>0.602435</td>\n",
       "      <td>2.126544</td>\n",
       "      <td>2.650824</td>\n",
       "      <td>1916.322860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>517.000000</td>\n",
       "      <td>6200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.112740</td>\n",
       "      <td>7.330990</td>\n",
       "      <td>3800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>227.750000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>3020.750000</td>\n",
       "      <td>72425.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.757082</td>\n",
       "      <td>9.205970</td>\n",
       "      <td>4892.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>765.500000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>3684.000000</td>\n",
       "      <td>102400.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.013901</td>\n",
       "      <td>11.330880</td>\n",
       "      <td>5800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1082.000000</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>4139.000000</td>\n",
       "      <td>129973.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.526340</td>\n",
       "      <td>13.233512</td>\n",
       "      <td>6900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1534.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>4658.000000</td>\n",
       "      <td>190000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>46.792019</td>\n",
       "      <td>18.168011</td>\n",
       "      <td>10800.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID  engine_power  age_in_days             km  previous_owners  \\\n",
       "count    86.000000     86.000000    86.000000      86.000000        86.000000   \n",
       "mean    724.406977     55.465116  3310.372093  103382.802326         1.267442   \n",
       "std     489.008610      8.210034  1165.134994   43041.072473         0.602435   \n",
       "min       3.000000     51.000000   517.000000    6200.000000         1.000000   \n",
       "25%     227.750000     51.000000  3020.750000   72425.000000         1.000000   \n",
       "50%     765.500000     51.000000  3684.000000  102400.000000         1.000000   \n",
       "75%    1082.000000     56.250000  4139.000000  129973.000000         1.000000   \n",
       "max    1534.000000     74.000000  4658.000000  190000.000000         4.000000   \n",
       "\n",
       "             lat        lon         price  \n",
       "count  86.000000  86.000000     86.000000  \n",
       "mean   43.799563  11.436682   6275.406977  \n",
       "std     2.126544   2.650824   1916.322860  \n",
       "min    38.112740   7.330990   3800.000000  \n",
       "25%    41.757082   9.205970   4892.500000  \n",
       "50%    45.013901  11.330880   5800.000000  \n",
       "75%    45.526340  13.233512   6900.000000  \n",
       "max    46.792019  18.168011  10800.000000  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsport.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e6e0b4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f35ec64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "db07df5a",
   "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>ID</th>\n",
       "      <th>model</th>\n",
       "      <th>engine_power</th>\n",
       "      <th>age_in_days</th>\n",
       "      <th>km</th>\n",
       "      <th>previous_owners</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>sport</td>\n",
       "      <td>74</td>\n",
       "      <td>4658</td>\n",
       "      <td>142228</td>\n",
       "      <td>1</td>\n",
       "      <td>45.503300</td>\n",
       "      <td>11.41784</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>sport</td>\n",
       "      <td>73</td>\n",
       "      <td>4049</td>\n",
       "      <td>76000</td>\n",
       "      <td>1</td>\n",
       "      <td>45.548000</td>\n",
       "      <td>11.54947</td>\n",
       "      <td>5600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>3653</td>\n",
       "      <td>89000</td>\n",
       "      <td>1</td>\n",
       "      <td>45.438301</td>\n",
       "      <td>10.99170</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>3531</td>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.976452</td>\n",
       "      <td>14.17228</td>\n",
       "      <td>4950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>3927</td>\n",
       "      <td>140000</td>\n",
       "      <td>2</td>\n",
       "      <td>40.755932</td>\n",
       "      <td>14.69019</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>1499</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>1400</td>\n",
       "      <td>60000</td>\n",
       "      <td>1</td>\n",
       "      <td>45.802021</td>\n",
       "      <td>9.18779</td>\n",
       "      <td>10800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1501</th>\n",
       "      <td>1502</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>3774</td>\n",
       "      <td>161900</td>\n",
       "      <td>1</td>\n",
       "      <td>44.906120</td>\n",
       "      <td>7.67723</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1504</th>\n",
       "      <td>1505</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>4322</td>\n",
       "      <td>95000</td>\n",
       "      <td>1</td>\n",
       "      <td>40.928501</td>\n",
       "      <td>14.20367</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>1532</td>\n",
       "      <td>sport</td>\n",
       "      <td>73</td>\n",
       "      <td>4505</td>\n",
       "      <td>127000</td>\n",
       "      <td>1</td>\n",
       "      <td>45.528511</td>\n",
       "      <td>9.59323</td>\n",
       "      <td>4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1533</th>\n",
       "      <td>1534</td>\n",
       "      <td>sport</td>\n",
       "      <td>51</td>\n",
       "      <td>3712</td>\n",
       "      <td>115280</td>\n",
       "      <td>1</td>\n",
       "      <td>45.069679</td>\n",
       "      <td>7.70492</td>\n",
       "      <td>5200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  model  engine_power  age_in_days      km  previous_owners  \\\n",
       "2        3  sport            74         4658  142228                1   \n",
       "8        9  sport            73         4049   76000                1   \n",
       "9       10  sport            51         3653   89000                1   \n",
       "27      28  sport            51         3531  100000                1   \n",
       "33      34  sport            51         3927  140000                2   \n",
       "...    ...    ...           ...          ...     ...              ...   \n",
       "1498  1499  sport            51         1400   60000                1   \n",
       "1501  1502  sport            51         3774  161900                1   \n",
       "1504  1505  sport            51         4322   95000                1   \n",
       "1531  1532  sport            73         4505  127000                1   \n",
       "1533  1534  sport            51         3712  115280                1   \n",
       "\n",
       "            lat       lon  price  \n",
       "2     45.503300  11.41784   4200  \n",
       "8     45.548000  11.54947   5600  \n",
       "9     45.438301  10.99170   6000  \n",
       "27    40.976452  14.17228   4950  \n",
       "33    40.755932  14.69019   5200  \n",
       "...         ...       ...    ...  \n",
       "1498  45.802021   9.18779  10800  \n",
       "1501  44.906120   7.67723   4500  \n",
       "1504  40.928501  14.20367   4500  \n",
       "1531  45.528511   9.59323   4750  \n",
       "1533  45.069679   7.70492   5200  \n",
       "\n",
       "[86 rows x 9 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfsport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e61f90d9",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dfsport' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOULIS~1\\AppData\\Local\\Temp/ipykernel_40008/925246444.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mdfsport\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ID'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'model'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'engine_power'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'lat'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'lon'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdfsport\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dfsport' is not defined"
     ]
    }
   ],
   "source": [
    "X =dfsport.drop(columns=['ID','model','engine_power','lat','lon','price'])\n",
    "Y = dfsport['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9d99b10f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOULIS~1\\AppData\\Local\\Temp/ipykernel_40008/295352597.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2f95b1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8017.82458566, 7910.56801183])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans = model.predict([[2190,70000,1],[2190,70000,2]])\n",
    "ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fc74eb27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the age of the car : 4658\n",
      "Enter the no of km : 142228\n",
      "Enter no of owners : 1\n",
      "Price :  [4258.47604219]\n"
     ]
    }
   ],
   "source": [
    "age = int(input(\"Enter the age of the car : \"))\n",
    "km = int(input(\"Enter the no of km : \"))\n",
    "owner = int(input(\"Enter no of owners : \"))\n",
    "price = model.predict([[age,km,owner]])\n",
    "print(\"Price : \",price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "edadfd0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the number of cars: 1\n",
      "Enter the car type:pop\n",
      "Enter the age pop the car : 4563\n",
      "Enter the no of km : 5896321\n",
      "Enter no of owners : 1\n",
      "Price : -64776 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "a= int(input(\"Enter the number of cars: \"))\n",
    "for i in range(1,a+1):\n",
    "    car_type = input(\"Enter the car type:\")\n",
    "    if(car_type ==\"sport\"):\n",
    "        X =dfsport.drop(columns=['ID','model','engine_power','lat','lon','price'])\n",
    "        Y = dfsport['price']\n",
    "        age = int(input(\"Enter the age of sport car : \"))\n",
    "        km = int(input(\"Enter the no of km : \"))\n",
    "        owner = int(input(\"Enter no of owners : \"))\n",
    "        price = model.predict([[age,km,owner]])\n",
    "        print(\"Price : \",price)\n",
    "        print()\n",
    "    elif(car_type ==\"lounge\"):\n",
    "        X =dflounge.drop(columns=['ID','model','engine_power','lat','lon','price'])\n",
    "        Y = dflounge['price']\n",
    "        age = int(input(\"Enter the age of lounge car : \"))\n",
    "        km = int(input(\"Enter the no of km : \"))\n",
    "        owner = int(input(\"Enter no of owners : \"))\n",
    "        price = model.predict([[age,km,owner]])\n",
    "        print(\"Price : \",price)\n",
    "        print()\n",
    "    elif(car_type ==\"pop\"):\n",
    "        X =dfpop.drop(columns=['ID','model','engine_power','lat','lon','price'])\n",
    "        Y = dfpop['price']\n",
    "        age = int(input(\"Enter the age pop the car : \"))\n",
    "        km = int(input(\"Enter the no of km : \"))\n",
    "        owner = int(input(\"Enter no of owners : \"))\n",
    "        price = model.predict([[age,km,owner]])\n",
    "        print(\"Price : %d \"%price)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "86bd4aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:2\n",
      "enter a valid car type\n",
      "Model:pop\n"
     ]
    }
   ],
   "source": [
    "car_type = input(\"Model:\")\n",
    "if(car_type != \"sport\" and car_type!= \"lounge\" and car_type!=\"pop\"):\n",
    "    print(\"enter a valid car type\")\n",
    "else:\n",
    "    a= int(input(\"Enter the number of cars: \"))\n",
    "    list = []\n",
    "    for i in range(1,a+1):\n",
    "        if(car_type ==\"sport\"):\n",
    "            X =dfsport.drop(columns=['ID','model','engine_power','lat','lon','price'])\n",
    "            Y = dfsport['price']\n",
    "            age = int(input(f\"Enter the age of sport car{i} : \"))\n",
    "            km = int(input(\"Enter the no of km : \"))\n",
    "            owner = int(input(\"Enter no of owners : \"))\n",
    "            price = model.predict([[age,km,owner]])\n",
    "            list.append(price[0])\n",
    "        elif(car_type ==\"lounge\"):\n",
    "            X =dflounge.drop(columns=['ID','model','engine_power','lat','lon','price'])\n",
    "            Y = dflounge['price']\n",
    "            age = int(input(\"Enter the age of lounge car : \"))\n",
    "            km = int(input(\"Enter the no of km : \"))\n",
    "            owner = int(input(\"Enter no of owners : \"))\n",
    "            price = model.predict([[age,km,owner]])\n",
    "            list.append(price[0])\n",
    "        elif(car_type ==\"pop\"):\n",
    "            X =dfpop.drop(columns=['ID','model','engine_power','lat','lon','price'])\n",
    "            Y = dfpop['price']\n",
    "            age = int(input(\"Enter the age pop the car : \"))\n",
    "            km = int(input(\"Enter the no of km : \"))\n",
    "            owner = int(input(\"Enter no of owners : \"))\n",
    "            price = model.predict([[age,km,owner]])\n",
    "            list.append(price[0])\n",
    "    for j in range (1,a+1):\n",
    "        print(list[j-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "a25885f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      age_in_days      km  previous_owners  model_lounge  model_pop  model_sport\n",
      "0             882   25000                1             1          0            0\n",
      "1            1186   32500                1             0          1            0\n",
      "2            4658  142228                1             0          0            1\n",
      "3            2739  160000                1             1          0            0\n",
      "4            3074  106880                1             0          1            0\n",
      "5            3623   70225                1             0          1            0\n",
      "6             731   11600                1             1          0            0\n",
      "7            1521   49076                1             1          0            0\n",
      "8            4049   76000                1             0          0            1\n",
      "9            3653   89000                1             0          0            1\n",
      "10            790   43286                1             0          1            0\n",
      "11            366   17500                1             1          0            0\n",
      "12            456   18450                1             1          0            0\n",
      "13           3835  120000                1             0          1            0\n",
      "14           1035   40500                1             1          0            0\n",
      "15           1096   28200                1             1          0            0\n",
      "16           4200  110000                1             1          0            0\n",
      "17           2223   96848                1             0          1            0\n",
      "18           2861   31000                1             1          0            0\n",
      "19            425   20030                1             1          0            0\n",
      "20            397   19037                1             1          0            0\n",
      "21           1886  110000                1             1          0            0\n",
      "22           1035    8000                1             1          0            0\n",
      "23            790   27595                1             1          0            0\n",
      "24           1583   14900                1             1          0            0\n",
      "25            366    9218                1             1          0            0\n",
      "26           3592  124000                1             0          1            0\n",
      "27           3531  100000                1             0          0            1\n",
      "28            762   28900                1             1          0            0\n",
      "29            670    4000                1             1          0            0\n",
      "30           2769   59216                1             1          0            0\n",
      "31           4169   99477                2             1          0            0\n",
      "32            821   21730                2             1          0            0\n",
      "33           3927  140000                2             0          0            1\n",
      "34            640   32033                2             1          0            0\n",
      "35           3653  138116                2             0          1            0\n",
      "36            852   17000                1             0          1            0\n",
      "37           3013   58527                1             0          1            0\n",
      "38            790   43100                1             0          0            1\n",
      "39           1858   13373                1             0          1            0\n",
      "40           4139  119000                1             0          0            1\n",
      "41            609   28500                1             0          1            0\n",
      "42           1096   83000                1             0          1            0\n",
      "43           4049   98000                1             1          0            0\n",
      "44            456   12693                1             1          0            0\n",
      "45            762   14586                1             1          0            0\n",
      "46            821   27640                1             1          0            0\n",
      "47           2039   49000                1             1          0            0\n",
      "48           3684  160000                1             0          0            1\n",
      "49           4596  107000                1             0          0            1\n",
      "50           2192   66000                1             1          0            0\n",
      "51            790   35000                1             0          0            1\n",
      "52           1186   24651                1             0          1            0\n",
      "53           3288   71900                1             0          0            1\n",
      "54            701   11200                1             1          0            0\n",
      "55           3288  124050                1             1          0            0\n",
      "56           1066   69395                1             0          1            0\n",
      "57            456   17961                1             1          0            0\n",
      "58            670   18669                1             1          0            0\n",
      "59           1431   22700                1             0          1            0\n",
      "60           4169   56000                1             1          0            0\n",
      "61            701   22669                1             1          0            0\n",
      "62           2557   50000                1             0          1            0\n",
      "63           2343   39743                1             1          0            0\n",
      "64            456   27466                1             1          0            0\n",
      "65            548   22609                1             1          0            0\n",
      "66           1461   94485                1             0          1            0\n",
      "67           4261  188000                1             0          0            1\n",
      "68           2343   94000                1             1          0            0\n",
      "69           2192   60786                1             0          1            0\n",
      "70           1096   41950                1             0          1            0\n",
      "71            456   16971                1             1          0            0\n",
      "72           4108   45300                1             1          0            0\n",
      "73           4108   45300                1             1          0            0\n",
      "74           3227   75359                1             1          0            0\n",
      "75            366   12585                1             1          0            0\n",
      "76           2101  103000                1             0          1            0\n",
      "77           2739   77149                3             0          0            1\n",
      "78            456   13587                3             1          0            0\n",
      "79           1797   55595                3             1          0            0\n",
      "80            397   17250                3             1          0            0\n",
      "81           4292  145000                3             0          0            1\n",
      "82            790   20849                1             1          0            0\n",
      "83           3988  107375                1             1          0            0\n",
      "84           2009  100000                1             0          1            0\n",
      "85            456   17841                1             1          0            0\n",
      "86            762   44778                1             1          0            0\n",
      "87           3258   78681                1             0          1            0\n",
      "88           4474  123000                1             0          1            0\n",
      "89            397   17912                1             1          0            0\n",
      "90           1066   69916                1             1          0            0\n",
      "91           2647   97700                1             1          0            0\n",
      "92            366   11500                1             0          1            0\n",
      "93            397   17250                1             1          0            0\n",
      "94            366   12372                1             1          0            0\n",
      "95           4292  165600                1             0          0            1\n",
      "96           1066   28000                1             0          1            0\n",
      "97           2009   86000                2             0          0            1\n",
      "98            456   18592                2             1          0            0\n",
      "99            731   41558                2             0          1            0\n",
      "100          4018   96000                2             0          1            0\n",
      "101           701   19000                1             1          0            0\n",
      "102           397   18183                1             1          0            0\n",
      "103          1066   69916                1             1          0            0\n",
      "104          2647   75500                1             0          1            0\n",
      "105          1827   39500                1             0          1            0\n",
      "106           397   17250                1             1          0            0\n",
      "107          3835  128000                1             0          1            0\n",
      "108           366   12929                1             0          1            0\n",
      "109          3562  113254                2             1          0            0\n",
      "110           366   18000                1             1          0            0\n",
      "111           397   16643                1             1          0            0\n",
      "112          2892   92300                1             1          0            0\n",
      "113           640   34873                1             1          0            0\n",
      "114          4018  102000                1             0          0            1\n",
      "115           397   16135                1             1          0            0\n",
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      "1519          670   30000                1             1          0            0\n",
      "1520         1035   15000                1             1          0            0\n",
      "1521         3774   85000                1             1          0            0\n",
      "1522          366   14618                1             1          0            0\n",
      "1523         2251   79800                1             0          1            0\n",
      "1524         2192   53300                1             0          1            0\n",
      "1525          790   41870                1             1          0            0\n",
      "1526         1705   23600                1             1          0            0\n",
      "1527          517    3000                1             0          1            0\n",
      "1528         2861  126000                1             1          0            0\n",
      "1529          731   22551                1             1          0            0\n",
      "1530          670   29000                1             1          0            0\n",
      "1531         4505  127000                1             0          0            1\n",
      "1532         1917   52008                1             0          1            0\n",
      "1533         3712  115280                1             0          0            1\n",
      "1534         3835  112000                1             1          0            0\n",
      "1535         2223   60457                1             0          1            0\n",
      "1536         2557   80750                1             1          0            0\n",
      "1537         1766   54276                1             0          1            0\n"
     ]
    }
   ],
   "source": [
    "newdf= df.drop(columns=['ID','engine_power','lat','lon','price'])\n",
    "newdf1 = pd.get_dummies(newdf,dtype= int)\n",
    "newdf2= df['price']\n",
    "print(newdf1.to_string())\n",
    "x1 = newdf1\n",
    "y2 = newdf2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "c2d64a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1=LinearRegression()\n",
    "model1 = model1.fit(x1,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "9f078df5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model: sport\n",
      "Age:2190\n",
      "km: 70000\n",
      "owner:1\n",
      "the price of the sport car is :7880\n"
     ]
    }
   ],
   "source": [
    "type1 = input(\"model: \")\n",
    "age = int(input(\"Age:\"))\n",
    "km = int(input(\"km: \"))\n",
    "owner = int(input(\"owner:\"))\n",
    "if(type1 == \"sport\"):\n",
    "    print(f\"the price of the {type1} car is :\"+str(int(model1.predict([[age,km,owner,1,0,0]])[0])))\n",
    "elif(type1 == \"lounge\"):\n",
    "    print(f\"the price of the {type1} car is :\"+str(int(model1.predict([[age,km,owner,1,0,0]])[0])))\n",
    "elif(type1 == \"pop\"):\n",
    "    print(f\"the price of the {type1} car is :\"+str(int(model1.predict([[age,km,owner,1,0,0]])[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3f9e5cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "6f3f381e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x1,y2,test_size=0.33,random_state = 2)\n",
    "x_train1,x_test1,y_train1,y_test1 = train_test_split(x1,y2,test_size=0.15,random_state = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "567b13d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1030, 6)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "4ca1dc9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(508, 6)\n"
     ]
    }
   ],
   "source": [
    "print(x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "3bac0b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalans = LinearRegression()\n",
    "finalans.fit(x_train,y_train)\n",
    "ans1 = finalans.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "f6004cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalans = LinearRegression()\n",
    "finalans.fit(x_train1,y_train1)\n",
    "ans2 = finalans.predict(x_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "8a116ee0",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Interrupted by user",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\MOULIS~1\\AppData\\Local\\Temp/ipykernel_18280/2065183989.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtype1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"model: \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Age:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mkm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"km: \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mowner\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"owner:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype1\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"sport\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Python Scripts\\lib\\site-packages\\ipykernel\\kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[1;34m(self, prompt)\u001b[0m\n\u001b[0;32m   1004\u001b[0m                 \u001b[1;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1005\u001b[0m             )\n\u001b[1;32m-> 1006\u001b[1;33m         return self._input_request(\n\u001b[0m\u001b[0;32m   1007\u001b[0m             \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1008\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"shell\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Python Scripts\\lib\\site-packages\\ipykernel\\kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[1;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[0;32m   1049\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m                 \u001b[1;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1051\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Interrupted by user\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1052\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid Message:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model: sport\n"
     ]
    }
   ],
   "source": [
    "type1 = input(\"model: \")\n",
    "age = int(input(\"Age:\"))\n",
    "km = int(input(\"km: \"))\n",
    "owner = int(input(\"owner:\"))\n",
    "if(type1 == \"sport\"):\n",
    "    print(f\"the price of the {type1} car is :\"+str(int(finalans.predict([[age,km,owner,0,0,1]])[0])))\n",
    "elif(type1 == \"lounge\"):\n",
    "    print(f\"the price of the {type1} car is :\"+str(int(finalans.predict([[age,km,owner,1,0,0]])[0])))\n",
    "elif(type1 == \"pop\"):\n",
    "    print(f\"the price of the {type1} car is :\"+str(int(finalans.predict([[age,km,owner,0,1,0]])[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "01411746",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "28a92bb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='KM', ylabel='Price'>"
      ]
     },
     "execution_count": 220,
     "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": [
    "result = pd.DataFrame(columns=['KM','Price'])\n",
    "result['KM'] = x_test['km']\n",
    "result['Price'] = ans1\n",
    "sns.lineplot(x='KM',y='Price',data=result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "c993967a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='KM', ylabel='Price'>"
      ]
     },
     "execution_count": 237,
     "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": [
    "result1 = pd.DataFrame(columns=['KM','Price'])\n",
    "result1['KM'] = x_test['km']\n",
    "result1['Price'] = ans1\n",
    "sns.lineplot(x='KM',y='Price',data=result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "93b5c448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='KM', ylabel='Price'>"
      ]
     },
     "execution_count": 245,
     "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": [
    "result = pd.DataFrame(columns=['KM','Price'])\n",
    "result1 = pd.DataFrame(columns=['KM','Price'])\n",
    "result['KM'] = x_test['km']\n",
    "result1['KM'] = x_test1['km']\n",
    "result['Price'] = ans1\n",
    "result1['Price'] = ans2\n",
    "sns.lineplot(x='KM',y='Price',data=result.head(100),label=\"Model1[0.15]\")\n",
    "sns.lineplot(x='KM',y='Price',data=result1.head(100),label=\"model2[0.33]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "055c840c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#in class task 02-02-2024\n",
    "data = pd.read_csv('hire.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "610b6e3a",
   "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>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>five</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10</td>\n",
       "      <td>65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>seven</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>three</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        NaN                    8.0                           9      50000\n",
       "1        NaN                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    NaN                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "37cf7a79",
   "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>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        True                  False                       False      False\n",
       "1        True                  False                       False      False\n",
       "2       False                  False                       False      False\n",
       "3       False                  False                       False      False\n",
       "4       False                  False                       False      False\n",
       "5       False                  False                       False      False\n",
       "6       False                   True                       False      False\n",
       "7       False                  False                       False      False"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e85ffa8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>experience</th>\n",
       "      <th>test_score(out of 10)</th>\n",
       "      <th>interview_score(out of 10)</th>\n",
       "      <th>salary($)</th>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eleven</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        NaN                    8.0                           9      50000\n",
       "1        NaN                    8.0                           6      45000\n",
       "2       five                    6.0                           7      60000\n",
       "3        two                   10.0                          10      65000\n",
       "4      seven                    9.0                           6      70000\n",
       "5      three                    7.0                          10      62000\n",
       "6        ten                    NaN                           7      72000\n",
       "7     eleven                    7.0                           8      80000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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    "data"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eed03d62",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['test_score(out of 10)'].fillna(data['test_score(out of 10)'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2a6d373b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>70000</td>\n",
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       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0        NaN               8.000000                           9      50000\n",
       "1        NaN               8.000000                           6      45000\n",
       "2       five               6.000000                           7      60000\n",
       "3        two              10.000000                          10      65000\n",
       "4      seven               9.000000                           6      70000\n",
       "5      three               7.000000                          10      62000\n",
       "6        ten               7.857143                           7      72000\n",
       "7     eleven               7.000000                           8      80000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "data"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc736a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['experience'].fillna('zero',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27641e3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
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       "      <td>62000</td>\n",
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       "      <th>6</th>\n",
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       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0       zero               8.000000                           9      50000\n",
       "1       zero               8.000000                           6      45000\n",
       "2       five               6.000000                           7      60000\n",
       "3        two              10.000000                          10      65000\n",
       "4      seven               9.000000                           6      70000\n",
       "5      three               7.000000                          10      62000\n",
       "6        ten               7.857143                           7      72000\n",
       "7     eleven               7.000000                           8      80000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25cee1c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: word2number in c:\\users\\mouliswaran\\documents\\python scripts\\lib\\site-packages (1.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install word2number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90c9285f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from word2number import w2n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2420740d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.experience = data.experience.apply(w2n.word_to_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da66dbdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
       "      <td>10</td>\n",
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       "      <td>7</td>\n",
       "      <td>72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
       "0           0               8.000000                           9      50000\n",
       "1           0               8.000000                           6      45000\n",
       "2           5               6.000000                           7      60000\n",
       "3           2              10.000000                          10      65000\n",
       "4           7               9.000000                           6      70000\n",
       "5           3               7.000000                          10      62000\n",
       "6          10               7.857143                           7      72000\n",
       "7          11               7.000000                           8      80000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9acc1c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "76ec0299",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=data.drop(columns=['salary($)'])\n",
    "y=data['salary($)']\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f100f8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total experience: 2\n",
      "tst_score: 9\n",
      "interview score : 6\n",
      "Salary : 53290\n"
     ]
    }
   ],
   "source": [
    "exp = int(input('total experience: '))\n",
    "tst = float(input('tst_score: '))\n",
    "inter = int(input('interview score : '))\n",
    "ans = model.predict([[exp,tst,inter]])[0]\n",
    "print(\"Salary : %d\"%ans)"
   ]
  }
 ],
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