car-price-prediction.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:01.859028Z","iopub.execute_input":"2023-08-11T01:44:01.859415Z","iopub.status.idle":"2023-08-11T01:44:01.864892Z","shell.execute_reply.started":"2023-08-11T01:44:01.859384Z","shell.execute_reply":"2023-08-11T01:44:01.863575Z"},"editable":false,"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"car = pd.read_csv('/kaggle/input/used-car-auction-prices/car_prices.csv', error_bad_lines=False)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:01.882898Z","iopub.execute_input":"2023-08-11T01:44:01.883838Z","iopub.status.idle":"2023-08-11T01:44:05.250181Z","shell.execute_reply.started":"2023-08-11T01:44:01.883798Z","shell.execute_reply":"2023-08-11T01:44:05.249142Z"},"editable":false,"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_32/829825274.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n\n\n  car = pd.read_csv('/kaggle/input/used-car-auction-prices/car_prices.csv', error_bad_lines=False)\nSkipping line 408163: expected 16 fields, saw 17\nSkipping line 417837: expected 16 fields, saw 17\nSkipping line 421291: expected 16 fields, saw 17\nSkipping line 424163: expected 16 fields, saw 17\n\nSkipping line 427042: expected 16 fields, saw 17\nSkipping line 427045: expected 16 fields, saw 17\nSkipping line 434426: expected 16 fields, saw 17\nSkipping line 444503: expected 16 fields, saw 17\nSkipping line 453796: expected 16 fields, saw 17\n\nSkipping line 461599: expected 16 fields, saw 17\nSkipping line 461614: expected 16 fields, saw 17\n\nSkipping line 492486: expected 16 fields, saw 17\nSkipping line 497010: expected 16 fields, saw 17\nSkipping line 497013: expected 16 fields, saw 17\nSkipping line 499085: expected 16 fields, saw 17\nSkipping line 501457: expected 16 fields, saw 17\nSkipping line 505301: expected 16 fields, saw 17\nSkipping line 505308: expected 16 fields, saw 17\nSkipping line 520463: expected 16 fields, saw 17\n\nSkipping line 528998: expected 16 fields, saw 17\nSkipping line 529011: expected 16 fields, saw 17\nSkipping line 529015: expected 16 fields, saw 17\nSkipping line 529624: expected 16 fields, saw 17\nSkipping line 548786: expected 16 fields, saw 17\nSkipping line 551224: expected 16 fields, saw 17\nSkipping line 554712: expected 16 fields, saw 17\n\n","output_type":"stream"}]},{"cell_type":"code","source":"car","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.252345Z","iopub.execute_input":"2023-08-11T01:44:05.253048Z","iopub.status.idle":"2023-08-11T01:44:05.297567Z","shell.execute_reply.started":"2023-08-11T01:44:05.253004Z","shell.execute_reply":"2023-08-11T01:44:05.296513Z"},"editable":false,"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"        year    make                model         trim       body  \\\n0       2015     Kia              Sorento           LX        SUV   \n1       2015     Kia              Sorento           LX        SUV   \n2       2014     BMW             3 Series   328i SULEV      Sedan   \n3       2015   Volvo                  S60           T5      Sedan   \n4       2014     BMW  6 Series Gran Coupe         650i      Sedan   \n...      ...     ...                  ...          ...        ...   \n558806  2015     Kia                 K900       Luxury      Sedan   \n558807  2012     Ram                 2500  Power Wagon   Crew Cab   \n558808  2012     BMW                   X5    xDrive35d        SUV   \n558809  2015  Nissan               Altima        2.5 S      sedan   \n558810  2014    Ford                F-150          XLT  SuperCrew   \n\n       transmission                vin state  condition  odometer   color  \\\n0         automatic  5xyktca69fg566472    ca        5.0   16639.0   white   \n1         automatic  5xyktca69fg561319    ca        5.0    9393.0   white   \n2         automatic  wba3c1c51ek116351    ca        4.5    1331.0    gray   \n3         automatic  yv1612tb4f1310987    ca        4.1   14282.0   white   \n4         automatic  wba6b2c57ed129731    ca        4.3    2641.0    gray   \n...             ...                ...   ...        ...       ...     ...   \n558806          NaN  knalw4d4xf6019304    in        4.5   18255.0  silver   \n558807    automatic  3c6td5et6cg112407    wa        5.0   54393.0   white   \n558808    automatic  5uxzw0c58cl668465    ca        4.8   50561.0   black   \n558809    automatic  1n4al3ap0fc216050    ga        3.8   16658.0   white   \n558810    automatic  1ftfw1et2eke87277    ca        3.4   15008.0    gray   \n\n       interior                                             seller    mmr  \\\n0         black                            kia motors america, inc  20500   \n1         beige                            kia motors america, inc  20800   \n2         black             financial services remarketing (lease)  31900   \n3         black                            volvo na rep/world omni  27500   \n4         black             financial services remarketing (lease)  66000   \n...         ...                                                ...    ...   \n558806    black                                   avis corporation  35300   \n558807    black                                    i -5 uhlmann rv  30200   \n558808    black             financial services remarketing (lease)  29800   \n558809    black  enterprise vehicle exchange / tra / rental / t...  15100   \n558810     gray                   ford motor credit company,llc pd  29600   \n\n        sellingprice                                 saledate  \n0              21500  Tue Dec 16 2014 12:30:00 GMT-0800 (PST)  \n1              21500  Tue Dec 16 2014 12:30:00 GMT-0800 (PST)  \n2              30000  Thu Jan 15 2015 04:30:00 GMT-0800 (PST)  \n3              27750  Thu Jan 29 2015 04:30:00 GMT-0800 (PST)  \n4              67000  Thu Dec 18 2014 12:30:00 GMT-0800 (PST)  \n...              ...                                      ...  \n558806         33000  Thu Jul 09 2015 07:00:00 GMT-0700 (PDT)  \n558807         30800  Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)  \n558808         34000  Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)  \n558809         11100  Thu Jul 09 2015 06:45:00 GMT-0700 (PDT)  \n558810         26700  Thu May 28 2015 05:30:00 GMT-0700 (PDT)  \n\n[558811 rows x 16 columns]","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>year</th>\n      <th>make</th>\n      <th>model</th>\n      <th>trim</th>\n      <th>body</th>\n      <th>transmission</th>\n      <th>vin</th>\n      <th>state</th>\n      <th>condition</th>\n      <th>odometer</th>\n      <th>color</th>\n      <th>interior</th>\n      <th>seller</th>\n      <th>mmr</th>\n      <th>sellingprice</th>\n      <th>saledate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2015</td>\n      <td>Kia</td>\n      <td>Sorento</td>\n      <td>LX</td>\n      <td>SUV</td>\n      <td>automatic</td>\n      <td>5xyktca69fg566472</td>\n      <td>ca</td>\n      <td>5.0</td>\n      <td>16639.0</td>\n      <td>white</td>\n      <td>black</td>\n      <td>kia motors america, inc</td>\n      <td>20500</td>\n      <td>21500</td>\n      <td>Tue Dec 16 2014 12:30:00 GMT-0800 (PST)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2015</td>\n      <td>Kia</td>\n      <td>Sorento</td>\n      <td>LX</td>\n      <td>SUV</td>\n      <td>automatic</td>\n      <td>5xyktca69fg561319</td>\n      <td>ca</td>\n      <td>5.0</td>\n      <td>9393.0</td>\n      <td>white</td>\n      <td>beige</td>\n      <td>kia motors america, inc</td>\n      <td>20800</td>\n      <td>21500</td>\n      <td>Tue Dec 16 2014 12:30:00 GMT-0800 (PST)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014</td>\n      <td>BMW</td>\n      <td>3 Series</td>\n      <td>328i SULEV</td>\n      <td>Sedan</td>\n      <td>automatic</td>\n      <td>wba3c1c51ek116351</td>\n      <td>ca</td>\n      <td>4.5</td>\n      <td>1331.0</td>\n      <td>gray</td>\n      <td>black</td>\n      <td>financial services remarketing (lease)</td>\n      <td>31900</td>\n      <td>30000</td>\n      <td>Thu Jan 15 2015 04:30:00 GMT-0800 (PST)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2015</td>\n      <td>Volvo</td>\n      <td>S60</td>\n      <td>T5</td>\n      <td>Sedan</td>\n      <td>automatic</td>\n      <td>yv1612tb4f1310987</td>\n      <td>ca</td>\n      <td>4.1</td>\n      <td>14282.0</td>\n      <td>white</td>\n      <td>black</td>\n      <td>volvo na rep/world omni</td>\n      <td>27500</td>\n      <td>27750</td>\n      <td>Thu Jan 29 2015 04:30:00 GMT-0800 (PST)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014</td>\n      <td>BMW</td>\n      <td>6 Series Gran Coupe</td>\n      <td>650i</td>\n      <td>Sedan</td>\n      <td>automatic</td>\n      <td>wba6b2c57ed129731</td>\n      <td>ca</td>\n      <td>4.3</td>\n      <td>2641.0</td>\n      <td>gray</td>\n      <td>black</td>\n      <td>financial services remarketing (lease)</td>\n      <td>66000</td>\n      <td>67000</td>\n      <td>Thu Dec 18 2014 12:30:00 GMT-0800 (PST)</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>558806</th>\n      <td>2015</td>\n      <td>Kia</td>\n      <td>K900</td>\n      <td>Luxury</td>\n      <td>Sedan</td>\n      <td>NaN</td>\n      <td>knalw4d4xf6019304</td>\n      <td>in</td>\n      <td>4.5</td>\n      <td>18255.0</td>\n      <td>silver</td>\n      <td>black</td>\n      <td>avis corporation</td>\n      <td>35300</td>\n      <td>33000</td>\n      <td>Thu Jul 09 2015 07:00:00 GMT-0700 (PDT)</td>\n    </tr>\n    <tr>\n      <th>558807</th>\n      <td>2012</td>\n      <td>Ram</td>\n      <td>2500</td>\n      <td>Power Wagon</td>\n      <td>Crew Cab</td>\n      <td>automatic</td>\n      <td>3c6td5et6cg112407</td>\n      <td>wa</td>\n      <td>5.0</td>\n      <td>54393.0</td>\n      <td>white</td>\n      <td>black</td>\n      <td>i -5 uhlmann rv</td>\n      <td>30200</td>\n      <td>30800</td>\n      <td>Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)</td>\n    </tr>\n    <tr>\n      <th>558808</th>\n      <td>2012</td>\n      <td>BMW</td>\n      <td>X5</td>\n      <td>xDrive35d</td>\n      <td>SUV</td>\n      <td>automatic</td>\n      <td>5uxzw0c58cl668465</td>\n      <td>ca</td>\n      <td>4.8</td>\n      <td>50561.0</td>\n      <td>black</td>\n      <td>black</td>\n      <td>financial services remarketing (lease)</td>\n      <td>29800</td>\n      <td>34000</td>\n      <td>Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)</td>\n    </tr>\n    <tr>\n      <th>558809</th>\n      <td>2015</td>\n      <td>Nissan</td>\n      <td>Altima</td>\n      <td>2.5 S</td>\n      <td>sedan</td>\n      <td>automatic</td>\n      <td>1n4al3ap0fc216050</td>\n      <td>ga</td>\n      <td>3.8</td>\n      <td>16658.0</td>\n      <td>white</td>\n      <td>black</td>\n      <td>enterprise vehicle exchange / tra / rental / t...</td>\n      <td>15100</td>\n      <td>11100</td>\n      <td>Thu Jul 09 2015 06:45:00 GMT-0700 (PDT)</td>\n    </tr>\n    <tr>\n      <th>558810</th>\n      <td>2014</td>\n      <td>Ford</td>\n      <td>F-150</td>\n      <td>XLT</td>\n      <td>SuperCrew</td>\n      <td>automatic</td>\n      <td>1ftfw1et2eke87277</td>\n      <td>ca</td>\n      <td>3.4</td>\n      <td>15008.0</td>\n      <td>gray</td>\n      <td>gray</td>\n      <td>ford motor credit company,llc pd</td>\n      <td>29600</td>\n      <td>26700</td>\n      <td>Thu May 28 2015 05:30:00 GMT-0700 (PDT)</td>\n    </tr>\n  </tbody>\n</table>\n<p>558811 rows × 16 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df = car[['make','condition','odometer', 'mmr', 'sellingprice']]","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.299339Z","iopub.execute_input":"2023-08-11T01:44:05.300318Z","iopub.status.idle":"2023-08-11T01:44:05.318892Z","shell.execute_reply.started":"2023-08-11T01:44:05.300280Z","shell.execute_reply":"2023-08-11T01:44:05.317523Z"},"editable":false,"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.321867Z","iopub.execute_input":"2023-08-11T01:44:05.322211Z","iopub.status.idle":"2023-08-11T01:44:05.340032Z","shell.execute_reply.started":"2023-08-11T01:44:05.322183Z","shell.execute_reply":"2023-08-11T01:44:05.338799Z"},"editable":false,"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"          make  condition  odometer    mmr  sellingprice\n0          Kia        5.0   16639.0  20500         21500\n1          Kia        5.0    9393.0  20800         21500\n2          BMW        4.5    1331.0  31900         30000\n3        Volvo        4.1   14282.0  27500         27750\n4          BMW        4.3    2641.0  66000         67000\n...        ...        ...       ...    ...           ...\n558806     Kia        4.5   18255.0  35300         33000\n558807     Ram        5.0   54393.0  30200         30800\n558808     BMW        4.8   50561.0  29800         34000\n558809  Nissan        3.8   16658.0  15100         11100\n558810    Ford        3.4   15008.0  29600         26700\n\n[558811 rows x 5 columns]","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>make</th>\n      <th>condition</th>\n      <th>odometer</th>\n      <th>mmr</th>\n      <th>sellingprice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Kia</td>\n      <td>5.0</td>\n      <td>16639.0</td>\n      <td>20500</td>\n      <td>21500</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Kia</td>\n      <td>5.0</td>\n      <td>9393.0</td>\n      <td>20800</td>\n      <td>21500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BMW</td>\n      <td>4.5</td>\n      <td>1331.0</td>\n      <td>31900</td>\n      <td>30000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Volvo</td>\n      <td>4.1</td>\n      <td>14282.0</td>\n      <td>27500</td>\n      <td>27750</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BMW</td>\n      <td>4.3</td>\n      <td>2641.0</td>\n      <td>66000</td>\n      <td>67000</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>558806</th>\n      <td>Kia</td>\n      <td>4.5</td>\n      <td>18255.0</td>\n      <td>35300</td>\n      <td>33000</td>\n    </tr>\n    <tr>\n      <th>558807</th>\n      <td>Ram</td>\n      <td>5.0</td>\n      <td>54393.0</td>\n      <td>30200</td>\n      <td>30800</td>\n    </tr>\n    <tr>\n      <th>558808</th>\n      <td>BMW</td>\n      <td>4.8</td>\n      <td>50561.0</td>\n      <td>29800</td>\n      <td>34000</td>\n    </tr>\n    <tr>\n      <th>558809</th>\n      <td>Nissan</td>\n      <td>3.8</td>\n      <td>16658.0</td>\n      <td>15100</td>\n      <td>11100</td>\n    </tr>\n    <tr>\n      <th>558810</th>\n      <td>Ford</td>\n      <td>3.4</td>\n      <td>15008.0</td>\n      <td>29600</td>\n      <td>26700</td>\n    </tr>\n  </tbody>\n</table>\n<p>558811 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df['make'].unique()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.341458Z","iopub.execute_input":"2023-08-11T01:44:05.341781Z","iopub.status.idle":"2023-08-11T01:44:05.422744Z","shell.execute_reply.started":"2023-08-11T01:44:05.341754Z","shell.execute_reply":"2023-08-11T01:44:05.421530Z"},"editable":false,"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array(['Kia', 'BMW', 'Volvo', 'Nissan', 'Chevrolet', 'Audi', 'Ford',\n       'Hyundai', 'Buick', 'Cadillac', 'Acura', 'Lexus', 'Infiniti',\n       'Jeep', 'Mercedes-Benz', 'Mitsubishi', 'Mazda', 'MINI',\n       'Land Rover', 'Lincoln', 'lincoln', 'Jaguar', 'Volkswagen',\n       'Toyota', 'Subaru', 'Scion', 'Porsche', nan, 'bmw', 'Dodge',\n       'FIAT', 'Chrysler', 'ford', 'Ferrari', 'Honda', 'GMC',\n       'mitsubishi', 'Ram', 'smart', 'chevrolet', 'Bentley', 'chrysler',\n       'pontiac', 'Pontiac', 'Saturn', 'Maserati', 'Mercury', 'HUMMER',\n       'landrover', 'cadillac', 'land rover', 'mercedes', 'mazda',\n       'toyota', 'lexus', 'gmc truck', 'honda', 'nissan', 'porsche',\n       'Saab', 'Suzuki', 'dodge', 'subaru', 'Oldsmobile', 'oldsmobile',\n       'hyundai', 'jeep', 'Isuzu', 'dodge tk', 'Geo', 'acura',\n       'volkswagen', 'suzuki', 'kia', 'audi', 'Rolls-Royce', 'gmc',\n       'maserati', 'mazda tk', 'mercury', 'buick', 'hyundai tk',\n       'mercedes-b', 'vw', 'Daewoo', 'chev truck', 'ford tk', 'plymouth',\n       'Plymouth', 'ford truck', 'Tesla', 'airstream', 'dot',\n       'Aston Martin', 'Fisker', 'Lamborghini', 'Lotus'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.424103Z","iopub.execute_input":"2023-08-11T01:44:05.424478Z","iopub.status.idle":"2023-08-11T01:44:05.624207Z","shell.execute_reply.started":"2023-08-11T01:44:05.424423Z","shell.execute_reply":"2023-08-11T01:44:05.622865Z"},"editable":false,"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"make            10301\ncondition       11794\nodometer           94\nmmr                 0\nsellingprice        0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_new = df.sample(1000)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.626156Z","iopub.execute_input":"2023-08-11T01:44:05.627016Z","iopub.status.idle":"2023-08-11T01:44:05.650797Z","shell.execute_reply.started":"2023-08-11T01:44:05.626982Z","shell.execute_reply":"2023-08-11T01:44:05.649664Z"},"editable":false,"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"df_new.info()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.652393Z","iopub.execute_input":"2023-08-11T01:44:05.653190Z","iopub.status.idle":"2023-08-11T01:44:05.678257Z","shell.execute_reply.started":"2023-08-11T01:44:05.653152Z","shell.execute_reply":"2023-08-11T01:44:05.677069Z"},"editable":false,"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 1000 entries, 391600 to 50592\nData columns (total 5 columns):\n #   Column        Non-Null Count  Dtype  \n---  ------        --------------  -----  \n 0   make          979 non-null    object \n 1   condition     987 non-null    float64\n 2   odometer      999 non-null    float64\n 3   mmr           1000 non-null   int64  \n 4   sellingprice  1000 non-null   int64  \ndtypes: float64(2), int64(2), object(1)\nmemory usage: 46.9+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df_new['condition'].unique","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.679677Z","iopub.execute_input":"2023-08-11T01:44:05.679998Z","iopub.status.idle":"2023-08-11T01:44:05.696796Z","shell.execute_reply.started":"2023-08-11T01:44:05.679971Z","shell.execute_reply":"2023-08-11T01:44:05.695530Z"},"editable":false,"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"<bound method Series.unique of 391600    1.9\n354754    1.9\n319290    4.6\n346806    4.5\n14597     3.6\n         ... \n389526    2.0\n378105    3.6\n540451    2.4\n10341     4.3\n50592     3.8\nName: condition, Length: 1000, dtype: float64>"},"metadata":{}}]},{"cell_type":"code","source":"df_new.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.701553Z","iopub.execute_input":"2023-08-11T01:44:05.701950Z","iopub.status.idle":"2023-08-11T01:44:05.714166Z","shell.execute_reply.started":"2023-08-11T01:44:05.701917Z","shell.execute_reply":"2023-08-11T01:44:05.713051Z"},"editable":false,"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"make            21\ncondition       13\nodometer         1\nmmr              0\nsellingprice     0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"# Condition has 19 null values\n# Make means car name has 19 null values\n# working on only numerical data","metadata":{"editable":false}},{"cell_type":"markdown","source":"","metadata":{"editable":false}},{"cell_type":"code","source":"backup = df_new.copy()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.716148Z","iopub.execute_input":"2023-08-11T01:44:05.716617Z","iopub.status.idle":"2023-08-11T01:44:05.726496Z","shell.execute_reply.started":"2023-08-11T01:44:05.716574Z","shell.execute_reply":"2023-08-11T01:44:05.725293Z"},"editable":false,"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"df_new.isnull().sum()\n","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.727598Z","iopub.execute_input":"2023-08-11T01:44:05.728003Z","iopub.status.idle":"2023-08-11T01:44:05.747042Z","shell.execute_reply.started":"2023-08-11T01:44:05.727965Z","shell.execute_reply":"2023-08-11T01:44:05.745805Z"},"editable":false,"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"make            21\ncondition       13\nodometer         1\nmmr              0\nsellingprice     0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_new = df_new.dropna(axis=0)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.748170Z","iopub.execute_input":"2023-08-11T01:44:05.749130Z","iopub.status.idle":"2023-08-11T01:44:05.759845Z","shell.execute_reply.started":"2023-08-11T01:44:05.749095Z","shell.execute_reply":"2023-08-11T01:44:05.758210Z"},"editable":false,"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"df_new.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.761964Z","iopub.execute_input":"2023-08-11T01:44:05.762373Z","iopub.status.idle":"2023-08-11T01:44:05.778179Z","shell.execute_reply.started":"2023-08-11T01:44:05.762336Z","shell.execute_reply":"2023-08-11T01:44:05.776863Z"},"editable":false,"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"make            0\ncondition       0\nodometer        0\nmmr             0\nsellingprice    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df_new","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.779558Z","iopub.execute_input":"2023-08-11T01:44:05.780312Z","iopub.status.idle":"2023-08-11T01:44:05.800904Z","shell.execute_reply.started":"2023-08-11T01:44:05.780281Z","shell.execute_reply":"2023-08-11T01:44:05.799970Z"},"editable":false,"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"              make  condition  odometer    mmr  sellingprice\n391600       Dodge        1.9  132775.0   3175          2200\n354754      Toyota        1.9  257395.0   1700          2300\n319290      Nissan        4.6   57950.0  17300         19400\n346806     Porsche        4.5   13089.0  81000         80000\n14597   Volkswagen        3.6   60800.0  10300         10300\n...            ...        ...       ...    ...           ...\n389526        Ford        2.0  101477.0  20400         18900\n378105        Ford        3.6  170738.0   7675          7300\n540451        Jeep        2.4   59281.0   7800          7100\n10341        Dodge        4.3   39694.0  19000         19700\n50592    Chevrolet        3.8   39390.0  11400         11500\n\n[966 rows x 5 columns]","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>make</th>\n      <th>condition</th>\n      <th>odometer</th>\n      <th>mmr</th>\n      <th>sellingprice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>391600</th>\n      <td>Dodge</td>\n      <td>1.9</td>\n      <td>132775.0</td>\n      <td>3175</td>\n      <td>2200</td>\n    </tr>\n    <tr>\n      <th>354754</th>\n      <td>Toyota</td>\n      <td>1.9</td>\n      <td>257395.0</td>\n      <td>1700</td>\n      <td>2300</td>\n    </tr>\n    <tr>\n      <th>319290</th>\n      <td>Nissan</td>\n      <td>4.6</td>\n      <td>57950.0</td>\n      <td>17300</td>\n      <td>19400</td>\n    </tr>\n    <tr>\n      <th>346806</th>\n      <td>Porsche</td>\n      <td>4.5</td>\n      <td>13089.0</td>\n      <td>81000</td>\n      <td>80000</td>\n    </tr>\n    <tr>\n      <th>14597</th>\n      <td>Volkswagen</td>\n      <td>3.6</td>\n      <td>60800.0</td>\n      <td>10300</td>\n      <td>10300</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>389526</th>\n      <td>Ford</td>\n      <td>2.0</td>\n      <td>101477.0</td>\n      <td>20400</td>\n      <td>18900</td>\n    </tr>\n    <tr>\n      <th>378105</th>\n      <td>Ford</td>\n      <td>3.6</td>\n      <td>170738.0</td>\n      <td>7675</td>\n      <td>7300</td>\n    </tr>\n    <tr>\n      <th>540451</th>\n      <td>Jeep</td>\n      <td>2.4</td>\n      <td>59281.0</td>\n      <td>7800</td>\n      <td>7100</td>\n    </tr>\n    <tr>\n      <th>10341</th>\n      <td>Dodge</td>\n      <td>4.3</td>\n      <td>39694.0</td>\n      <td>19000</td>\n      <td>19700</td>\n    </tr>\n    <tr>\n      <th>50592</th>\n      <td>Chevrolet</td>\n      <td>3.8</td>\n      <td>39390.0</td>\n      <td>11400</td>\n      <td>11500</td>\n    </tr>\n  </tbody>\n</table>\n<p>966 rows × 5 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"\n\n\n# Assuming df_new is your DataFrame\nmakes_to_drop = ['Isuzu', 'lexus','FIAT', 'landrover','jeep']  # Add more makes if needed\ndf_new = df_new[~df_new['make'].isin(makes_to_drop)]\n\n","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.802182Z","iopub.execute_input":"2023-08-11T01:44:05.802725Z","iopub.status.idle":"2023-08-11T01:44:05.809696Z","shell.execute_reply.started":"2023-08-11T01:44:05.802693Z","shell.execute_reply":"2023-08-11T01:44:05.808731Z"},"editable":false,"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"# Here none of the value is null","metadata":{"editable":false}},{"cell_type":"code","source":"df_new.describe()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.810659Z","iopub.execute_input":"2023-08-11T01:44:05.811010Z","iopub.status.idle":"2023-08-11T01:44:05.846276Z","shell.execute_reply.started":"2023-08-11T01:44:05.810978Z","shell.execute_reply":"2023-08-11T01:44:05.844751Z"},"editable":false,"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"        condition       odometer           mmr  sellingprice\ncount  963.000000     963.000000    963.000000    963.000000\nmean     3.446937   67374.731049  13802.596054  13709.341641\nstd      0.930751   52260.857906   9251.781084   9324.536800\nmin      1.000000     134.000000    150.000000    150.000000\n25%      2.700000   27960.500000   7300.000000   7300.000000\n50%      3.600000   51326.000000  12250.000000  12200.000000\n75%      4.200000   98854.500000  18500.000000  18450.000000\nmax      5.000000  338382.000000  82000.000000  83100.000000","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>condition</th>\n      <th>odometer</th>\n      <th>mmr</th>\n      <th>sellingprice</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>963.000000</td>\n      <td>963.000000</td>\n      <td>963.000000</td>\n      <td>963.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3.446937</td>\n      <td>67374.731049</td>\n      <td>13802.596054</td>\n      <td>13709.341641</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.930751</td>\n      <td>52260.857906</td>\n      <td>9251.781084</td>\n      <td>9324.536800</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>134.000000</td>\n      <td>150.000000</td>\n      <td>150.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.700000</td>\n      <td>27960.500000</td>\n      <td>7300.000000</td>\n      <td>7300.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3.600000</td>\n      <td>51326.000000</td>\n      <td>12250.000000</td>\n      <td>12200.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>4.200000</td>\n      <td>98854.500000</td>\n      <td>18500.000000</td>\n      <td>18450.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>5.000000</td>\n      <td>338382.000000</td>\n      <td>82000.000000</td>\n      <td>83100.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# All the data are related to others data so we work on it","metadata":{"editable":false}},{"cell_type":"markdown","source":"# Model work","metadata":{"editable":false}},{"cell_type":"code","source":"x = df_new.drop(columns='sellingprice')\ny = df_new['sellingprice']","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.847833Z","iopub.execute_input":"2023-08-11T01:44:05.848770Z","iopub.status.idle":"2023-08-11T01:44:05.855825Z","shell.execute_reply.started":"2023-08-11T01:44:05.848736Z","shell.execute_reply":"2023-08-11T01:44:05.854584Z"},"editable":false,"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"x","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.857335Z","iopub.execute_input":"2023-08-11T01:44:05.858447Z","iopub.status.idle":"2023-08-11T01:44:05.880499Z","shell.execute_reply.started":"2023-08-11T01:44:05.858377Z","shell.execute_reply":"2023-08-11T01:44:05.879664Z"},"editable":false,"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"              make  condition  odometer    mmr\n391600       Dodge        1.9  132775.0   3175\n354754      Toyota        1.9  257395.0   1700\n319290      Nissan        4.6   57950.0  17300\n346806     Porsche        4.5   13089.0  81000\n14597   Volkswagen        3.6   60800.0  10300\n...            ...        ...       ...    ...\n389526        Ford        2.0  101477.0  20400\n378105        Ford        3.6  170738.0   7675\n540451        Jeep        2.4   59281.0   7800\n10341        Dodge        4.3   39694.0  19000\n50592    Chevrolet        3.8   39390.0  11400\n\n[963 rows x 4 columns]","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>make</th>\n      <th>condition</th>\n      <th>odometer</th>\n      <th>mmr</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>391600</th>\n      <td>Dodge</td>\n      <td>1.9</td>\n      <td>132775.0</td>\n      <td>3175</td>\n    </tr>\n    <tr>\n      <th>354754</th>\n      <td>Toyota</td>\n      <td>1.9</td>\n      <td>257395.0</td>\n      <td>1700</td>\n    </tr>\n    <tr>\n      <th>319290</th>\n      <td>Nissan</td>\n      <td>4.6</td>\n      <td>57950.0</td>\n      <td>17300</td>\n    </tr>\n    <tr>\n      <th>346806</th>\n      <td>Porsche</td>\n      <td>4.5</td>\n      <td>13089.0</td>\n      <td>81000</td>\n    </tr>\n    <tr>\n      <th>14597</th>\n      <td>Volkswagen</td>\n      <td>3.6</td>\n      <td>60800.0</td>\n      <td>10300</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>389526</th>\n      <td>Ford</td>\n      <td>2.0</td>\n      <td>101477.0</td>\n      <td>20400</td>\n    </tr>\n    <tr>\n      <th>378105</th>\n      <td>Ford</td>\n      <td>3.6</td>\n      <td>170738.0</td>\n      <td>7675</td>\n    </tr>\n    <tr>\n      <th>540451</th>\n      <td>Jeep</td>\n      <td>2.4</td>\n      <td>59281.0</td>\n      <td>7800</td>\n    </tr>\n    <tr>\n      <th>10341</th>\n      <td>Dodge</td>\n      <td>4.3</td>\n      <td>39694.0</td>\n      <td>19000</td>\n    </tr>\n    <tr>\n      <th>50592</th>\n      <td>Chevrolet</td>\n      <td>3.8</td>\n      <td>39390.0</td>\n      <td>11400</td>\n    </tr>\n  </tbody>\n</table>\n<p>963 rows × 4 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.881673Z","iopub.execute_input":"2023-08-11T01:44:05.882466Z","iopub.status.idle":"2023-08-11T01:44:05.889913Z","shell.execute_reply.started":"2023-08-11T01:44:05.882409Z","shell.execute_reply":"2023-08-11T01:44:05.889101Z"},"editable":false,"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"391600     2200\n354754     2300\n319290    19400\n346806    80000\n14597     10300\n          ...  \n389526    18900\n378105     7300\n540451     7100\n10341     19700\n50592     11500\nName: sellingprice, Length: 963, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:05.891138Z","iopub.execute_input":"2023-08-11T01:44:05.892089Z","iopub.status.idle":"2023-08-11T01:44:06.466800Z","shell.execute_reply.started":"2023-08-11T01:44:05.892057Z","shell.execute_reply":"2023-08-11T01:44:06.465264Z"},"editable":false,"trusted":true},"execution_count":23,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler,OneHotEncoder,LabelEncoder\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import r2_score\nfrom sklearn.compose import ColumnTransformer\n","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.468315Z","iopub.execute_input":"2023-08-11T01:44:06.468751Z","iopub.status.idle":"2023-08-11T01:44:06.595845Z","shell.execute_reply.started":"2023-08-11T01:44:06.468717Z","shell.execute_reply":"2023-08-11T01:44:06.594815Z"},"editable":false,"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.598235Z","iopub.execute_input":"2023-08-11T01:44:06.599147Z","iopub.status.idle":"2023-08-11T01:44:06.607652Z","shell.execute_reply.started":"2023-08-11T01:44:06.599099Z","shell.execute_reply":"2023-08-11T01:44:06.606297Z"},"editable":false,"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"print(df_new.columns)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.609240Z","iopub.execute_input":"2023-08-11T01:44:06.609607Z","iopub.status.idle":"2023-08-11T01:44:06.623222Z","shell.execute_reply.started":"2023-08-11T01:44:06.609577Z","shell.execute_reply":"2023-08-11T01:44:06.621724Z"},"editable":false,"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Index(['make', 'condition', 'odometer', 'mmr', 'sellingprice'], dtype='object')\n","output_type":"stream"}]},{"cell_type":"code","source":"categorical_columns = ['make', ]  ","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.624993Z","iopub.execute_input":"2023-08-11T01:44:06.625315Z","iopub.status.idle":"2023-08-11T01:44:06.635960Z","shell.execute_reply.started":"2023-08-11T01:44:06.625288Z","shell.execute_reply":"2023-08-11T01:44:06.634971Z"},"editable":false,"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"print(x.columns)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.638822Z","iopub.execute_input":"2023-08-11T01:44:06.639514Z","iopub.status.idle":"2023-08-11T01:44:06.650982Z","shell.execute_reply.started":"2023-08-11T01:44:06.639462Z","shell.execute_reply":"2023-08-11T01:44:06.649733Z"},"editable":false,"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"Index(['make', 'condition', 'odometer', 'mmr'], dtype='object')\n","output_type":"stream"}]},{"cell_type":"code","source":"preprocessor = ColumnTransformer(\n    transformers=[\n        ('num', StandardScaler(), ~x_train.columns.isin(categorical_columns)),\n        ('cat', OneHotEncoder(), categorical_columns)\n    ])","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.652779Z","iopub.execute_input":"2023-08-11T01:44:06.653301Z","iopub.status.idle":"2023-08-11T01:44:06.663123Z","shell.execute_reply.started":"2023-08-11T01:44:06.653260Z","shell.execute_reply":"2023-08-11T01:44:06.662113Z"},"editable":false,"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"pipe = Pipeline([\n    ('preprocessor', preprocessor),\n    ('linear_regression', LinearRegression())\n])","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.669487Z","iopub.execute_input":"2023-08-11T01:44:06.669907Z","iopub.status.idle":"2023-08-11T01:44:06.676948Z","shell.execute_reply.started":"2023-08-11T01:44:06.669869Z","shell.execute_reply":"2023-08-11T01:44:06.675587Z"},"editable":false,"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"pipe.fit(x_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.678107Z","iopub.execute_input":"2023-08-11T01:44:06.678575Z","iopub.status.idle":"2023-08-11T01:44:06.756191Z","shell.execute_reply.started":"2023-08-11T01:44:06.678534Z","shell.execute_reply":"2023-08-11T01:44:06.755146Z"},"editable":false,"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"Pipeline(steps=[('preprocessor',\n                 ColumnTransformer(transformers=[('num', StandardScaler(),\n                                                  array([False,  True,  True,  True])),\n                                                 ('cat', OneHotEncoder(),\n                                                  ['make'])])),\n                ('linear_regression', LinearRegression())])","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n                                                  array([False,  True,  True,  True])),\n                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n                                                  [&#x27;make&#x27;])])),\n                (&#x27;linear_regression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n                                                  array([False,  True,  True,  True])),\n                                                 (&#x27;cat&#x27;, OneHotEncoder(),\n                                                  [&#x27;make&#x27;])])),\n                (&#x27;linear_regression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n                                 array([False,  True,  True,  True])),\n                                (&#x27;cat&#x27;, OneHotEncoder(), [&#x27;make&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[False  True  True  True]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;make&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_predict = pipe.predict(x_test)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.757611Z","iopub.execute_input":"2023-08-11T01:44:06.758029Z","iopub.status.idle":"2023-08-11T01:44:06.771259Z","shell.execute_reply.started":"2023-08-11T01:44:06.757989Z","shell.execute_reply":"2023-08-11T01:44:06.770229Z"},"editable":false,"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"raw","source":"r2_score(y_test,y_pred)","metadata":{"editable":false}},{"cell_type":"code","source":"r2_score(y_test,y_predict)","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.772729Z","iopub.execute_input":"2023-08-11T01:44:06.773126Z","iopub.status.idle":"2023-08-11T01:44:06.786429Z","shell.execute_reply.started":"2023-08-11T01:44:06.773094Z","shell.execute_reply":"2023-08-11T01:44:06.785102Z"},"editable":false,"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"0.9637609044739502"},"metadata":{}}]},{"cell_type":"code","source":"import pickle\npickle_out = open(\"classifier.pkl\", \"wb\")\npickle.dump(pipe, pickle_out)\npickle_out.close()","metadata":{"execution":{"iopub.status.busy":"2023-08-11T01:44:06.788310Z","iopub.execute_input":"2023-08-11T01:44:06.789288Z","iopub.status.idle":"2023-08-11T01:44:06.795822Z","shell.execute_reply.started":"2023-08-11T01:44:06.789245Z","shell.execute_reply":"2023-08-11T01:44:06.794666Z"},"editable":false,"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"\n","metadata":{"editable":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"editable":false},"execution_count":null,"outputs":[]}]}