ML Projects/used-car-price-prediction.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":6478229,"sourceType":"datasetVersion","datasetId":3742543},{"sourceId":8619955,"sourceType":"datasetVersion","datasetId":5159726}],"dockerImageVersionId":30732,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd \nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:48:32.919994Z","iopub.execute_input":"2024-06-08T08:48:32.920284Z","iopub.status.idle":"2024-06-08T08:48:35.685990Z","shell.execute_reply.started":"2024-06-08T08:48:32.920257Z","shell.execute_reply":"2024-06-08T08:48:35.684717Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/kagglex-skill-dataset/train.csv\")\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:48:41.775760Z","iopub.execute_input":"2024-06-08T08:48:41.776686Z","iopub.status.idle":"2024-06-08T08:48:42.133720Z","shell.execute_reply.started":"2024-06-08T08:48:41.776629Z","shell.execute_reply":"2024-06-08T08:48:42.132393Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   id    brand          model  model_year  milage fuel_type  \\\n0   0     Ford   F-150 Lariat        2018   74349  Gasoline   \n1   1      BMW          335 i        2007   80000  Gasoline   \n2   2   Jaguar      XF Luxury        2009   91491  Gasoline   \n3   3      BMW   X7 xDrive40i        2022    2437    Hybrid   \n4   4  Pontiac  Firebird Base        2001  111000  Gasoline   \n\n                                              engine  \\\n0      375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel   \n1  300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n2       300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel   \n3  335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n4      200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel   \n\n                     transmission ext_col int_col       accident clean_title  \\\n0                    10-Speed A/T    Blue    Gray  None reported         Yes   \n1                     6-Speed M/T   Black   Black  None reported         Yes   \n2                     6-Speed A/T  Purple   Beige  None reported         Yes   \n3  Transmission w/Dual Shift Mode    Gray   Brown  None reported         Yes   \n4                             A/T   White   Black  None reported         Yes   \n\n   price  \n0  11000  \n1   8250  \n2  15000  \n3  63500  \n4   7850  ","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>brand</th>\n      <th>model</th>\n      <th>model_year</th>\n      <th>milage</th>\n      <th>fuel_type</th>\n      <th>engine</th>\n      <th>transmission</th>\n      <th>ext_col</th>\n      <th>int_col</th>\n      <th>accident</th>\n      <th>clean_title</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>Ford</td>\n      <td>F-150 Lariat</td>\n      <td>2018</td>\n      <td>74349</td>\n      <td>Gasoline</td>\n      <td>375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>10-Speed A/T</td>\n      <td>Blue</td>\n      <td>Gray</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>11000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>BMW</td>\n      <td>335 i</td>\n      <td>2007</td>\n      <td>80000</td>\n      <td>Gasoline</td>\n      <td>300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>6-Speed M/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>8250</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>Jaguar</td>\n      <td>XF Luxury</td>\n      <td>2009</td>\n      <td>91491</td>\n      <td>Gasoline</td>\n      <td>300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed A/T</td>\n      <td>Purple</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>15000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>BMW</td>\n      <td>X7 xDrive40i</td>\n      <td>2022</td>\n      <td>2437</td>\n      <td>Hybrid</td>\n      <td>335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>Gray</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>63500</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>Pontiac</td>\n      <td>Firebird Base</td>\n      <td>2001</td>\n      <td>111000</td>\n      <td>Gasoline</td>\n      <td>200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>7850</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:48:45.660644Z","iopub.execute_input":"2024-06-08T08:48:45.661060Z","iopub.status.idle":"2024-06-08T08:48:45.742289Z","shell.execute_reply.started":"2024-06-08T08:48:45.661025Z","shell.execute_reply":"2024-06-08T08:48:45.741088Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 54273 entries, 0 to 54272\nData columns (total 13 columns):\n #   Column        Non-Null Count  Dtype \n---  ------        --------------  ----- \n 0   id            54273 non-null  int64 \n 1   brand         54273 non-null  object\n 2   model         54273 non-null  object\n 3   model_year    54273 non-null  int64 \n 4   milage        54273 non-null  int64 \n 5   fuel_type     54273 non-null  object\n 6   engine        54273 non-null  object\n 7   transmission  54273 non-null  object\n 8   ext_col       54273 non-null  object\n 9   int_col       54273 non-null  object\n 10  accident      54273 non-null  object\n 11  clean_title   54273 non-null  object\n 12  price         54273 non-null  int64 \ndtypes: int64(4), object(9)\nmemory usage: 5.4+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:48:49.100822Z","iopub.execute_input":"2024-06-08T08:48:49.101221Z","iopub.status.idle":"2024-06-08T08:48:49.138302Z","shell.execute_reply.started":"2024-06-08T08:48:49.101190Z","shell.execute_reply":"2024-06-08T08:48:49.137190Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"                 id    model_year         milage         price\ncount  54273.000000  54273.000000   54273.000000  5.427300e+04\nmean   27136.000000   2015.091979   72746.175667  3.921844e+04\nstd    15667.409917      5.588909   50469.490448  7.282634e+04\nmin        0.000000   1974.000000     100.000000  2.000000e+03\n25%    13568.000000   2012.000000   32268.000000  1.550000e+04\n50%    27136.000000   2016.000000   66107.000000  2.800000e+04\n75%    40704.000000   2019.000000  102000.000000  4.500000e+04\nmax    54272.000000   2024.000000  405000.000000  2.954083e+06","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>model_year</th>\n      <th>milage</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>54273.000000</td>\n      <td>54273.000000</td>\n      <td>54273.000000</td>\n      <td>5.427300e+04</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>27136.000000</td>\n      <td>2015.091979</td>\n      <td>72746.175667</td>\n      <td>3.921844e+04</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>15667.409917</td>\n      <td>5.588909</td>\n      <td>50469.490448</td>\n      <td>7.282634e+04</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>1974.000000</td>\n      <td>100.000000</td>\n      <td>2.000000e+03</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>13568.000000</td>\n      <td>2012.000000</td>\n      <td>32268.000000</td>\n      <td>1.550000e+04</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>27136.000000</td>\n      <td>2016.000000</td>\n      <td>66107.000000</td>\n      <td>2.800000e+04</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>40704.000000</td>\n      <td>2019.000000</td>\n      <td>102000.000000</td>\n      <td>4.500000e+04</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>54272.000000</td>\n      <td>2024.000000</td>\n      <td>405000.000000</td>\n      <td>2.954083e+06</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:48:53.115427Z","iopub.execute_input":"2024-06-08T08:48:53.115864Z","iopub.status.idle":"2024-06-08T08:48:53.178106Z","shell.execute_reply.started":"2024-06-08T08:48:53.115830Z","shell.execute_reply":"2024-06-08T08:48:53.176813Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"id              0\nbrand           0\nmodel           0\nmodel_year      0\nmilage          0\nfuel_type       0\nengine          0\ntransmission    0\next_col         0\nint_col         0\naccident        0\nclean_title     0\nprice           0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:48:56.330313Z","iopub.execute_input":"2024-06-08T08:48:56.330789Z","iopub.status.idle":"2024-06-08T08:48:56.402037Z","shell.execute_reply.started":"2024-06-08T08:48:56.330752Z","shell.execute_reply":"2024-06-08T08:48:56.400752Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:48:59.650806Z","iopub.execute_input":"2024-06-08T08:48:59.651182Z","iopub.status.idle":"2024-06-08T08:48:59.658889Z","shell.execute_reply.started":"2024-06-08T08:48:59.651154Z","shell.execute_reply":"2024-06-08T08:48:59.657674Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"(54273, 13)"},"metadata":{}}]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2024-06-08T08:49:02.841084Z","iopub.execute_input":"2024-06-08T08:49:02.841572Z","iopub.status.idle":"2024-06-08T08:49:02.849635Z","shell.execute_reply.started":"2024-06-08T08:49:02.841534Z","shell.execute_reply":"2024-06-08T08:49:02.848485Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Index(['id', 'brand', 'model', 'model_year', 'milage', 'fuel_type', 'engine',\n       'transmission', 'ext_col', 'int_col', 'accident', 'clean_title',\n       'price'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"markdown","source":"## EDA","metadata":{}},{"cell_type":"markdown","source":"Before we go ahead we can drop the `id` column","metadata":{}},{"cell_type":"code","source":"new_df = df.drop(\"id\", axis = 1)[:]\nnew_df","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:49:38.308260Z","iopub.execute_input":"2024-06-08T09:49:38.308711Z","iopub.status.idle":"2024-06-08T09:49:38.336712Z","shell.execute_reply.started":"2024-06-08T09:49:38.308678Z","shell.execute_reply":"2024-06-08T09:49:38.335460Z"},"trusted":true},"execution_count":104,"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":"         brand                             model  model_year  milage  \\\n0         Ford                      F-150 Lariat        2018   74349   \n1          BMW                             335 i        2007   80000   \n2       Jaguar                         XF Luxury        2009   91491   \n3          BMW                      X7 xDrive40i        2022    2437   \n4      Pontiac                     Firebird Base        2001  111000   \n...        ...                               ...         ...     ...   \n54268      BMW                      X6 xDrive50i        2017   29000   \n54269     Audi                   A4 2.0T Premium        2015   94634   \n54270  Porsche                         Cayenne S        2013   40989   \n54271  Porsche                 911 Carrera 4 GTS        2023    1518   \n54272     Audi  A5 Sportback S line Premium Plus        2021   35000   \n\n           fuel_type                                             engine  \\\n0           Gasoline      375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel   \n1           Gasoline  300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n2           Gasoline       300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel   \n3             Hybrid  335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n4           Gasoline      200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel   \n...              ...                                                ...   \n54268       Gasoline       445.0HP 4.4L 8 Cylinder Engine Gasoline Fuel   \n54269  E85 Flex Fuel  220.0HP 2.0L 4 Cylinder Engine Flex Fuel Capab...   \n54270       Gasoline      420.0HP 3.6L V6 Cylinder Engine Gasoline Fuel   \n54271       Gasoline                               4.0L H6 24V GDI DOHC   \n54272         Hybrid  261.0HP 2.0L 4 Cylinder Engine Gasoline/Mild E...   \n\n                            transmission ext_col int_col  \\\n0                           10-Speed A/T    Blue    Gray   \n1                            6-Speed M/T   Black   Black   \n2                            6-Speed A/T  Purple   Beige   \n3         Transmission w/Dual Shift Mode    Gray   Brown   \n4                                    A/T   White   Black   \n...                                  ...     ...     ...   \n54268                        8-Speed A/T   White   Brown   \n54269                        6-Speed A/T   Black   Black   \n54270     Transmission w/Dual Shift Mode   White   Black   \n54271  8-Speed Automatic with Auto-Shift   Beige   Brown   \n54272                        8-Speed A/T   Black   Black   \n\n                                     accident clean_title   price  \n0                               None reported         Yes   11000  \n1                               None reported         Yes    8250  \n2                               None reported         Yes   15000  \n3                               None reported         Yes   63500  \n4                               None reported         Yes    7850  \n...                                       ...         ...     ...  \n54268                           None reported         Yes   29000  \n54269  At least 1 accident or damage reported         Yes    6500  \n54270  At least 1 accident or damage reported         Yes   18950  \n54271                           None reported         Yes  194965  \n54272                           None reported         Yes   37499  \n\n[54273 rows x 12 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>brand</th>\n      <th>model</th>\n      <th>model_year</th>\n      <th>milage</th>\n      <th>fuel_type</th>\n      <th>engine</th>\n      <th>transmission</th>\n      <th>ext_col</th>\n      <th>int_col</th>\n      <th>accident</th>\n      <th>clean_title</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Ford</td>\n      <td>F-150 Lariat</td>\n      <td>2018</td>\n      <td>74349</td>\n      <td>Gasoline</td>\n      <td>375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>10-Speed A/T</td>\n      <td>Blue</td>\n      <td>Gray</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>11000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BMW</td>\n      <td>335 i</td>\n      <td>2007</td>\n      <td>80000</td>\n      <td>Gasoline</td>\n      <td>300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>6-Speed M/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>8250</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Jaguar</td>\n      <td>XF Luxury</td>\n      <td>2009</td>\n      <td>91491</td>\n      <td>Gasoline</td>\n      <td>300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed A/T</td>\n      <td>Purple</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>15000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BMW</td>\n      <td>X7 xDrive40i</td>\n      <td>2022</td>\n      <td>2437</td>\n      <td>Hybrid</td>\n      <td>335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>Gray</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>63500</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Pontiac</td>\n      <td>Firebird Base</td>\n      <td>2001</td>\n      <td>111000</td>\n      <td>Gasoline</td>\n      <td>200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>7850</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    </tr>\n    <tr>\n      <th>54268</th>\n      <td>BMW</td>\n      <td>X6 xDrive50i</td>\n      <td>2017</td>\n      <td>29000</td>\n      <td>Gasoline</td>\n      <td>445.0HP 4.4L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>White</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>29000</td>\n    </tr>\n    <tr>\n      <th>54269</th>\n      <td>Audi</td>\n      <td>A4 2.0T Premium</td>\n      <td>2015</td>\n      <td>94634</td>\n      <td>E85 Flex Fuel</td>\n      <td>220.0HP 2.0L 4 Cylinder Engine Flex Fuel Capab...</td>\n      <td>6-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>6500</td>\n    </tr>\n    <tr>\n      <th>54270</th>\n      <td>Porsche</td>\n      <td>Cayenne S</td>\n      <td>2013</td>\n      <td>40989</td>\n      <td>Gasoline</td>\n      <td>420.0HP 3.6L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>18950</td>\n    </tr>\n    <tr>\n      <th>54271</th>\n      <td>Porsche</td>\n      <td>911 Carrera 4 GTS</td>\n      <td>2023</td>\n      <td>1518</td>\n      <td>Gasoline</td>\n      <td>4.0L H6 24V GDI DOHC</td>\n      <td>8-Speed Automatic with Auto-Shift</td>\n      <td>Beige</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>194965</td>\n    </tr>\n    <tr>\n      <th>54272</th>\n      <td>Audi</td>\n      <td>A5 Sportback S line Premium Plus</td>\n      <td>2021</td>\n      <td>35000</td>\n      <td>Hybrid</td>\n      <td>261.0HP 2.0L 4 Cylinder Engine Gasoline/Mild E...</td>\n      <td>8-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>37499</td>\n    </tr>\n  </tbody>\n</table>\n<p>54273 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"new_df.hist(bins = 50, figsize=(20, 15));","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:49:41.307805Z","iopub.execute_input":"2024-06-08T09:49:41.308184Z","iopub.status.idle":"2024-06-08T09:49:42.514990Z","shell.execute_reply.started":"2024-06-08T09:49:41.308155Z","shell.execute_reply":"2024-06-08T09:49:42.513762Z"},"trusted":true},"execution_count":105,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1500 with 4 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= new_df.corr(numeric_only = True)\ncorr_matrix[\"price\"].sort_values(ascending = False)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:49:47.517342Z","iopub.execute_input":"2024-06-08T09:49:47.518621Z","iopub.status.idle":"2024-06-08T09:49:47.532367Z","shell.execute_reply.started":"2024-06-08T09:49:47.518579Z","shell.execute_reply":"2024-06-08T09:49:47.531155Z"},"trusted":true},"execution_count":106,"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"price         1.000000\nmodel_year    0.216150\nmilage       -0.248927\nName: price, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"As we can see here the price is affected by year of launch and the milage. Low milage and new cars have higher prices","metadata":{}},{"cell_type":"code","source":"import matplotlib.ticker as ticker\nsns.barplot(x = \"brand\", y = \"price\", data = new_df)\nplt.yscale('log')\nplt.xticks(rotation=90)\nplt.tick_params(axis='x', which='major', labelsize= 6)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:49:50.392379Z","iopub.execute_input":"2024-06-08T09:49:50.392815Z","iopub.status.idle":"2024-06-08T09:49:53.518152Z","shell.execute_reply.started":"2024-06-08T09:49:50.392782Z","shell.execute_reply":"2024-06-08T09:49:53.516928Z"},"trusted":true},"execution_count":107,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"the price of Bugatti seems to be very high and it maybe an outlier. We must check other outliers","metadata":{}},{"cell_type":"code","source":"len(new_df)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:49:56.907267Z","iopub.execute_input":"2024-06-08T09:49:56.907770Z","iopub.status.idle":"2024-06-08T09:49:56.915308Z","shell.execute_reply.started":"2024-06-08T09:49:56.907734Z","shell.execute_reply":"2024-06-08T09:49:56.914026Z"},"trusted":true},"execution_count":108,"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"54273"},"metadata":{}}]},{"cell_type":"code","source":"new_df.sort_values(by = \"price\", ascending = False)[:50]","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:49:59.628186Z","iopub.execute_input":"2024-06-08T09:49:59.628627Z","iopub.status.idle":"2024-06-08T09:49:59.684431Z","shell.execute_reply.started":"2024-06-08T09:49:59.628593Z","shell.execute_reply":"2024-06-08T09:49:59.683286Z"},"trusted":true},"execution_count":109,"outputs":[{"execution_count":109,"output_type":"execute_result","data":{"text/plain":"               brand                              model  model_year  milage  \\\n39761      Chevrolet                  Suburban 1500 LTZ        2010  194000   \n37341        Porsche                     911 Carrera 4S        2005   28500   \n41065      Chevrolet                           Tahoe LT        2017  117780   \n19661           Audi               A7 3.0T Premium Plus        2017   54500   \n5584   Mercedes-Benz            SL-Class SL500 Roadster        2004    2533   \n7849             GMC           Sierra 1500 SLE Crew Cab        2005  231500   \n14389            GMC            Sierra 1500 SL Crew Cab        2006  231500   \n7620       Chevrolet                      Corvette Base        2005  231581   \n34049          Acura                    RDX PMC Edition        2021    2353   \n29826         Toyota                    Sequoia Limited        2002   17300   \n10374      Chevrolet                 Silverado 1500 LTZ        2015   23442   \n52848      Chevrolet                      Corvette Base        2005   13300   \n47611         Hummer                            H2 Base        2005  145000   \n20007            RAM                       1500 Laramie        2019   10458   \n13913      Chevrolet                          Tahoe LTZ        2006  110000   \n26175      Chevrolet                     Monte Carlo SS        2007   79000   \n48311        Bugatti            Veyron 16.4 Grand Sport        2011    6330   \n19606         Toyota                     Tacoma TRD Pro        2018   40698   \n42779    Lamborghini                        570S Spider        2018   17761   \n41646  Mercedes-Benz                   S-Class S 63 AMG        2011   37100   \n37520           Audi             R8 5.2 V10 performance        2011   12245   \n37355           Jeep                     Wrangler Sport        2016   74582   \n6052         Porsche                     911 Carrera 4S        2005   28800   \n34484  Mercedes-Benz                      E-Class E 350        2013   63496   \n13284  Mercedes-Benz                      AMG G 63 Base        2022    2950   \n32844        Porsche                          Cayenne S        2023    1500   \n5794   Mercedes-Benz                           AMG GT R        2017    3811   \n13385  Mercedes-Benz  AMG GLE AMG GLE 63 S-Model 4MATIC        2018    2800   \n38273        Porsche                      911 Carrera S        2015   28470   \n47387           Audi     RS 7 4.0T Performance Prestige        2020   22000   \n49610           Audi                 RS 7 4.0T Prestige        2016   64800   \n2568         Porsche                            911 GT3        2019    1568   \n9891            Ford                     F-350 Platinum        2022    7500   \n2550            Ford              Expedition Timberline        2023    8911   \n53405            GMC              Yukon Denali Ultimate        2023   23600   \n789    Mercedes-Benz               AMG C 43 Base 4MATIC        2017   80300   \n15200      Chevrolet            Corvette Stingray w/1LT        2020   11089   \n33133        Genesis                           G70 2.0T        2023   15198   \n49718    Rolls-Royce                         Ghost Base        2007   79000   \n39705           Audi                   A7 3.0T Prestige        2021   15000   \n3256            Ford                     F-250 Platinum        2023   89980   \n8804         Porsche                        911 Turbo S        2021     154   \n45245        Bentley                Continental GT Base        2009   25500   \n47689       Chrysler                        200 Limited        2021   14684   \n8362     Lamborghini                          Urus Base        2021   16876   \n28400           Ford                 Expedition Limited        2023   93400   \n13898            BMW                       X7 xDrive40i        2023   14360   \n21905      Chevrolet            Corvette Stingray w/1LT        2023   15500   \n50813          Dodge                       Viper SRT-10        2004    3763   \n53192  Mercedes-Benz            Sprinter 3500 High Roof        2019   39000   \n\n      fuel_type                                             engine  \\\n39761  Gasoline       355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel   \n37341  Gasoline  355.0HP 3.8L Flat 6 Cylinder Engine Gasoline Fuel   \n41065  Gasoline       355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel   \n19661    Diesel                3.0L V6 Cylinder Engine Diesel Fuel   \n5584   Gasoline       302.0HP 5.0L 8 Cylinder Engine Gasoline Fuel   \n7849   Gasoline       355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel   \n14389  Gasoline       295.0HP 5.3L 8 Cylinder Engine Gasoline Fuel   \n7620   Gasoline       400.0HP 6.0L 8 Cylinder Engine Gasoline Fuel   \n34049  Gasoline                         2.0L I4 16V GDI DOHC Turbo   \n29826  Gasoline       381.0HP 5.7L 8 Cylinder Engine Gasoline Fuel   \n10374  Gasoline       355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel   \n52848  Gasoline       430.0HP 6.2L 8 Cylinder Engine Gasoline Fuel   \n47611  Gasoline       325.0HP 6.0L 8 Cylinder Engine Gasoline Fuel   \n20007  Gasoline                               5.7L V8 16V MPFI OHV   \n13913  Gasoline       355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel   \n26175  Gasoline      200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel   \n48311  Gasoline                   8.0L W16 64V GDI DOHC Twin Turbo   \n19606  Gasoline                               3.5L V6 24V PDI DOHC   \n42779  Gasoline                              5.2L V10 40V GDI DOHC   \n41646  Gasoline       577.0HP 5.5L 8 Cylinder Engine Gasoline Fuel   \n37520  Gasoline                              5.2L V10 40V GDI DOHC   \n37355  Gasoline      285.0HP 3.6L V6 Cylinder Engine Gasoline Fuel   \n6052   Gasoline                    3.0L H6 24V GDI DOHC Twin Turbo   \n34484  Gasoline      302.0HP 3.5L V6 Cylinder Engine Gasoline Fuel   \n13284  Gasoline       577.0HP 4.0L 8 Cylinder Engine Gasoline Fuel   \n32844  Gasoline       400.0HP 4.8L 8 Cylinder Engine Gasoline Fuel   \n5794   Gasoline       577.0HP 4.0L 8 Cylinder Engine Gasoline Fuel   \n13385  Gasoline       577.0HP 5.5L 8 Cylinder Engine Gasoline Fuel   \n38273  Gasoline  385.0HP 3.8L Flat 6 Cylinder Engine Gasoline Fuel   \n47387  Gasoline       605.0HP 4.0L 8 Cylinder Engine Gasoline Fuel   \n49610  Gasoline       560.0HP 4.0L 8 Cylinder Engine Gasoline Fuel   \n2568   Gasoline  475.0HP 3.8L Flat 6 Cylinder Engine Gasoline Fuel   \n9891     Diesel         475.0HP 6.7L 8 Cylinder Engine Diesel Fuel   \n2550   Gasoline                    3.5L V6 24V PDI DOHC Twin Turbo   \n53405  Gasoline       420.0HP 6.2L 8 Cylinder Engine Gasoline Fuel   \n789    Gasoline      362.0HP 3.0L V6 Cylinder Engine Gasoline Fuel   \n15200  Gasoline       490.0HP 6.2L 8 Cylinder Engine Gasoline Fuel   \n33133  Gasoline       252.0HP 2.0L 4 Cylinder Engine Gasoline Fuel   \n49718  Gasoline      624.0HP 6.6L 12 Cylinder Engine Gasoline Fuel   \n39705    Hybrid  335.0HP 3.0L V6 Cylinder Engine Gasoline/Mild ...   \n3256     Diesel         475.0HP 6.7L 8 Cylinder Engine Diesel Fuel   \n8804   Gasoline                    3.8L H6 24V GDI DOHC Twin Turbo   \n45245  Gasoline      552.0HP 6.0L 12 Cylinder Engine Gasoline Fuel   \n47689  Gasoline                              3.6L V6 24V MPFI DOHC   \n8362   Gasoline                    4.0L V8 32V GDI DOHC Twin Turbo   \n28400  Gasoline      375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel   \n13898    Hybrid  375.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n21905  Gasoline       490.0HP 6.2L 8 Cylinder Engine Gasoline Fuel   \n50813  Gasoline       550.0HP 5.4L 8 Cylinder Engine Gasoline Fuel   \n53192    Diesel        188.0HP 3.0L V6 Cylinder Engine Diesel Fuel   \n\n                            transmission               ext_col     int_col  \\\n39761                                A/T                Silver       Black   \n37341                        6-Speed M/T                  Gray       Black   \n41065                                A/T                 Black       Black   \n19661                        8-Speed A/T                 Black       Brown   \n5584                                 A/T                 White       Beige   \n7849                         4-Speed A/T                   Red       Beige   \n14389                                A/T                   Red        Gray   \n7620                         4-Speed A/T                  Gray       Beige   \n34049                 10-Speed Automatic                   Red       Ebony   \n29826                                A/T                Silver        Gray   \n10374                                A/T                   Red        Gray   \n52848                        6-Speed M/T                   Red       Beige   \n47611                                A/T                Silver       Beige   \n20007                  8-Speed Automatic                   Red       Black   \n13913     Transmission w/Dual Shift Mode                   Red       Beige   \n26175                        4-Speed A/T                   Red       Black   \n48311  7-Speed Automatic with Auto-Shift                 White       Black   \n19606                            6-Speed                 Black       Black   \n42779  7-Speed Automatic with Auto-Shift           Rosso Corsa       White   \n41646                        7-Speed A/T                 Black       Black   \n37520  7-Speed Automatic with Auto-Shift                 White       White   \n37355                        4-Speed A/T                 White       Black   \n6052                      6-Speed Manual                 White         Red   \n34484                        7-Speed A/T                   Red       Black   \n13284                                A/T                 White       Black   \n32844                        8-Speed A/T                  Gray        Gray   \n5794                         7-Speed A/T                  Gray       Black   \n13385                        7-Speed A/T                 White       Black   \n38273                        6-Speed M/T                  Gray       Black   \n47387                        8-Speed A/T                  Blue       White   \n49610                        8-Speed A/T                  Gray       Brown   \n2568                         7-Speed A/T                 White       Black   \n9891                                 A/T                  Gray        Gray   \n2550                  10-Speed Automatic  Dark Matter Metallic  Black Onyx   \n53405                       10-Speed A/T                 Black       Black   \n789                          9-Speed A/T                  Gray       Black   \n15200     Transmission w/Dual Shift Mode                   Red       Black   \n33133                        8-Speed A/T                 Black       Black   \n49718                        8-Speed A/T                Silver       Black   \n39705                                A/T                  Gray       Brown   \n3256                                 A/T                 Brown       Black   \n8804   8-Speed Automatic with Auto-Shift          Python Green       Black   \n45245                        6-Speed M/T                 Black       Black   \n47689                  5-Speed Automatic                 Black           –   \n8362                   8-Speed Automatic                 Black       Brown   \n28400                       10-Speed A/T                 Black       Black   \n13898                        8-Speed A/T                 Black       Beige   \n21905                                A/T                  Gray       Black   \n50813                        6-Speed M/T                  Blue       Black   \n53192                                A/T                 White       Black   \n\n                                     accident clean_title    price  \n39761  At least 1 accident or damage reported         Yes  2954083  \n37341                           None reported         Yes  2954083  \n41065                           None reported         Yes  2954083  \n19661  At least 1 accident or damage reported         Yes  2954083  \n5584                            None reported         Yes  2954083  \n7849   At least 1 accident or damage reported         Yes  2954083  \n14389                           None reported         Yes  2954083  \n7620                            None reported         Yes  2954083  \n34049                           None reported         Yes  2954083  \n29826  At least 1 accident or damage reported         Yes  2954083  \n10374  At least 1 accident or damage reported         Yes  2954083  \n52848                           None reported         Yes  2954083  \n47611                           None reported         Yes  2954083  \n20007                           None reported         Yes  2954083  \n13913  At least 1 accident or damage reported         Yes  2954083  \n26175                           None reported         Yes  1950995  \n48311                           None reported         Yes  1950995  \n19606                           None reported         Yes  1950995  \n42779                           None reported         Yes  1950995  \n41646                           None reported         Yes  1950995  \n37520                           None reported         Yes  1950995  \n37355                           None reported         Yes  1950995  \n6052                            None reported         Yes  1950995  \n34484                           None reported         Yes  1950995  \n13284                           None reported         Yes  1599000  \n32844                           None reported         Yes  1599000  \n5794                            None reported         Yes  1599000  \n13385                           None reported         Yes  1599000  \n38273                           None reported         Yes  1599000  \n47387                           None reported         Yes  1599000  \n49610                           None reported         Yes  1599000  \n2568                            None reported         Yes  1599000  \n9891                            None reported         Yes   749950  \n2550                            None reported         Yes   749950  \n53405                           None reported         Yes   749950  \n789                             None reported         Yes   749950  \n15200                           None reported         Yes   749950  \n33133                           None reported         Yes   749950  \n49718                           None reported         Yes   749950  \n39705                           None reported         Yes   749950  \n3256                            None reported         Yes   749950  \n8804                            None reported         Yes   749950  \n45245                           None reported         Yes   749950  \n47689                           None reported         Yes   749950  \n8362                            None reported         Yes   749950  \n28400                           None reported         Yes   749950  \n13898                           None reported         Yes   749950  \n21905                           None reported         Yes   695000  \n50813                           None reported         Yes   695000  \n53192  At least 1 accident or damage reported         Yes   695000  ","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>brand</th>\n      <th>model</th>\n      <th>model_year</th>\n      <th>milage</th>\n      <th>fuel_type</th>\n      <th>engine</th>\n      <th>transmission</th>\n      <th>ext_col</th>\n      <th>int_col</th>\n      <th>accident</th>\n      <th>clean_title</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>39761</th>\n      <td>Chevrolet</td>\n      <td>Suburban 1500 LTZ</td>\n      <td>2010</td>\n      <td>194000</td>\n      <td>Gasoline</td>\n      <td>355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>Silver</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>37341</th>\n      <td>Porsche</td>\n      <td>911 Carrera 4S</td>\n      <td>2005</td>\n      <td>28500</td>\n      <td>Gasoline</td>\n      <td>355.0HP 3.8L Flat 6 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed M/T</td>\n      <td>Gray</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>41065</th>\n      <td>Chevrolet</td>\n      <td>Tahoe LT</td>\n      <td>2017</td>\n      <td>117780</td>\n      <td>Gasoline</td>\n      <td>355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>19661</th>\n      <td>Audi</td>\n      <td>A7 3.0T Premium Plus</td>\n      <td>2017</td>\n      <td>54500</td>\n      <td>Diesel</td>\n      <td>3.0L V6 Cylinder Engine Diesel Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>Black</td>\n      <td>Brown</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>5584</th>\n      <td>Mercedes-Benz</td>\n      <td>SL-Class SL500 Roadster</td>\n      <td>2004</td>\n      <td>2533</td>\n      <td>Gasoline</td>\n      <td>302.0HP 5.0L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>White</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>7849</th>\n      <td>GMC</td>\n      <td>Sierra 1500 SLE Crew Cab</td>\n      <td>2005</td>\n      <td>231500</td>\n      <td>Gasoline</td>\n      <td>355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>4-Speed A/T</td>\n      <td>Red</td>\n      <td>Beige</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>14389</th>\n      <td>GMC</td>\n      <td>Sierra 1500 SL Crew Cab</td>\n      <td>2006</td>\n      <td>231500</td>\n      <td>Gasoline</td>\n      <td>295.0HP 5.3L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>Red</td>\n      <td>Gray</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>7620</th>\n      <td>Chevrolet</td>\n      <td>Corvette Base</td>\n      <td>2005</td>\n      <td>231581</td>\n      <td>Gasoline</td>\n      <td>400.0HP 6.0L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>4-Speed A/T</td>\n      <td>Gray</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>34049</th>\n      <td>Acura</td>\n      <td>RDX PMC Edition</td>\n      <td>2021</td>\n      <td>2353</td>\n      <td>Gasoline</td>\n      <td>2.0L I4 16V GDI DOHC Turbo</td>\n      <td>10-Speed Automatic</td>\n      <td>Red</td>\n      <td>Ebony</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>29826</th>\n      <td>Toyota</td>\n      <td>Sequoia Limited</td>\n      <td>2002</td>\n      <td>17300</td>\n      <td>Gasoline</td>\n      <td>381.0HP 5.7L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>Silver</td>\n      <td>Gray</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>10374</th>\n      <td>Chevrolet</td>\n      <td>Silverado 1500 LTZ</td>\n      <td>2015</td>\n      <td>23442</td>\n      <td>Gasoline</td>\n      <td>355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>Red</td>\n      <td>Gray</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>52848</th>\n      <td>Chevrolet</td>\n      <td>Corvette Base</td>\n      <td>2005</td>\n      <td>13300</td>\n      <td>Gasoline</td>\n      <td>430.0HP 6.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed M/T</td>\n      <td>Red</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>47611</th>\n      <td>Hummer</td>\n      <td>H2 Base</td>\n      <td>2005</td>\n      <td>145000</td>\n      <td>Gasoline</td>\n      <td>325.0HP 6.0L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>Silver</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>20007</th>\n      <td>RAM</td>\n      <td>1500 Laramie</td>\n      <td>2019</td>\n      <td>10458</td>\n      <td>Gasoline</td>\n      <td>5.7L V8 16V MPFI OHV</td>\n      <td>8-Speed Automatic</td>\n      <td>Red</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>13913</th>\n      <td>Chevrolet</td>\n      <td>Tahoe LTZ</td>\n      <td>2006</td>\n      <td>110000</td>\n      <td>Gasoline</td>\n      <td>355.0HP 5.3L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>Red</td>\n      <td>Beige</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>2954083</td>\n    </tr>\n    <tr>\n      <th>26175</th>\n      <td>Chevrolet</td>\n      <td>Monte Carlo SS</td>\n      <td>2007</td>\n      <td>79000</td>\n      <td>Gasoline</td>\n      <td>200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>4-Speed A/T</td>\n      <td>Red</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>48311</th>\n      <td>Bugatti</td>\n      <td>Veyron 16.4 Grand Sport</td>\n      <td>2011</td>\n      <td>6330</td>\n      <td>Gasoline</td>\n      <td>8.0L W16 64V GDI DOHC Twin Turbo</td>\n      <td>7-Speed Automatic with Auto-Shift</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>19606</th>\n      <td>Toyota</td>\n      <td>Tacoma TRD Pro</td>\n      <td>2018</td>\n      <td>40698</td>\n      <td>Gasoline</td>\n      <td>3.5L V6 24V PDI DOHC</td>\n      <td>6-Speed</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>42779</th>\n      <td>Lamborghini</td>\n      <td>570S Spider</td>\n      <td>2018</td>\n      <td>17761</td>\n      <td>Gasoline</td>\n      <td>5.2L V10 40V GDI DOHC</td>\n      <td>7-Speed Automatic with Auto-Shift</td>\n      <td>Rosso Corsa</td>\n      <td>White</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>41646</th>\n      <td>Mercedes-Benz</td>\n      <td>S-Class S 63 AMG</td>\n      <td>2011</td>\n      <td>37100</td>\n      <td>Gasoline</td>\n      <td>577.0HP 5.5L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>7-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>37520</th>\n      <td>Audi</td>\n      <td>R8 5.2 V10 performance</td>\n      <td>2011</td>\n      <td>12245</td>\n      <td>Gasoline</td>\n      <td>5.2L V10 40V GDI DOHC</td>\n      <td>7-Speed Automatic with Auto-Shift</td>\n      <td>White</td>\n      <td>White</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>37355</th>\n      <td>Jeep</td>\n      <td>Wrangler Sport</td>\n      <td>2016</td>\n      <td>74582</td>\n      <td>Gasoline</td>\n      <td>285.0HP 3.6L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>4-Speed A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>6052</th>\n      <td>Porsche</td>\n      <td>911 Carrera 4S</td>\n      <td>2005</td>\n      <td>28800</td>\n      <td>Gasoline</td>\n      <td>3.0L H6 24V GDI DOHC Twin Turbo</td>\n      <td>6-Speed Manual</td>\n      <td>White</td>\n      <td>Red</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>34484</th>\n      <td>Mercedes-Benz</td>\n      <td>E-Class E 350</td>\n      <td>2013</td>\n      <td>63496</td>\n      <td>Gasoline</td>\n      <td>302.0HP 3.5L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>7-Speed A/T</td>\n      <td>Red</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1950995</td>\n    </tr>\n    <tr>\n      <th>13284</th>\n      <td>Mercedes-Benz</td>\n      <td>AMG G 63 Base</td>\n      <td>2022</td>\n      <td>2950</td>\n      <td>Gasoline</td>\n      <td>577.0HP 4.0L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>32844</th>\n      <td>Porsche</td>\n      <td>Cayenne S</td>\n      <td>2023</td>\n      <td>1500</td>\n      <td>Gasoline</td>\n      <td>400.0HP 4.8L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>Gray</td>\n      <td>Gray</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>5794</th>\n      <td>Mercedes-Benz</td>\n      <td>AMG GT R</td>\n      <td>2017</td>\n      <td>3811</td>\n      <td>Gasoline</td>\n      <td>577.0HP 4.0L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>7-Speed A/T</td>\n      <td>Gray</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>13385</th>\n      <td>Mercedes-Benz</td>\n      <td>AMG GLE AMG GLE 63 S-Model 4MATIC</td>\n      <td>2018</td>\n      <td>2800</td>\n      <td>Gasoline</td>\n      <td>577.0HP 5.5L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>7-Speed A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>38273</th>\n      <td>Porsche</td>\n      <td>911 Carrera S</td>\n      <td>2015</td>\n      <td>28470</td>\n      <td>Gasoline</td>\n      <td>385.0HP 3.8L Flat 6 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed M/T</td>\n      <td>Gray</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>47387</th>\n      <td>Audi</td>\n      <td>RS 7 4.0T Performance Prestige</td>\n      <td>2020</td>\n      <td>22000</td>\n      <td>Gasoline</td>\n      <td>605.0HP 4.0L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>Blue</td>\n      <td>White</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>49610</th>\n      <td>Audi</td>\n      <td>RS 7 4.0T Prestige</td>\n      <td>2016</td>\n      <td>64800</td>\n      <td>Gasoline</td>\n      <td>560.0HP 4.0L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>Gray</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>2568</th>\n      <td>Porsche</td>\n      <td>911 GT3</td>\n      <td>2019</td>\n      <td>1568</td>\n      <td>Gasoline</td>\n      <td>475.0HP 3.8L Flat 6 Cylinder Engine Gasoline Fuel</td>\n      <td>7-Speed A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>1599000</td>\n    </tr>\n    <tr>\n      <th>9891</th>\n      <td>Ford</td>\n      <td>F-350 Platinum</td>\n      <td>2022</td>\n      <td>7500</td>\n      <td>Diesel</td>\n      <td>475.0HP 6.7L 8 Cylinder Engine Diesel Fuel</td>\n      <td>A/T</td>\n      <td>Gray</td>\n      <td>Gray</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>2550</th>\n      <td>Ford</td>\n      <td>Expedition Timberline</td>\n      <td>2023</td>\n      <td>8911</td>\n      <td>Gasoline</td>\n      <td>3.5L V6 24V PDI DOHC Twin Turbo</td>\n      <td>10-Speed Automatic</td>\n      <td>Dark Matter Metallic</td>\n      <td>Black Onyx</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>53405</th>\n      <td>GMC</td>\n      <td>Yukon Denali Ultimate</td>\n      <td>2023</td>\n      <td>23600</td>\n      <td>Gasoline</td>\n      <td>420.0HP 6.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>10-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>789</th>\n      <td>Mercedes-Benz</td>\n      <td>AMG C 43 Base 4MATIC</td>\n      <td>2017</td>\n      <td>80300</td>\n      <td>Gasoline</td>\n      <td>362.0HP 3.0L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>9-Speed A/T</td>\n      <td>Gray</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>15200</th>\n      <td>Chevrolet</td>\n      <td>Corvette Stingray w/1LT</td>\n      <td>2020</td>\n      <td>11089</td>\n      <td>Gasoline</td>\n      <td>490.0HP 6.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>Red</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>33133</th>\n      <td>Genesis</td>\n      <td>G70 2.0T</td>\n      <td>2023</td>\n      <td>15198</td>\n      <td>Gasoline</td>\n      <td>252.0HP 2.0L 4 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>49718</th>\n      <td>Rolls-Royce</td>\n      <td>Ghost Base</td>\n      <td>2007</td>\n      <td>79000</td>\n      <td>Gasoline</td>\n      <td>624.0HP 6.6L 12 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>Silver</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>39705</th>\n      <td>Audi</td>\n      <td>A7 3.0T Prestige</td>\n      <td>2021</td>\n      <td>15000</td>\n      <td>Hybrid</td>\n      <td>335.0HP 3.0L V6 Cylinder Engine Gasoline/Mild ...</td>\n      <td>A/T</td>\n      <td>Gray</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>3256</th>\n      <td>Ford</td>\n      <td>F-250 Platinum</td>\n      <td>2023</td>\n      <td>89980</td>\n      <td>Diesel</td>\n      <td>475.0HP 6.7L 8 Cylinder Engine Diesel Fuel</td>\n      <td>A/T</td>\n      <td>Brown</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>8804</th>\n      <td>Porsche</td>\n      <td>911 Turbo S</td>\n      <td>2021</td>\n      <td>154</td>\n      <td>Gasoline</td>\n      <td>3.8L H6 24V GDI DOHC Twin Turbo</td>\n      <td>8-Speed Automatic with Auto-Shift</td>\n      <td>Python Green</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>45245</th>\n      <td>Bentley</td>\n      <td>Continental GT Base</td>\n      <td>2009</td>\n      <td>25500</td>\n      <td>Gasoline</td>\n      <td>552.0HP 6.0L 12 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed M/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>47689</th>\n      <td>Chrysler</td>\n      <td>200 Limited</td>\n      <td>2021</td>\n      <td>14684</td>\n      <td>Gasoline</td>\n      <td>3.6L V6 24V MPFI DOHC</td>\n      <td>5-Speed Automatic</td>\n      <td>Black</td>\n      <td>–</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>8362</th>\n      <td>Lamborghini</td>\n      <td>Urus Base</td>\n      <td>2021</td>\n      <td>16876</td>\n      <td>Gasoline</td>\n      <td>4.0L V8 32V GDI DOHC Twin Turbo</td>\n      <td>8-Speed Automatic</td>\n      <td>Black</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>28400</th>\n      <td>Ford</td>\n      <td>Expedition Limited</td>\n      <td>2023</td>\n      <td>93400</td>\n      <td>Gasoline</td>\n      <td>375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>10-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>13898</th>\n      <td>BMW</td>\n      <td>X7 xDrive40i</td>\n      <td>2023</td>\n      <td>14360</td>\n      <td>Hybrid</td>\n      <td>375.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>8-Speed A/T</td>\n      <td>Black</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>749950</td>\n    </tr>\n    <tr>\n      <th>21905</th>\n      <td>Chevrolet</td>\n      <td>Corvette Stingray w/1LT</td>\n      <td>2023</td>\n      <td>15500</td>\n      <td>Gasoline</td>\n      <td>490.0HP 6.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>Gray</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>695000</td>\n    </tr>\n    <tr>\n      <th>50813</th>\n      <td>Dodge</td>\n      <td>Viper SRT-10</td>\n      <td>2004</td>\n      <td>3763</td>\n      <td>Gasoline</td>\n      <td>550.0HP 5.4L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed M/T</td>\n      <td>Blue</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>695000</td>\n    </tr>\n    <tr>\n      <th>53192</th>\n      <td>Mercedes-Benz</td>\n      <td>Sprinter 3500 High Roof</td>\n      <td>2019</td>\n      <td>39000</td>\n      <td>Diesel</td>\n      <td>188.0HP 3.0L V6 Cylinder Engine Diesel Fuel</td>\n      <td>A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>695000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"new_df = new_df[new_df[\"price\"] <=  749950] # drop values \nnew_df","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:04.968014Z","iopub.execute_input":"2024-06-08T09:50:04.968494Z","iopub.status.idle":"2024-06-08T09:50:05.000115Z","shell.execute_reply.started":"2024-06-08T09:50:04.968454Z","shell.execute_reply":"2024-06-08T09:50:04.998915Z"},"trusted":true},"execution_count":110,"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"         brand                             model  model_year  milage  \\\n0         Ford                      F-150 Lariat        2018   74349   \n1          BMW                             335 i        2007   80000   \n2       Jaguar                         XF Luxury        2009   91491   \n3          BMW                      X7 xDrive40i        2022    2437   \n4      Pontiac                     Firebird Base        2001  111000   \n...        ...                               ...         ...     ...   \n54268      BMW                      X6 xDrive50i        2017   29000   \n54269     Audi                   A4 2.0T Premium        2015   94634   \n54270  Porsche                         Cayenne S        2013   40989   \n54271  Porsche                 911 Carrera 4 GTS        2023    1518   \n54272     Audi  A5 Sportback S line Premium Plus        2021   35000   \n\n           fuel_type                                             engine  \\\n0           Gasoline      375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel   \n1           Gasoline  300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n2           Gasoline       300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel   \n3             Hybrid  335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n4           Gasoline      200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel   \n...              ...                                                ...   \n54268       Gasoline       445.0HP 4.4L 8 Cylinder Engine Gasoline Fuel   \n54269  E85 Flex Fuel  220.0HP 2.0L 4 Cylinder Engine Flex Fuel Capab...   \n54270       Gasoline      420.0HP 3.6L V6 Cylinder Engine Gasoline Fuel   \n54271       Gasoline                               4.0L H6 24V GDI DOHC   \n54272         Hybrid  261.0HP 2.0L 4 Cylinder Engine Gasoline/Mild E...   \n\n                            transmission ext_col int_col  \\\n0                           10-Speed A/T    Blue    Gray   \n1                            6-Speed M/T   Black   Black   \n2                            6-Speed A/T  Purple   Beige   \n3         Transmission w/Dual Shift Mode    Gray   Brown   \n4                                    A/T   White   Black   \n...                                  ...     ...     ...   \n54268                        8-Speed A/T   White   Brown   \n54269                        6-Speed A/T   Black   Black   \n54270     Transmission w/Dual Shift Mode   White   Black   \n54271  8-Speed Automatic with Auto-Shift   Beige   Brown   \n54272                        8-Speed A/T   Black   Black   \n\n                                     accident clean_title   price  \n0                               None reported         Yes   11000  \n1                               None reported         Yes    8250  \n2                               None reported         Yes   15000  \n3                               None reported         Yes   63500  \n4                               None reported         Yes    7850  \n...                                       ...         ...     ...  \n54268                           None reported         Yes   29000  \n54269  At least 1 accident or damage reported         Yes    6500  \n54270  At least 1 accident or damage reported         Yes   18950  \n54271                           None reported         Yes  194965  \n54272                           None reported         Yes   37499  \n\n[54241 rows x 12 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>brand</th>\n      <th>model</th>\n      <th>model_year</th>\n      <th>milage</th>\n      <th>fuel_type</th>\n      <th>engine</th>\n      <th>transmission</th>\n      <th>ext_col</th>\n      <th>int_col</th>\n      <th>accident</th>\n      <th>clean_title</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Ford</td>\n      <td>F-150 Lariat</td>\n      <td>2018</td>\n      <td>74349</td>\n      <td>Gasoline</td>\n      <td>375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>10-Speed A/T</td>\n      <td>Blue</td>\n      <td>Gray</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>11000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BMW</td>\n      <td>335 i</td>\n      <td>2007</td>\n      <td>80000</td>\n      <td>Gasoline</td>\n      <td>300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>6-Speed M/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>8250</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Jaguar</td>\n      <td>XF Luxury</td>\n      <td>2009</td>\n      <td>91491</td>\n      <td>Gasoline</td>\n      <td>300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed A/T</td>\n      <td>Purple</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>15000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BMW</td>\n      <td>X7 xDrive40i</td>\n      <td>2022</td>\n      <td>2437</td>\n      <td>Hybrid</td>\n      <td>335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>Gray</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>63500</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Pontiac</td>\n      <td>Firebird Base</td>\n      <td>2001</td>\n      <td>111000</td>\n      <td>Gasoline</td>\n      <td>200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>7850</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    </tr>\n    <tr>\n      <th>54268</th>\n      <td>BMW</td>\n      <td>X6 xDrive50i</td>\n      <td>2017</td>\n      <td>29000</td>\n      <td>Gasoline</td>\n      <td>445.0HP 4.4L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>White</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>29000</td>\n    </tr>\n    <tr>\n      <th>54269</th>\n      <td>Audi</td>\n      <td>A4 2.0T Premium</td>\n      <td>2015</td>\n      <td>94634</td>\n      <td>E85 Flex Fuel</td>\n      <td>220.0HP 2.0L 4 Cylinder Engine Flex Fuel Capab...</td>\n      <td>6-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>6500</td>\n    </tr>\n    <tr>\n      <th>54270</th>\n      <td>Porsche</td>\n      <td>Cayenne S</td>\n      <td>2013</td>\n      <td>40989</td>\n      <td>Gasoline</td>\n      <td>420.0HP 3.6L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>18950</td>\n    </tr>\n    <tr>\n      <th>54271</th>\n      <td>Porsche</td>\n      <td>911 Carrera 4 GTS</td>\n      <td>2023</td>\n      <td>1518</td>\n      <td>Gasoline</td>\n      <td>4.0L H6 24V GDI DOHC</td>\n      <td>8-Speed Automatic with Auto-Shift</td>\n      <td>Beige</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>194965</td>\n    </tr>\n    <tr>\n      <th>54272</th>\n      <td>Audi</td>\n      <td>A5 Sportback S line Premium Plus</td>\n      <td>2021</td>\n      <td>35000</td>\n      <td>Hybrid</td>\n      <td>261.0HP 2.0L 4 Cylinder Engine Gasoline/Mild E...</td>\n      <td>8-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>37499</td>\n    </tr>\n  </tbody>\n</table>\n<p>54241 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Preparing Data For The Model","metadata":{}},{"cell_type":"code","source":"new_df.shape","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:08.407611Z","iopub.execute_input":"2024-06-08T09:50:08.408037Z","iopub.status.idle":"2024-06-08T09:50:08.415119Z","shell.execute_reply.started":"2024-06-08T09:50:08.408004Z","shell.execute_reply":"2024-06-08T09:50:08.413915Z"},"trusted":true},"execution_count":111,"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"(54241, 12)"},"metadata":{}}]},{"cell_type":"code","source":"current_year = 2024\nnew_df= new_df.assign(age = current_year - new_df[\"model_year\"])","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:11.477698Z","iopub.execute_input":"2024-06-08T09:50:11.478154Z","iopub.status.idle":"2024-06-08T09:50:11.489769Z","shell.execute_reply.started":"2024-06-08T09:50:11.478120Z","shell.execute_reply":"2024-06-08T09:50:11.488310Z"},"trusted":true},"execution_count":112,"outputs":[]},{"cell_type":"code","source":"new_df.drop(\"model_year\", axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:14.467263Z","iopub.execute_input":"2024-06-08T09:50:14.467731Z","iopub.status.idle":"2024-06-08T09:50:14.483654Z","shell.execute_reply.started":"2024-06-08T09:50:14.467696Z","shell.execute_reply":"2024-06-08T09:50:14.482254Z"},"trusted":true},"execution_count":113,"outputs":[]},{"cell_type":"code","source":"new_df","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:18.603098Z","iopub.execute_input":"2024-06-08T09:50:18.603615Z","iopub.status.idle":"2024-06-08T09:50:18.629274Z","shell.execute_reply.started":"2024-06-08T09:50:18.603576Z","shell.execute_reply":"2024-06-08T09:50:18.627990Z"},"trusted":true},"execution_count":114,"outputs":[{"execution_count":114,"output_type":"execute_result","data":{"text/plain":"         brand                             model  milage      fuel_type  \\\n0         Ford                      F-150 Lariat   74349       Gasoline   \n1          BMW                             335 i   80000       Gasoline   \n2       Jaguar                         XF Luxury   91491       Gasoline   \n3          BMW                      X7 xDrive40i    2437         Hybrid   \n4      Pontiac                     Firebird Base  111000       Gasoline   \n...        ...                               ...     ...            ...   \n54268      BMW                      X6 xDrive50i   29000       Gasoline   \n54269     Audi                   A4 2.0T Premium   94634  E85 Flex Fuel   \n54270  Porsche                         Cayenne S   40989       Gasoline   \n54271  Porsche                 911 Carrera 4 GTS    1518       Gasoline   \n54272     Audi  A5 Sportback S line Premium Plus   35000         Hybrid   \n\n                                                  engine  \\\n0          375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel   \n1      300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n2           300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel   \n3      335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...   \n4          200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel   \n...                                                  ...   \n54268       445.0HP 4.4L 8 Cylinder Engine Gasoline Fuel   \n54269  220.0HP 2.0L 4 Cylinder Engine Flex Fuel Capab...   \n54270      420.0HP 3.6L V6 Cylinder Engine Gasoline Fuel   \n54271                               4.0L H6 24V GDI DOHC   \n54272  261.0HP 2.0L 4 Cylinder Engine Gasoline/Mild E...   \n\n                            transmission ext_col int_col  \\\n0                           10-Speed A/T    Blue    Gray   \n1                            6-Speed M/T   Black   Black   \n2                            6-Speed A/T  Purple   Beige   \n3         Transmission w/Dual Shift Mode    Gray   Brown   \n4                                    A/T   White   Black   \n...                                  ...     ...     ...   \n54268                        8-Speed A/T   White   Brown   \n54269                        6-Speed A/T   Black   Black   \n54270     Transmission w/Dual Shift Mode   White   Black   \n54271  8-Speed Automatic with Auto-Shift   Beige   Brown   \n54272                        8-Speed A/T   Black   Black   \n\n                                     accident clean_title   price  age  \n0                               None reported         Yes   11000    6  \n1                               None reported         Yes    8250   17  \n2                               None reported         Yes   15000   15  \n3                               None reported         Yes   63500    2  \n4                               None reported         Yes    7850   23  \n...                                       ...         ...     ...  ...  \n54268                           None reported         Yes   29000    7  \n54269  At least 1 accident or damage reported         Yes    6500    9  \n54270  At least 1 accident or damage reported         Yes   18950   11  \n54271                           None reported         Yes  194965    1  \n54272                           None reported         Yes   37499    3  \n\n[54241 rows x 12 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>brand</th>\n      <th>model</th>\n      <th>milage</th>\n      <th>fuel_type</th>\n      <th>engine</th>\n      <th>transmission</th>\n      <th>ext_col</th>\n      <th>int_col</th>\n      <th>accident</th>\n      <th>clean_title</th>\n      <th>price</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Ford</td>\n      <td>F-150 Lariat</td>\n      <td>74349</td>\n      <td>Gasoline</td>\n      <td>375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>10-Speed A/T</td>\n      <td>Blue</td>\n      <td>Gray</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>11000</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BMW</td>\n      <td>335 i</td>\n      <td>80000</td>\n      <td>Gasoline</td>\n      <td>300.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>6-Speed M/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>8250</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Jaguar</td>\n      <td>XF Luxury</td>\n      <td>91491</td>\n      <td>Gasoline</td>\n      <td>300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>6-Speed A/T</td>\n      <td>Purple</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>15000</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BMW</td>\n      <td>X7 xDrive40i</td>\n      <td>2437</td>\n      <td>Hybrid</td>\n      <td>335.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>Gray</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>63500</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Pontiac</td>\n      <td>Firebird Base</td>\n      <td>111000</td>\n      <td>Gasoline</td>\n      <td>200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>7850</td>\n      <td>23</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    </tr>\n    <tr>\n      <th>54268</th>\n      <td>BMW</td>\n      <td>X6 xDrive50i</td>\n      <td>29000</td>\n      <td>Gasoline</td>\n      <td>445.0HP 4.4L 8 Cylinder Engine Gasoline Fuel</td>\n      <td>8-Speed A/T</td>\n      <td>White</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>29000</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>54269</th>\n      <td>Audi</td>\n      <td>A4 2.0T Premium</td>\n      <td>94634</td>\n      <td>E85 Flex Fuel</td>\n      <td>220.0HP 2.0L 4 Cylinder Engine Flex Fuel Capab...</td>\n      <td>6-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>6500</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>54270</th>\n      <td>Porsche</td>\n      <td>Cayenne S</td>\n      <td>40989</td>\n      <td>Gasoline</td>\n      <td>420.0HP 3.6L V6 Cylinder Engine Gasoline Fuel</td>\n      <td>Transmission w/Dual Shift Mode</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>18950</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>54271</th>\n      <td>Porsche</td>\n      <td>911 Carrera 4 GTS</td>\n      <td>1518</td>\n      <td>Gasoline</td>\n      <td>4.0L H6 24V GDI DOHC</td>\n      <td>8-Speed Automatic with Auto-Shift</td>\n      <td>Beige</td>\n      <td>Brown</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>194965</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>54272</th>\n      <td>Audi</td>\n      <td>A5 Sportback S line Premium Plus</td>\n      <td>35000</td>\n      <td>Hybrid</td>\n      <td>261.0HP 2.0L 4 Cylinder Engine Gasoline/Mild E...</td>\n      <td>8-Speed A/T</td>\n      <td>Black</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>37499</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>54241 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test_data = pd.read_csv(\"/kaggle/input/kagglex-skill-dataset/test.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:23.692732Z","iopub.execute_input":"2024-06-08T09:50:23.693167Z","iopub.status.idle":"2024-06-08T09:50:23.849042Z","shell.execute_reply.started":"2024-06-08T09:50:23.693132Z","shell.execute_reply":"2024-06-08T09:50:23.847904Z"},"trusted":true},"execution_count":115,"outputs":[]},{"cell_type":"code","source":"X_test = test_data[:]","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:26.037734Z","iopub.execute_input":"2024-06-08T09:50:26.038200Z","iopub.status.idle":"2024-06-08T09:50:26.046132Z","shell.execute_reply.started":"2024-06-08T09:50:26.038166Z","shell.execute_reply":"2024-06-08T09:50:26.044552Z"},"trusted":true},"execution_count":116,"outputs":[]},{"cell_type":"code","source":"new_df.drop(\"model\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:29.568146Z","iopub.execute_input":"2024-06-08T09:50:29.568603Z","iopub.status.idle":"2024-06-08T09:50:29.582153Z","shell.execute_reply.started":"2024-06-08T09:50:29.568569Z","shell.execute_reply":"2024-06-08T09:50:29.580868Z"},"trusted":true},"execution_count":117,"outputs":[]},{"cell_type":"code","source":"X_test = X_test.assign(age = current_year - X_test[\"model_year\"])\nX_test.drop(\"model_year\", axis = 1, inplace = True)\nX_test.drop(\"model\", axis = 1, inplace = True)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:35.257856Z","iopub.execute_input":"2024-06-08T09:50:35.258351Z","iopub.status.idle":"2024-06-08T09:50:35.280903Z","shell.execute_reply.started":"2024-06-08T09:50:35.258315Z","shell.execute_reply":"2024-06-08T09:50:35.279494Z"},"trusted":true},"execution_count":118,"outputs":[]},{"cell_type":"code","source":"new_df[\"engine\"].unique()[:10]","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:50:40.507567Z","iopub.execute_input":"2024-06-08T09:50:40.507997Z","iopub.status.idle":"2024-06-08T09:50:40.524992Z","shell.execute_reply.started":"2024-06-08T09:50:40.507962Z","shell.execute_reply":"2024-06-08T09:50:40.523445Z"},"trusted":true},"execution_count":119,"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":"array(['375.0HP 3.5L V6 Cylinder Engine Gasoline Fuel',\n       '300.0HP 3.0L Straight 6 Cylinder Engine Gasoline Fuel',\n       '300.0HP 4.2L 8 Cylinder Engine Gasoline Fuel',\n       '335.0HP 3.0L Straight 6 Cylinder Engine Gasoline/Mild Electric Hybrid',\n       '200.0HP 3.8L V6 Cylinder Engine Gasoline Fuel',\n       '140.0HP 1.8L 4 Cylinder Engine Gasoline Fuel',\n       '333.0HP 3.0L V6 Cylinder Engine Gasoline Fuel',\n       '193.0HP 2.5L 4 Cylinder Engine Gasoline Fuel',\n       '200.0HP 2.0L 4 Cylinder Engine Gasoline Fuel',\n       '300.0HP 3.7L V6 Cylinder Engine Gasoline Fuel'], dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"import re\n\ndef extract_features(engine_desc):\n    \n    hp_match = re.search(r'(\\d+(\\.\\d+)?)HP', engine_desc)\n    horsepower = float(hp_match.group(1)) if hp_match else None\n    \n    displacement_match = re.search(r'(\\d+(\\.\\d+)?)L', engine_desc)\n    displacement = float(displacement_match.group(1)) if displacement_match else None\n\n    cylinders_match = re.search(r'(\\d+(\\.\\d+)?) Cylinder', engine_desc)\n    cylinders = float(cylinders_match.group(1)) if cylinders_match else None\n\n    return horsepower, displacement, cylinders","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:51:02.898446Z","iopub.execute_input":"2024-06-08T09:51:02.898866Z","iopub.status.idle":"2024-06-08T09:51:02.908001Z","shell.execute_reply.started":"2024-06-08T09:51:02.898835Z","shell.execute_reply":"2024-06-08T09:51:02.906003Z"},"trusted":true},"execution_count":120,"outputs":[]},{"cell_type":"code","source":"new_df[['horsepower', 'displacement', 'cylinders']] = new_df['engine'].apply(extract_features).apply(pd.Series)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:51:06.347256Z","iopub.execute_input":"2024-06-08T09:51:06.347694Z","iopub.status.idle":"2024-06-08T09:51:15.950142Z","shell.execute_reply.started":"2024-06-08T09:51:06.347658Z","shell.execute_reply":"2024-06-08T09:51:15.948916Z"},"trusted":true},"execution_count":121,"outputs":[]},{"cell_type":"code","source":"X_test[['horsepower', 'displacement', 'cylinders']] = X_test['engine'].apply(extract_features).apply(pd.Series)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:51:15.952430Z","iopub.execute_input":"2024-06-08T09:51:15.952870Z","iopub.status.idle":"2024-06-08T09:51:21.982499Z","shell.execute_reply.started":"2024-06-08T09:51:15.952836Z","shell.execute_reply":"2024-06-08T09:51:21.981258Z"},"trusted":true},"execution_count":122,"outputs":[]},{"cell_type":"code","source":"X_test = X_test.drop(\"engine\", axis = 1)\nnew_df = new_df.drop(\"engine\", axis = 1)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T09:51:21.984608Z","iopub.execute_input":"2024-06-08T09:51:21.984991Z","iopub.status.idle":"2024-06-08T09:51:22.001875Z","shell.execute_reply.started":"2024-06-08T09:51:21.984960Z","shell.execute_reply":"2024-06-08T09:51:22.000255Z"},"trusted":true},"execution_count":123,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import KMeans\n\ndff_ = new_df[:]\n\nfeatures = ['horsepower', 'displacement', 'cylinders']\ndf_features = dff_[features]\n\ndf_features = df_features.fillna(df_features.mean())\n\nscaler = StandardScaler()\ndf_scaled = scaler.fit_transform(df_features)\n\nkmeans = KMeans(n_init = 1, n_clusters=3, random_state=42)\n\ndff_['cluster'] = kmeans.fit_predict(df_scaled)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:06:23.900574Z","iopub.execute_input":"2024-06-08T10:06:23.900951Z","iopub.status.idle":"2024-06-08T10:06:24.072280Z","shell.execute_reply.started":"2024-06-08T10:06:23.900924Z","shell.execute_reply":"2024-06-08T10:06:24.070914Z"},"trusted":true},"execution_count":141,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/2971467737.py:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  dff_['cluster'] = kmeans.fit_predict(df_scaled)\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.impute import KNNImputer\n\ndef impute_cluster(group):\n    imputer = KNNImputer(n_neighbors=2)\n    group[features] = imputer.fit_transform(group[features])\n    return group\n\ndf_imputed = (\n    dff_.groupby('cluster')\n    .apply(lambda group: impute_cluster(group))\n    .reset_index(drop=True)\n)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:11:45.612079Z","iopub.execute_input":"2024-06-08T10:11:45.612492Z","iopub.status.idle":"2024-06-08T10:11:55.788474Z","shell.execute_reply.started":"2024-06-08T10:11:45.612461Z","shell.execute_reply":"2024-06-08T10:11:55.787218Z"},"trusted":true},"execution_count":145,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/3456981258.py:9: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n  .apply(lambda group: impute_cluster(group))\n","output_type":"stream"}]},{"cell_type":"code","source":"X_ = X_test[:]\n\nX_features = X_[features]\n\nX_features = X_features.fillna(X_features.mean())\n\nscaler = StandardScaler()\nX_scaled = scaler.fit_transform(X_features)\n\nkmeans = KMeans(n_init = 1, n_clusters=3, random_state=42)\n\nX_['cluster'] = kmeans.fit_predict(X_scaled)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:16:02.631542Z","iopub.execute_input":"2024-06-08T10:16:02.631974Z","iopub.status.idle":"2024-06-08T10:16:02.800061Z","shell.execute_reply.started":"2024-06-08T10:16:02.631943Z","shell.execute_reply":"2024-06-08T10:16:02.798813Z"},"trusted":true},"execution_count":148,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/539148929.py:12: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  X_['cluster'] = kmeans.fit_predict(X_scaled)\n","output_type":"stream"}]},{"cell_type":"code","source":"X_test_imputed = (\n    X_.groupby('cluster')\n    .apply(lambda group: impute_cluster(group))\n    .reset_index(drop=True)\n)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:16:11.536105Z","iopub.execute_input":"2024-06-08T10:16:11.536645Z","iopub.status.idle":"2024-06-08T10:16:15.569526Z","shell.execute_reply.started":"2024-06-08T10:16:11.536607Z","shell.execute_reply":"2024-06-08T10:16:15.568264Z"},"trusted":true},"execution_count":149,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/1905705197.py:3: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n  .apply(lambda group: impute_cluster(group))\n","output_type":"stream"}]},{"cell_type":"code","source":"df_imputed = df_imputed.drop(\"cluster\", axis = 1)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:19:55.411290Z","iopub.execute_input":"2024-06-08T10:19:55.411742Z","iopub.status.idle":"2024-06-08T10:19:55.422891Z","shell.execute_reply.started":"2024-06-08T10:19:55.411709Z","shell.execute_reply":"2024-06-08T10:19:55.421232Z"},"trusted":true},"execution_count":156,"outputs":[]},{"cell_type":"code","source":"X_test_imputed = X_test_imputed.drop(\"cluster\", axis = 1)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:20:34.391773Z","iopub.execute_input":"2024-06-08T10:20:34.392198Z","iopub.status.idle":"2024-06-08T10:20:34.402105Z","shell.execute_reply.started":"2024-06-08T10:20:34.392165Z","shell.execute_reply":"2024-06-08T10:20:34.400914Z"},"trusted":true},"execution_count":158,"outputs":[]},{"cell_type":"code","source":"X_test_imputed = X_test_imputed.drop(\"id\", axis = 1)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:21:53.678211Z","iopub.execute_input":"2024-06-08T10:21:53.679375Z","iopub.status.idle":"2024-06-08T10:21:53.689940Z","shell.execute_reply.started":"2024-06-08T10:21:53.679331Z","shell.execute_reply":"2024-06-08T10:21:53.688538Z"},"trusted":true},"execution_count":163,"outputs":[]},{"cell_type":"code","source":"X_test_imputed.shape","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:22:49.501657Z","iopub.execute_input":"2024-06-08T10:22:49.502089Z","iopub.status.idle":"2024-06-08T10:22:49.509677Z","shell.execute_reply.started":"2024-06-08T10:22:49.502056Z","shell.execute_reply":"2024-06-08T10:22:49.508380Z"},"trusted":true},"execution_count":168,"outputs":[{"execution_count":168,"output_type":"execute_result","data":{"text/plain":"(36183, 12)"},"metadata":{}}]},{"cell_type":"code","source":"X = df_imputed.drop(\"price\", axis = 1)\ny = df_imputed[\"price\"]","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:23:17.951214Z","iopub.execute_input":"2024-06-08T10:23:17.951649Z","iopub.status.idle":"2024-06-08T10:23:17.965363Z","shell.execute_reply.started":"2024-06-08T10:23:17.951617Z","shell.execute_reply":"2024-06-08T10:23:17.964250Z"},"trusted":true},"execution_count":170,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.2, random_state = 42)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:23:20.921077Z","iopub.execute_input":"2024-06-08T10:23:20.921495Z","iopub.status.idle":"2024-06-08T10:23:20.952649Z","shell.execute_reply.started":"2024-06-08T10:23:20.921465Z","shell.execute_reply":"2024-06-08T10:23:20.951396Z"},"trusted":true},"execution_count":171,"outputs":[]},{"cell_type":"code","source":"X_train.shape, X_val.shape, y_train.shape, y_val.shape","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:23:24.141178Z","iopub.execute_input":"2024-06-08T10:23:24.141647Z","iopub.status.idle":"2024-06-08T10:23:24.150831Z","shell.execute_reply.started":"2024-06-08T10:23:24.141614Z","shell.execute_reply":"2024-06-08T10:23:24.148908Z"},"trusted":true},"execution_count":172,"outputs":[{"execution_count":172,"output_type":"execute_result","data":{"text/plain":"((43392, 12), (10849, 12), (43392,), (10849,))"},"metadata":{}}]},{"cell_type":"code","source":"X_train","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:23:27.261195Z","iopub.execute_input":"2024-06-08T10:23:27.261636Z","iopub.status.idle":"2024-06-08T10:23:27.286226Z","shell.execute_reply.started":"2024-06-08T10:23:27.261600Z","shell.execute_reply":"2024-06-08T10:23:27.284690Z"},"trusted":true},"execution_count":173,"outputs":[{"execution_count":173,"output_type":"execute_result","data":{"text/plain":"           brand  milage fuel_type  transmission ext_col int_col  \\\n23622       Ford   17500    Hybrid           A/T     Red   Black   \n38100       Ford   50780    Diesel   6-Speed A/T    Blue    Gray   \n18215      Volvo   60660  Gasoline   8-Speed A/T    Gray   Black   \n13047     Toyota   52000  Gasoline   9-Speed A/T   White   Black   \n33404     Nissan   61450  Gasoline   5-Speed M/T   Black    Gray   \n...          ...     ...       ...           ...     ...     ...   \n11284       Ford   64552  Gasoline  10-Speed A/T   White    Gray   \n44732  Chevrolet   72000  Gasoline           A/T    Blue   Beige   \n38158       Ford   72000    Diesel   6-Speed A/T    Gray   Black   \n860       Toyota   19000    Hybrid  10-Speed A/T  Orange   Black   \n15795       Land   47200  Gasoline   8-Speed A/T   Black    Gray   \n\n                                     accident clean_title  age  horsepower  \\\n23622                           None reported         Yes    2       318.0   \n38100  At least 1 accident or damage reported         Yes    7       450.0   \n18215  At least 1 accident or damage reported         Yes    6       316.0   \n13047                           None reported         Yes    4       295.0   \n33404  At least 1 accident or damage reported         Yes   14       122.0   \n...                                       ...         ...  ...         ...   \n11284  At least 1 accident or damage reported         Yes    6       325.0   \n44732                           None reported         Yes    5       355.0   \n38158                           None reported         Yes    5       450.0   \n860                             None reported         Yes    2       437.0   \n15795  At least 1 accident or damage reported         Yes    6       340.0   \n\n       displacement  cylinders  \n23622           3.3        6.0  \n38100           6.7        8.0  \n18215           2.0        4.0  \n13047           3.5        6.0  \n33404           1.8        4.0  \n...             ...        ...  \n11284           2.7        6.0  \n44732           5.3        8.0  \n38158           6.7        8.0  \n860             3.4        6.0  \n15795           3.0        6.0  \n\n[43392 rows x 12 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>brand</th>\n      <th>milage</th>\n      <th>fuel_type</th>\n      <th>transmission</th>\n      <th>ext_col</th>\n      <th>int_col</th>\n      <th>accident</th>\n      <th>clean_title</th>\n      <th>age</th>\n      <th>horsepower</th>\n      <th>displacement</th>\n      <th>cylinders</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>23622</th>\n      <td>Ford</td>\n      <td>17500</td>\n      <td>Hybrid</td>\n      <td>A/T</td>\n      <td>Red</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2</td>\n      <td>318.0</td>\n      <td>3.3</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>38100</th>\n      <td>Ford</td>\n      <td>50780</td>\n      <td>Diesel</td>\n      <td>6-Speed A/T</td>\n      <td>Blue</td>\n      <td>Gray</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>7</td>\n      <td>450.0</td>\n      <td>6.7</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>18215</th>\n      <td>Volvo</td>\n      <td>60660</td>\n      <td>Gasoline</td>\n      <td>8-Speed A/T</td>\n      <td>Gray</td>\n      <td>Black</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>6</td>\n      <td>316.0</td>\n      <td>2.0</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>13047</th>\n      <td>Toyota</td>\n      <td>52000</td>\n      <td>Gasoline</td>\n      <td>9-Speed A/T</td>\n      <td>White</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>4</td>\n      <td>295.0</td>\n      <td>3.5</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>33404</th>\n      <td>Nissan</td>\n      <td>61450</td>\n      <td>Gasoline</td>\n      <td>5-Speed M/T</td>\n      <td>Black</td>\n      <td>Gray</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>14</td>\n      <td>122.0</td>\n      <td>1.8</td>\n      <td>4.0</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    </tr>\n    <tr>\n      <th>11284</th>\n      <td>Ford</td>\n      <td>64552</td>\n      <td>Gasoline</td>\n      <td>10-Speed A/T</td>\n      <td>White</td>\n      <td>Gray</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>6</td>\n      <td>325.0</td>\n      <td>2.7</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>44732</th>\n      <td>Chevrolet</td>\n      <td>72000</td>\n      <td>Gasoline</td>\n      <td>A/T</td>\n      <td>Blue</td>\n      <td>Beige</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>5</td>\n      <td>355.0</td>\n      <td>5.3</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>38158</th>\n      <td>Ford</td>\n      <td>72000</td>\n      <td>Diesel</td>\n      <td>6-Speed A/T</td>\n      <td>Gray</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>5</td>\n      <td>450.0</td>\n      <td>6.7</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>860</th>\n      <td>Toyota</td>\n      <td>19000</td>\n      <td>Hybrid</td>\n      <td>10-Speed A/T</td>\n      <td>Orange</td>\n      <td>Black</td>\n      <td>None reported</td>\n      <td>Yes</td>\n      <td>2</td>\n      <td>437.0</td>\n      <td>3.4</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>15795</th>\n      <td>Land</td>\n      <td>47200</td>\n      <td>Gasoline</td>\n      <td>8-Speed A/T</td>\n      <td>Black</td>\n      <td>Gray</td>\n      <td>At least 1 accident or damage reported</td>\n      <td>Yes</td>\n      <td>6</td>\n      <td>340.0</td>\n      <td>3.0</td>\n      <td>6.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>43392 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Dealing With Categorical Values","metadata":{}},{"cell_type":"code","source":"X_train.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:31:49.710673Z","iopub.execute_input":"2024-06-08T10:31:49.711110Z","iopub.status.idle":"2024-06-08T10:31:49.762861Z","shell.execute_reply.started":"2024-06-08T10:31:49.711076Z","shell.execute_reply":"2024-06-08T10:31:49.761377Z"},"trusted":true},"execution_count":187,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 43392 entries, 23622 to 15795\nData columns (total 12 columns):\n #   Column        Non-Null Count  Dtype  \n---  ------        --------------  -----  \n 0   brand         43392 non-null  object \n 1   milage        43392 non-null  float64\n 2   fuel_type     43392 non-null  object \n 3   transmission  43392 non-null  object \n 4   ext_col       43392 non-null  object \n 5   int_col       43392 non-null  object \n 6   accident      43392 non-null  object \n 7   clean_title   43392 non-null  object \n 8   age           43392 non-null  float64\n 9   horsepower    43392 non-null  float64\n 10  displacement  43392 non-null  float64\n 11  cylinders     43392 non-null  float64\ndtypes: float64(5), object(7)\nmemory usage: 4.3+ MB\n","output_type":"stream"}]},{"cell_type":"code","source":"X_train.columns == X_test_imputed.columns","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:24:49.471357Z","iopub.execute_input":"2024-06-08T10:24:49.471856Z","iopub.status.idle":"2024-06-08T10:24:49.480065Z","shell.execute_reply.started":"2024-06-08T10:24:49.471817Z","shell.execute_reply":"2024-06-08T10:24:49.478895Z"},"trusted":true},"execution_count":176,"outputs":[{"execution_count":176,"output_type":"execute_result","data":{"text/plain":"array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n        True,  True,  True])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nnum_col = [\"milage\",\"age\", \"horsepower\", \"displacement\", \"cylinders\"]\nscaler = StandardScaler()\nX_train[num_col] = scaler.fit_transform(X_train[num_col])\nX_val[num_col] = scaler.transform(X_val[num_col])\nX_test_imputed[num_col] = scaler.transform(X_test_imputed[num_col])","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:27:16.966701Z","iopub.execute_input":"2024-06-08T10:27:16.967161Z","iopub.status.idle":"2024-06-08T10:27:17.001976Z","shell.execute_reply.started":"2024-06-08T10:27:16.967130Z","shell.execute_reply":"2024-06-08T10:27:17.000327Z"},"trusted":true},"execution_count":177,"outputs":[]},{"cell_type":"code","source":"cat_features_one_hot = ['brand', 'fuel_type', 'transmission', 'ext_col', 'int_col', 'accident', 'clean_title'] ","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:34:12.355160Z","iopub.execute_input":"2024-06-08T10:34:12.355681Z","iopub.status.idle":"2024-06-08T10:34:12.361563Z","shell.execute_reply.started":"2024-06-08T10:34:12.355645Z","shell.execute_reply":"2024-06-08T10:34:12.360185Z"},"trusted":true},"execution_count":192,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import OneHotEncoder\n\nencoder = OneHotEncoder(sparse_output = False, handle_unknown='ignore')\nX_train_encoded = encoder.fit_transform(X_train[cat_features_one_hot])\nX_val_encoded = encoder.transform(X_val[cat_features_one_hot])\nX_test_encoded = encoder.transform(X_test_imputed[cat_features_one_hot])","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:35:16.155097Z","iopub.execute_input":"2024-06-08T10:35:16.155568Z","iopub.status.idle":"2024-06-08T10:35:16.930388Z","shell.execute_reply.started":"2024-06-08T10:35:16.155534Z","shell.execute_reply":"2024-06-08T10:35:16.929175Z"},"trusted":true},"execution_count":195,"outputs":[]},{"cell_type":"markdown","source":"### Training Model","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error, r2_score\n\ndef model_result(model, X_val, y_val):\n    y_pred = model.predict(X_val)\n    mse = mean_squared_error(y_val, y_pred)\n    rmse = np.sqrt(mse)\n    r2 = r2_score(y_val, y_pred)\n    \n    print(f\"MSE: {mse}\")\n    print(f\"RMSE: {rmse}\")\n    print(f\"r2_score: {r2}\")","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:35:43.815247Z","iopub.execute_input":"2024-06-08T10:35:43.815700Z","iopub.status.idle":"2024-06-08T10:35:43.823768Z","shell.execute_reply.started":"2024-06-08T10:35:43.815665Z","shell.execute_reply":"2024-06-08T10:35:43.822052Z"},"trusted":true},"execution_count":198,"outputs":[]},{"cell_type":"code","source":"import xgboost as xgb \n\nxgb = xgb.XGBRegressor()\nxgb.fit(X_train_encoded, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:38:42.785091Z","iopub.execute_input":"2024-06-08T10:38:42.785549Z","iopub.status.idle":"2024-06-08T10:38:48.342198Z","shell.execute_reply.started":"2024-06-08T10:38:42.785504Z","shell.execute_reply":"2024-06-08T10:38:48.341022Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"XGBRegressor(base_score=None, booster=None, callbacks=None,\n             colsample_bylevel=None, colsample_bynode=None,\n             colsample_bytree=None, device=None, early_stopping_rounds=None,\n             enable_categorical=False, eval_metric=None, feature_types=None,\n             gamma=None, grow_policy=None, importance_type=None,\n             interaction_constraints=None, learning_rate=None, max_bin=None,\n             max_cat_threshold=None, max_cat_to_onehot=None,\n             max_delta_step=None, max_depth=None, max_leaves=None,\n             min_child_weight=None, missing=nan, monotone_constraints=None,\n             multi_strategy=None, n_estimators=None, n_jobs=None,\n             num_parallel_tree=None, random_state=None, ...)","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>XGBRegressor(base_score=None, booster=None, callbacks=None,\n             colsample_bylevel=None, colsample_bynode=None,\n             colsample_bytree=None, device=None, early_stopping_rounds=None,\n             enable_categorical=False, eval_metric=None, feature_types=None,\n             gamma=None, grow_policy=None, importance_type=None,\n             interaction_constraints=None, learning_rate=None, max_bin=None,\n             max_cat_threshold=None, max_cat_to_onehot=None,\n             max_delta_step=None, max_depth=None, max_leaves=None,\n             min_child_weight=None, missing=nan, monotone_constraints=None,\n             multi_strategy=None, n_estimators=None, n_jobs=None,\n             num_parallel_tree=None, random_state=None, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n             colsample_bylevel=None, colsample_bynode=None,\n             colsample_bytree=None, device=None, early_stopping_rounds=None,\n             enable_categorical=False, eval_metric=None, feature_types=None,\n             gamma=None, grow_policy=None, importance_type=None,\n             interaction_constraints=None, learning_rate=None, max_bin=None,\n             max_cat_threshold=None, max_cat_to_onehot=None,\n             max_delta_step=None, max_depth=None, max_leaves=None,\n             min_child_weight=None, missing=nan, monotone_constraints=None,\n             multi_strategy=None, n_estimators=None, n_jobs=None,\n             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"model_result(xgb, X_train_encoded, y_train)\nmodel_result(xgb, X_val_encoded, y_val)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:40:32.310190Z","iopub.execute_input":"2024-06-08T10:40:32.310648Z","iopub.status.idle":"2024-06-08T10:40:32.452859Z","shell.execute_reply.started":"2024-06-08T10:40:32.310610Z","shell.execute_reply":"2024-06-08T10:40:32.451879Z"},"trusted":true},"execution_count":204,"outputs":[{"name":"stdout","text":"MSE: 1368355167.3329737\nRMSE: 36991.28501867668\nr2_score: 0.3196819067155007\nMSE: 1624572109.2072742\nRMSE: 40305.98105997762\nr2_score: 0.14691195988275074\n","output_type":"stream"}]},{"cell_type":"code","source":"import lightgbm as lgb\n\nlgb = lgb.LGBMRegressor()\nlgb.fit(X_train_encoded, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:42:08.685311Z","iopub.execute_input":"2024-06-08T10:42:08.686632Z","iopub.status.idle":"2024-06-08T10:42:09.467774Z","shell.execute_reply.started":"2024-06-08T10:42:08.686590Z","shell.execute_reply":"2024-06-08T10:42:09.466487Z"},"trusted":true},"execution_count":208,"outputs":[{"name":"stdout","text":"[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005218 seconds.\nYou can set `force_row_wise=true` to remove the overhead.\nAnd if memory is not enough, you can set `force_col_wise=true`.\n[LightGBM] [Info] Total Bins 268\n[LightGBM] [Info] Number of data points in the train set: 43392, number of used features: 134\n[LightGBM] [Info] Start training from score 37941.887329\n","output_type":"stream"},{"execution_count":208,"output_type":"execute_result","data":{"text/plain":"LGBMRegressor()","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"model_result(lgb, X_train_encoded, y_train)\nmodel_result(lgb, X_val_encoded, y_val)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:42:24.105306Z","iopub.execute_input":"2024-06-08T10:42:24.105778Z","iopub.status.idle":"2024-06-08T10:42:24.535512Z","shell.execute_reply.started":"2024-06-08T10:42:24.105743Z","shell.execute_reply":"2024-06-08T10:42:24.534268Z"},"trusted":true},"execution_count":209,"outputs":[{"name":"stdout","text":"MSE: 1542285654.5075848\nRMSE: 39271.944878088034\nr2_score: 0.23320723974047408\nMSE: 1519614439.6980276\nRMSE: 38982.232359089285\nr2_score: 0.20202686187414842\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"Best parameters found: \", random_search.best_params_)\nprint(\"Best RMSE found: \", np.sqrt(-random_search.best_score_))","metadata":{"execution":{"iopub.status.busy":"2024-06-08T10:57:27.995991Z","iopub.status.idle":"2024-06-08T10:57:27.996535Z","shell.execute_reply.started":"2024-06-08T10:57:27.996229Z","shell.execute_reply":"2024-06-08T10:57:27.996247Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\n\ntraining_data = tf.data.Dataset.from_tensor_slices((X_train_encoded, y_train))\nvalidation_data = tf.data.Dataset.from_tensor_slices((X_val_encoded, y_val))\n\nbatch_size = 32\nauto = tf.data.AUTOTUNE \n\ntrain_data = training_data.batch(batch_size, num_parallel_calls = auto).cache().prefetch(buffer_size = auto)\nval_data = validation_data.batch(batch_size, num_parallel_calls = auto).cache().prefetch(buffer_size = auto)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T11:33:39.180671Z","iopub.execute_input":"2024-06-08T11:33:39.181102Z","iopub.status.idle":"2024-06-08T11:33:55.643312Z","shell.execute_reply.started":"2024-06-08T11:33:39.181070Z","shell.execute_reply":"2024-06-08T11:33:55.642029Z"},"trusted":true},"execution_count":216,"outputs":[{"name":"stderr","text":"2024-06-08 11:33:41.430107: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-06-08 11:33:41.430253: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-06-08 11:33:41.579594: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"code","source":"model = tf.keras.Sequential([\n    tf.keras.layers.InputLayer(shape = (478,)),\n    tf.keras.layers.Dense(128, activation = \"relu\"),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(64, activation = 'relu'),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(1)\n])\n\nmodel.summary()\n\nearly_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 5, restore_best_weights = True, verbose = 1)\n\nmodel.compile(loss = tf.keras.losses.MeanSquaredError(),\n              optimizer = tf.keras.optimizers.Adam())\n\nmodel.fit(train_data, epochs = 200, validation_data = val_data, callbacks=[early_stopping])","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:24:56.182161Z","iopub.execute_input":"2024-06-08T12:24:56.182598Z","iopub.status.idle":"2024-06-08T12:27:51.055317Z","shell.execute_reply.started":"2024-06-08T12:24:56.182565Z","shell.execute_reply":"2024-06-08T12:27:51.054100Z"},"trusted":true},"execution_count":254,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_8\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_8\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense_26 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m61,312\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_27 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_28 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">61,312</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m69,633\u001b[0m (272.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,633</span> (272.00 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m69,633\u001b[0m (272.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">69,633</span> (272.00 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"Epoch 1/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 2754502656.0000 - val_loss: 1780164224.0000\nEpoch 2/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1756276864.0000 - val_loss: 1689618432.0000\nEpoch 3/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1688686080.0000 - val_loss: 1651938688.0000\nEpoch 4/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1656191616.0000 - val_loss: 1630690944.0000\nEpoch 5/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1635583872.0000 - val_loss: 1616139264.0000\nEpoch 6/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1622200448.0000 - val_loss: 1604788224.0000\nEpoch 7/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1610985728.0000 - val_loss: 1595074304.0000\nEpoch 8/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1604912768.0000 - val_loss: 1586047744.0000\nEpoch 9/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1592652416.0000 - val_loss: 1577420288.0000\nEpoch 10/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1582389376.0000 - val_loss: 1569371392.0000\nEpoch 11/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1574664192.0000 - val_loss: 1561814016.0000\nEpoch 12/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1567736192.0000 - val_loss: 1554918784.0000\nEpoch 13/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1559409920.0000 - val_loss: 1548739456.0000\nEpoch 14/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1556558848.0000 - val_loss: 1543122432.0000\nEpoch 15/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1551742208.0000 - val_loss: 1538303232.0000\nEpoch 16/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - loss: 1548351744.0000 - val_loss: 1533954304.0000\nEpoch 17/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1544561664.0000 - val_loss: 1530203520.0000\nEpoch 18/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1536886784.0000 - val_loss: 1526843648.0000\nEpoch 19/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1531277696.0000 - val_loss: 1524376064.0000\nEpoch 20/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1522063872.0000 - val_loss: 1521986432.0000\nEpoch 21/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1528345472.0000 - val_loss: 1520275200.0000\nEpoch 22/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1523327744.0000 - val_loss: 1519039360.0000\nEpoch 23/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1519694848.0000 - val_loss: 1517852032.0000\nEpoch 24/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1517049344.0000 - val_loss: 1516987776.0000\nEpoch 25/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1519567744.0000 - val_loss: 1516246016.0000\nEpoch 26/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1513319680.0000 - val_loss: 1515736832.0000\nEpoch 27/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1513148288.0000 - val_loss: 1515138304.0000\nEpoch 28/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1512485632.0000 - val_loss: 1514991488.0000\nEpoch 29/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 2ms/step - loss: 1508802304.0000 - val_loss: 1514644608.0000\nEpoch 30/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1512466816.0000 - val_loss: 1514712448.0000\nEpoch 31/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1509035520.0000 - val_loss: 1515040256.0000\nEpoch 32/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1509637248.0000 - val_loss: 1515004416.0000\nEpoch 33/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1508653696.0000 - val_loss: 1515159808.0000\nEpoch 34/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1505680256.0000 - val_loss: 1515154560.0000\nEpoch 34: early stopping\nRestoring model weights from the end of the best epoch: 29.\n","output_type":"stream"},{"execution_count":254,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x78af81f6d420>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = model.predict(X_val_encoded)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:16:25.242045Z","iopub.execute_input":"2024-06-08T12:16:25.242489Z","iopub.status.idle":"2024-06-08T12:16:25.954673Z","shell.execute_reply.started":"2024-06-08T12:16:25.242452Z","shell.execute_reply":"2024-06-08T12:16:25.953281Z"},"trusted":true},"execution_count":234,"outputs":[{"name":"stdout","text":"\u001b[1m340/340\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:17:47.656561Z","iopub.execute_input":"2024-06-08T12:17:47.657105Z","iopub.status.idle":"2024-06-08T12:17:47.665011Z","shell.execute_reply.started":"2024-06-08T12:17:47.657067Z","shell.execute_reply":"2024-06-08T12:17:47.663854Z"},"trusted":true},"execution_count":242,"outputs":[{"execution_count":242,"output_type":"execute_result","data":{"text/plain":"array([[61818.09 ],\n       [15938.352],\n       [24404.934],\n       ...,\n       [59617.234],\n       [69001.54 ],\n       [42095.28 ]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"predictions = np.round(y_pred).astype(int)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:19:07.110926Z","iopub.execute_input":"2024-06-08T12:19:07.111338Z","iopub.status.idle":"2024-06-08T12:19:07.117515Z","shell.execute_reply.started":"2024-06-08T12:19:07.111310Z","shell.execute_reply":"2024-06-08T12:19:07.115956Z"},"trusted":true},"execution_count":245,"outputs":[]},{"cell_type":"code","source":"mse = mean_squared_error(y_val, predictions)\nrmse = np.sqrt(mse)\n\nmse, rmse","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:20:30.071072Z","iopub.execute_input":"2024-06-08T12:20:30.071548Z","iopub.status.idle":"2024-06-08T12:20:30.087105Z","shell.execute_reply.started":"2024-06-08T12:20:30.071512Z","shell.execute_reply":"2024-06-08T12:20:30.085673Z"},"trusted":true},"execution_count":249,"outputs":[{"execution_count":249,"output_type":"execute_result","data":{"text/plain":"(1516421164.598304, 38941.25273534872)"},"metadata":{}}]},{"cell_type":"code","source":"model_result(model, X_val_encoded, y_val)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:28:06.006081Z","iopub.execute_input":"2024-06-08T12:28:06.006550Z","iopub.status.idle":"2024-06-08T12:28:07.395930Z","shell.execute_reply.started":"2024-06-08T12:28:06.006512Z","shell.execute_reply":"2024-06-08T12:28:07.394484Z"},"trusted":true},"execution_count":255,"outputs":[{"name":"stdout","text":"\u001b[1m340/340\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\nMSE: 1514644639.1253653\nRMSE: 38918.435723001065\nr2_score: 0.2046365813235208\n","output_type":"stream"}]},{"cell_type":"code","source":"model2 = tf.keras.Sequential([\n    tf.keras.layers.InputLayer(shape=(478,)),\n    tf.keras.layers.Dense(128, activation=\"relu\"),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(64, activation='relu'),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(32, activation='relu'),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(16, activation='relu'),\n    tf.keras.layers.Dense(1)\n])\n\nmodel2.summary()\n\n\nearly_stopping = tf.keras.callbacks.EarlyStopping(\n    monitor='val_loss', patience=5, restore_best_weights=True, verbose=1)\n\nlr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(\n    monitor='val_loss', factor=0.2, patience=3, min_lr=1e-6, verbose=1)\n\nmodel2.compile(loss=tf.keras.losses.MeanSquaredError(),\n              optimizer=tf.keras.optimizers.Adam())\n\nmodel2.fit(train_data, epochs=200, validation_data=val_data,\n          callbacks=[early_stopping, lr_scheduler])","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:34:46.786000Z","iopub.execute_input":"2024-06-08T12:34:46.786449Z","iopub.status.idle":"2024-06-08T12:36:20.605941Z","shell.execute_reply.started":"2024-06-08T12:34:46.786394Z","shell.execute_reply":"2024-06-08T12:36:20.604611Z"},"trusted":true},"execution_count":258,"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_10\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense_32 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m61,312\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_33 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_34 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │         \u001b[38;5;34m2,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_35 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │           \u001b[38;5;34m528\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_36 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m17\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">61,312</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_35 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">528</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_36 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">17</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m72,193\u001b[0m (282.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">72,193</span> (282.00 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m72,193\u001b[0m (282.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">72,193</span> (282.00 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"Epoch 1/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - loss: 2256586240.0000 - val_loss: 1621271168.0000 - learning_rate: 0.0010\nEpoch 2/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1638029952.0000 - val_loss: 1570927232.0000 - learning_rate: 0.0010\nEpoch 3/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1595184512.0000 - val_loss: 1544692864.0000 - learning_rate: 0.0010\nEpoch 4/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1567744768.0000 - val_loss: 1527597824.0000 - learning_rate: 0.0010\nEpoch 5/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1548074880.0000 - val_loss: 1522012416.0000 - learning_rate: 0.0010\nEpoch 6/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1539388416.0000 - val_loss: 1518504704.0000 - learning_rate: 0.0010\nEpoch 7/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1537161088.0000 - val_loss: 1519219840.0000 - learning_rate: 0.0010\nEpoch 8/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1530685824.0000 - val_loss: 1515937152.0000 - learning_rate: 0.0010\nEpoch 9/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1527867136.0000 - val_loss: 1517653760.0000 - learning_rate: 0.0010\nEpoch 10/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1526511616.0000 - val_loss: 1520469760.0000 - learning_rate: 0.0010\nEpoch 11/200\n\u001b[1m1347/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 1524704256.0000\nEpoch 11: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1525371904.0000 - val_loss: 1521480832.0000 - learning_rate: 0.0010\nEpoch 12/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1519071360.0000 - val_loss: 1514344832.0000 - learning_rate: 2.0000e-04\nEpoch 13/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1510822144.0000 - val_loss: 1514089728.0000 - learning_rate: 2.0000e-04\nEpoch 14/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1505602688.0000 - val_loss: 1514612736.0000 - learning_rate: 2.0000e-04\nEpoch 15/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1505846656.0000 - val_loss: 1514987648.0000 - learning_rate: 2.0000e-04\nEpoch 16/200\n\u001b[1m1353/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 1510026880.0000\nEpoch 16: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1510295424.0000 - val_loss: 1514645248.0000 - learning_rate: 2.0000e-04\nEpoch 17/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1506423680.0000 - val_loss: 1514528512.0000 - learning_rate: 4.0000e-05\nEpoch 18/200\n\u001b[1m1356/1356\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - loss: 1498062592.0000 - val_loss: 1514457984.0000 - learning_rate: 4.0000e-05\nEpoch 18: early stopping\nRestoring model weights from the end of the best epoch: 13.\n","output_type":"stream"},{"execution_count":258,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.history.History at 0x78af84fd4a00>"},"metadata":{}}]},{"cell_type":"code","source":"model_result(model2, X_val_encoded, y_val)","metadata":{"execution":{"iopub.status.busy":"2024-06-08T12:36:37.592584Z","iopub.execute_input":"2024-06-08T12:36:37.593026Z","iopub.status.idle":"2024-06-08T12:36:38.959752Z","shell.execute_reply.started":"2024-06-08T12:36:37.592994Z","shell.execute_reply":"2024-06-08T12:36:38.958271Z"},"trusted":true},"execution_count":259,"outputs":[{"name":"stdout","text":"\u001b[1m340/340\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\nMSE: 1514089645.024828\nRMSE: 38911.30484865328\nr2_score: 0.20492801734339383\n","output_type":"stream"}]}]}