{"id":19024481,"url":"https://github.com/danielstankw/car_classification_and_regression","last_synced_at":"2026-04-29T21:30:18.673Z","repository":{"id":203570425,"uuid":"709917017","full_name":"danielstankw/Car_Classification_and_Regression","owner":"danielstankw","description":"Regression  \u0026 Classification model using a car data. 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Problem Statement\nThe goal is to build one or more models, capable of predicting vehicle trim and dealer listing price given the other twenty-six variables provided.\n\n# Models in the dataset\nThe dataset set consists of two models of a car:  \n- Caddilac XT5 (*left*)\n- Jeep Cherokee (*right*)\n\n\u003cdiv style=\"display: flex; justify-content: space-between;\"\u003e\n  \u003cimg src=\"img/Cadilac_img.jpg\" alt=\"Description of the Image\" width=\"500\"/\u003e\n  \u003cimg src=\"img/Jeep_img.jfif\" alt=\"Description of the Image\" width=\"415\"/\u003e\n\u003c/div\u003e\n\n\n## Data Structure\nThe data consists of `Test_Dataset.csv` and `Training_DataSet.csv`.\n\nThe structure of the `Test_Dataset.csv` is as follows:\n\n| Parameter            | Type    | Description                                                                                                      |\n|----------------------|---------|------------------------------------------------------------------------------------------------------------------|\n| ListingID            | int64   | Unique key that identifies each listing                                                                          |\n| SellerCity           | object  | Seller city                                                                                                      |\n| SellerIsPriv         | bool    | Boolean that indicates if the listing if from a private seller                                                   |\n| SellerListSrc        | object  | Seller listing source identifier                                                                                 |\n| SellerName           | object  | Seller name                                                                                                      |\n| SellerRating         | float64 | Seller rating (continuous over [0,5] with 5 being a favorable rating)                                             |\n| SellerRevCnt         | int64   | Seller review count                                                                                              |\n| SellerState          | object  | Seller state                                                                                                     |\n| SellerZip            | float64 | Seller zip code                                                                                                  |\n| VehCertified         | bool    | Boolean that indicates if the listing has a manufacturer certification (generally indicates extended warranty)   |\n| VehColorExt          | object  | Vehicle exterior color                                                                                           |\n| VehColorInt          | object  | Vehicle interior color                                                                                           |\n| VehDriveTrain        | object  | Vehicle drivetrain (rear/front/all wheel drive)                                                                  |\n| VehEngine            | object  | Vehicle engine (generally includes displacement size, whether it is turbocharged, sometimes includes fuel type)  |\n| VehFeats             | object  | Vehicle features as listed by the seller in a semi-structured list format                                        |\n| VehFuel              | object  | Vehicle fuel type                                                                                                |\n| VehHistory           | object  | Vehicle ownership history in a semi-structured format                                                            |\n| VehListdays          | float64 | Duration (in days) the vehicle listing has been active                                                           |\n| VehMake              | object  | Vehicle make (manufacturer)                                                                                      |\n| VehMileage           | float64 | Vehicle mileage                                                                                                  |\n| VehModel             | object  | Vehicle model                                                                                                    |\n| VehPriceLabel        | object  | A classification label applied by the listing site                                                              |\n| VehSellerNotes       | object  | Unstructured text the seller has entered                                                                         |\n| VehSellerStockNum    | object  | Vehicle seller stock number                                                                                      |\n| VehTransmission      | object  | Vehicle transmission type                                                                                        |\n| VehYear              | int64   | Vehicle model year (not necessarily the year it was manufactured)                                                |\n| Vehicle_Trim         | object  | Vehicle trim                                                                                                     |\n| Dealer_Listing_Price | float64 | Vehicle listing price, dependent variable to be predicted.                                                       |\n\n\n# Vehicle Trims\nDespite having only two models of a car, each of the models comes in variety of trims. In order to facilitate learning, classes with little representation in the dataset were dropped or merged with similar classes. \nDecission on which trim can be merged with which trim, was made based on the literature survey.\n\n\u003cdiv style=\"display: flex; justify-content: space-between;\"\u003e\n  \u003cimg src=\"img/Cadilac_Trim.png\" alt=\"Description of the Image\" width=\"450\"/\u003e\n  \u003cimg src=\"img/Jeep_Trim.png\" alt=\"Description of the Image\" width=\"455\"/\u003e\n\u003c/div\u003e\n\n# Price Distribution\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"img/Vehicle_Prices.png\" alt=\"Description of the Image\" width=\"500\"/\u003e\n\u003c/p\u003e\n\n# Training\nBefore training the data was heavily prepreocessed using pipeline and appropiate transforms:\n- Missing values were imputed\n- New features were created\n- Categorical features were one-hot-encoded\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"img/pipeline.PNG\" alt=\"Description of the Image\" width=\"500\"/\u003e\n\u003c/p\u003e\n\nThe baseline model without hyperparameter tunning resulted in:\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"img/class_report.PNG\" alt=\"Description of the Image\" width=\"500\"/\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"img/conf_matrix.png\" alt=\"Description of the Image\" width=\"500\"/\u003e\n\u003c/p\u003e\n\n# Hyperparameter Tunning\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanielstankw%2Fcar_classification_and_regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanielstankw%2Fcar_classification_and_regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanielstankw%2Fcar_classification_and_regression/lists"}