This project focuses on predicting the gearbox type (Automatic, Manual, or Tiptronic) of second-hand cars using machine learning classification models.
A web crawler was developed to collect a dataset, which is then used for data analysis, feature engineering, and model training.
This is a classification project that predicts the gearbox type (Automatic, Manual, or Tiptronic) of second-hand cars.
The dataset was created using a custom web crawler, and various machine learning models were tested to find the best classifier.
The project involves data preprocessing, feature selection, and model evaluation.
The dataset consists of second-hand car listings scraped from a website.
It includes the following features:
| Feature | Description |
|---|---|
| Year | Year of manufacture |
| Model | Car make and model |
| KM | Total mileage (kilometers driven) |
| Hand | Number of previous owners |
| CM | Engine volume (in liters) |
| Color | Car color |
| Area | Geographic location of the car |
| Gearbox | Target variable: Automatic, Manual, Tiptronic |
| Price | Car's listed price |
| IsNew | Whether the car is new (1 = Yes, 0 = No) |
| IsSaved | If the car is marked as saved (1 = Yes, 0 = No) |
| WithoutAccidents | Whether the car has accident history (1 = No accidents, 0 = Had accidents) |
Best Model Performance: The best model achieved an accuracy of 90%, successfully classifying cars into Automatic, Manual, or Tiptronic.