This project shows:
- ability of writing solid,structured Python code
- ability of using existing utilities(libraries) for processing and analyzing data.
- ability of utilizing Machine Learning models:
- CatBoost
- XGBoost
- LightGBM
- analytical and data pre-processing skills
Project involves:
- Data preprocessing
- Machine Learning model research:
- Selection of model parameters
- Choosing the best model to solve the problem
Provided data - historical data: technical specifications, packages and prices of cars.
| Column | Description | Column type |
|---|---|---|
| DateCrawled | the date the application was downloaded from the database | features |
| VehicleType | vehicle type | features |
| RegistrationYear | registration year | features |
| Gearbox | gearbox type | features |
| Power | power (hp) | features |
| Model | model | features |
| Kilometer | mileage (km) | features |
| RegistrationMonth | vehicle registration month | features |
| FuelType | fuel type | features |
| Brand | brand | features |
| NotRepaired | whether the car has been repaired or not | features |
| DateCreated | date of application form creation | features |
| NumberOfPictures | how many pictures of the car | features |
| PostalCode | postal code of the application form owner (user) | features |
| LastSeen | the date of the user's last activity | features |
| Price | price (euro) | target |
A used car service is developing an app where you can find out the market value of your car. We need to build a Machine Learning model to quickly determine the value of the car.
The company is concerned about:
- the quality of the prediction
- prediction speed
- learning time
pandas numpy sklearn xgboost catboost lightgbm matplotlib seaborn scipy