determining_car_price

System to determine car value

Jupyter Notebook viewer

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:

1. Data preprocessing
2. Machine Learning model research:
   • Selection of model parameters
   • Choosing the best model to solve the problem

Data

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

Task

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.

Task details

The company is concerned about:

• the quality of the prediction
• prediction speed
• learning time

Libraries used

pandas numpy sklearn xgboost catboost lightgbm matplotlib seaborn scipy