Used Car Price Prediction Project

This project aims to predict the price of used cars based on various features of the cars such as mileage, model year, make, and model. Kaggle Link : Click Here

Data Source

The data used in this project is sourced from Kaggle : https://www.kaggle.com/datasets/farhanhossein/used-vehicles-for-sale. This dataset consists of 24,199 used vehicles listed for sale in 2023. These vehicles were listed for sale within 25KM proximity of downtown Toronto, Ontario, Canada. The vehicles were found for sale on Autotrader.ca.

Tools and Technologies Used

This project is implemented using Python programming language and the following libraries and frameworks:

• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

Modeling Approach

The modeling approach used in this project is a regression problem. We trained several machine learning algorithms such as lasso regression, decision tree regression, random forest regression, and gradient boosting regression to predict the price of used cars. The performance of the models is evaluated using metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared.

Conclusion

This project provides a solution for predicting the price of used cars based on several features. The machine learning models used in the project showed promising results with low MSE and RMSE values. Further improvements to the project can be made by incorporating additional tuning on the hyperparameters.