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Predicting Prices of Used Cars with Python and ML

1M.NAGA KEERTHI, 2JONNADULA DURGA RAO

1Assistant Professor, Department Of MCA, 2MCA Final Semester,

1Master of Computer Applications,

1Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

Abstract:

This project presents a comprehensive machine learning-based web application designed to predict prices of used cars using Python. The system begins with data preparation, where a cleaned dataset is ingested and structured with key automotive features such as brand, model, year, fuel type, and kilometres driven. The data undergoes preprocessing and feature selection to ensure optimal input for a regression model. A Linear Regression model trained on historical car listings is used to predict prices based on user inputs. The model’s performance is evaluated using standard metrics like R-squared and Mean Squared Error. A user-friendly Flask web interface, enhanced with modern CSS styling, allows real-time interaction: users can select car attributes and instantly receive price estimates. This integrated solution bridges data science with interactive deployment, offering valuable insights for buyers, sellers, and automotive analysts.

Index Terms: Used car price prediction, Machine Learning, Python, Linear Regression, Flask Web Application, Data Preprocessing, Feature Engineering, Interactive Prediction, Web Deployment