SmartPrice Auto: AI Predicts the Value of Pre-Owned Cars

• Tech Stack: Python, Scikit-Learn, Pandas, Matplotlib, Regression Models
• Github URL: Project Link

Developed a machine learning model that predicts the price of pre-owned cars based on various features such as make, model, age, mileage, and more. The project involved extensive data cleaning and exploratory data analysis (EDA) to ensure high-quality data for training the model.

Utilized multiple regression algorithms, including linear regression, decision trees, and random forests, to determine the best model for price prediction. Evaluated each model’s performance using regression metrics like R-squared, MAE, and RMSE.

Conducted in-depth analysis of each algorithm's strengths and weaknesses, ensuring a deep understanding of their behavior and the factors influencing price predictions. This helped select the best-performing model for predicting car prices with higher accuracy.

This project demonstrates the practical application of machine learning techniques for real-world data problems, offering a valuable tool for assessing the price of used cars in the automotive market.