Used Car Price Prediction Engine

A predictive machine learning system designed to estimate used car market prices based on multiple vehicle specifications such as mileage, brand, model year, transmission type, and fuel type.

Key Contributions & Architecture:

Data Preprocessing & Cleaning: Handled missing values, outliers, and feature encoding for high-cardinality categorical variables using Pandas and NumPy.

Model Development: Trained and fine-tuned a Random Forest Regressor, achieving optimal regression metrics (RMSE / R² Score) through cross-validation and hyperparameter optimization.

Deployment & Packaging: Built an inference API to serve predictions in real time and packaged the application using Docker to ensure consistency across environments.

Technologies Used: Python, Scikit-Learn, Pandas, NumPy, Random Forest