This repository contains the code and documentation for a machine learning model designed to predict the price of used cars. This project was developed as part of the KaggleX Fellowship competition. The model leverages various features of the cars, such as make, model, year, and more, to accurately estimate their market price.
- Data Preprocessing: Handling missing values, feature engineering, and normalization.
- Exploratory Data Analysis (EDA): Visualizations and insights to understand data distributions and relationships.
- Model Training: Multiple models evaluated, including Linear Regression, Decision Trees, Random Forest, and Gradient Boosting.
- Hyperparameter Tuning: Optimized model parameters using Grid Search and Random Search techniques.
- Model Evaluation: Detailed evaluation using metrics such as RMSE, MAE, and R² score.