In this project, I tested several machine learning models to predict used car prices based on factors like mileage, year, and state population. Each model was evaluated based on its accuracy (RMSE, MAE, and R²) and how well it captured patterns in the data.
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Modles I tried
Linear Regression – A simple baseline model to compare others against.
CART – A single decision tree that helps explain how predictions are made.
Neural Network – Non-linear model that tries to learn deeper patterns.
Random Forest – A group of decision trees that improves accuracy.
XGBoost – The most accurate model, using advanced boosting techniques.
KEY Takeaway
XGBoost performed the best overall, but each model helped reveal different insights about the data.
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