This project developed a predictive model to estimate the market value of used vehicles for Rusty Bargain, leveraging historical sales data and various car specifications. The goal was to create a model that balances accuracy, inference speed, and training efficiency, ultimately supporting real-time pricing predictions.
🔢 Numerical Methods & Comparing Methods 📉 Gradient Descent & Stochastic Gradient Descent (SGD) 🔁 Iterative Methods 🧠 Neural Networks 🧐 Computational Complexity & Algorithm Analysis 🚀 Gradient Boosting 👯♀️ Ensembles
- This project uses pandas, pyplot, numpy, seaborn, time, lightgbm, catboost, and several sklearn libraries. It requires python 3.11. There data file for this project I was unable to upload due to upload limitations.