Application of supervised learning and deep learning methods to predict car resale price
• Positioned among the top 10 out of 86 teams in the Kaggle competition by employing machine learning models including random forest, gradient boosting, and neural networks to forecast the price of used cars based on Python.
• Built a recommender system that allows users to select a car based on browsing habits and personal preferences.
• Investigated the correlation between the cost of a new car and its rate of price depreciation using linear regression techniques.