Constructed a predictive model with over 1.2 million samples, to help investors make informed decisions before selecting loans using Python, NumPy, and Pandas.
Applied Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms to train and evaluate the model to predict the default rate.
Histogram and boxplot applied to help visualize the data.
Please find the relevant datasets in https://www.kaggle.com/datasets/wordsforthewise/lending-club
This project is a COMP 562 Machine Learning Group Project from
Zhennan Feng, Kang Du, Jingtong E, and Zihao Fang
under the supervision of
Professor Jorge Silva