Capstone project for Machine Learning Engineer course, Udacity. It is based on the “Home Credit Default Risk” Kaggle Competition.

Used libraries: Pandas Numpy Seaborn Missingno Matplotlib Xgboost Sklearn Pickle

Please download the data here: https://www.kaggle.com/c/home-credit-default-risk/data

References: “Home Credit Default Risk” - https://www.kaggle.com/c/home-credit-default-risk “Understanding AUC - ROC Curve” - https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5 “XGBoost: A Scalable Tree Boosting System” - https://arxiv.org/abs/1603.02754 “Complete Guide to Parameter Tuning in XGBoost (with codes in Python)” - https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/