This project is about churn modelling (binary classification task) with Machine Learning and Neural Networks in Python, you can expect:

• statistical analysis (t-student and Jarque-Bera tests, Correlations, Mutual Information)
• feature selection (feature importances, sensitivity, statistical methods)
• feature engineering (monotonic transforms, binarization, label encoding, interactions)
• lots of ML algorithms (Logits, Random Forests, XGBoosts, SVMs, kNNs, Neural Nets, ensembling)
• n-Folds Cross Validation study
• bootstrap simulation
• several notebooks in Polish (lots of Python code)

GitHub repository

• Machine Learning
• Neural Networks
• notebook
• Python
• statistics