Use historical loan application data to predict whether or not an applicant will be able to repay a loan.

Expected output: a probability of default prediction, between 0 and 1.

The dataset is provided by Home Credit

• ✔️ Exploratory Data Analysis (EDA);
• ✔️ Data cleaning;
• ✔️ Feature engineering;
• ✔️ Imbalanced classes management;
• ✔️ Model training: Naïve Bayes, Logistic Regression, Stochastic Gradient Descent, Random Forest, LightGBM
• ✔️ Model evaluation: AUC (Area Under the Curve), Recall, F1-Score
• ✔️ Hyperparameters optimization;
• ✔️ Evaluation of variable importance with SHAP (Shapley Additive exPlanations).

scikit-learn, LightGBM, SHAP

• Start here a gentle introduction - Will Koehrsen on Kaggle
• Collection of useful functions - Ann Antonova on Kaggle
• Introduction to Imbalanced Classification - Jason Brownlee (machinelearningmastery.com)
• Reduce memory usage on large datasets with datatype conversion
• Baseline: scikit-learn - Naive Bayes (Gaussian)
• Linear models: scikit-learn - Logistic Regression; scikit-learn - Stochastic Gradient Descent (SGD)
• Non Linear models : Random Forest Classifier (ensemble method); LightGBM (Gradient Boosting Machine) documentation
• Cross-validation: evaluating estimator performance; cross_validate
• Evaluation metrics : Evaluation of models with scikit-learn
• Tuning the hyperparameters of an estimator; GridSearchCV
• Interpretable Machine Learning with SHAP

• Threshold moving for Imbalanced Classification - Jason Brownlee (machinelearningmastery.com)
• Imbalanced Data in NLP and Computer Vision - Analytics Vidhya
• Bayesian Optimization- Jason Brownlee (machinelearningmastery.com)