This is my work for AI HACK qualification, my goal was to explore as many classification models as i can, i tried some feature engineering techniques and modified multiple featues. The models I used are KNN, Random Forest, Decision Tree, MLP, AdaBoost, XGBoost I used ROC/AUC to compare between models and accuracy aswell finally I chose the best models and applied Stacking to them which gave me the best result. I explored aswell other techniques such as PCA, LDA and SMOTE because the data was unbalanced, I also built a small NN using Keras. The data can be found on Zindi : https://zindi.africa/competitions/financial-inclusion-in-africa/data