This project is a bank project. The dataset is a CSV containing many features such as age, job, marital status, and education. The project bank file performs ETL on this data to make it cleaner and easier to analyze. I run decision tree, naive bayes, and logistic regression classifiers on this data to predict whether the customer would make a deposit to the bank. Then I build an ensemble model, one with voting, one with bagging, and one with stacking, and compare the three. I reached an accuracy score of 0.9 with the stacking model.
Use Numpy and Pandas, many SKlearn libraries to import naive bayes, decision tree, KNN, and other classifiers for the ensemble models.