This is a project performed to predict the Uber Rider Churns. This notebook demonstrates a typical ML pipeline that can be followed for a Class Imbalance Classification Problem.
Here data preprocessing, Exploratory data analysis(EDA), and different feature selection methods were utilized to bring down to the most significant features. Once they were done, different models were experimented to get the best F1 score against the Cross-validation.