Customer Churn prediction using Decision tree and Random Forest
App at https://customer-churn-predictor.herokuapp.com/
This is a mini-project for my Data Mining Class (18CSE355T). In this project , customer credit card data was used and customer churn was predicted. The data included features like Credit Score , Age , Gender , Tenure , Balance and Estimated Salary etc. After EDA of the data , LabelEncoder was used to encode Gender and other categorical data. Finally , this data was used to train Decision Tree Model and Random Forest Model.The random forest model was effective in it's prediction than Decision Tree with slightly better F1 score.