Members:
- Aditi Mittal (RA1911003010226)
- Anweasha Saha (RA1911003010235)
- R. Vijay (RA1911003010239)
Mini Project Work
Overview
- In this project, we have used customer credit card data and customer churn has been predicted on its basis.
- The data included features like Credit Score, Age, Gender, Tenure, Balance, Estimated Salary, etc.
- After doing Exploratory Data Analysis (EDA) of the dataset, 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 more effective in its prediction than Decision Tree with a slightly better F1 score.
WEB APPLICATION - Customer Churn Prediction