This project is a part of Bussiness Analytics class Course aimed toward understanding customer churn behavior in telecommunication sector.
In the project we will analyze different attributes of customer that helps us to estimate likelihood that customer will churn and select the best subset of that. We will try different approach for predicting customer churn and select the best one.
We have used Python language throughout this project and the libraries that we have used
includes:
1. Pandas (for EDA)
2. Matplotlib, Seaborn, Plotly (for better visualization)
3. Numpy (for mathematical modeling)
4. Scikit-learn (for different models)
5. Imblearn (for Oversampling)