Bank Customer Churn Prediction

This project compares the performance of three supervised classification techniques to suggest an efficient model to predict customer churn in banking industry

Objective

1. Design and implement a data pipeline for pre-processing the raw dataset.
2. Compare the performance of three supervised classification techniques to suggest an efficient model for predicting which customer will leave the bank

Dataset overview

Dataset: Link

Summary: This dataset comprises of bank customers and their behavior. It is an advantageous dataset for banks to know what leads a client towards the decision to leave the bank. Churn prevention allows companies to develop loyalty programs and retention campaigns to keep as many customers as possible.

Problem: Predict which customer can leave the bank

Describe: Dataset has 10000 rows and 14 columns (features) as described below:

• RowNumber: Corresponds to the record (row) number and has no effect on the output.
• CustomerId: Contains random values and has no effect on customer leaving the bank.
• Surname: The surname of a customer has no impact on their decision to leave the bank.
• CreditScore: Can have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.
• Geography: A customer’s location can affect their decision to leave the bank.
• Gender: Tt’s interesting to explore whether gender plays a role in a customer leaving the bank.
• Age: This is certainly relevant, since older customers are less likely to leave their bank than younger ones.
• Tenure: Refers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.
• Balance: Also a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.
• NumOfProducts: Refers to the number of products that a customer has purchased through the bank.
• HasCrCard: Denotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank.
• IsActiveMember: Active customers are less likely to leave the bank.
• EstimatedSalary: As with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.
• Exited: Whether or not the customer left the bank.

Technologies

Language: Python

Framework/Libary: Python, Pandas, Numpy, Scikit-Learn

Machine Learning Algorithms: Logistic Regression, Decision Trees, XGBoost (Extreme Gradient Boosting)

Metrics: Accuracy, F1-score, Precison, Recall

Contact

Author: Nguyen Hoang Long

Facebook: https://www.facebook.com/umhummmm/

Email: [email protected]