This project compares the performance of three supervised classification techniques to suggest an efficient model to predict customer churn in banking industry
- Design and implement a data pipeline for pre-processing the raw dataset.
- Compare the performance of three supervised classification techniques to suggest an efficient model for predicting which customer will leave the bank
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.
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
Author: Nguyen Hoang Long
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