Authors: Tosif Raza Mansoori
Abstract: Customer retention has become one of the most significant challenges faced by modern banking organizations. Due to increasing competition in the financial sector, customers can easily switch from one bank to another if they are dissatisfied with the services provided. Therefore, predicting customer churn has become an important business problem, as retaining existing customers is generally more cost-effective than acquiring new ones. This project presents a Machine Learning-based Bank Customer Churn Prediction Dashboard developed using Python and Streamlit. The objective of the project is to analyze customer information and accurately predict whether a customer is likely to discontinue banking services. Along with prediction, the dashboard provides interactive visualizations and business insights that assist organizations in making informed decisions. The project begins with data collection and preprocessing, where duplicate records and unnecessary attributes are removed. Categorical variables are converted into numerical values using Label Encoding, and numerical features are standardized using StandardScaler. The cleaned dataset is then used to train multiple Machine Learning classification models. Three Machine Learning algorithms were implemented and compared, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier. Their performances were evaluated using Accuracy Score, Precision, Recall, F1-Score, and Confusion Matrix. Experimental results showed that the Random Forest classifier achieved the highest prediction accuracy of 86.25%, making it the final model selected for deployment. To improve usability, the trained model was integrated into an interactive Streamlit dashboard. Users can enter customer details and instantly receive churn predictions along with prediction confidence, customer risk level, and business recommendations. The dashboard also includes interactive data visualizations, customer analytics, dataset exploration, feature importance analysis, and model comparison charts. Overall, this project demonstrates how Machine Learning and Data Analytics can support banking organizations in reducing customer churn, improving customer retention strategies, and making data-driven business decisions.
