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https://github.com/kaushikrohida/bank-customer-data-prep

Cleaning and Exploring the bank customer data to prepare it for machine learning models
https://github.com/kaushikrohida/bank-customer-data-prep

business eda finance geospatial python

Last synced: 14 days ago
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Cleaning and Exploring the bank customer data to prepare it for machine learning models

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README 

        
# Bank Customer Churn Analysis



## Table of Contents

1. [Project Overview](#project-overview)

2. [Dataset](#dataset)

3. [Methodology](#methodology)

4. [Key Findings](#key-findings)

5. [Insights and Recommendations](#insights-and-recommendations)

6. [Future Work](#future-work)

7. [Conclusion](#conclusion)

8. [Contributing](#contributing)



## Project Overview



This repository contains a comprehensive analysis of customer churn for a banking institution. Customer churn, the phenomenon where customers cease their relationship with a company, is a critical concern in the banking sector. This project aims to:



1. Identify key factors contributing to customer churn

2. Develop predictive models to forecast potential churners

3. Provide actionable insights to improve customer retention strategies



By leveraging machine learning techniques and data analysis, we seek to empower the bank with the tools and knowledge to proactively address customer attrition.



## Dataset



The analysis is based on the "Bank_Churn_Messy.xlsx" dataset, which includes various customer-related attributes such as:



- Demographics (age, gender)

- Account information (balance, number of products)

- Behavioral data (credit score, activity level)

- Churn status (target variable)



The raw data requires cleaning and preprocessing before analysis.



## Methodology



Our analysis follows these key steps:



1. **Data Preprocessing**

   - Handling missing values

   - Encoding categorical variables

   - Feature scaling and normalization



2. **Exploratory Data Analysis (EDA)**

   - Distribution analysis of key features

   - Correlation studies between features and churn

   - Identification of outliers and anomalies



3. **Feature Engineering**

   - Creation of new features (e.g., customer segments, tenure groups)

   - Feature selection based on importance and correlation



4. **Model Building**

   - Implementation of multiple classification models:

     - Logistic Regression

     - Random Forest

     - XGBoost

   - Hyperparameter tuning using cross-validation



5. **Model Evaluation**

   - Performance assessment using metrics such as:

     - Accuracy

     - Precision

     - Recall

     - F1 Score

     - AUC-ROC



6. **Interpretation and Insights**

   - Identification of top churn predictors

   - Development of actionable recommendations



## Key Findings



- Customer tenure is inversely correlated with churn probability

- Account balance shows a negative relationship with churn likelihood

- The number of products held by a customer impacts their propensity to churn

- Certain age groups exhibit higher churn rates than others



## Insights and Recommendations



Based on our analysis, we recommend the following strategies to reduce customer churn:



1. Implement a tiered loyalty program to incentivize longer customer tenure

2. Develop personalized product offerings for high-risk customer segments

3. Enhance customer engagement through targeted communication strategies

4. Offer financial advisory services to customers with fluctuating account balances

5. Conduct regular satisfaction surveys to identify and address pain points



## Future Work



To further enhance this project, consider:



- Incorporating more advanced machine learning techniques (e.g., neural networks)

- Analyzing the impact of macroeconomic factors on churn

- Developing a real-time churn prediction system

- Conducting A/B tests to validate the effectiveness of retention strategies



## Conclusion



This customer churn analysis project has provided valuable insights into the factors driving customer attrition in the banking sector. Through comprehensive data analysis and machine learning modeling, we have identified key predictors of churn and developed a robust framework for predicting at-risk customers. The recommendations provided offer actionable strategies for improving customer retention, while the predictive models enable proactive intervention before customers decide to leave.



The success of this project demonstrates the power of data-driven decision-making in addressing critical business challenges. By implementing the suggested recommendations and continuing to refine the analysis, banks can work towards reducing churn rates and building stronger, longer-lasting relationships with their customers.



## Contributing



We welcome contributions to improve the analysis or extend the project's scope. Please feel free to fork the repository, make changes, and submit a pull request.