Datasets:

EthanGabis
/

Banking_Customer_Churn_Prediction_Dataset

# Assignment #1: EDA & Dataset - Bank Customer Churn Analysis

---

## Overview

This project presents an exploratory data analysis (EDA) of a bank customer churn dataset. The analysis aims to uncover key factors influencing customer churn and provide actionable insights for customer retention strategies.

## Dataset Information

- **Source**: Bank Customer Churn Dataset
- **Size**: 10,000 records
- **Features**: Multiple demographic and behavioral attributes including age, credit score, balance, number of products, and activity status
- **Target Variable**: `Exited` (whether a customer churned or not)

## Project Structure

The analysis follows a structured approach covering:

1. **Data Loading and Initial Exploration**
   - Dataset inspection and basic statistics
   - Initial data quality assessment

2. **Data Cleaning and Preprocessing**
   - Handling missing values
   - Data type conversions
   - Feature scaling and transformation

3. **Outlier Detection and Handling**
   - IQR (Interquartile Range) method applied to numerical features
   - Analysis of outliers in: CreditScore, Age, Tenure, Balance, EstimatedSalary, and NumOfProducts
   - ![Outlier](Outliers.png)

5. **Exploratory Data Analysis**
   - Descriptive statistics
   - Distribution analysis of key features
   - Correlation analysis

6. **Data Visualization**
   - Churn rate analysis by age groups
   - Active member status impact on churn
   - Product holdings vs. churn rate
   - Correlation matrix heatmap

## Key Findings

### Outlier Analysis
- **Result**: No significant outliers were detected using the IQR method
- **Implication**: The dataset shows a robust distribution across all numerical features
- **Dataset Integrity**: All 10,000 records were retained for analysis

### Churn Statistics
- **Overall Churn Rate**: 20.37% of customers in the dataset
- **Churn Distribution**: Approximately 2,037 customers churned out of 10,000

### Critical Churn Factors

#### 1. Age Demographics
- **High-Risk Group**: Customers aged 40-60 show significantly higher churn rates
- **Low-Risk Groups**: Both younger customers (under 30) and older customers (above 60) demonstrate greater stability
- **Actionable Insight**: Targeted retention strategies should focus on the mid-40s to late 50s demographic

#### 2. Customer Activity Status
- **Key Finding**: Inactive members represent a high-risk group for churn
- **Correlation**: Weak negative correlation (-0.1561) between active membership and churn
- **Recommendation**: Engagement strategies are crucial to maintain customer involvement

#### 3. Product Holdings
- **Optimal Range**: Customers with 1-2 products show the lowest churn rates
- **Surprising Pattern**: Customers holding 3-4 products exhibit unexpectedly higher churn rates
- **Positive Correlation**: 0.1854 correlation between number of products and churn
- **Investigation Needed**: This counterintuitive finding suggests potential issues with:
  - Product utility for multi-product holders
  - Customer service quality for complex portfolios
  - Aggressive cross-selling practices that may backfire

#### 4. Other Notable Correlations
- **Age**: Moderate positive correlation (0.3124) with churn
- **Balance**: Weak positive correlation (0.1185) with churn
- **Credit Score**: Negligible linear relationship with churn
- **Tenure**: Very weak correlation with churn
- **Estimated Salary**: Negligible impact on churn

## Data Processing Highlights

### Feature Scaling
- Successfully applied StandardScaler to numerical features
- Achieved means close to 0 and standard deviations close to 1
- Maintained original values for interpretability in visualizations

### Data Quality
- **Missing Values**: Properly handled (if any were present)
- **Data Integrity**: All records maintained after outlier analysis
- **Feature Engineering**: Original product counts preserved alongside scaled versions

## Visualizations Included

1. **Age Distribution and Churn Analysis**: Bar plot showing churn rates across age groups
2. **Active Member Status Impact**: Comparative analysis of churn between active and inactive members
3. **Product Holdings Analysis**: Churn rate visualization by number of products held
4. **Correlation Matrix Heatmap**: Comprehensive view of relationships between all numerical features

## Technologies Used

- **Python 3**
- **pandas**: Data manipulation and analysis
- **numpy**: Numerical computations
- **matplotlib**: Data visualization
- **seaborn**: Statistical data visualization
- **sklearn**: Data preprocessing (StandardScaler)

## Business Recommendations

Based on the analysis, the following retention strategies are recommended:

1. **Age-Targeted Programs**: Develop specialized retention programs for customers aged 40-60
2. **Engagement Initiatives**: Implement proactive engagement campaigns for inactive members
3. **Product Portfolio Review**: 
   - Investigate why multi-product (3-4) customers churn at higher rates
   - Review customer service quality for complex product portfolios
   - Reassess cross-selling strategies
4. **Balance-Based Interventions**: Consider targeted offers for customers with higher balances who show churn risk

## Churn Analysis by Age
![Churn by Age Group](Churn_by_age.png)

## Churn Analysis by Active Member
![Churn by Active Member](Churn_by_ActiveMember.png)

## Churn Analysis by Number of Products
![Churn by products](Churn_NumberOfProducts.png)

## Correlation HeatMap
![Correlation HeatMap](Correlation_heatMap.png)

## How to Use This Analysis

1. Review the Jupyter notebook for detailed code and step-by-step analysis
2. Watch the video presentation for a quick overview of findings
3. Examine visualizations to understand churn patterns
4. Apply the business recommendations to develop targeted retention strategies

## Author

Ethan Gabis  
ID: 209926781

## Video Presentation

https://youtu.be/3QpimSUF6BI


## Acknowledgments

This analysis was completed as part of Assignment #1 for the Data Science course, focusing on building foundational EDA skills and communicating data-driven insights effectively.

---

*Last Updated: November 2025*

	# Assignment #1: EDA & Dataset - Bank Customer Churn Analysis

	---

	## Overview

	This project presents an exploratory data analysis (EDA) of a bank customer churn dataset. The analysis aims to uncover key factors influencing customer churn and provide actionable insights for customer retention strategies.

	## Dataset Information

	- Source: Bank Customer Churn Dataset
	- Size: 10,000 records
	- Features: Multiple demographic and behavioral attributes including age, credit score, balance, number of products, and activity status
	- Target Variable: `Exited` (whether a customer churned or not)

	## Project Structure

	The analysis follows a structured approach covering:

	1. Data Loading and Initial Exploration
	- Dataset inspection and basic statistics
	- Initial data quality assessment

	2. Data Cleaning and Preprocessing
	- Handling missing values
	- Data type conversions
	- Feature scaling and transformation

	3. Outlier Detection and Handling
	- IQR (Interquartile Range) method applied to numerical features
	- Analysis of outliers in: CreditScore, Age, Tenure, Balance, EstimatedSalary, and NumOfProducts
	- ![Outlier](Outliers.png)

	5. Exploratory Data Analysis
	- Descriptive statistics
	- Distribution analysis of key features
	- Correlation analysis

	6. Data Visualization
	- Churn rate analysis by age groups
	- Active member status impact on churn
	- Product holdings vs. churn rate
	- Correlation matrix heatmap

	## Key Findings

	### Outlier Analysis
	- Result: No significant outliers were detected using the IQR method
	- Implication: The dataset shows a robust distribution across all numerical features
	- Dataset Integrity: All 10,000 records were retained for analysis

	### Churn Statistics
	- Overall Churn Rate: 20.37% of customers in the dataset
	- Churn Distribution: Approximately 2,037 customers churned out of 10,000

	### Critical Churn Factors

	#### 1. Age Demographics
	- High-Risk Group: Customers aged 40-60 show significantly higher churn rates
	- Low-Risk Groups: Both younger customers (under 30) and older customers (above 60) demonstrate greater stability
	- Actionable Insight: Targeted retention strategies should focus on the mid-40s to late 50s demographic

	#### 2. Customer Activity Status
	- Key Finding: Inactive members represent a high-risk group for churn
	- Correlation: Weak negative correlation (-0.1561) between active membership and churn
	- Recommendation: Engagement strategies are crucial to maintain customer involvement

	#### 3. Product Holdings
	- Optimal Range: Customers with 1-2 products show the lowest churn rates
	- Surprising Pattern: Customers holding 3-4 products exhibit unexpectedly higher churn rates
	- Positive Correlation: 0.1854 correlation between number of products and churn
	- Investigation Needed: This counterintuitive finding suggests potential issues with:
	- Product utility for multi-product holders
	- Customer service quality for complex portfolios
	- Aggressive cross-selling practices that may backfire

	#### 4. Other Notable Correlations
	- Age: Moderate positive correlation (0.3124) with churn
	- Balance: Weak positive correlation (0.1185) with churn
	- Credit Score: Negligible linear relationship with churn
	- Tenure: Very weak correlation with churn
	- Estimated Salary: Negligible impact on churn

	## Data Processing Highlights

	### Feature Scaling
	- Successfully applied StandardScaler to numerical features
	- Achieved means close to 0 and standard deviations close to 1
	- Maintained original values for interpretability in visualizations

	### Data Quality
	- Missing Values: Properly handled (if any were present)
	- Data Integrity: All records maintained after outlier analysis
	- Feature Engineering: Original product counts preserved alongside scaled versions

	## Visualizations Included

	1. Age Distribution and Churn Analysis: Bar plot showing churn rates across age groups
	2. Active Member Status Impact: Comparative analysis of churn between active and inactive members
	3. Product Holdings Analysis: Churn rate visualization by number of products held
	4. Correlation Matrix Heatmap: Comprehensive view of relationships between all numerical features

	## Technologies Used

	- Python 3
	- pandas: Data manipulation and analysis
	- numpy: Numerical computations
	- matplotlib: Data visualization
	- seaborn: Statistical data visualization
	- sklearn: Data preprocessing (StandardScaler)

	## Business Recommendations

	Based on the analysis, the following retention strategies are recommended:

	1. Age-Targeted Programs: Develop specialized retention programs for customers aged 40-60
	2. Engagement Initiatives: Implement proactive engagement campaigns for inactive members
	3. Product Portfolio Review:
	- Investigate why multi-product (3-4) customers churn at higher rates
	- Review customer service quality for complex product portfolios
	- Reassess cross-selling strategies
	4. Balance-Based Interventions: Consider targeted offers for customers with higher balances who show churn risk

	## Churn Analysis by Age
	![Churn by Age Group](Churn_by_age.png)

	## Churn Analysis by Active Member
	![Churn by Active Member](Churn_by_ActiveMember.png)

	## Churn Analysis by Number of Products
	![Churn by products](Churn_NumberOfProducts.png)

	## Correlation HeatMap
	![Correlation HeatMap](Correlation_heatMap.png)

	## How to Use This Analysis

	1. Review the Jupyter notebook for detailed code and step-by-step analysis
	2. Watch the video presentation for a quick overview of findings
	3. Examine visualizations to understand churn patterns
	4. Apply the business recommendations to develop targeted retention strategies

	## Author

	Ethan Gabis
	ID: 209926781

	## Video Presentation

	https://youtu.be/3QpimSUF6BI


	## Acknowledgments

	This analysis was completed as part of Assignment #1 for the Data Science course, focusing on building foundational EDA skills and communicating data-driven insights effectively.

	---

	Last Updated: November 2025