📊 Customer Churn Intelligence

End-to-end customer churn analysis using SQL, Python, exploratory data analysis (EDA), and machine learning to identify churn patterns and predict customer retention risk.

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📌 Project Overview

Customer churn is one of the most important business challenges for subscription-based companies. This project analyzes customer behavior to identify the key factors influencing churn and develops a machine learning model to predict customers at risk of leaving.

The project combines SQL analysis, data visualization, exploratory data analysis (EDA), and predictive modeling to simulate a real-world business analytics workflow.

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🎯 Objectives

• Perform data cleaning and preprocessing
• Analyze customer churn behavior using SQL and EDA
• Visualize customer churn patterns and trends
• Build a machine learning model for churn prediction
• Generate actionable business insights for customer retention

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🛠 Tools & Technologies

• Python
• Pandas
• SQLite
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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🗄 SQL-Based Analysis

SQL queries were used to analyze:

• Churn rate by contract type
• Average monthly charges by churn status
• Customer tenure patterns
• Customer retention behavior

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📈 Exploratory Data Analysis (EDA)

The project includes visual analysis of:

• Churn distribution
• Contract type vs churn
• Monthly charges vs churn
• Tenure vs churn

Charts are stored in the charts/ directory.

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📊 Sample Visualization

Churn Distribution

Tenure vs Churn

Contract Type vs Churn

Internet Service VS Churn

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🤖 Machine Learning

A Logistic Regression model was developed to predict customer churn using customer demographic and service-related features.

Model evaluation includes:

• Accuracy score
• Confusion matrix
• Business-focused interpretation

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📊 Confusion Matrix

The confusion matrix below evaluates the model’s ability to correctly classify churned and retained customers.

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📌 Key Insights

• Customers with month-to-month contracts show significantly higher churn rates
• Customers paying $65+/month churn at 2x the rate
• Customers with shorter tenure are more likely to leave
• The model successfully identifies a large portion of churn-risk customers

Detailed findings are available in reports/insights.md.

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# 📁 Project Structure

customer-churn-intelligence/
│
├── 01_data/
├── 02_notebooks/
│   └── customer_churn_analysis.ipynb
│
├── 03_charts/
├── 04_reports/
│   └── insights.md
│
├── 05_sql/
├── README.md
└── requirements.txt

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🚀 How to Run

1. Clone the repository

2. Install dependencies:

pip install -r requirements.txt

3. Launch Jupyter Notebook:

jupyter notebook

4. Open and run the notebook from the notebooks/ folder.

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✅ Conclusion

This project demonstrates an end-to-end data analytics workflow including:

• Data cleaning and preprocessing
• SQL-based business analysis
• Exploratory data analysis (EDA)
• Data visualization
• Machine learning model development
• Business-focused interpretation of results

The insights generated from this analysis can help businesses improve customer retention strategies and reduce customer attrition.