Customer Churn Analysis

Project Overview

This project analyzes customer churn patterns for a telecommunications company using machine learning techniques. The analysis identifies key factors contributing to customer attrition and predicts which customers are at risk of leaving, enabling proactive retention strategies.

Objectives

• Understand Churn Patterns: Analyze historical customer data to identify trends and characteristics of churned customers
• Identify Risk Factors: Determine which features have the highest impact on customer churn
• Build Predictive Models: Develop machine learning models to predict customer churn with high accuracy
• Actionable Insights: Provide business recommendations for reducing churn rates

Dataset Description

The dataset contains customer information with the following characteristics:

• Total Records: Multiple customer profiles with service usage and demographic data
• Features: Customer tenure, contract type, monthly charges, total charges, internet service type, and additional services
• Target Variable: Churn (Yes/No) - whether the customer has left the company
• Data Format: CSV file with comprehensive customer attributes

Technologies Used

• Python: Primary programming language for data analysis and modeling
• Jupyter Notebook: Interactive development environment (TCA.ipynb)
• Libraries:
  • Pandas: Data manipulation and exploration
  • NumPy: Numerical computations
  • Scikit-learn: Machine learning algorithms
  • Matplotlib/Seaborn: Data visualization
• SQL: Data querying and exploration
• Machine Learning: Classification algorithms for churn prediction

Analysis Components

1. Exploratory Data Analysis (EDA)

• Distribution of customer demographics
• Churn rate analysis across different segments
• Correlation analysis between features and churn
• Service usage patterns

2. Data Preprocessing

• Handling missing values
• Feature encoding (categorical to numerical)
• Feature scaling for model compatibility
• Train-test data split

3. Predictive Modeling

• Logistic Regression
• Decision Trees
• Random Forest
• Gradient Boosting (if applicable)
• Model evaluation using accuracy, precision, recall, and F1-score

4. Key Insights

• Identification of high-risk customer segments
• Impact of contract type on churn
• Relationship between tenure and customer retention
• Service type influence on churn probability

Files in Repository

• Customer_Churn.csv: Raw customer dataset with all features and churn labels
• TCA.ipynb: Jupyter notebook containing complete analysis, visualizations, and model building
• Teco Customer Churn Analysys.pdf: Comprehensive analysis report with findings and recommendations
• README.md: Project documentation (this file)

Key Findings

• Month-to-month contracts show significantly higher churn rates
• Customers with longer tenure are less likely to churn
• Specific service combinations correlate with higher retention
• Early intervention for at-risk customers can improve retention

How to Use

1. Open the Jupyter Notebook: Run TCA.ipynb to view the complete analysis
2. Review the Dataset: Load Customer_Churn.csv for raw data exploration
3. Check the Report: Read Teco Customer Churn Analysys.pdf for executive summary
4. Modify Analysis: Update parameters in the notebook for different scenarios

Model Performance

The predictive models achieved strong performance metrics:

• Accuracy: High classification accuracy in identifying potential churners
• Precision: Reliable identification of true positive churn cases
• Recall: Effective capture of customers at risk
• F1-Score: Balanced performance across metrics

Recommendations

1. Target Retention Campaigns: Focus on month-to-month customers with targeted retention offers
2. Service Bundling: Encourage customers to adopt multiple services to increase lifetime value
3. Early Engagement: Implement onboarding programs for new customers
4. Proactive Support: Use predictive scores to identify at-risk customers for proactive outreach
5. Contract Incentives: Offer discounts for longer-term contract commitments

Future Enhancements

• Incorporate additional external data sources
• Develop real-time churn prediction API
• Implement deep learning models for improved predictions
• Create automated alerting system for high-risk customers
• Build customer lifetime value (CLV) models

Technologies & Concepts

• Machine Learning: Supervised learning, classification
• Statistics: Correlation analysis, statistical testing
• Data Science: EDA, feature engineering, model evaluation
• Business Analytics: Customer segmentation, retention analysis

Author

Bhuvan CW

License

This project is open source and available for educational and research purposes.