A comprehensive project to analyze and predict customer churn using advanced analytics, data visualization, and machine learning. The project aims to help businesses understand why customers leave and how to reduce churn rates.
Customer churn is a critical metric for many businesses. This project explores churn patterns, identifies key factors, and builds predictive models to anticipate at-risk customers. The project includes:
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Model Building (Logistic Regression, Random Forest, etc.)
- Results interpretation & actionable insights
- Clean and modular Python code
- Jupyter Notebooks for step-by-step analysis
- Visualizations with Matplotlib/Seaborn
- Machine learning models for churn prediction
- Clear documentation and comments
The dataset used is a sample of customer records with features such as:
- Demographics (age, gender, etc.)
- Account information
- Customer behavior (usage, complaints, etc.)
- Churn status
Note: Data file is included for demonstration purposes. Replace with your own data for production use.
git clone https://github.com/Asad-In-data/Customer_Churn_Analysis.git
cd Customer_Churn_Analysis
pip install -r requirements.txt- Open the main Jupyter notebook:
Customer_Churn_Analysis.ipynb - Run the notebook cells step by step.
- Review EDA, feature engineering, and model results.
- Modify the code for your own dataset as needed.
- Key churn factors are visualized with charts and plots
- Model performance metrics (accuracy, recall, precision, etc.)
- Feature importance analysis
- Actionable recommendations
Contributions, issues, and feature requests are welcome!
Please open an issue or submit a pull request.
Asad Analyst
GitHub @Asad In Data
Email: asadalich56@gmail.com
© 2025 AsadInData - Customer Churn Analysis