Welcome to the Customer Churn Prediction project, a Supervised Machine Learning endeavor focused on customer churn analysis and retention strategies. In this project, we diligently adhered to the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By examining data variables and employing various techniques, we aim to predict and mitigate customer churn.
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Project Workflow:
- Data Collection: Customer data was collected from various sources, including SQL Server tables and CSV/Excel files, covering demographics, services, payment, and churn status.
- Data Processing: Collected data was transformed into a suitable format using libraries like pyodbc and pandas, creating a comprehensive dataset.
- Exploratory Data Analysis (EDA): Extensive EDA was performed, including data summarization, handling missing values, and univariate and bivariate analyses, using pandas, numpy, matplotlib, and seaborn.
- Data Preprocessing: Data was cleaned, missing values handled, and categorical variables transformed, making it ready for in-depth analysis with pandas.
- Hypothesis Testing: Hypotheses on customer churn were formulated and tested using various statistical methods, including scipy's hypothesis tests.
- Visual Insights: Compelling visualizations were created to answer key questions about customer churn, using matplotlib and seaborn.
- Power BI Deployment: Analysis and visuals were deployed in Power BI for interactive exploration and sharing.
- Data Balancing: Class imbalance was addressed using the SMOTETomek technique.
- Model Training and Evaluation: Multiple machine learning models were trained, evaluated, and assessed, using classification reports with F1-score as the evaluation metric.
- Model Fine-Tuning: The best-performing model was fine-tuned as needed.
- Advanced Model Improvement: Hyperparameter tuning was performed for selected models using GridSearchCV.
- Future Predictions: The trained model can be deployed for ongoing predictions on new data, allowing proactive measures for customer retention.
To run the code in this repository, ensure you have the following Python packages installed. You can install them using pip:
pip install pyodbc sqlalchemy lightgbm catboost python-dotenv pandas numpy matplotlib seaborn scipy
pip install scikit-learn xgboostWe take our analysis to the next level with Power BI's Python scripting tool:
| Name | Article Link | Github |
|---|---|---|
| Israel Anaba Ayamga | Defying Telco Churn: Leveraging Machine Learning for Customer Retention Success | Israel_Anaba |
This project is licensed under the MIT License.
Contributions are welcome! Feel free to open an issue or submit a pull request.
I would like to express my gratitude to the Azubi Africa Data Analyst Program for their support and for offering valuable projects as part of this program. Not forgeting my scrum masters on this project Rachel Appiah-Kubi & Emmanuel Koupoh
For questions or feedback, please contact Israel Anaba Ayamga.