This project focuses on predicting customer churn for PowerCo, an energy provider. The goal is to analyze customer behavior, identify key drivers of churn, and recommend strategies to improve customer retention.
The project is structured into the following steps:
Understanding the business problem, defining objectives, and formulating hypotheses. 📄 Read more
Performing data visualization and statistical analysis to uncover patterns and insights. 📄 Read more
Selecting key features, preprocessing data, and building predictive models. 📄 Read more
Interpreting model results, drawing business insights, and suggesting actionable recommendations. 📄 Read more
📂 customer-churn-prediction
│── 📜 README.md # Main documentation
│── 📜 step1_business_understanding.md # Business understanding & hypothesis framing
│── 📜 step2_eda.md # Exploratory data analysis
│── 📜 step3_feature_engineering_modeling.md # Feature engineering & modeling
│── 📜 step4_findings_recommendations.md # Findings & recommendations
│── 📂 data # Raw and processed datasets
│── 📂 notebooks # Jupyter notebooks for analysis and modeling
│── 📂 models # Trained models and results
│── 📂 images # Embedded images and visualizations
- Clone the repository:
git clone https://github.com/yourusername/customer-churn-prediction.git
- Run the notebooks step by step to explore the analysis.
Feel free to contribute by submitting issues or pull requests!
This project is licensed under the MIT License.
🎯 Predict churn, retain customers, and optimize business growth! 🚀