Built an XGBoost classification model to predict customer conversion and identify key drivers influencing whether users place an order. Conducted exploratory data analysis (EDA) to uncover behavioral and transactional patterns. Performed cohort analysis to measure customer retention over time, identify drop-off points, and understand lifecycle behavior. Delivered insights to improve the restaurant discovery experience and boost overall conversion rates.
- Machine Learning: XGBoost classification model for predicting customer conversion
- Exploratory Data Analysis: Uncovered behavioral and transactional patterns
- Cohort Analysis: Measured customer retention and identified drop-off points
- Business Impact: Actionable insights to improve conversion rates and user experience
- Python
- XGBoost
- Pandas
- NumPy
- Matplotlib/Seaborn
- Scikit-learn
- Identified key drivers influencing customer conversion
- Measured customer retention trends over time
- Discovered critical drop-off points in the customer lifecycle
- Provided recommendations to enhance the restaurant discovery experience
├── notebooks/
├── deck/
└── README.md
pip install xgboost pandas numpy matplotlib seaborn scikit-learn- Clone the repository
- Install dependencies
- Run the Jupyter notebooks in the
notebooks/directory - Review the analysis and model results
The XGBoost model successfully predicted customer conversion with high accuracy, enabling targeted interventions to improve user engagement and conversion rates.
- Implement real-time prediction pipeline
- Explore additional feature engineering
- Test ensemble methods for improved performance
- Develop A/B testing framework for recommendations
For questions or collaboration opportunities, feel free to reach out via GitHub.