This project analyzes a real-world business problem: understanding what factors influence whether someone who signs up for Uber actually goes on to complete their first trip as a driver. By leveraging predictive modeling and data visualization, we aim to help Uber refine its driver onboarding strategy and improve activation rates.
When brainstorming project ideas, we wanted something enjoyable and impactful. Our goal was to extract valuable insights from a relatively small dataset—showcasing how companies can still make smart decisions with limited data. We chose a StrataScratch dataset because it offered an authentic, real-world scenario where we could dig deep into a company's operational data and draw meaningful conclusions.
How can Uber use limited signup data to predict whether a new driver will complete their first trip?
Our project dives into identifying the most significant predictors of driver activation, offering actionable recommendations to improve recruitment and onboarding processes.
- Dataset: Contains driver signup data with features such as signup channel, device, referral source, and more.
- Target Variable:
started_driving(binary classification: 1 if the driver completed their first trip, 0 otherwise) - Models Used:
- Random Forest Classifier
- XGBoost (LightBoost)
- Evaluation Metrics:
- Accuracy
- Feature Importance
- Confusion Matrix
- 🐍 Python (pandas, scikit-learn, xgboost, matplotlib, seaborn)
- 📊 Power BI for dashboards and data storytelling
- 🤖 Machine Learning for classification and feature selection
- Dealing with missing or inconsistent values.
- Balancing between adding engineered features and preventing overfitting.
- Translating model insights into actionable business recommendations.
- Created an end-to-end machine learning pipeline.
- Built an interactive Power BI dashboard to support business storytelling.
- Collaborated effectively as a team to deliver both technical depth and business value.
- Applied ML algorithms to a real business classification problem.
- Gained experience in building interpretable models and refining them through feature selection.
- Developed compelling visualizations in Power BI that drive decision-making.
- Learned how to turn raw data into a coherent and persuasive story for stakeholders.
If external datasets become available, the next logical step is to compare our findings with a competitor like Lyft. This could uncover platform-specific behavioral insights and highlight where Uber might improve to gain a competitive edge in driver acquisition and retention.
├── data/ # Raw and cleaned datasets
├── notebooks/ # Jupyter Notebooks with EDA and modeling
├── visuals/ # Power BI exports and graphs
├── models/ # Trained model files (if applicable)
└── README.md # Project documentation (you are here)
Project by: Kento Morita, Phuong Duong, Vincent Perez, Ariel Liang We’re a group of business analytics students passionate about solving real-world problems through data and machine learning.