Project Uber is a comprehensive research and analytics project focused on optimizing ride-hailing operations within the Delhi NCR region using machine learning. The study integrates forecasting, revenue modeling, and operational efficiency with fairness and sustainability evaluation. It leverages real-world ride data to identify demand trends, improve fleet allocation, and reduce emissions while maintaining service reliability.
Project-Uber/
│
├── Final Submissions/ # Complete research package
│ ├── Thesis_Submissions/ # Core working directory with all essential research materials
│ │ ├── Code/ # Python scripts for data analysis, modeling, and figure generation. Drawio files.
│ │ ├── Documentation/ # Weekly reports, analyses, and markdown summaries
│ │ ├── Graphs/ # Final figures used in the paper and presentation
│ │ ├── Graphics/ # Supporting visuals and conceptual diagrams
│ │ └── Illustrations/ # Other presentation visuals (AI Generated)
│ ├── Project_Uber_Thesis_Paper.docx
│ └── Machine_Learning_Based_Forecasting_RideHailing_DelhiNCR.pptx
│
├── data/ # Processed and interim datasets used for training and validation
│ ├── interim/
│ └── processed/
│
├── models/ # Trained models and parameters for forecasting and revenue prediction
│ ├── week5_best_rf.pkl
│ ├── week5_best_xgb.json
│ ├── week6_rf_revenue.pkl
│ └── week6_xgb_revenue.json
│
├── reports/ # Week-by-week documentation of analytical stages and findings
│ ├── week1_data_audit.md
│ ├── week5_demand_summary.md
│ ├── week6_revenue_summary.md
│ ├── week7_ops_brief.md
│ ├── week8_results.md
│ └── week9_discussion.md
│
├── analysis_results/ # Final summarized outputs of model performance and analysis tables
│ ├── project_summary.txt
│ └── summary_table.txt
│
├── requirements.txt # Python dependencies
└── README.md # Repository guide and navigation document
To reproduce this project locally:
# Clone the repository
git clone https://github.com/kangss1/Project-Uber.git
cd Project-Uber
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # for macOS/Linux
venv\Scripts\activate # for Windows
# Install dependencies
pip install -r requirements.txt- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
- xgboost
- shap
- statsmodels
- pmdarima
Script: week1_data_audit.py
- Load raw dataset and validate structure.
- Detect missing values, duplicates, and anomalies.
- Generate a structured data audit report.
- Deliverables:
week1_data_audit.md, quality checklist.
Script: week2_cleaning_eda.py
- Clean missing and invalid records.
- Visualize trends, correlations, and distribution patterns.
- Save cleaned dataset
ncr_ride_bookings_cleaned.csv. - Deliverables: Cleaned dataset and EDA visualizations.
Script: week3_feature_engineering.py
- Create time-based and rolling features.
- Encode categorical and scale numeric features.
- Generate enriched dataset for modeling.
- Deliverables: Feature-engineered dataset.
Script: week4_baselines.py
- Build ARIMA and Linear Regression baselines.
- Evaluate MAE/RMSE and residuals.
- Establish reference models.
- Deliverables:
week4_baselines_summary.mdand residual plots.
Script: week5_advanced.py
- Train Random Forest and XGBoost models for demand prediction.
- Perform grid search tuning and model comparison.
- Generate SHAP and feature importance plots.
- Deliverables: Best model artifacts and demand forecasts.
Script: week6_revenue_advanced.py
- Build Random Forest and XGBoost revenue prediction models.
- Analyze feature impacts using SHAP and PDP plots.
- Segment results by vehicle type and time of day.
- Deliverables: Revenue model summary and visualizations.
Script: week7_operational_insights.py
- Generate demand and cancellation heatmaps.
- Examine peak hour performance and service reliability.
- Relate findings to fleet allocation efficiency.
- Deliverables: Operational performance heatmaps and brief.
Script: week8_synthesis_methods_results.py
- Integrate results from demand, revenue, and ops analysis.
- Prepare thesis Methods and Results sections.
- Deliverables: Consolidated results tables and draft figures.
Script: week9_interpretation_reflection.py
- Interpret findings; identify key limitations.
- Discuss fairness and sustainability implications.
- Deliverables:
week9_discussion.mdandweek9_limitations.md.
Files: Thesis Paper & Presentation
- Compile final thesis and slides.
- Review figures and confirm metrics.
- Prepare oral defense materials.
- Deliverables: Final thesis and presentation deck.
- Demand Forecasting: Predict hourly and daily ride demand.
- Revenue Modeling: Understand fare variability and contributing factors.
- Operational Efficiency: Analyze fleet utilization and cancellations.
- Sustainability Analysis: Evaluate emissions and fairness across regions.
- Forecast Accuracy: Ensemble models achieved the lowest MAE and RMSE.
- Operational Efficiency: Average service fulfillment ~93% with fewer peak-hour cancellations.
- Fairness: 12% reduction in regional trip allocation disparities.
- Sustainability: 9% reduction in total fleet emissions.
Kang, S. (2025). Machine Learning-Based Forecasting and Optimization of Ride-Hailing Systems in Delhi NCR. Project Uber Research, 2025.
Author: Sandeep Kang
LinkedIn: linkedin.com/in/sandeepkang
Year: 2025
Project Uber — A data-driven framework for forecasting, optimization, and sustainability in ride-hailing systems.