This project dives into Uber trip data from New York City to uncover patterns, trends, and insights that can help predict trip demand. By analyzing millions of Uber pickups, we aim to build machine learning models that can forecast when and where demand will be highest. This kind of analysis is crucial for optimizing operations, improving customer experiences, and making data-driven decisions.
The dataset includes 4.5 million Uber pickups from April to September 2014 and 14.3 million pickups from January to June 2015. We explore everything from peak demand times to popular pickup locations, and even predict future trip demand using advanced machine learning techniques like Random Forest, XGBoost, and Gradient Boosted Regression Trees (GBRT). To boost accuracy, we also combine these models into an ensemble model.
- Data Cleaning & Prep: We start by cleaning and organizing the raw Uber trip data to make it ready for analysis.
- Exploratory Data Analysis (EDA): We visualize the data to uncover trends, like when and where Uber trips are most frequent.
- Feature Engineering: We extract useful information from the data, such as the hour of the day, day of the week, and month, to help our models make better predictions.
- Model Building: We train and test machine learning models to predict trip demand.
- Ensemble Modeling: We combine the predictions from multiple models to create a more accurate forecast.
- Visualizations: We create clear and insightful charts to communicate our findings.
- Programming Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, XGBoost
- Tools: Jupyter Notebook, VS Code
- Domain: Data Analysis, Machine Learning, Time Series Forecasting
The data comes from the NYC Taxi & Limousine Commission (TLC) and includes:
- Uber Trip Data: Details like pickup times, locations (latitude/longitude), and base codes.
- Non-Uber Data: Trip information from other for-hire vehicle companies.
- Aggregate Stats: Daily pickup data for hundreds of companies.
This dataset was originally used in FiveThirtyEight stories to analyze Uber's impact on NYC traffic, transit, and competition with traditional taxis.
- Peak Demand: The busiest times are during evening rush hours (5 PM - 8 PM) and on weekends.
- Popular Locations: Most pickups happen in Manhattan, with significant activity in Brooklyn and Queens.
- Best Model: XGBoost performed the best, with a Mean Absolute Percentage Error (MAPE) of 8.37%.
- Ensemble Model: By combining XGBoost, Random Forest, and GBRT, we achieved a MAPE of 8.60%, making our predictions even more reliable.
- Clone the Repository:
git clone https://github.com/your-username/uber-trip-analysis.git
- Install Dependencies:
pip install -r requirements.txt
- Open the Jupyter Notebook:
- Explore the code and analysis in
uber_trip_analysis.ipynb. - Run the cells to preprocess data, train models, and visualize results.
- Explore the code and analysis in
If you’d like to contribute to this project, feel free to open an issue or submit a pull request! We welcome any improvements or new ideas.
This project is open-source and available under the MIT License. Check out the LICENSE file for more details.
- The dataset was sourced from the NYC Taxi & Limousine Commission (TLC).
- Inspired by FiveThirtyEight's analysis of Uber's impact on NYC.
Understanding trip demand isn’t just about numbers—it’s about improving the experience for both drivers and riders. By predicting when and where demand will be highest, Uber can allocate resources more efficiently, reduce wait times, and even adjust pricing dynamically. This project is a step toward making ride-sharing smarter and more responsive to real-world needs.
Check out the full analysis in the Jupyter Notebook to see how we turned raw data into actionable insights! 🚀
Let me know if you’d like to tweak this further! 😊