Predicting Taxi Demand Demo

[NripeshN]

Objective: Leverage ivy to build a predictive model that forecasts taxi demand in a city, integrating historical taxi usage data, weather conditions, and city events. This initiative aims to assist taxi companies in optimizing fleet management and enhancing customer service by anticipating demand fluctuations.

Task Details:

• Dataset: The project will use the City of Chicago's Taxi Trips dataset, accessible at City of Chicago Taxi Trips 2013-2023. This dataset provides a comprehensive record of taxi trips, including timestamps and trip details, which, combined with weather data and event schedules, can form a solid basis for demand prediction.

• Expected Output: Participants are expected to deliver a Jupyter notebook that outlines the entire model development process. This process includes data cleaning and preprocessing, exploratory data analysis, feature engineering, model training, and evaluation. The trained model files should also be included in the submission.

• Submission Directory: Ensure that your completed Jupyter notebook and associated model files are placed within the Contributor_demos/Predicting Taxi Demand subdirectory of the unifyai/demos repository.

How to Contribute:

1. Fork the unifyai/demos repository to your GitHub account.
2. Clone the forked repository to your local machine.
3. Create a new branch dedicated to your work on the Predicting Taxi Demand demo.
4. Develop your predictive model, documenting the process thoroughly in the Jupyter notebook.
5. Store your notebook and model files in the specified Contributor_demos/Predicting Taxi Demand directory.
6. After finalizing your model, push the changes to your forked repository.
7. Open a Pull Request (PR) to the unifyai/demos repository with a clear and descriptive title, such as "Predicting Taxi Demand Demo Submission".

Contribution Guidelines:

• Ensure your code is well-documented for clarity and ease of understanding by others.
• Provide a concise summary of your approach, significant findings, and any obstacles encountered in the PR description to offer insights into your project journey.

[KatoStevenMubiru]

Hi @NripeshN , I'm interested in working on this project and would like to be assigned to it. Could you please assign it to me?