Objective: Develop a machine learning model using ivy to predict the availability of parking spaces in urban areas. This model should factor in various elements such as time of day, day of the week, and weather conditions. The project aims to mitigate congestion and enhance traffic flow, offering a practical solution to a common urban challenge.
Task Details:
Dataset: Use the dataset provided by the Berkeley DeepDrive (BDD) initiative, available at BDD Data, which includes diverse data that could be relevant for predicting parking space availability. While the dataset encompasses various types of data, focus on extracting and utilizing information pertinent to parking space prediction.
Expected Output: Contributors are expected to submit a Jupyter notebook that encapsulates the entire model development lifecycle, including data preprocessing, feature selection, model training, and performance evaluation. Additionally, the trained model files should be included in the submission.
Submission Directory: Please place your completed Jupyter notebook and associated model files in the Contributor_demos/Predicting Parking Space Availability subdirectory within the unifyai/demos repository.
How to Contribute:
Start by forking the unifyai/demos repository to your GitHub account.
Clone the forked repository to your local environment.
Create a distinct branch for your contributions related to this specific use case.
Develop your predictive model, ensuring comprehensive documentation within the Jupyter notebook.
Store your notebook and model in the specified Contributor_demos/Predicting Parking Space Availability directory.
After finalizing your work, push the changes to your forked repository.
Initiate a Pull Request (PR) to the unifyai/demos repository, with a clear title like "Predicting Parking Space Availability Demo Submission".
Contribution Guidelines:
Your code should be well-documented to ensure clarity and facilitate replication by others.
In your PR, include a concise summary of your approach, key findings, and any significant hurdles you overcame during the project.
Objective: Develop a machine learning model using ivy to predict the availability of parking spaces in urban areas. This model should factor in various elements such as time of day, day of the week, and weather conditions. The project aims to mitigate congestion and enhance traffic flow, offering a practical solution to a common urban challenge.
Task Details:
Dataset: Use the dataset provided by the Berkeley DeepDrive (BDD) initiative, available at BDD Data, which includes diverse data that could be relevant for predicting parking space availability. While the dataset encompasses various types of data, focus on extracting and utilizing information pertinent to parking space prediction.
Expected Output: Contributors are expected to submit a Jupyter notebook that encapsulates the entire model development lifecycle, including data preprocessing, feature selection, model training, and performance evaluation. Additionally, the trained model files should be included in the submission.
Submission Directory: Please place your completed Jupyter notebook and associated model files in the
Contributor_demos/Predicting Parking Space Availabilitysubdirectory within theunifyai/demosrepository.How to Contribute:
unifyai/demosrepository to your GitHub account.Contributor_demos/Predicting Parking Space Availabilitydirectory.unifyai/demosrepository, with a clear title like "Predicting Parking Space Availability Demo Submission".Contribution Guidelines: