Objective: Develop a machine learning model with ivy to detect fraudulent transactions in credit card data. This project aims to address the critical issue of financial fraud, leveraging advanced analytics to identify suspicious activities.
Task Details:
Dataset: The project will utilize the Credit Card Fraud Detection dataset available on Kaggle, accessible here: Credit Card Fraud Detection Dataset. This dataset contains transactions made by credit cards in September 2013 by European cardholders. Transactions are labeled as fraudulent or genuine, providing a comprehensive basis for developing a detection model.
Expected Output: Contributors are expected to submit a Jupyter notebook that comprehensively documents the fraud detection model's development process. This includes data exploration, preprocessing, feature engineering, model training, and evaluation stages. The submission should also include the trained model files.
Submission Directory: Your completed Jupyter notebook and model files should be placed in the Contributor_demos/Credit Card Fraud Detection subdirectory within the unifyai/demos repository.
How to Contribute:
Fork the unifyai/demos repository to your GitHub account.
Clone the forked repository to your local machine.
Create a new branch specifically for your work on the Credit Card Fraud Detection demo.
Develop your model, ensuring to document each step in the Jupyter notebook, from data analysis to the application of machine learning algorithms for fraud detection.
Save your notebook and model files in the Contributor_demos/Credit Card Fraud Detection directory.
Push your branch to your forked repository once your work is complete.
Submit a Pull Request (PR) to the unifyai/demos repository, making sure your PR title clearly indicates the project, such as "Credit Card Fraud Detection Demo Submission".
Contribution Guidelines:
Make sure your code is well-documented to facilitate understanding and replication by others.
Provide a summary of your methodology, significant findings, and any challenges you faced in the PR description, offering insights into your development process.
Review and Feedback:
Submissions will be reviewed on a rolling basis. Feedback or requests for modifications will be communicated through the PR discussion. Your contribution will be merged once it meets the project's standards and objectives, contributing significantly to the fight against financial fraud.
This project offers a meaningful opportunity to apply machine learning to a real-world problem with significant societal impact. We eagerly anticipate your innovative solutions and contributions to enhancing the security of financial transactions.
Objective: Develop a machine learning model with ivy to detect fraudulent transactions in credit card data. This project aims to address the critical issue of financial fraud, leveraging advanced analytics to identify suspicious activities.
Task Details:
Dataset: The project will utilize the Credit Card Fraud Detection dataset available on Kaggle, accessible here: Credit Card Fraud Detection Dataset. This dataset contains transactions made by credit cards in September 2013 by European cardholders. Transactions are labeled as fraudulent or genuine, providing a comprehensive basis for developing a detection model.
Expected Output: Contributors are expected to submit a Jupyter notebook that comprehensively documents the fraud detection model's development process. This includes data exploration, preprocessing, feature engineering, model training, and evaluation stages. The submission should also include the trained model files.
Submission Directory: Your completed Jupyter notebook and model files should be placed in the
Contributor_demos/Credit Card Fraud Detectionsubdirectory within theunifyai/demosrepository.How to Contribute:
unifyai/demosrepository to your GitHub account.Contributor_demos/Credit Card Fraud Detectiondirectory.unifyai/demosrepository, making sure your PR title clearly indicates the project, such as "Credit Card Fraud Detection Demo Submission".Contribution Guidelines:
Review and Feedback:
Submissions will be reviewed on a rolling basis. Feedback or requests for modifications will be communicated through the PR discussion. Your contribution will be merged once it meets the project's standards and objectives, contributing significantly to the fight against financial fraud.
This project offers a meaningful opportunity to apply machine learning to a real-world problem with significant societal impact. We eagerly anticipate your innovative solutions and contributions to enhancing the security of financial transactions.