Credit Card Fraud Detection

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Credit Card Fraud Detection :red_circle: Aim : Implement different algorithms like decision trees, logistic regression, and artificial neural networks to see which gives better accuracy. Compare the results of each algorithm and understand the behavior of models. :red_circle: Dataset : https://www.kaggle.com/mlg-ulb/creditcardfraud :red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

Hello, ML-Crate contributors, this issue is only for the contribution purposes and allocated only to the participants of SWOC 2.0 Open Source Program.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.
• A Demo Project has been created to guide you about the whole structure of presenting the Project in this repository. Here's the link of the Demo Project - https://github.com/abhisheks008/ML-Crate/tree/main/Project-Demo-Folder

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.
• This issue is only for 'SWOC' contributors of 'ML-Crate' project.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Participant ID :
• Approach for this Project :
• Are you a participant of SWOC 2.0?
  • [ ] YES
  • [ ] No

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[sankalp-srivastava]

Full name : Sankalp Srivastava GitHub Profile Link : www.github.com/sankalp-srivastava Participant ID : 857 Approach for this Project : cleaning and visualizing the data then creating baseline models with random forest, xgboost, lightBGM and cnn model and the model performing the best will go through hyperparameter tuning for best accuracy. Are you a participant of SWOC 2.0? YES

[ASLManasa]

@sankalp-srivastava go Ahead! Complete it within 3 Days.