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The task is to classify credit card transactions as fraudulent or legitimate.

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CreditCardFraudDetection

Dataset used: https://www.kaggle.com/mlg-ulb/creditcardfraud/

Participants are encouraged to explore the dataset on their own and apply classification algorithm(s) which they seem appropriate. A starter Jupyter Notebook is provided for participants to kick start their analysis. Participants are encouraged to perform exploratory analysis of data before jumping on to algorithms to classify.

Quick Start:

  • Fork and Clone this repo using the following command:
$ git clone https://github.com/<username>/CreditCardFraudDetection.git
$ cd CreditCardFraudDetection/Notebooks/
  • Make a copy of FraudDetection.ipynb and rename it as FraudDetection-x.ipynb (where x is unique for each participant).
  • Stage your changes, commit, and push using the following commands:
$ git add .
$ git commit -m "<commit message>"
$ git push origin
  • Once you are satisfied with your work, create a Pull Request (PR) on the BX-GY branch of this repository. (where X stands for batch number and Y stands for group number alloted to each participant).

  • Example a participant of Batch 1 Group 2 has been alloted the number 3.

    • The participant must name the file as FraudDetection-3.ipynb
    • The participant must work on his/her fork.
    • Once satisfied, the participant will create a PR on the B1-G2 branch of this repository.

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