https://github.com/abrar2652/python-machine-learning-ai-implementation-in-credit-card-scam-detection
It detects and labels the output as fraud and not fraud according to the test dataset. Since it's a binary classification logistic regression provided better results than that of the other classifiers
https://github.com/abrar2652/python-machine-learning-ai-implementation-in-credit-card-scam-detection
Science Score: 13.0%
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Low similarity (6.6%) to scientific vocabulary
Repository
It detects and labels the output as fraud and not fraud according to the test dataset. Since it's a binary classification logistic regression provided better results than that of the other classifiers
Basic Info
- Host: GitHub
- Owner: Abrar2652
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Size: 518 KB
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Python-Machine-Learning-AI-implementation-in-Credit-Card-Scam-Detection
It analyzes the input and labels the output by proper detection, whether it's fraud or not fraud, according to the test dataset. Since it's a binary classification, logistic regression provided better results than that of the other classifiers.
AI implementation in Credit Card Scam Detection
Dataset Collection.
I used the open-source dataset available on Kaggle. Here is the link to the dataset: https://www.kaggle.com/mlg-ulb/creditcardfraud.
How to use the notebook.
- Download the dataset from kaggle.
- Clone the repository.
- Run the notebook, and you will be able to see the results and visualize the data.
- If you don't want to clone then you just simply run these codes and you can observe the desired result.
Owner
- Name: Md. Abrar Jahin
- Login: Abrar2652
- Kind: user
- Location: Bangladesh
- Company: OIST
- Website: https://devpost.com/abrar-jahin-2652?ref_content=user-portfolio&ref_feature=portfolio&ref_medium=global-nav
- Twitter: AbrarJa02766068
- Repositories: 7
- Profile: https://github.com/Abrar2652
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