https://github.com/avinandanbose/credit-card-fraud-detection-machine-learning-
Credit Card Fraud Detection using Python and Machine Learning.
https://github.com/avinandanbose/credit-card-fraud-detection-machine-learning-
artificial-intelligence credit-card creditcard decision-trees logistic-regression machine-learning machine-learning-algorithms matplotlib matplotlib-pyplot oversampling pca python quantile-transformer random-over-sampling random-under-sampling seaborn smote-sampling stratified-cross-validation support-vector-machines tomek-link-elimination
Last synced: 7 months ago
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Credit Card Fraud Detection using Python and Machine Learning.
- Host: GitHub
- URL: https://github.com/avinandanbose/credit-card-fraud-detection-machine-learning-
- Owner: AvinandanBose
- License: gpl-3.0
- Created: 2021-12-02T19:55:44.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-01-02T13:38:08.000Z (almost 4 years ago)
- Last Synced: 2025-01-26T17:11:23.236Z (9 months ago)
- Topics: artificial-intelligence, credit-card, creditcard, decision-trees, logistic-regression, machine-learning, machine-learning-algorithms, matplotlib, matplotlib-pyplot, oversampling, pca, python, quantile-transformer, random-over-sampling, random-under-sampling, seaborn, smote-sampling, stratified-cross-validation, support-vector-machines, tomek-link-elimination
- Language: Jupyter Notebook
- Homepage:
- Size: 77.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Credit-Card-Fraud-Detection-Machine-Learning-
It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Such
problems can be tackled with Data Science and its importance, along with Machine Learning, cannot be overstated. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications.
