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https://github.com/ameykasbe/credit-card-fraud-detection-on-imbalanced-dataset
Examined data preprocessing techniques and performance of six different predictive models in Python to credit card fraud detection problem on an imbalanced dataset. Algorithms implemented - Logistic Regression, K Nearest Neighbours, Support Vector Classification, Naïve Bayes Classifier, Decision Tree Classifier, and Random Forest Classifier.
https://github.com/ameykasbe/credit-card-fraud-detection-on-imbalanced-dataset
classification machine-learning matplotlib numpy pandas python scikit-learn seaborn
Last synced: about 2 months ago
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Examined data preprocessing techniques and performance of six different predictive models in Python to credit card fraud detection problem on an imbalanced dataset. Algorithms implemented - Logistic Regression, K Nearest Neighbours, Support Vector Classification, Naïve Bayes Classifier, Decision Tree Classifier, and Random Forest Classifier.
- Host: GitHub
- URL: https://github.com/ameykasbe/credit-card-fraud-detection-on-imbalanced-dataset
- Owner: ameykasbe
- Created: 2021-03-30T08:02:40.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-30T08:03:36.000Z (almost 4 years ago)
- Last Synced: 2023-09-14T21:19:55.868Z (over 1 year ago)
- Topics: classification, machine-learning, matplotlib, numpy, pandas, python, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage: https://colab.research.google.com/github/ameykasbe/credit-card-fraud-detection-on-imbalanced-dataset/blob/master/Credit%20Card%20Fraud%20Detection%20on%20Imbalanced%20Dataset.ipynb
- Size: 695 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0