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https://github.com/khyatimahendru/balancing-data-smote

In this project, I have worked on the problem of Credit Card Fraud Detection. The data is highly imbalanced with the positive class (fraud) accounting merely for 0.172% of the data. In classification problem balancing your data is extremely important. Here I have described how accuracy should not be the only criteria to judge model performance.
https://github.com/khyatimahendru/balancing-data-smote

classification classification-algorithims data-science imbalanced-data machine-learning smote

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In this project, I have worked on the problem of Credit Card Fraud Detection. The data is highly imbalanced with the positive class (fraud) accounting merely for 0.172% of the data. In classification problem balancing your data is extremely important. Here I have described how accuracy should not be the only criteria to judge model performance.

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# Balancing-data-SMOTE
In this project, I work on the problem of Credit Card Fraud Detection. The data is highly imbalanced with the positive class (fraud) accounting merely for 0.172% of the data. In classification problem balancing your data is extremely important. Here I explore how accuracy should not be the only criteria to judge model performance.

You can get the dataset from Kaggle: https://www.kaggle.com/mlg-ulb/creditcardfraud/downloads/creditcardfraud.zip/

This notebook was in support of my article on Balancing data using SMOTE.
Find it at
https://medium.com/analytics-vidhya/balance-your-data-using-smote-98e4d79fcddb