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This repo consists of a python notebook that demonstrates how to predict fraud transactions from a highly imbalanced dataset. The data is from a Kaggle dataset 'Credit Card Fraud Detection'.

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CreditCard_Fraud_Detection

This repo consists of a python notebook that demonstrates how to predict fraud transactions from a highly imbalanced dataset. The data is from a Kaggle dataset 'Credit Card Fraud Detection'. The data states that the features are scaled and anonymized for security reasons. The techniques used in the notebook for dealing with imbalance are:

  1. Undersampling
  2. Oversampling through SMOTE

The algorithms used are:

  1. LogisiticRegression
  2. KNearest
  3. Support Vector Classifier
  4. DecisionTreeClassifier
  5. Deep Neural Network through Keras.

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This repo consists of a python notebook that demonstrates how to predict fraud transactions from a highly imbalanced dataset. The data is from a Kaggle dataset 'Credit Card Fraud Detection'.

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