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Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.

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data-science gradient-boosting-classifier imbalanced-data logistic-regression machine-learning-algorithms random-forest-classifier shallow-neural-network support-vector-classifier under-sampling

credit-card-fraud-detection's Introduction

Performance metrics (classification report) for each model on the balanced dataset:

Logistic Regression:

Precision Recall F1-Score Support
Not Fraud 0.93 0.99 0.96 69
Fraud 0.99 0.93 0.96 73
Accuracy 0.96 142
Macro Avg 0.96 0.96 0.96 142
Weighted Avg 0.96 0.96 0.96 142

Shallow NN:

Precision Recall F1-Score Support
Not Fraud 0.86 1.00 0.93 69
Fraud 1.00 0.85 0.92 73
Accuracy 0.92 142
Macro Avg 0.93 0.92 0.92 142
Weighted Avg 0.93 0.92 0.92 142

Random Forest:

Precision Recall F1-Score Support
Not Fraud 0.91 1.00 0.95 69
Fraud 1.00 0.90 0.95 73
Accuracy 0.95 142
Macro Avg 0.95 0.95 0.95 142
Weighted Avg 0.96 0.95 0.95 142

Gradient Boosting:

Precision Recall F1-Score Support
Not Fraud 0.93 0.94 0.94 69
Fraud 0.94 0.93 0.94 73
Accuracy 0.94 142
Macro Avg 0.94 0.94 0.94 142
Weighted Avg 0.94 0.94 0.94 142

SVC:

Precision Recall F1-Score Support
Not Fraud 0.93 0.97 0.95 69
Fraud 0.97 0.93 0.95 73
Accuracy 0.95 142
Macro Avg 0.95 0.95 0.95 142
Weighted Avg 0.95 0.95 0.95 142

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