The dataset is about fraudulent transactions that happened in 2 days in Europe. Fraudulent transactions account for 0.17% of all transactions. The dataset contains train_data, test_data and test_data_hidden. All three have no missing values. Exploratory data analysis was done. To remedy the negative effect of unbalanced dataset, both oversampling and undersampling technique were used. Then, neural network algorithms were used along with GridSearch class to get optimum parameters for the model. Ensemble methods such as Random Forest and XGBoost were also used. The metric for evaluation for the entire notebook is f1 score. The code in Jupyter notebook and well-documented and self-explanatory. Comments give enough information not worth repeating here. For more information, refer to the Jupyter notebook file.
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