Credit Card Fraud Detection In Python Using Scikit-Learn and Snap ML
I am always on the lookout for ways to improve credit card fraud detection. Recently, I have been experimenting with two popular machine learning libraries in Python — Scikit-Learn and Snap ML — to see how well they perform on a real dataset. I recently completed involving credit card fraud detection using the Python programming language and two powerful machine learning libraries: Scikit-Learn and Snap ML.
The dataset I used is from September 2013 and includes information about transactions made by credit cardholders in Europe. It contains a variety of features, including the amount of the transaction, the location of the merchant, and the type of card used.
Snap ML is a high-performance IBM library for ML modeling. It provides highly-efficient CPU/GPU implementations of linear models and tree-based models. Snap ML not only accelerates ML algorithms through system awareness, but also offers novel ML algorithms with best-in-class accuracy
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I used the Scikit-Learn and Snap ML libraries in Python to train and evaluate two Support Vector Machine models using a real dataset of credit card transactions made by European cardholders in September 2013. The goal was to predict whether a transaction was legitimate or not based on various features such as transaction amount, location, and type of purchase.
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To train the models, I first split the dataset into a training and testing set and used the training set to fit the models. I then used the testing set to evaluate the performance of each model and calculated the ROC-AUC score, which is a measure of the model’s ability to correctly classify transactions as either legitimate or fraudulent.
After analyzing the results, I found that both the Scikit-Learn and Snap ML models performed well, with the Snap ML model slightly outperforming the Scikit-Learn model. However, both models showed strong potential for detecting fraudulent credit card transactions with high accuracy.
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Overall, this project highlighted the effectiveness of using machine learning techniques like Support Vector Machines to identify fraudulent credit card transactions. I look forward to continuing to explore new ways to leverage the power of data and machine learning to solve complex problems and make a positive impact in the world.
