Financial fraud in this environment is the fast-growing issue since the mobile channel can facilitate nearly any type of payments. Since in this environment any malicious activities can be took happens like fraud transaction Such problems can be tackled by data science and its importance, along with Machine Learning, cannot be overstated. As we know there are many technology which day by day comes in market and there are hoaxer who studied the technology and used them in malicious activities like fraud detection. So our project objective is to detect the fraudulent cases while minimizing the fraud cases by applying 2 major algorithms-LOCAL OUTLIER FACTOR AND ISOLATION FOREST ALGORITHM. This totally works on pre-processing data. Keywords-Credit card fraud, applications of machine learning, data science, isolation forest algorithm, local outlier factor
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The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not.
rt1599/credit-card-fraud-detection
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The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not.
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