Challenge: Detect Fraudulent Transactions
Automating fraud detection is a critical application of machine learning in FinTech. Traditional rule-based systems often fall short when faced with evolving fraud tactics, but unsupervised learning algorithms can help identify unusual behavior in transaction data. In this challenge, you will use Python and scikit-learn's IsolationForest to detect anomalies within a set of transaction amounts. The IsolationForest algorithm works by isolating observations that are different from the majority—making it well-suited for flagging potentially fraudulent transactions without the need for labeled data. By integrating machine learning into fraud detection workflows, you can improve accuracy and adapt to new patterns of suspicious activity.
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Write a function that detects anomalous transactions using the IsolationForest algorithm from scikit-learn. The function should return the indices of transactions flagged as anomalies and print the number of anomalies detected.
- Fit an IsolationForest model to the list of transaction amounts.
- Predict anomalies using the trained model.
- Identify the indices of transactions predicted as anomalies.
- Print the number of anomalies detected.
- Return a list of the anomaly indices.
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Automating fraud detection is a critical application of machine learning in FinTech. Traditional rule-based systems often fall short when faced with evolving fraud tactics, but unsupervised learning algorithms can help identify unusual behavior in transaction data. In this challenge, you will use Python and scikit-learn's IsolationForest to detect anomalies within a set of transaction amounts. The IsolationForest algorithm works by isolating observations that are different from the majority—making it well-suited for flagging potentially fraudulent transactions without the need for labeled data. By integrating machine learning into fraud detection workflows, you can improve accuracy and adapt to new patterns of suspicious activity.
Swipe to start coding
Write a function that detects anomalous transactions using the IsolationForest algorithm from scikit-learn. The function should return the indices of transactions flagged as anomalies and print the number of anomalies detected.
- Fit an IsolationForest model to the list of transaction amounts.
- Predict anomalies using the trained model.
- Identify the indices of transactions predicted as anomalies.
- Print the number of anomalies detected.
- Return a list of the anomaly indices.
Рішення
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