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I n t his assignment, you will be using neural network to classify whether a transaction i s f raudulent or not. We provided the r equired dataset and you will have to design a neural network architecture t hat accurately i denties the f rauds. The data contains 28 undened features which dene the type of transaction. 1. Find the distribution of each class and explain why the data is skewed. 2. There are many approaches that were developed for handling the skewed data. For this problem, we will use an approach called 'under sampling' where we randomly delete instances from over-represented class. You have to reduce the dataset such that both classes have equal distribution. 3. Split the reduced data into train, valid and test datasets. Now train a neural network classier (your choice) and calculate precision & recall using confusion matrix. 4. Explain what each metric (precision and recall) represents for this dataset. Which do you think is more important?.

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