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What is Report about?

This report details an assignment on the application of feed-forward neural networks (FNNs) for diabetes prediction, focusing on model architecture evaluation and performance metrics under an imbalanced dataset. The final selected model, a three-hidden-layer FNN, achieved high accuracy but failed to predict minority class instances, highlighting the limitations of accuracy in imbalanced classification. Recommendations for future improvements include using class-weighted loss functions and resampling techniq

Author

hasinarafat03

Language

EN