Open ranamanish674zu opened 1 month ago
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I will look at this PR later.
I used deep learning (simple feed forward neural network) on the given datasest, but due to small size of dataset or something else model gone overfitted(training accuracy-100 %, testing accuracy-69%, Overfitting occurs when your model learns the training data too well, capturing noise and details that do not generalize to new, unseen data ) , so to resolve it i used Regularization(regularization techniques like L2 regularization (weight decay) to the model) , Dropout(Introduce dropout layers to the model to randomly set a fraction of input units to 0 at each update during training time, which helps prevent overfitting) , Early Stopping(Use early stopping to halt training when the validation loss stops improving) for balanced the training and testing accuracy. So after applying this i get training accuracy-above 80 % and testing accuracy-75%. after this i save the model into deeplearning.pkl file, if we developers want use this model then they can use by importing this file in the code. that's i solved issue #57
![image](https://github.com/BamaCharanChhandogi/Diabetes-Prediction/assets/119676300/6d9f0630-fc96-4bc6-9b00-1a8da5c0aebb)