Deep-learning-Classification-Models

1) Iris Classification

A core deep learning model has been implemented using the Keras library. The Iris flower dataset -iris.csv is used to build the model and perform classification tasks. The model has two hidden layers with activation function 'relu' and output layer with activation function 'softmax'.

Compilation of the model is performed using loss - categorical_crossentropy and metrics - accuracy.

Result:

Initial testing accuracy obtained was 73.33%. Improved this accuracy to 80.00% by hyper-parameter tuning and altering the layer structure.

2) Spam Classification

A core deep learning model has been implemented using the Keras library. The file Spam-Classification is used to build the model and perform classification tasks. The model has two hidden layers with activation function 'relu' and output layer with activation function 'softmax'. Further the training, validation and testing loss has been
minimised adding an additional hidden layer.

Compilation of the model is performed using loss - categorical_crossentropy and metrics - accuracy.

Result:

Training accuracy - 99% and testing accuracy 95.33%.