Classifier Model using Multi Layer Perceptron neural network to classify csv data

• The classification model is build using Multi layer perceptron neural network as it is efficient in building classifying models because it has different layers(input,hidden,output) through which data is propagated and classify the features efficiently.

• All the columns(features,conformation_name,ID) except class and molecule_name are taken as features(X) and class is taken as Label(Y).

• The label is 0 if compound is Non Musk and 1 if compound is Musk.

• The accuracy and loss graph are plotted for validation and training data.