Task

Given dataset contains following columns
molecule_name - this contains 102 class of molecule
conformation_name - All of which are unique
f1 - f166 - These are the features of every molecule and its conformation
class - this column contains 0 or 1 (1 for musk and 0 for non-musk)

So the classification model can be made either to classify between musk or no_musk or 102 categories of molecules.

Pre Processing

I have used StandardScaler for scaling the input data.
This is necessary because we don’t know on what scale and range of the features are in. StandardScaler makes our data such that its mean value of 0 and standard deviation of 1. So that it helps in efficiency training our neural network model.

Models

I have trained two deep learning algorithms viz, Deep Neural Network and Convolutional Neural Network for both musk vs non-musk classification and also 102 category of molecules classification.

All these models performed well. But as there are 102 categories of molecules containing musk and non musk molecules. I have applied transfer learning as first training 102 category molecules and used the same model and added 2 layers to train for musk vs non musk.
By using transfer learning I achieved 100% accuracy in musk vs non musk classification.

Details of which are described below. Link for the jupyter notebooks on github and colab along with the link for the model files of all the models are at the end of this document in the form of table.

Accuracy Table

	musk vs non-musk	102 class of molecules
Deep neural network	Accuracy - 0.99924242424	Accuracy - 0.971969696969
Convolutional neural network	Accuracy - 0.99848484848	Accuracy - 0.956818181818
Transfer learning	Accuracy - 1.0 or 100%

Code and model files

	musk vs non-musk	102 class of molecules
Deep neural network	Github Colab model.h5	Github Colab model.h5
Convolutional neural network	Github Colab model.h5	Github Colab model.h5
Transfer learning	Github Colab model.h5