Classifying Celestial Objects using Neural Networks

Original article was published on Deep Learning on Medium

Problem Statement-

The SDSS is a large collection of detailed 3D data about the universe, collecting information about the billions of celestial objects in the sky. What makes exploration difficult is the fact that the amount of data collected is enormous. The task can be made simpler by identifying the celestial objects represented by each observation. Hence, the task is to classify the observations into different classes (Star, Galaxy, Quasar).

Approach-

Seeing the dataset we can figure out that this is a case of multi-label classification. So, we will use an artificial neural network to perform this task and train it on the above dataset.

Data preprocessing-

1. The first step is to remove the unnecessary columns that do not contribute to the process of classification. After removing these columns we are left with the following columns.

Final dataset to be used

2. Next, we will divide the dataset into the matrix of features and the independent variable.

3. Now, we can split the dataset into the training set and the test/validation set.

4. And finally, we will scale it to ease the computation done by the model.

Applying the model

Now we can design the neural net and apply it on the dataset and see it’s performance.

We will make a loop that will add hidden layers to a model after each iteration so that we can see the effect of adding hidden layers to our neural network.