Galaxy Classification: A machine learning approach for classifying
  shapes using numerical data
Anusha Guruprasad
The classification of galaxies as spirals or ellipticals is a crucial task in
understanding their formation and evolution. With the arrival of large-scale
astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers
now have access to images of a vast number of galaxies. However, the visual
inspection of these images is an impossible task for humans due to the sheer
number of galaxies to be analyzed. To solve this problem, the Galaxy Zoo
project was created to engage thousands of citizen scientists to classify the
galaxies based on their visual features. In this paper, we present a machine
learning model for galaxy classification using numerical data from the Galaxy
Zoo[5] project. Our model utilizes a convolutional neural network architecture
to extract features from galaxy images and classify them into spirals or
ellipticals. We demonstrate the effectiveness of our model by comparing its
performance with that of human classifiers using a subset of the Galaxy Zoo
dataset. Our results show that our model achieves high accuracy in classifying
galaxies and has the potential to significantly enhance our understanding of
the formation and evolution of galaxies.