In this article, I will introduce you to the Galaxy Classification Model with Machine Learning. The first galaxy was observed by a Persian. The first galaxy was observed by a Persian astronomer Abd al-Rahman over 1,000 years ago, and it was first believed to be an unknown extended structure.
The first galaxy was observed by a Persian astronomer Abd al-Rahman over 1,000 years ago, and it was first believed to be an unknown extended structure. which is now known as Messier-31 or the infamous Andromeda Galaxy. From that point on, these unknown structures are more frequently observed and recorded, but it took more than 9 centuries for astronomers to manifest on an agreement that they were not just astronomical objects, but entire galaxies. In this article, I will introduce you to the Galaxy Classification Model with Machine Learning.
As the discoveries and classification of galaxies increased, several astronomers observed the divergent morphologies. Then, they started grouping previously reported galaxies and newly discovered galaxies based on morphological features which then formed a meaningful classification scheme.
Astronomy in this contemporary era has evolved massively in parallel with advances in computing over the years. Sophisticated computational techniques such as machine learning models are much more efficient now due to the dramatically increased efficiency in computer performance and huge data available to us today.
Long Centuries ago, the galaxy classification was done by hand with a massive group of experienced people, who used to evaluate the results by using cross-validation algorithm. With this inspiration here I will introduce you to a Galaxy Classification Model with Machine Learning.
The dataset that I am using is very large, so you need to show patience while downloading it. The dataset can be downloaded from here.
Now, let’s start this task of creating a Galaxy Classification Model by importing all the necessary packages:
Now, as you can see, I have imported all the packages, now let’s start reading the data and exploring it to have a quick look at what we are going to work with:
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import cufflinks as cf cf.go_offline() %matplotlib inline #Reading the data from google.colab import files uploaded = files.upload() zoo = pd.read_csv('GalaxyZoo1_DR_table2.csv') zoo.head()
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