In this tutorial we will perform a STAR/GALAXY separation using a real dataset from S-PLUS. This data were already matched with SDSS (DR15) spectroscopical data and it will be used to train and test the supervised classifiers. The final step (not included in this tutorial) is to use the trained model to predict the classification of your unknown objects.
This tutorial will be entirely in Python 3 and we will go through the following topics:
- Introduction to
Pandas - Data visualization with
seaborn - Classification methods with
sklearn
Have fun!
- Open Google Colab: https://colab.research.google.com/notebooks/welcome.ipynb
- Go to File > Open Notebook
- Select the GITHUB tab
- Enter this address https://github.com/marixko/Supervised_Learning_Tutorial.git
- It will show you the .ipynb file, click the "Open notebook in a new tab" icon
or
- Click in the .ipynb file listed above
- A new page will open and click in "Open in Colab" button
Finally, go to File > Save a copy in Drive... and you are set!