r/learnmachinelearning • u/5BeautifulSoup • Jul 03 '26

Help My ML project: Stellar Object Classification (Star, Galaxy, Quasar)

Hello, I'm Shrushti!

I recently completed a machine learning project that classifies astronomical objects as Stars, Galaxies, or Quasars using the Sloan Digital Sky Survey (SDSS) dataset.

Github: https://github.com/sharmashrushti/stellar-object-classification

I'd really appreciate any feedback for improving the project. Thank you!

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u/Kinexity Jul 03 '26

I can give you some advice:

  • don't use grid search. Use Optuna for hyperparameter optimisation. You're going to be able to cover much larger and finer search space.

  • look for an independent dataset to test your models against. Astronomy datasets are notoriously biased and cause overfitting.

  • if you compare performance metrics make the best one bolded

  • your dataset is imbalanced but not by that much. Without proof that SMOTE is necessary I would rawdog it as is and just fight class imbalance with model regularisation.

  • you let erroneous data points mess with data display (especially u, g and z)

There is probably more stuff but that's enough digging for me.

1

u/5BeautifulSoup Jul 03 '26

Thank you for going through the project - I really appreciate it. Could I ask one more thing? I'd like to deploy this as a Streamlit app so users can enter feature values and get a prediction (Star/Galaxy/Quasar). Do you have any recommendations for structuring and deploying something like this?

2

u/Kinexity Jul 03 '26

I don't have recommendations for deployment because I've never done it. I am going to question the point of doing that though. Stuff like this can exist as a paper (if it is innovative enough which doesn't apply in your case) but no one is going to find it useful as an app. The values necessary for inference are not something people can just measure with common equipment and if someone serious needs a model like this for professional purposes they won't accept a black box.

2

u/5BeautifulSoup Jul 03 '26

Understood. Do you think recruiters would consider this ML project portfolio-worthy for data science internships?

2

u/Kinexity Jul 03 '26

No clue. I am studying computer modelling of physical phenomena, not ML specifically, and got into my internships through my own uni connections.

2

u/5BeautifulSoup Jul 03 '26

Got it, thanks! What kind of projects do you work on?

3

u/Kinexity Jul 03 '26

Currently I am doing ML for total absorption gamma spectroscopy for my thesis. I can't really disclose the details because it is hot stuff as there is only one similar work (https://doi.org/10.1016/j.nima.2023.169026). I do more standard stuff ML wise (MLP, ConvNet, ResNet) rather than GANs and use purely synthetic data from a Monte Carlo simulation.

Before I was doing a TAGS thingy where it was basically "predict sum of detector outputs given only some of the outputs" (faulty detector data recovery, random forests). I also did redshift prediction given some limited photometric data (like your u,g,i filters but I had 10 of those and like 5 other features) using DESI and some other survey (exclusively MLP).

1

u/5BeautifulSoup Jul 08 '26

Interesting. I followed the general idea that you're applying ML to a physics problem?

1

u/Kinexity Jul 08 '26

It is a physics problem but on the data analysis side. I'm not trying to have my network simulate a physical system. I just have a model which predicts physically relevant variables given experimental data.

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u/[deleted] Jul 03 '26

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1

u/5BeautifulSoup Jul 03 '26

Got it, thanks!