Ansareze/stellar_object_classification

stellarobjectclassification

Stellar Object Detection using Machine Learning Models =========================================================

This project aims to classify stellar objects based on given features using various machine learning algorithms. After testing multiple models, Gradient Boosting was identified as the best-performing model. The final model was deployed using Gradio for an interactive web-based interface.

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Project Overview -------------------

Classifying stellar objects, such as stars, galaxies, and quasars, is crucial in astronomical studies. This project applies multiple supervised learning algorithms to detect and classify stellar objects efficiently. The best-performing model, Gradient Boosting, was deployed on Gradio to provide real-time predictions.

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Models Used --------------

The following machine learning models were implemented and compared:

1. 1.Decision Tree
2. 2.Random Forest
3. 3.K-Nearest Neighbors (KNN)
4. 4.Support Vector Machines (SVM)
5. 5.Gradient Boosting
6. 6.AdaBoost
7. 7.XGBoost
8. 8.Neural Network

Gradient Boosting superior performance in terms of accuracy, precision, and recall compared to other models.