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README.md

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-license: apache-2.0
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+---
+title: Stellar Classification Dataset - SDSS17
+emoji: 🌟
+tags:
+  - astronomy
+  - stellar-classification
+  - spectral-data
+  - machine-learning
+  - sdss
+---
+
+# Stellar Classification Dataset - SDSS17
+
+## Welcome to the Dataset!
+
+Get ready to explore the cosmos with the **Stellar Classification Dataset** from the Sloan Digital Sky Survey (SDSS) Data Release 17! This dataset contains **100,000 observations** of celestial objects—stars, galaxies, and quasars—captured through their spectral characteristics. Whether you're an astronomer studying the universe, a data scientist building classification models, or a student curious about the night sky, this dataset offers a fantastic opportunity to dive into the world of stellar classification.
+
+## Context
+
+In astronomy, **stellar classification** is the process of categorizing stars, galaxies, and quasars based on their **spectral characteristics**—the unique "fingerprints" of light they emit. This classification is a cornerstone of astronomy, helping us understand how stars are distributed in our Milky Way galaxy and beyond. The discovery that the Andromeda Galaxy was separate from our own sparked a wave of galaxy surveys, made possible by increasingly powerful telescopes. This dataset builds on that legacy, providing spectral data to classify celestial objects and uncover insights about the universe.
+
+## Dataset Description
+
+### Content
+
+The dataset includes **100,000 observations** from the SDSS, each described by **17 feature columns** and **1 class column**. Here's what each column represents:
+
+- **obj_ID**: Unique identifier for the object in the SDSS image catalog (CAS).
+- **alpha**: Right Ascension angle (J2000 epoch, in degrees).
+- **delta**: Declination angle (J2000 epoch, in degrees).
+- **u**: Ultraviolet filter magnitude in the photometric system.
+- **g**: Green filter magnitude in the photometric system.
+- **r**: Red filter magnitude in the photometric system.
+- **i**: Near-infrared filter magnitude in the photometric system.
+- **z**: Infrared filter magnitude in the photometric system.
+- **run_ID**: Run number identifying the specific scan.
+- **rerun_ID**: Rerun number specifying how the image was processed.
+- **cam_col**: Camera column identifying the scanline within the run.
+- **field_ID**: Field number identifying each field.
+- **spec_obj_ID**: Unique ID for optical spectroscopic objects (objects with the same `spec_obj_ID` share the same class).
+- **class**: Object class, labeled as `STAR`, `GALAXY`, or `QUASAR`.
+- **redshift**: Redshift value, indicating the increase in wavelength due to the object’s motion or cosmic expansion.
+- **plate**: Plate ID, identifying each SDSS plate.
+- **MJD**: Modified Julian Date, indicating when the data was collected.
+- **fiber_ID**: Fiber ID, identifying the fiber that directed light to the focal plane.
+
+### Format
+
+- **File**: Likely stored as a CSV file (e.g., `stellar_classification.csv`) in the `data/` directory.
+- **Size**: 100,000 rows, 18 columns (17 features + 1 class).
+
+### Source
+
+The data is sourced from the **Sloan Digital Sky Survey (SDSS) Data Release 17 (DR17)**, a publicly available collection of astronomical observations. The dataset was curated by fedesoriano and hosted on Kaggle.
+
+## Use Cases
+
+This dataset is a treasure trove for various applications:
+
+- **Astronomy Research**: Study the distribution and properties of stars, galaxies, and quasars in the universe.
+- **Machine Learning**: Train classification models to predict whether an object is a star, galaxy, or quasar based on spectral features.
+- **Data Visualization**: Create stunning visualizations of celestial objects’ spectral characteristics or spatial distributions.
+- **Education**: Use in astronomy or data science courses to teach spectral classification and ML techniques.
+- **Exploratory Analysis**: Investigate relationships between redshift, photometric filters, and object classes.
+
+## Similar Datasets
+
+If you’re interested in related datasets, check out these:
+
+- **CERN Proton Collision Dataset**: Particle collision data for high-energy physics research. [Link](#)
+- **Airfoil Self-Noise Dataset**: Acoustic data for aerodynamic studies. [Link](#)
+- **CERN Electron Collision Data**: Electron collision events from CERN experiments. [Link](#)
+- **Wind Speed Prediction Dataset**: Meteorological data for wind forecasting. [Link](#)
+- **Spanish Wine Quality Dataset**: Chemical properties for wine quality classification. [Link](#)
+
+*Note*: Links are placeholders as specific URLs were not provided. Replace with actual links if available.
+
+## Citation
+
+To give credit to the dataset creator, please cite:
+
+> fedesoriano. (January 2022). Stellar Classification Dataset - SDSS17. Retrieved  (January 2022) from https://www.kaggle.com/fedesoriano/stellar-classification-dataset-sdss17.
+
+For the SDSS data source, cite:
+
+> Abdurro’uf et al., The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data (Abdurro’uf et al., submitted to ApJS) [arXiv:2112.02026].
+
+## Acknowledgements
+
+The data is released under the **public domain** by the Sloan Digital Sky Survey (SDSS) as part of Data Release 17 (DR17). For more details on the SDSS license, visit: http://www.sdss.org/science/image-gallery/.
+
+We thank the SDSS team for making this data publicly available and fedesoriano for curating and sharing the dataset on Kaggle.
+
+## License
+
+Public Domain (see SDSS license for details: http://www.sdss.org/science/image-gallery/).
+
+---
+
+Have questions or ideas? Open a GitHub issue or join the discussion on Hugging Face. Clear skies and happy exploring!