{"id":24520440,"url":"https://github.com/siddh-coder/celestial-body-classifier","last_synced_at":"2026-05-17T02:39:26.109Z","repository":{"id":272143089,"uuid":"915644213","full_name":"siddh-coder/Celestial-Body-Classifier","owner":"siddh-coder","description":null,"archived":false,"fork":false,"pushed_at":"2025-01-12T13:55:35.000Z","size":26217,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-21T19:53:58.010Z","etag":null,"topics":["machine-learning","streamlit"],"latest_commit_sha":null,"homepage":"https://celestial-body-classifier-by-sid.streamlit.app","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/siddh-coder.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-12T12:29:29.000Z","updated_at":"2025-01-12T13:55:39.000Z","dependencies_parsed_at":null,"dependency_job_id":"fe4766ca-435a-4769-8fd8-ae7ccde426c8","html_url":"https://github.com/siddh-coder/Celestial-Body-Classifier","commit_stats":null,"previous_names":["siddh-coder/celestial-body-classifier"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/siddh-coder/Celestial-Body-Classifier","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siddh-coder%2FCelestial-Body-Classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siddh-coder%2FCelestial-Body-Classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siddh-coder%2FCelestial-Body-Classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siddh-coder%2FCelestial-Body-Classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/siddh-coder","download_url":"https://codeload.github.com/siddh-coder/Celestial-Body-Classifier/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siddh-coder%2FCelestial-Body-Classifier/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279159424,"owners_count":26116470,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-16T02:00:06.019Z","response_time":53,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","streamlit"],"created_at":"2025-01-22T02:22:34.788Z","updated_at":"2025-10-16T05:23:58.379Z","avatar_url":"https://github.com/siddh-coder.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Star Classification with Pre-trained Machine Learning Models\n\n## Project Overview\nThis project is a web-based application that uses **7 pre-trained machine learning models** to classify celestial objects—such as stars, galaxies, and quasars—based on their photometric properties. The app is built using **Streamlit** for its user interface and supports interactive inputs to predict the class of celestial objects using various models.\n\n---\n\n## Features\n1. **Input Photometric Data:**\n   - Users can provide values for features like “U, G, R, I, Z” magnitudes, right ascension (“Alpha”), declination (“Delta”), and redshift.\n\n2. **Multiple Pre-trained Models:**\n   - Predictions are made using:\n     - Support Vector Machine (SVM)\n     - Random Forest\n     - Logistic Regression\n     - K-Nearest Neighbors (KNN)\n     - Decision Tree\n     - Gradient Boosting\n     - Naive Bayes\n\n3. **Final Classification:**\n   - The app provides individual model predictions and a final classification based on majority voting.\n\n4. **Educational Content:**\n   - A detailed explanation of the significance of photometric filters (U, G, R, I, Z) in astronomy is included.\n   - Users can learn about the importance of magnitudes and how they help classify celestial objects.\n\n---\n\n## How to Run the Application\n\n### Prerequisites\nEnsure you have the following installed:\n- Python (\u003e= 3.8)\n- pip (Python package installer)\n\n### Installation\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/your-repo/star-classification-app.git\n   cd star-classification-app\n   ```\n2. Install the required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. Ensure the pre-trained models (`*.pkl` files) are in the root directory.\n\n### Run the Application\n1. Start the Streamlit server:\n   ```bash\n   streamlit run app.py\n   ```\n2. Open your browser and navigate to the local URL provided (e.g., `http://localhost:8501`).\n\n---\n\n## Project Structure\n```\nstar-classification-app/\n|-- app.py                # Main Streamlit application\n|-- requirements.txt      # Required Python libraries\n|-- svm_classifier.pkl\n|-- random_forest_classifier.pkl\n|-- logistic_regression_model.pkl\n|-- k_nearest_neighbors_model.pkl\n|-- decision_tree_model.pkl\n|-- gradient_boosting_model.pkl\n|-- naive_bayes_model.pkl\n|-- scaler.pkl            # Scaler for feature preprocessing\n|-- label_encoder.pkl     # Encoder for target labels\n|-- README.md             # Project documentation (this file)\n```\n\n---\n\n## Inputs and Outputs\n\n### Inputs\nUsers are required to provide the following inputs through the sidebar:\n- **Alpha (α):** Right ascension of the celestial object.\n- **Delta (δ):** Declination of the celestial object.\n- **U, G, R, I, Z:** Magnitudes in respective photometric bands.\n- **Redshift:** A measure of the object’s distance and velocity.\n\n### Outputs\n- Predictions from all 7 machine learning models.\n- Final classification based on majority voting.\n- Educational insights about magnitudes and filters.\n\n---\n\n## Explanation of U, G, R, I, Z Filters\n\n### What Are They?\n**U, G, R, I, Z** are photometric filters used to observe celestial objects in specific wavelength ranges:\n- **U (Ultraviolet):** 300-400 nm\n- **G (Green):** 400-550 nm\n- **R (Red):** 550-700 nm\n- **I (Infrared):** 700-850 nm\n- **Z (Near-Infrared):** 850-1000 nm\n\n### Importance in Astronomy\nThese filters measure how much light an object emits in each wavelength band, enabling astronomers to:\n- **Classify objects** (e.g., stars, galaxies, quasars).\n- **Determine distances** using redshift.\n- **Analyze properties** like temperature, composition, and age.\n\n### Magnitudes\nMagnitudes are a logarithmic measure of brightness:\n- **Smaller values = brighter objects.**\n- A difference of 5 magnitudes corresponds to a brightness ratio of 100.\n\n---\n\n## Future Improvements\n- Adding more classifiers to enhance prediction reliability.\n- Incorporating real-time data visualization.\n- Enabling upload functionality for batch predictions.\n- Expanding educational content to include visual aids and examples.\n\n---\n\n## Contributors\n- Siddharth Tripathi - Project Developer\n\n---\n\n## Acknowledgments\n- **Kaggle:** For providing data(https://www.kaggle.com/datasets/fedesoriano/stellar-classification-dataset-sdss17)\n- **Streamlit:** For the user-friendly web interface.\n- **scikit-learn:** For powerful machine learning tools.\n\n---\n\nThank you for using the Star Classification App! Feel free to contribute or raise issues to improve this project further.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsiddh-coder%2Fcelestial-body-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsiddh-coder%2Fcelestial-body-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsiddh-coder%2Fcelestial-body-classifier/lists"}