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Clone the repository:\n\n```bash\ngit clone https://github.com/shivamr021/student-admission-predictor.git\ncd student-admission-predictor\n````\n\n2. Install required packages:\n\n```bash\npip install -r requirements.txt\n```\n\n3. Run the Streamlit app:\n\n```bash\nstreamlit run streamlit_app.py\n```\n\n---\n\n## 📁 File Structure\n\n```bash\n.\n├── streamlit_app.py          # Streamlit app (interactive UI)\n├── admission_data.csv        # Dataset (used for training)\n├── README.md                 # This file\n├── requirements.txt          # Dependencies\n```\n\n---\n\n## 💡 Learning Outcome\n\n* Implemented end-to-end **binary classification** without using machine learning libraries\n* Learned to build a basic **ML app with Streamlit**\n* Understood core ML evaluation metrics\n* Practiced data preprocessing and normalization\n\n---\n\n## 🌐 Tech Stack\n\n* Python\n* NumPy\n* Pandas\n* Matplotlib\n* Streamlit\n\n---\n\n## ✨ Future Improvements\n\n* Add model trained using `scikit-learn` for comparison\n* Allow file upload to test batch data\n* Deploy advanced model on platforms like HuggingFace or Render\n\n---\n\n## 👤 Author\n\n**Shivam Rathod**\n📬 [LinkedIn](https://linkedin.com/in/shivamrathod021)\n\n---\n\n## 📜 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshivamr021%2Fstudent-admission-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshivamr021%2Fstudent-admission-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshivamr021%2Fstudent-admission-predictor/lists"}