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Dataset\n│\n├── notebooks/\n│   └── EDA.ipynb                  # Exploratory data analysis\n│\n├── src/\n│   ├── preprocess.py              # Data preprocessing\n│   ├── train_model.py             # Train Linear Regression model and evaluation of model\n│   └── predict.py                 # Prediction function\n│\n├── app/\n│   └── app.py                     # Streamlit web application\n│\n├── models/\n│   └── linear_regression.pkl      # Saved trained model\n│\n├── requirements.txt               # Dependencies\n└── README.md                      # Project documentation\n```\n\n\n📊 Workflow\n\n1. Load Data → `pandas`\n2. EDA → Visualize Hours vs Marks\n3. Train Model → `LinearRegression` from scikit-learn\n4. Evaluate Model → MAE, MSE, R² Score\n5. Save Model → `joblib`\n6. Deploy App → User enters hours → Predict marks\n\n\n\n🔮 Example Prediction\n\nInput: `5 hours of study`\nOutput: `Predicted Marks ≈ 55%`\n\n\n\n▶️ How to Run\n\n🔧 1. Clone Repo\n\n```bash\ngit clone https://github.com/your-username/student-marks-predictor.git\ncd student-marks-predictor\n```\n\n📦 2. Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n🏋️ 3. Train Model\n\n```bash\npython src/train_model.py\n```\n\n🌐 4. Run Web App\n\n```bash\nstreamlit run app/app.py\n```\n\n\n\n📈 Results\n\n* Regression line fitting study hours vs marks\n* R² Score close to **1.0** for small datasets\n* Predictions within a reasonable error margin\n\n\n🤝 Contributing\n\nContributions are welcome!\n\n* Fork the repo\n* Create a new branch\n* Make changes and commit\n* Submit a pull request\n\n\n📜 License\n\nThis project is licensed under the **MIT License**.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fizhaan0%2Fpredict-marks-based-on-study-hours","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fizhaan0%2Fpredict-marks-based-on-study-hours","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fizhaan0%2Fpredict-marks-based-on-study-hours/lists"}