GitHub Project Description:
📊 Supervised Learning for Student Percentage Prediction
This project utilizes logistic regression in a supervised learning setting to predict students' percentages based on relevant features. By employing machine learning techniques, we aim to provide a robust and accurate model for educational performance forecasting.
🚀 Key Features:
- Logistic Regression Model: Implemented a logistic regression algorithm to model the relationship between input features and student performance.
- Data Preprocessing: Conducted thorough data preprocessing, handling missing values, and encoding categorical variables to ensure the model's effectiveness.
- Evaluation Metrics: Utilized appropriate evaluation metrics such as accuracy, precision, recall, and F1-score to assess the model's performance.
- Scalability: Designed the solution to scale with larger datasets, allowing for broader applications and improved predictive accuracy.
🛠️ Technology Stack:
- Python: Leveraged the power of Python for data manipulation, preprocessing, and implementing the logistic regression model using popular libraries like NumPy and pandas.
- Scikit-Learn: Incorporated Scikit-Learn for efficient machine learning tools, model training, and evaluation.
- GitHub Actions: Implemented CI/CD workflows using GitHub Actions for automated testing and deployment.
📈 Usage:
- Clone the repository.
- Install dependencies using
pip install -r requirements.txt. - Run the Jupyter notebook to explore the data, train the model, and evaluate its performance.
🤝 Contributions: Contributions are welcome! Whether it's optimizing the model, improving data preprocessing techniques, or enhancing documentation, we encourage collaboration to make this project even better.
🔗 References:
📚 Acknowledgments: We extend our gratitude to the open-source community, without which this project would not be possible. Let's continue learning and improving together!
🌐 Explore the Code: GitHub Repository
Feel free to star the repository if you find it helpful, and don't hesitate to open issues or pull requests. Happy coding! 🚀