{"id":26060443,"url":"https://github.com/pramodyasahan/grade-predictor","last_synced_at":"2026-04-30T11:33:22.010Z","repository":{"id":211889996,"uuid":"730192702","full_name":"pramodyasahan/grade-predictor","owner":"pramodyasahan","description":"This project aims to predict student performance based on various features such as job, study time, failures, absences, and first and second period grades. 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The project utilizes a linear regression model from the `scikit-learn` library in Python.\n\n## Getting Started\n\n### Prerequisites\n- Python 3.x\n- PyCharm (or any other IDE)\n\n### Installation\n1. **Clone the Repository**: \n   ```bash\n   git clone https://github.com/pramodyasahan/grade-predictor\n   ```\n\n2. **Install Required Libraries**:\n   - `pandas` for data manipulation and analysis\n   - `numpy` for numerical operations\n   - `matplotlib` for plotting graphs\n   - `scikit-learn` for machine learning tools\n\n   You can install these using pip:\n   ```bash\n   pip install pandas numpy matplotlib scikit-learn\n   ```\n\n3. **Download the Dataset**: \n   Download the dataset from [Student Performance Dataset](https://archive.ics.uci.edu/dataset/320/student+performance) and place it in the project directory.\n\n## Project Structure\n\n- `student-mat.csv`: The dataset file.\n- `main.py`: The main Python script with data preprocessing, training, and evaluation.\n\n## Running the Project\n\n1. Open the project in PyCharm.\n2. Ensure that the dataset `student-mat.csv` is in the correct directory.\n3. Run `main.py` to start the training and evaluation process.\n\n## Code Explanation\n\n### Data Preprocessing\n- **Feature Selection**: Selects specific columns from the dataset as features.\n- **One-Hot Encoding**: Applies one-hot encoding to categorical features.\n- **Data Splitting**: Splits the dataset into training and testing sets.\n\n### Model Training\n- **Linear Regression**: Uses the `LinearRegression` class from `scikit-learn` to train the model on the training set.\n\n### Prediction and Evaluation\n- **Predicting Grades**: The model predicts grades on the test set.\n- **Comparison Plot**: A plot is generated to compare predicted grades against actual grades.\n\n## Theoretical Background\n\n- **Linear Regression**: A statistical method that models the relationship between a dependent variable and one or more independent variables.\n- **One-Hot Encoding**: A process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions.\n- **Training and Testing Split**: This concept involves dividing the dataset into two parts: training data to train the model, and testing data to evaluate its performance.\n\n## Contribution\nFeel free to fork this repository and contribute to its development. Any contributions you make are greatly appreciated.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpramodyasahan%2Fgrade-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpramodyasahan%2Fgrade-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpramodyasahan%2Fgrade-predictor/lists"}