This project leverages machine learning to predict student performance based on various demographic, academic, and behavioral factors. By analyzing student learning patterns, this model aims to provide actionable insights for educators, institutions, and policymakers to enhance educational outcomes.
Understanding student performance drivers is crucial for improving educational strategies. This project applies data-driven approaches to predict student learning outcomes using machine learning algorithms. By identifying key influencing factors, the model helps in making informed decisions to support student success.
The dataset comprises various student attributes, including:
- Demographics: Age, gender, parental education level
- Academic Performance: Past grades, attendance records
- Behavioral & Social Factors: Study time, participation in extracurricular activities
- Technological & Environmental Factors: Internet access, home study environment
The model incorporates multiple predictive features:
| Feature Name | Description |
|---|---|
| Gender | Male or Female |
| Age | Age of the student |
| Parental Education | Highest education level attained by parents |
| Study Time | Weekly study hours |
| Failures | Number of past class failures |
| Extracurriculars | Participation in activities outside of academics |
| Higher Education Plan | Whether the student aspires for higher studies |
| Internet Access | Availability of internet at home |
The model development process includes:
- Data Preprocessing: Handling missing values, encoding categorical variables, feature scaling.
- Exploratory Data Analysis (EDA): Visualizing and understanding data trends.
- Model Selection: Evaluating different machine learning models to find the most accurate one.
- Training & Evaluation: Measuring model performance with key metrics like accuracy, precision, recall, and F1-score.
For an in-depth analysis, refer to the Jupyter Notebook:
📌 Model Building Notebook
The model was evaluated on real-world student data, achieving the following performance:
| Metric | Score |
|---|---|
| Accuracy | 74% |
| F1-Score | 83% |
🚀 Fine-tuning and hyperparameter optimization can further improve these results.
Ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
Clone the repository:
git clone https://github.com/SpyDplayz/Predict-Student-Learning.git
cd Predict-Student-Learning