Predicting Student Performance Using Machine Learning | Educational Project

Project Description

Predicting student performance in mathematics is essential for educators, parents, and policymakers to identify students who may need additional support. This project utilizes machine learning techniques to predict student performance based on various factors such as gender, ethnicity, parental level of education, lunch type, and test preparation course. The goal is to develop a tool that can provide insights into factors influencing student performance and help tailor educational strategies accordingly.

Note: This project is for educational purposes only.

Features

• Predicts student performance in mathematics based on multiple factors.
• Provides insights into the influence of gender, ethnicity, parental level of education, lunch type, and test preparation course on student performance.
• User-friendly interface for inputting student information and obtaining predictions.

Dataset

The dataset used for training the machine learning model is sourced from Kaggle – Students Performance in Exams. It contains information about students’ demographics, parental education, lunch type, test preparation course, and their corresponding math scores.

Model Training

The machine learning model is trained using a supervised learning algorithm, such as a decision tree or random forest, to predict the math score based on the input features. The dataset is split into training and testing sets to evaluate the model’s performance.

Technology Stack

• Python: Programming language for model development and implementation.
• Machine Learning: Utilized for predictive analytics.
• Pandas: Data manipulation and analysis library.
• NumPy: Numerical computing library for efficient array operations.
• Scikit-learn: Machine learning library for model training and evaluation.
• Flask: Web framework for building the application backend.
• HTML, CSS: Frontend development for user interface design.

Installation Steps

1. Install Dependencies:

   python -m pip install --user -r requirements.txt

2. Run the Application:

   python app.py

   Access the application through the provided URL.

Conclusion

The Predicting Student Performance Using Machine Learning project showcases the application of machine learning techniques in educational settings. By accurately predicting student performance based on various factors, educators and policymakers can identify students who may require additional support and tailor educational strategies accordingly. This project emphasizes the importance of leveraging data-driven approaches to improve student outcomes and educational equity.