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Student Exam Performance Indicator

An End-to-End Flask-based Web Application to Analyze and Predict Student Exam Performance.

Table of Contents

Project Overview

The Student Exam Performance Indicator is a web application designed to assist teachers and educational institutions in analyzing student exam performance and gaining insights to improve educational outcomes. By leveraging machine learning, this tool predicts a student's performance and suggests personalized educational strategies for improvement.

The key objectives of this project are:

  • To visualize trends in student performance using interactive charts.
  • To predict exam results using machine learning models with an R² score of 0.879.
  • To help teachers tailor their teaching strategies based on predicted outcomes.

Features

  • Data Visualization: Provides insightful visualizations of student performance data (e.g., distribution of scores, correlation between variables, etc.).
  • Prediction Engine: Predicts student exam scores based on multiple attributes like gender, parental level of education, and previous scores.
  • Interactive Dashboard: User-friendly interface where teachers can explore and analyze the predictions.
  • Customizable Reports: Generates reports for each student, highlighting areas of improvement and strengths.

Tech Stack

  • Backend: Flask (Python)
  • Frontend: HTML, CSS, Bootstrap
  • Database: SQLite (can be replaced with any relational database)
  • Machine Learning: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
  • Deployment: Docker (optional), Gunicorn (optional)

Data Pipeline

  1. Data Ingestion: Loading the student performance data from a CSV file.

  2. Exploratory Data Analysis (EDA):

    • Visualizations of student performance using tools like Matplotlib and Seaborn.
    • Key insights include score distribution, feature relationships, and performance trends.

  3. Data Preprocessing:

    • Handling missing data, categorical encoding (one-hot encoding), and normalization.
    • Splitting the data into training and testing sets.

  4. Model Training:

    • Multiple models were trained, including Linear Regression, Random Forest, and XGBoost.
    • Hyperparameter tuning using GridSearchCV was performed to optimize performance.

  5. Model Evaluation:

    • The selected model achieved an R² score of 0.879, making it ideal for predicting student exam scores.
    • Metrics used: Mean Squared Error (MSE), R² Score, and Root Mean Squared Error (RMSE).

Model Evaluation

After experimenting with various models, Random Forest emerged as the best-performing model for this problem. It was evaluated on the test set, and the following metrics were achieved:

  • R² Score: 0.879
  • Mean Squared Error (MSE):
  • Root Mean Squared Error (RMSE):

The model was selected for its ability to generalize well on unseen data and its interpretability in a classroom setting.

Setup Instructions

Prerequisites

  • Python 3.10+
  • Flask
  • Scikit-learn
  • Pandas, NumPy, Matplotlib, Seaborn

Installation

  1. Clone the repository: git clone https://github.com/SamJoeSilvano/mlproject.git

  2. Navigate to the project directory: cd student-exam-performance-indicator

  3. Install dependencies: pip install -r requirements.txt

  4. Run the Flask app: python app.py

  5. Open your browser and go to http://127.0.0.1:5000/ to access the web app.

Usage

  1. Data Upload: Teachers can upload student performance data in CSV format.
  2. Visualizations: The application will display various visualizations for analyzing the data.
  3. Prediction: Use the trained model to predict student scores based on inputs such as gender, previous scores, and parental education.
  4. Report Generation: Teachers can generate performance reports to assist in personalized teaching strategies.

Future Enhancements

  • Addition of More ML Models: Incorporate more advanced algorithms like Neural Networks for improved predictions.
  • Dynamic Report Generation: Automatically generate PDFs of performance analysis and recommendations for each student.
  • User Authentication: Implement a secure login system for teachers and administrators.
  • Integration with Real-Time Data: Allow the app to fetch data directly from student databases in real-time.

Contributing

Contributions are welcome! Please follow these steps:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature-branch).
  3. Commit your changes (git commit -am 'Add new feature').
  4. Push to the branch (git push origin feature-branch).
  5. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

  • Special thanks to the Flask and Scikit-learn communities for their invaluable resources.
  • Thank you to all contributors for making this project possible!