🎓 Student Success Predictor

Predicting student outcomes using early engagement data

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Project Overview

The Student Success Predictor is a machine learning-based web application designed to help identify students who may be at risk of underperforming in a course.

By analyzing early engagement indicators such as:

• Attendance rate
• Study hours per week
• Prior grades
• Extracurricular involvement
• Parental support

...the model helps instructors and advisors intervene early and support at-risk students.

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Tech Stack

• Python: pandas, scikit-learn, joblib
• Streamlit: Web dashboard for user interaction
• Jupyter Notebook: For data exploration and model training
• Git & GitHub: Version control and deployment
• (Optional) Docker for containerization and cloud deployment

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Project Structure

Student-Success-Predictor/
│
├── README.md <-- Project overview (this file)
├── LICENSE <-- Open-source license (MIT)
├── .gitignore <-- Ignore unnecessary files
│
├── data/
│ ├── raw/ <-- Raw input data (e.g., Kaggle CSV)
│ └── processed/ <-- Cleaned data ready for modeling
│
├── notebooks/
│ └── 01_EDA_Modeling.ipynb <-- Data analysis & model training notebook
│
├── src/
│ ├── data_preprocessing.py <-- Data cleaning and feature prep
│ ├── train_model.py <-- Model training script
│ └── predict.py <-- Prediction logic
│
├── models/
│ └── student_success_model.pkl <-- Trained ML model
│
├── dashboard/
│ ├── app.py <-- Streamlit app code
│ └── requirements.txt <-- Required Python packages
│
└── docs/
└── methodology.md <-- Documentation on model + ethics

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How to Run Locally

1. Clone the repo

git clone https://github.com/NEEJUSINGH/Student-Success-Predictor.git
cd Student-Success-Predictor

2. Create a virtual environment (recommended)

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r dashboard/requirements.txt

4. Run the Streamlit app

cd dashboard
streamlit run app.py

Deliverables

• Trained ML model (student_success_model.pkl)

• Interactive web dashboard (Streamlit)

• Jupyter Notebook for data exploration & training

• Sample student dataset (simulated)

• Documentation (docs/methodology.md)

Future Improvements

Integrate real-time LMS API data (e.g., D2L Brightspace)

Build role-based dashboard views for instructors or advisors

Include more features like quiz scores, login frequency

Add intervention recommendation engine

Fully deploy via Streamlit Cloud, Docker, or Heroku

License

This project is licensed under the MIT License.

Live Demo

Launch the App

Test the app by uploading a CSV with the following columns:

Gender, AttendanceRate, StudyHoursPerWeek, PreviousGrade, ExtracurricularActivities, ParentalSupport