This project analyzes student data and create a machine learning model to predict third term student performance. The primary goal is to predict G3 grades without using grades from the first two semesters (G1 and G2). I use Sci-kit Learn to make Linear Regression, SVM Regression, Lasso Regression, Ridge Regression, Random Forest Regression, and stacking ensemble method.
- Student Performance Datasets downloaded from UC Irvine Machine Learning Repository 5/12/2025
- Data Exploration: Jupyter notebooks in VS Code.
- Analysis: Python with the Pandas, Numpy, and Sci-kit Learn packages for data cleaning.
- Visualizations : Initial visualizations using Matplotlib in Jupyter notebooks.
To run this project, follow these steps:
- Clone the repository:
git clone https://github.com/NicholasJCampbell/student_grades_project.git - Install the necessary dependencies:
pip install -r requirements.txt - Explore the Jupyter notebooks or scripts in the respective folders.
- After you have cloned the repo to your machine, navigate to the project folder in GitBash/Terminal.
- Create a virtual environment in the project folder.
- Activate the virtual environment.
- Install the required packages.
- When you are done working on your repo, deactivate the virtual environment.
Virtual Environment Commands
| Command | Linux/Mac | GitBash |
|---|---|---|
| Create | python3 -m venv venv |
python -m venv venv |
| Activate | source venv/bin/activate |
source venv/Scripts/activate |
| Install | pip install -r requirements.txt |
pip install -r requirements.txt |
| Deactivate | deactivate |
deactivate |