This open-source Python project performs student performance analysis using Linear Regression.
It aims to predict student scores or academic outcomes based on input features such as study hours, attendance, or other performance metrics.
It’s a beginner-friendly project to understand how machine learning models—especially regression algorithms—can be used to analyze real-world data and make predictions.
You can:
- Explore the dataset and visualize student performance trends.
- Train a Linear Regression model using scikit-learn.
- Evaluate and visualize the model’s performance with error metrics and prediction plots.
Feel free to modify, improve, or expand this project as you wish!
The dataset contains information related to student learning and outcomes.
Example columns may include:
- Hours Studied – number of hours a student studied.
- Previous Scores – marks from past tests or exams.
- Attendance (%) – percentage of classes attended.
- Final Score / Performance – target variable to predict.
The dataset can be customized or replaced with any CSV containing similar features.
This project uses:
- Python 🐍
- Pandas
- NumPy
- Matplotlib / Seaborn
- Scikit-learn
All dependencies are listed in the requirements.txt file.
To install them, run:
pip install -r requirements.txt
## 🤖 Author
GitHub Profile → [https://github.com/Hacknova49](https://github.com/Hacknova49)