Project Overview
This project utilizes linear regression to predict students' academic performance based on attributes like study hours, attendance, and grades. It demonstrates the practical application of machine learning in education, providing insights for better academic strategies.
Objectives
- Predict students' final grades accurately using linear regression.
- Analyze how study habits, attendance, and other factors influence academic performance.
- Demonstrate the use of Python libraries and statistical methods for predictive modeling.
- Provide educators and students with actionable insights for performance improvement.
Tools and Technologies
Python Libraries:
- Programming Language: Python (main language for implementation).
- Visualization: Matplotlib, Seaborn
- Data Processing and Analysis: Pandas, NumPy, Scikit-learn
- Additional Libraries/Packages: Google Colab
How to Run the Project
-------INSTRUCTIONS-------
- Open the provided Jupyter Notebook (student_performance_prediction.ipynb) in Google Colab.
- Ensure the dataset file is uploaded to your Google Colab environment before running any code. Note: A stable internet connection is required.
- Run the code cell by cell in the notebook. This allows you to follow the step-by-step implementation and view intermediate results.
- Visualize predictions and generated plots.