This project demonstrates a simple machine learning approach to predict factors that influence student grades using a dataset stored in a CSV file.
The dataset contains information about students from different nationalities and grade levels, along with key determining factors such as:
- Number of hands raised
- Number of attendances
- Hours studied
- And more
The goal of this project is to analyze these factors and predict their impact on student performance.
Several classifiers and machine learning models have been implemented and compared to achieve the most accurate predictions of the factors affecting student marks.
To better understand the results, visual aids have been generated, including:
- Graphs for data insights
- Confusion matrices for model evaluation
This project provides a hands-on demonstration of applying machine learning techniques to an educational dataset, highlighting the relationship between study habits, engagement, and student success.