This project aims to predict student performance and analyze factors contributing to academic success or dropout using machine learning techniques. It employs clustering algorithms to identify patterns in student data and develops predictive models to forecast student outcomes based on various features.
Python
Pandas
NumPy
Matplotlib
Seaborn
Machine Learning
- Clustering Analysis:
- Utilizes clustering algorithms to group students based on similarities in academic performance, behavior, and demographic attributes.
- Identifies distinct clusters representing different student profiles, such as high achievers, struggling students, and potential dropouts.
- Predictive Modeling:
- Develops machine learning models to forecast student outcomes, including final grades, graduation probability, and dropout likelihood.
- Uses algorithms like logistic regression, decision trees, and ensemble methods to learn from historical student data and make predictions for future cohorts.
- Data Analysis: Enhanced skills in exploratory data analysis, feature engineering, and visualization to derive actionable insights from complex datasets.
- Machine Learning Techniques: Developed proficiency in applying machine learning algorithms such as clustering and predictive modeling to real-world problems in education.
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Mohammed Thabrez G
