Skip to content

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.

Notifications You must be signed in to change notification settings

mdthabrez/Student-Performance-Analysis-and-Prediction

Repository files navigation

STUDENT PERFORMANCE PREDICTION AND ANALYSIS

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.

Technologies and Concepts Used

Python
Pandas
NumPy
Matplotlib
Seaborn
Machine Learning

Features

  • 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.

What did I learn in this project?

  • 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.

Project Report

Click to view PROJECT REPORT.

🔗 Contact

Mohammed Thabrez G

linkedin

About

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.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published