Back to Projects
Machine Learningintermediate

Academic Risk & Engagement Prediction System

This project focuses on predicting student academic outcomes based on demographic, academic, and behavioral data. It demonstrates how educational institutions can use Machine Learning to identify students at risk, understand performance drivers, and make data-driven decisions to improve learning outcomes.The project emphasizes classification and probabilistic modeling, proper data preprocessing, feature importance analysis, and model evaluation.

12 lectures

What You Will Learn

Understanding educational datasets and translating academic features into ML signals
Performing EDA to uncover patterns affecting student performance
Feature engineering for categorical and numerical academic data
Interpreting model results to explain why students succeed or struggle
Structuring ML experiments cleanly using notebooks and reusable code
Writing modular, production-ready ML code with configuration files
Deploying an ML application using Streamlit Cloud with a user-friendly UI

System Architecture

Academic Risk & Engagement Prediction System Architecture Diagram

High-level architecture overview of the Academic Risk & Engagement Prediction System .

What You'll Build

  • Multi-Objective Academic Predictor
  • Reproducible Data Preprocessing Pipeline
  • Interpretable Dashboard
  • Early Warning Systems
Academic Risk & Engagement Prediction System
Premium
One Subscription. 40+ Projects. Unlimited Access.
AccessMobile & Web