Student Success Predictor: A Student Academic Performance Prediction System Based on Logistic Regression

Student Success Predictor：基于逻辑回归的学生学业表现预测系统

Original Author/Maintainer: Hesandu-Ruwanpathirana Source Platform: GitHub Original Link: https://github.com/Hesandu-Ruwanpathirana/student-success-predictor Release Date: 2026年5月24日

This project is an end-to-end machine learning application designed to predict academic risks (pass/fail) by analyzing students' learning behavior data. Built using the Python ecosystem toolchain (Scikit-learn, Streamlit, etc.), it covers the complete workflow from data loading, preprocessing, model training to deployment, making it a good example for understanding the ML project lifecycle.

Project Background and Significance of Educational Data Mining

With the deepening of educational informatization, schools have accumulated a large amount of student learning behavior data. How to use this data to identify students with academic difficulties early and provide interventions is an important topic in educational data mining. This project presents a lightweight yet complete solution to help implement data-driven academic early warning.

Technology Stack and Model Design

Technology Selection: Uses pandas (data processing), Scikit-learn (model training), Streamlit (interactive application), joblib (model persistence). The technology combination is mature and stable, suitable for prototype development in the education field.

Model Selection: Logistic regression (binary classification algorithm) was chosen for reasons including: strong interpretability (coefficients reflect feature impacts), high computational efficiency, low risk of overfitting, and suitability for academic prediction (pass/fail binary classification).

Input Features: Study duration, attendance rate, number of completed assignments, quiz scores, sleep duration—covering dimensions of learning input, participation, and physical/mental state.

End-to-End Process Analysis

1. Data Preparation: Load CSV data → Clean (handle missing/outlier values) → Feature engineering → Split into training/test sets.
2. Model Training: Select LogisticRegression → Fit to training set → Hyperparameter tuning → Cross-validation to evaluate generalization ability.
3. Model Persistence: Serialize the model into a .pkl file using joblib for easy deployment.
4. Application Deployment: Build an interactive interface with Streamlit, supporting user input of parameters → Real-time prediction → Display classification results and pass probability.

Application Scenarios and Value

Early Warning System: Identify high-risk students, provide intervention support for counselors, and feedback academic status to parents. Student Feedback: Students can self-assess their learning data, get suggestions for behavior adjustment, and set improvement goals. Educational Research: Provides a data collection framework and reference for analysis methods; open-source code facilitates reproduction and expansion.

Project Highlights and Areas for Improvement

Highlights:

• Completeness: Covers the entire ML project lifecycle;
• Simplicity: Reasonable technology stack without over-engineering;
• Interactivity: Streamlit application makes the model easy to demo;
• Educational value: Clear code structure, suitable as a learning case.

Improvement Directions: Database integration, expand dataset, optimize UI, add evaluation metrics (confusion matrix/ROC curve), introduce more features (social activities/mental health).

Privacy Ethics and Summary

Privacy Considerations: When deploying, attention should be paid to data desensitization, access permission control, and avoiding algorithmic bias.

Summary: This project is a small yet elegant educational ML application that demonstrates the use of logistic regression in educational scenarios. It is suitable for beginners to understand the end-to-end process and provides an academic early warning prototype for edtech practitioners. Its value lies in using data to gain insights into learning patterns and empower educational decision-making.