Which algorithms are used in this student stress prediction project?

The system implements and compares two classification algorithms: Random Forest and Decision Tree. Random Forest achieved the best performance with 89.4% accuracy and an F1-score of 0.893.

What features are used for stress prediction?

The system uses 20 features including anxiety level, depression, self-esteem, sleep quality, academic performance, study load, social support, peer pressure, living conditions, and engineered features like Stress Index, Academic Stress, Social Stress, and Wellbeing Score.

What is the accuracy of this stress prediction model?

The Random Forest model achieves 89.4% test accuracy with precision of 89.4%, recall of 89.4%, F1-score of 0.893, and ROC-AUC of 0.938, making it highly reliable for student mental health screening.

Who can benefit from this student stress prediction system?

Educational institutions can use it for early intervention, counseling centers for student prioritization, administrators for resource allocation, and researchers for studying student mental health patterns.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, imbalanced-learn (SMOTE), Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Student Stress Prediction Using Machine Learning

The Student Stress Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts student stress levels (Low, Moderate, or High) using psychological, academic, social, and environmental factors. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like Random Forest and Decision Tree to achieve 89.4% accuracy.

The system leverages 20 features including anxiety level, depression, self-esteem, sleep quality, academic performance, and engineered features like Stress Index and Academic Stress. It provides interactive visualizations, feature importance analysis, model comparison, and real-time stress prediction to help educational institutions identify at-risk students and provide timely interventions.

Python 3.8+ Machine Learning Random Forest Decision Tree SMOTE Scikit-learn imbalanced-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class Stress Level Prediction
Random Forest (89.4% Accuracy)
Decision Tree (84.7% Accuracy)
5 Engineered Features
Feature Importance Analysis
Real-time Stress Prediction
Model Performance Comparison
Interactive Visualizations
5-Fold Cross-Validation
Flask Web Application

Algorithms Used

🌲 Random Forest Classifier

Ensemble learning with 100 trees, captures non-linear relationships effectively

🎯 Accuracy: 89.4%

🌳 Decision Tree Classifier

Interpretable tree-based model, provides clear decision rules

🎯 Accuracy: 84.7%

🔧 Feature Engineering

Stress Index, Academic Stress, Social Stress, Environmental Stress, Wellbeing Score

📊 Features: 25

🔍 5-Fold CV

Cross-validation for model stability and generalization

📊 RF: 88.5% ± 0.84%

Methodology & Workflow

1 Data Loading & Inspection
Stress dataset with 1,100 records and 20 features

2 Data Preprocessing
Cleaning, encoding, feature scaling with StandardScaler

3 Feature Engineering
Stress Index, Academic Stress, Social Stress, Environmental Stress, Wellbeing Score

4 Model Training
Random Forest & Decision Tree with SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 5-fold CV

6 Web Deployment
Flask web app for real-time stress prediction

Model Performance Comparison

Metric	Random Forest	Decision Tree	Best
Accuracy	0.894	0.847	Random Forest
Precision	0.894	0.848	Random Forest
Recall	0.894	0.847	Random Forest
F1-Score	0.893	0.846	Random Forest
ROC-AUC	0.938	0.892	Random Forest
CV Mean (5-Fold)	0.885	0.839	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (1,100 records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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