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Student Performance Prediction Using Machine Learning

Project Overview

The goal of this project is to predict whether a student will pass or fail (G3 >= 10) using study habits, attendance, and demographic features

The scope consists of preprocessing, feature engineering, binary classification, and interpretability via SHAP

Technology Used

  • Github
  • VSCode
  • Python
  • NumPy
  • Scikit-learn
  • Matplotlib / Seaborn

Setup

Install Scikit-Learn, Shap, Randomforest, Matplotlib, Pandas, Seaborn, and XGBoost into the python runtime environment

Dataset

  • Overview
    • Total instances: 649
    • Features: 30
    • Missing values: 0
    • Target variable: G3 (final grade from 0–20)
  • Key Dataset Notes
    • Two Portuguese secondary schools - Math & Portuguese subjects
    • G3 strongly correlated with G1 & G2
    • G1 & G2 excluded to prevent data leakage
    • Pass threshold: G3 >= 10 | Fail: G3 < 10
  • Feature Categories
    • Demographic: Sex, age, address, family size, cohabitation
    • Parental: Mother’s & Father’s education & occupations
    • School-Related: School choice reason, guardian, travel time
    • Behavioral/Lifestyle: Study time, absences, alcohol use, internet, activities

Team Contributions

  • Kaitlin Sloan — Logistic Regression Build Logistic Regression pipeline Preprocess data (encoding + scaling) Train with/without G1 & G2 Evaluate using F1, ROC-AUC, confusion matrix Write results summary + push notebook

  • Praneetha Voona — Random Forest Build Random Forest pipeline Perform hyperparameter tuning + 5-fold CV Evaluate using F1, ROC-AUC, confusion matrix Plot feature importances Write results summary + push notebook

  • Joshua Grewe — Gradient Boosting Build XGBoost/LightGBM pipeline Train using stratified 5-fold CV Compare with/without G1 & G2 Evaluate using F1, ROC-AUC, confusion matrix Write results summary + push notebook

  • Justin Gurkin — SHAP Fairness Analysis Train baseline Random Forest for SHAP Run SHAP analysis on features Analyze parental education bias impact Write results summary + push notebook

  • Manihams Suraparaju — Ensemble + Report Build VotingClassifier ensemble (LR + RF + XGBoost) Evaluate using F1, ROC-AUC, confusion matrix Compile IEEE final report Push final notebook/report

Preprocessing

  • Data Quality Check:
    • Verified zero missing values across all 33 features
    • Ensured dataset was ready for immediate Modeling
  • Creating Target Variable
    • Used G3 as Binary Classification: Pass = G3 >= 10, Fail = G3 <10
    • Dropped G1, G2, G3 from features to prevent data leakage
  • Preprocessing Pipeline:
    • Categorical Features: OneHotEncoder(drop first category to avoid multicollinearity)
    • Numeric Features: StandardScaler (mean =0, standard deviation = 1)
  • Had an 80/20 train/test split with stratification to maintain class balance( random_state = 42)

Logistic Regression

  • Model Set Up:
    • Established baseline performance for binary classification
    • Linear Model predicting probability of pass/ fail outcomes
    • Max iterations set to 500 to ensure convergence on preprocessed data
  • Performance Metrics:
    • F1 Score 0.9346 (93.46%), meaning there was excellent precision-recall balance
    • ROC-AUC: 0.9790 (97.90%), meaning there was a near-perfect class separation
  • Confusion Matrix:
  • True Negatives: 22 | False Positives: 4
  • False Negatives: 3 | True Positives: 50
  • Only 7 total Misclassifications = 91.1 Accuracy
  • Key Insight: Emphasizes that simple linear Models achieve strong performance

Random Forest

  • Converted final grade (G3) into pass/fail label; removed G1, G2, and G3 to prevent data leakage.
  • Built preprocessing pipeline: One-Hot Encoding (categorical) + Standardization (numeric).
  • Performed 80/20 stratified train-test split to preserve class balance.
  • Tuned the model by testing different parameter combinations using Grid Search with 5-fold cross-validation to find the best-performing setup.
  • Evaluating using F1-score, ROC-AUC, and confusion matrix.
  • Achieved improved classification performance over baseline.
  • Key insight: Feature importance shows the strongest predictors of student success.

Gradient Boosting

  • XGBoost Model with stratified 5-fold cross-validation
  • Two feature sets: with and without G1/G2
  • Goal: measure impact of previous grades on predictions
  • Compare realistic features vs prediction power
  • Without G1/G2:
    • F1: 0.784, ROC-AUC: 0.625
    • Bias toward predicting “pass”
    • High pass recall (0.89), low fail recall (0.15)
  • With G1/G2:
    • F1: 0.920, ROC-AUC: 0.942
    • Improved accuracy and class separation
    • Model was much more reliable
  • G1 & G2 are strong features for predicting
  • GB Model relies on previous performance, not behavior
  • Dependence on G1/G2 is limiting since it may not always be available

Model Evaluation Metrics

  • F1 Score
  • ROC-AUC
  • Confusion Matrix
  • Cross Validation Performance
  • Feature Importance

Findings

MODEL F1 SCORE ROC-AUC NOTES
Logistic Regression 0.9346 0.9790 Leaky baseline (kept G3)
Random Forest 0.7705 0.6067 No G1/G2/G3
XGBoost (without G1/G2) 0.7840 0.6248 Leak-free,real world setting
XGBoost (with G1/G2) 0.9200 0.9419 Mid-term prediction
  • Failing students were consistently the hardest to classify across all models.
  • Without grade features, absences, failures, and study time were the strongest predictors.
  • Models with prior grades performed much better, but are only usable once the semester has started, highlighting a trade-off between accuracy and real-world applicability.

Contributors

Justin Gurkin, Kaitlin Sloan, Manihams Suraparaju, Praneetha Voona, Joshua Grewe