🎓 Student Performance Prediction (UCI Dataset)

This machine learning project predicts student performance based on various academic, demographic, and behavioral factors. The goal is to classify whether a student is likely to pass or fail using supervised classification techniques.

📂 Dataset

• Source: UCI Machine Learning Repository – Student Performance
• Files used: student-mat.csv (Mathematics performance data)

📊 Features

The dataset includes:

• Demographic info (e.g., sex, age, address)
• Family and social info (e.g., famsize, Pstatus, schoolsup)
• Academic info (e.g., studytime, failures, absences, grades G1, G2, G3)

Target variable:

• pass → Created as 1 if G3 >= 10, otherwise 0

✅ Project Steps

• Data Loading and Preprocessing
• Feature Engineering
• Encoding categorical variables
• Train/Test Split
• Model training (Random Forest Classifier)
• Model evaluation (Accuracy, Classification Report)
• Visualizations:
  • Grade distribution
  • Correlation heatmap
  • Study time vs Pass
  • Feature importance

🧠 ML Model Used

• RandomForestClassifier (from sklearn.ensemble)
• Accuracy achieved: XX% (fill with your model score)

📈 Visualizations

• Histogram of grades
• Heatmap of feature correlations
• Boxplots of study time vs pass/fail
• Barplot of feature importances

🧪 Requirements

Install required packages:

pip install -r requirements.txt