Student Score Analysis and Prediction

A comprehensive data analysis and machine learning project focused on analyzing student performance using the UCI student dataset containing 33 features across 396 instances.

Dataset Features

The analysis includes key student attributes:

• Academic Performance (Grades)
• Attendance (Absences)
• Health Metrics
• Lifestyle Factors:
  • Daily/Weekly Alcohol Consumption
  • Free Time Management
  • Internet Usage
• Academic Factors:
  • Study Time
  • Travel Time to School

Technical Stack

Python Libraries

• NumPy: Numerical computations and array operations
• Pandas: Data manipulation and analysis
• Seaborn: Statistical data visualization
• Matplotlib: Creating static, animated, and interactive visualizations
• Scikit-learn: Machine learning implementations
• Pickle: Model serialization

Machine Learning Models

• K-means Clustering: Student grouping analysis
• Principal Component Analysis (PCA): Dimensionality reduction
• Decision Tree: Classification and prediction
• Random Forest: Ensemble learning for improved accuracy
• Linear Regression: Score prediction

Analysis Workflow

1. Data Loading and Preprocessing
2. Exploratory Data Analysis
3. Feature Engineering
4. Model Training and Evaluation
5. Performance Prediction

Goals

• Analyze factors affecting student performance
• Predict student scores based on various features
• Identify key patterns in student behavior and academic performance
• Generate actionable insights for educational improvement

Author

Discord Email