📚 Student Performance Analytics Platform

An advanced analytics platform for investigating correlations between student lifestyle patterns and academic outcomes through machine learning and statistical analysis.

🎯 Key Features

• Robust Data Processing Pipeline

  • Automated data cleaning and normalization
  • Missing value imputation using advanced techniques
  • Feature engineering and selection

• Comprehensive Analytics Suite

  • Time-series analysis of study patterns
  • Multi-dimensional correlation studies
  • Predictive modeling using ML algorithms

• Interactive Visualizations

  • Dynamic Plotly dashboards
  • Real-time data exploration
  • Custom reporting capabilities

🛠️ Technical Stack

Core Analysis: Python, NumPy, Pandas
ML Framework: scikit-learn, TensorFlow
Visualization: Plotly, Seaborn
Statistics: SciPy, StatsModels

📊 Dataset Schema

student_metrics = {
        'behavioral_metrics': ['study_hours', 'sleep_pattern', 'social_media_usage'],
        'academic_metrics': ['attendance', 'exam_scores', 'participation'],
        'environmental_factors': ['internet_quality', 'study_environment'],
        'psychological_indicators': ['stress_level', 'motivation_score']
}

🚀 Quick Start

1. Environment Setup

   python -m venv venv
   source venv/bin/activate  # Unix
   pip install -r requirements.txt

2. Run Analysis

   python src/main.py --data-path /path/to/data --analysis-type full

📈 Key Findings

Factor	Correlation	Significance
Study Hours	0.78	p < 0.001
Sleep Quality	0.65	p < 0.001
Social Media	-0.45	p < 0.01

🤝 Contributing

Please see CONTRIBUTING.md for guidelines.

📝 License

MIT License - see LICENSE.md

📧 Contact

For queries: research@studentanalytics.io

Note: This is a research project. See our documentation for methodology details.