📊 Student Result Management System

A comprehensive system for managing and analyzing student results using Python, PySpark, and Streamlit. This project handles data for 10,000 students across 6 subjects, providing detailed analytics and an interactive dashboard.

🌟 Features

1. Data Generation & Management

• Generates realistic data for 10,000 students
• Creates profiles with student ID, name, age, and batch information
• Generates marks for 6 core subjects with automatic grade assignment:
  • Electronics
  • Programming
  • Database
  • Data Science
  • Mathematics
  • DSA

2. Data Analysis (Using PySpark)

• Statistical Analysis:

  • Overall performance metrics
  • Subject-wise analysis
  • Grade distribution
  • Pass/Fail statistics

• Performance Metrics:

  • Average marks with standard deviation
  • Minimum and maximum marks
  • Pass percentages by subject
  • Subject-wise performance trends

3. Interactive Dashboard

Student Search Features

• Search by Student ID or Name
• Comprehensive student profile view
• Individual performance metrics
• Color-coded grade visualization
• Subject-wise performance charts

Analytics Dashboard

• Key Performance Indicators
  • Overall average marks
  • Pass rate with distinction percentage
  • Highest/lowest marks
  • Total entries and statistics

• Performance Analysis
  • Interactive subject-wise performance charts
  • Grade distribution pie charts
  • Detailed statistical tables
  • Pass/Fail analysis by subject

🛠️ Technology Stack

• Python 3.x
• PySpark 3.5.4
• Streamlit 1.42.2
• Pandas 2.2.3
• Plotly 6.0.0
• Additional libraries:
  • Faker (for data generation)
  • Matplotlib (for visualization)
  • Seaborn (for statistical plots)

📋 Prerequisites

1. Python 3.x
2. Java Runtime Environment (for PySpark)
3. Required Python packages:

pip install -r requirements.txt

🚀 Installation & Setup

1. Clone the repository:

git clone https://github.com/divyanshi/student-result-management.git
cd student-result-management

2. Create required directories:

mkdir data
mkdir analysis_results

3. Install dependencies:

pip install -r requirements.txt

💻 Usage

1. Generate student data:

python data_generator.py

This will create:

• students.csv (10,000 student profiles)
• marks.csv (60,000 mark entries)

2. Run Spark analysis:

python spark_analysis.py

Generates analysis files:

• overall_stats.csv
• subject_stats.csv
• grade_dist.csv
• performance_metrics.csv
• subject_performance.csv

3. Launch the dashboard:

streamlit run dashboard.py

📁 Project Structure

student-result-management/
├── data/                      # Data storage
│   ├── students.csv          # Student profiles
│   └── marks.csv            # Student marks
├── analysis_results/         # Analysis output
│   ├── overall_stats.csv
│   ├── subject_stats.csv
│   ├── grade_dist.csv
│   ├── performance_metrics.csv
│   └── subject_performance.csv
├── screenshots/              # Documentation images
├── src/
│   ├── data_generator.py    # Data generation script
│   ├── spark_analysis.py    # PySpark analysis
│   └── dashboard.py         # Streamlit dashboard
├── requirements.txt         # Dependencies
└── README.md               # Documentation

📊 Dashboard Guide

Student Search Tab

1. Enter student ID (e.g., STU00001) or name
2. View comprehensive student profile:
   • Personal information
   • Academic performance
   • Subject-wise marks
   • Performance visualization

Analysis Tab

1. Key Metrics View

   • Overall performance indicators
   • Quick statistics

2. Subject Analysis

   • Interactive performance charts
   • Detailed subject statistics

3. Grade Distribution

   • Visual grade distribution
   • Percentage breakdowns

🔧 Customization

You can modify:

1. Number of students (data_generator.py):

def generate_student_profiles(num_students=10000):

2. Subject list (data_generator.py):

subjects = ['Electronics', 'Programming', 'Database', 
            'Data_Science', 'Mathematics', 'DSA']

3. Grade boundaries (data_generator.py):

'A+' if x >= 90 else
'A' if x >= 80 else
# ... etc

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Authors

• [Your Name]
• [Other Contributors]

🙏 Acknowledgments

• Built as part of the Data Engineering course project
• Thanks to all contributors and testers
• Special thanks to [Your Institution/Organization]

📞 Support