🎓 Student Performance Prediction System

A comprehensive Streamlit web application that predicts student academic performance using machine learning models. The system provides both individual and batch predictions for student results and marks.

🌟 Features

🤖 Machine Learning Models

• Result Prediction Model: Predicts whether a student will Pass (1) or Fail (0)
• Marks Prediction Model: Predicts numerical marks for students

📊 Prediction Types

1. Individual Result Prediction (Pass/Fail)
2. Individual Marks Prediction (Numerical marks)
3. Batch Analysis (10 students with historical data and future predictions)

🎯 Key Functionalities

Individual Predictions

• Real-time predictions for single students
• Interactive input forms with sliders and select boxes
• Confidence scores and performance categories
• Visual progress bars and performance indicators

Batch Analysis

• 10 Sample Students with 3 years of historical data
• One-click predictions for next academic year
• Dual predictions: Both results AND marks
• Comprehensive analytics and performance distribution
• Multiple view modes: Summary, Detailed Marks, Full Details

📈 Analytics & Insights

• Pass/Fail statistics and probabilities
• Performance categorization (Excellent, Good, Average, Needs Improvement)
• Subject-wise marks analysis
• Historical trend visualization
• Risk assessment for at-risk students

🚀 Installation

Prerequisites

• Python 3.7+
• pip package manager

Step 1: Clone/Download the Project

git clone <repository-url>
cd student-performance-predictor

Step 2: Install Dependencies

pip install -r requirements.txt

Step 3: Prepare Model Files

Ensure you have both trained model files in the project directory:

• student_result_model.pkl - Result prediction model
• student_marks_model.pkl - Marks prediction model

Step 4: Run the Application

streamlit run app.py

The application will open in your default web browser at http://localhost:8501

📁 Project Structure

student-performance-predictor/
│
├── app.py                 # Main Streamlit application
├── student_result_model.pkl    # Result prediction model
├── student_marks_model.pkl     # Marks prediction model
├── README.md              # This documentation
└── requirements.txt       # Python dependencies

🎮 How to Use

1. Individual Result Prediction

• Navigate to the "Result Prediction" tab
• Fill in student details:
  • Year, Gender, Attendance percentage
  • Study hours per day
  • Extra activity level
  • Subject marks (Math, Science, English, Social Studies)
• Click "Predict Result" to get Pass/Fail prediction

2. Individual Marks Prediction

• Go to the "Marks Prediction" tab
• Provide student information:
  • Year, Gender, Attendance
  • Study hours, Previous year marks
  • Parent education level
• Click "Predict Marks" for numerical marks prediction

3. Batch Analysis

• Select the "Batch Analysis" tab
• View 10 sample students with 3 years of historical data
• Select individual students to see detailed history
• Click "Predict Next Year Results & Marks" for batch predictions
• Explore different views: Summary, Detailed Marks, Student Details
• Download complete predictions as CSV

🔧 Model Input Formats

Result Prediction Model Expects:

{
    'Year': int,
    'Gender': ['Male', 'Female'],
    'Attendance': int (0-100),
    'Study_Hours': float,
    'Extra_Activity': ['Low', 'Medium', 'High'],
    'Math': int (0-100),
    'Science': int (0-100),
    'English': int (0-100),
    'Social': int (0-100)
}

Marks Prediction Model Expects:

{
    'year': str,
    'gender': ['Male', 'Female'],
    'attendance': int (0-100),
    'study_hours': float,
    'previous_year_marks': int (0-100),
    'parent_education': ['High School', 'Graduate', 'Post Graduate', 'Doctorate']
}

📊 Output Interpretation

Result Predictions

• PASS ✅: Student is predicted to pass
• FAIL ❌: Student is predicted to fail
• Pass Probability: Confidence percentage (0-100%)

Marks Predictions

• Excellent 🎉: 80+ marks
• Good 📚: 60-79 marks
• Average 💪: 40-59 marks
• Needs Improvement 📝: Below 40 marks

Performance Categories

• Excellent (80-100 marks): Outstanding performance
• Good (60-79 marks): Solid performance
• Average (40-59 marks): Room for improvement
• Needs Improvement (0-39 marks): Requires attention

🛠️ Technical Details

Built With

• Streamlit: Web application framework
• Pandas: Data manipulation and analysis
• Joblib: Model serialization and loading
• Scikit-learn: Machine learning models (assumed)

Features

• Responsive Design: Works on desktop and mobile
• Real-time Predictions: Instant results
• Data Caching: Optimized performance
• Error Handling: Graceful error management
• Export Functionality: Download predictions as CSV

📈 Use Cases

For Educators

• Identify at-risk students early
• Plan targeted interventions
• Monitor class performance trends
• Generate performance reports

For Students

• Self-assessment and goal setting
• Understand performance factors
• Identify areas for improvement

For Administrators

• Institutional performance analysis
• Resource allocation planning
• Curriculum improvement insights

Note: This application requires pre-trained machine learning models (student_result_model.pkl and student_marks_model.pkl) to function properly. Ensure these files are in the same directory as the application.