🎓 Placement Predictor

A comprehensive machine learning application that predicts student placement status based on various academic and personal factors.

Features

• Interactive Web Interface: Beautiful Streamlit-based UI with multiple pages
• Data Analysis: Comprehensive visualizations and insights
• Real-time Predictions: Instant placement predictions with confidence scores
• Model Performance: Detailed metrics and confusion matrix
• Responsive Design: Modern, user-friendly interface

Pages

1. 🏠 Home: Overview with key metrics and insights
2. 📊 Data Analysis: Interactive visualizations and data exploration
3. 🎯 Predict Placement: Input form for making predictions
4. 📈 Model Performance: Model evaluation metrics and confusion matrix

Installation

1. Clone or download the project files

2. Install dependencies:

   pip install -r requirements.txt

3. Run the training script first (if not already done):

   python placement_predictor.py

4. Launch the web application:

   streamlit run app.py

Usage

Making Predictions

1. Navigate to the "🎯 Predict Placement" page

2. Fill in the student details:

   • CGPA: Cumulative Grade Point Average (6.0-10.0)
   • Internships: Number of internships completed
   • Projects: Number of projects completed
   • Workshops/Certifications: Number of workshops/certifications
   • Aptitude Test Score: Score in aptitude tests (0-100)
   • Soft Skills Rating: Rating of soft skills (1.0-5.0)
   • Extracurricular Activities: Yes/No
   • Placement Training: Yes/No
   • SSC Marks: Secondary School Certificate marks (0-100)
   • HSC Marks: Higher Secondary Certificate marks (0-100)

3. Click "Predict Placement" to get results

Understanding Results

• Placed: Student is predicted to get placed
• Not Placed: Student is predicted to not get placed
• Confidence Score: How confident the model is in its prediction
• Raw Prediction: The actual numerical prediction value

Data Analysis Features

• Distribution Plots: Visualize CGPA distribution and placement status
• Correlation Analysis: See which features most influence placement
• Key Metrics: Average scores and placement rates
• Model Performance: Accuracy, precision, recall, and F1-score

Model Details

• Algorithm: Random Forest Regressor
• Features: 11 student characteristics
• Target: Binary placement status (Placed/Not Placed)
• Preprocessing: One-hot encoding for categorical variables, standardization for numerical features

File Structure

placement/
├── app.py                    # Streamlit web application
├── placement_predictor.py    # Training script
├── placementdata .csv        # Dataset
├── requirements.txt          # Python dependencies
├── README.md                # This file
├── placement_prediction.pkl  # Trained model (generated)
├── transformer.pkl          # Data transformer (generated)
└── scaler.pkl              # Data scaler (generated)

Technical Details

• Framework: Streamlit for web interface
• Visualization: Plotly for interactive charts
• Machine Learning: Scikit-learn for model training
• Data Processing: Pandas for data manipulation
• Styling: Custom CSS for enhanced UI

Troubleshooting

1. Model files not found: Run python placement_predictor.py first
2. Port already in use: Use streamlit run app.py --server.port 8502
3. Dependencies issues: Update pip and reinstall requirements

Contributing

Feel free to contribute by:

• Adding new features
• Improving the UI/UX
• Enhancing the model performance
• Adding more visualizations

License

This project is open source and available under the MIT License.