This project predicts whether a student is likely to get placed based on academic performance, certifications, and skill ratings using an AdaBoost classifier. A user-friendly web application is built with Streamlit, allowing real-time predictions and saving results to a MySQL database.
Predicting student placement outcomes can help institutions identify and guide students who may need additional training. This model provides an efficient and interactive way to assess placement likelihood based on multiple student parameters.
- Python
- Streamlit – Web app UI
- AdaBoost Classifier (Scikit-learn) – Prediction model
- Joblib – Model serialization
- MySQL – Backend data storage
- NumPy – Numerical operations
- Algorithm: AdaBoostClassifier
- Target:
Placement Status(1 = Placed, 0 = Not Placed) - Features:
- CGPA
- Number of Internships
- Projects Completed
- Workshop/Certification Count
- Aptitude Test Score
- Soft Skills Rating
- SSC & HSC Marks
- Extracurricular Activities
- Placement Training Attended
- Accepts input via sliders, text fields, and select boxes
- Predicts placement status in real time
- Displays results interactively using Streamlit
- Automatically saves predictions to MySQL database
-placement-prediction/ -│ -├── app.py # Streamlit application code -├── Model_AdaBoost.pkl # Trained AdaBoost model -├── requirements.txt # Python dependencies -├── README.md # Project documentation
- Input: CGPA = 8.1, Projects = 3, Internships = 1, Soft Skills = 7
- Output: ✅ Student is likely to be Placed
- This project is intended for educational use and should not be used as the sole basis for real-world decision-making in student career planning.