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Mini project · Semester 5 · Computer Science & Engineering

Student Placement Prediction Using Machine Learning

Python, Flask, Machine Learning, Scikit-learn, Pandas, NumPy, HTML, CSS

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Inside the project

Actual project screenshots
Student Placement Prediction - Input Form
Student Placement Prediction - Input Form
THE IDEA

About this project

Student Placement Prediction Using Machine Learning is a web-based mini project that predicts whether a student is likely to get placed based on academic and skill-related information.

The system considers inputs such as CGPA, internships, projects, certifications, aptitude score, communication skills and backlogs. A Machine Learning model processes these details and provides a placement prediction along with the probability.

The project includes dataset handling, model training, prediction and a simple Flask-based web interface. It is suitable for students who want to understand the practical implementation of Machine Learning with Python and Flask.

What this project does

  • • Student placement prediction using Machine Learning
  • • Placement probability prediction
  • • Simple and user-friendly web interface
  • • Accepts academic and skill-related student details
  • • Uses a trained Scikit-learn Machine Learning model
  • • Flask-based backend
  • • Dataset-based model training
  • • Saved ML model for quick predictions
  • • Responsive prediction form
  • • Easy to understand and modify for student projects

Requirements

• Python 3.9 or above
• pip
• Flask
• Pandas
• NumPy
• Scikit-learn
• Joblib
• Web browser
• VS Code or any Python IDE (optional)

How to run the project

1. Download and extract the project ZIP file.

2. Open the extracted project folder in Command Prompt or VS Code Terminal.

3. Create a virtual environment:

py -m venv venv

4. Activate the virtual environment:

venv\Scripts\activate

5. Install the required packages:

pip install -r requirements.txt

6. Train the Machine Learning model:

python train_model.py

7. Start the Flask application:

python app.py

8. Open your browser and visit:

http://127.0.0.1:5000

9. Enter the student details and click the prediction button to view the placement prediction.

Make it your own

Explore the implementation, understand the decisions and adapt the project to your requirements.

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