🎓 Placement Predictor Model

A full-stack application that leverages Machine Learning to predict student placement outcomes based on academic performance, technical skills, and extracurricular activities. It provides personalized feedback, actionable suggestions, and a probabilistic placement score.

🚀 Live Demo

Placement Predictor App

✨ Key Features

• Predictive Analytics: Calculates a placement probability score utilizing a robust Machine Learning model (Support Vector Machine) trained on comprehensive student data.
• Personalized Suggestions: Offers actionable, prioritized feedback based on critical factors like backlogs, internships, CGPA, and technical skills to improve placement chances.
• Interactive UI: Built with React, featuring a clean, responsive interface, animations (Framer Motion), and dynamic charts (Chart.js) to visualize data effectively.
• Feature Importance: Highlights the most significant factors influencing placement success (e.g., Backlogs, Technical Skills, Internships).
• Comprehensive EDA: Explains the underlying data through exploratory data analysis metrics, helping users understand general placement trends.

📸 Visual Elements & Screenshots

Here are some of the key sections of the application:

1. Hero Section & Dashboard

The landing page providing an overview of the predictor and quick access to the prediction form.

2. Prediction Form

An intuitive form where students input their academic details, test scores, and project experience.

3. Analytics & EDA

Exploratory Data Analysis insights and placement statistics derived from the model data.

🛠️ Tech Stack

Frontend

• React.js (Vite for fast builds)
• Chart.js & react-chartjs-2 (Data visualization)
• Framer Motion (Animations)
• react-confetti (Success celebrations)

Backend

• FastAPI (High-performance Python API)
• Uvicorn (ASGI server)
• Pydantic (Data validation)

Machine Learning

• Scikit-Learn (Model training, evaluation, and scaling)
• Pandas & NumPy (Data manipulation)
• Pickle (Model serialization)

📁 Project Structure

placement-predictor/
├── api/                  # FastAPI Backend
│   ├── app.py            # Main API application & endpoints
│   └── requirements.txt  # Python dependencies for API
├── data/                 # Datasets
│   └── student_academic_placement_performance_dataset.csv
├── ml/                   # Machine Learning Scripts
│   ├── train_models.py   # Training pipeline & evaluation
│   └── requirements.txt  # Python dependencies for ML
├── model/                # Serialized Models & Artifacts
│   ├── best_model.pkl    # Trained SVM Model
│   ├── scaler.pkl        # Feature scaler
│   ├── label_encoders.pkl# Categorical encoders
│   └── model_info.json   # Model performance metrics
├── public/               # Static assets
│   └── assets/           # Application screenshots
├── src/                  # React Frontend
│   ├── components/       # UI Components (Hero, Predictor, ModelInfo, etc.)
│   ├── styles/           # CSS stylesheets
│   ├── App.jsx           # Main React component
│   └── main.jsx          # React entry point
├── index.html            # HTML template
├── package.json          # Node.js dependencies
└── vite.config.js        # Vite configuration

🧠 Machine Learning Model Details

The application evaluates several models before selecting the best performer. Based on our evaluation:

• Best Model: Support Vector Machine (SVM)
• Accuracy: 94.3%
• F1-Score: 82.46%
• ROC-AUC: 97.84%

Other models evaluated include Logistic Regression (89.1% Acc), Random Forest (Overfitted), and K-Nearest Neighbors (90.2% Acc). The model takes 15 key features into account, prioritizing academic consistency and practical experience.

💻 Installation & Local Setup

Follow these steps to run the project on your local machine.

Prerequisites

• Node.js (v16 or higher)
• Python (v3.8 or higher)

1. Clone the repository

git clone https://github.com/your-username/placement-predictor.git cd placement-predictor

2. Frontend Setup

Install Node dependencies and start the Vite development server: npm install npm run dev

The frontend will typically run on http://localhost:5173.

3. Backend Setup

Open a new terminal window, navigate to the api directory, set up a virtual environment, and run the FastAPI server:

cd api python -m venv venv

Activate virtual environment: On Windows: venv\Scripts\activate On macOS/Linux: source venv/bin/activate

Install dependencies: pip install -r requirements.txt

Start the FastAPI server: python app.py

The backend API will run on http://localhost:8000.

4. (Optional) Retrain the Model

If you wish to retrain the machine learning models with new data: cd ml pip install -r requirements.txt python train_models.py

📡 API Endpoints

The FastAPI backend exposes the following endpoints:

• GET / - API health check.
• POST /predict - Accepts student data and returns the placement prediction, probability, and personalized suggestions.
• GET /model-info - Returns evaluation metrics of the deployed machine learning model.
• GET /feature-importance - Returns the top factors influencing placement predictions.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

📝 License

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