TR Placement Package Predictor

A web application that predicts placement packages based on CGPA using a linear regression model.

Overview

This application uses a simple linear regression model to predict the placement package a student might receive based on their CGPA. It features:

• User-friendly interface with modern design
• Real-time predictions
• Celebratory animations when predictions are made
• User counter that tracks total users and predictions
• Visitor tracking for analytics
• Feedback system for user comments
• Responsive design that works on mobile and desktop

Technology Stack

• Backend: Flask (Python)
• Database: MongoDB Atlas (with fallback to file storage)
• Frontend: HTML, CSS, JavaScript
• CSS Framework: Bootstrap 4
• Animations: Canvas Confetti
• Icons: Font Awesome
• Deployment: Render

Directory Structure

├── app.py                 # Main Flask application
├── templates/             # HTML templates
│   └── index.html         # Main page template
├── static/                # Static assets
│   └── style.css          # CSS styles
├── requirements.txt       # Python dependencies
├── render.yaml            # Render configuration
├── Procfile               # For Gunicorn
└── README.md              # Project documentation

Local Development

1. Clone the repository:

git clone https://github.com/yourusername/tr-placement-calculator.git
cd tr-placement-calculator

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

python app.py

4. Visit http://127.0.0.1:5000/ in your browser.

Deployment to Render

This application is configured for easy deployment on Render:

1. Push your code to a GitHub repository
2. In Render dashboard, create a new Web Service
3. Connect your GitHub repository
4. Set up the MongoDB connection:
   • Create a MongoDB Atlas account
   • Set up a free cluster
   • Create a database user
   • Get your connection string
   • Add it as the MONGO_URI environment variable in Render

Features

Prediction System

• Enter your CGPA and get an instant prediction
• Results are displayed with a celebration animation
• Based on a linear regression model

User Tracking

• Tracks unique users with cookies
• Counts total predictions made
• Stores visitor data for analytics

Feedback System

• Collects user feedback with a rating system
• Stores feedback in MongoDB for later review
• Includes name, email, message, and star rating

Contributors

• Designed & Developed by Aman Sharma

Model Details

The linear regression model follows the equation: