🎓 CollegeCompass AI

Navigate your college journey with AI-driven recommendations.

CollegeCompass AI is a full-stack AI-powered MHT CET College Recommendation System that predicts the most suitable colleges for students based on their percentile, category, branch preference, and historical admission trends.

The application combines Machine Learning, Recommendation Systems, and a modern Angular frontend to provide intelligent college recommendations categorized as Safe, Target, and Dream colleges.

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🚀 Features

• 🎯 Personalized college recommendations
• 🤖 Machine Learning powered recommendation engine
• 📊 Safe / Target / Dream classification
• 📈 AI-based recommendation scoring
• 📚 Historical MHT CET cutoff analysis
• ⚡ Modern Angular frontend
• 🌐 Flask REST API backend
• 🧠 Random Forest Machine Learning model
• 📂 Branch & Category filtering
• 🎨 Clean responsive UI with animations

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🛠 Tech Stack

Frontend

• Angular
• TypeScript
• Tailwind CSS
• HTML
• CSS

Backend

• Flask
• Python

Machine Learning

• Scikit-learn
• Pandas
• NumPy
• Joblib

Dataset

• Historical MHT CET CAP Round Cutoff Data

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🏗 Project Architecture

                  Student

                     │

                     ▼

          Angular Frontend (UI)

                     │

         HTTP REST API Request

                     │

                     ▼

              Flask Backend

                     │

                     ▼

        Recommendation Engine

          │               │

          │               ▼

          │      Random Forest Model

          │

          ▼

 Historical MHT CET Dataset

          │

          ▼

 Safe • Target • Dream Colleges

          │

          ▼

      Recommendation Results

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📂 Project Structure

CollegeCompass AI
│
├── application/        # Flask API Routes
├── artifacts/          # Saved ML Models
├── config/             # Configuration Files
├── dataset/            # MHT CET Dataset
├── frontend/           # Angular Application
├── notebook/           # Model Training Notebooks
├── src/
│   ├── components/
│   ├── ml/
│   ├── pipeline/
│   └── utils/
├── static/
├── templates/
├── app.py
├── requirements.txt
└── README.md

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🤖 Machine Learning Pipeline

1. Load historical MHT CET cutoff dataset
2. Clean and preprocess data
3. Feature Engineering
4. Encode categorical variables
5. Train Random Forest Classifier
6. Save trained model
7. Integrate model with Flask API
8. Generate AI-powered recommendations

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📊 Recommendation Categories

🟢 Safe

• High probability of admission

🎯 Target

• Moderate probability

🔥 Dream

• Competitive admission chances

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💡 Recommendation Factors

Recommendations are generated using:

• Student Percentile
• Category
• Preferred Branch
• Historical Cutoffs
• Previous Admission Trends
• Machine Learning Prediction
• Recommendation Score

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📷 Screenshots

Home Page

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Prediction Page

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Loading Screen

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Results Page

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⚙ Installation

Clone Repository

git clone https://github.com/yourusername/CollegeCompass-AI.git

Backend

pip install -r requirements.txt

Run

python app.py

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Frontend

cd frontend
npm install
ng serve

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📡 API

Get Recommendations

POST /recommend

Example Request

{
  "percentile": 98.54,
  "category": "OPEN",
  "branch": "Computer Engineering"
}

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Future Improvements

• Deep Learning Recommendation Model
• Student Login
• College Comparison
• College Details Page
• Admission Probability Visualization
• LLM-based Career Guidance
• Cloud Deployment

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👨‍💻 Author

Krish Parmani

Artificial Intelligence & Data Science Engineering Student

Built with ❤️ using Angular, Flask and Machine Learning.