🎯 Career Recommendation System

An AI-powered Career Recommendation System that helps students and professionals explore their best-suited career paths based on skills, interests, and preferences.
The system provides personalized recommendations, motivational guidance, and actionable first steps for each career.

It’s built using a robust AI backend, a dynamic frontend, and a self-curated dataset diverse_Career_dataset.csv for accurate and diverse recommendations.

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📌 Table of Contents

• Key Features
• Backend (AI & ML)
• Frontend (User Interface)
• Backend (Web Server & API)
• Benefits
• Technology Stack
• Future Improvements

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🌟 Key Features

• AI-driven career recommendations using hierarchical classification
• Default career suggestions for students who skip the questionnaire
• Motivational lines and initial steps for each recommended career
• Interactive frontend with progress tracking, real-time validation, animations, and dark/light mode
• Backend built with Flask, RESTful API design, session management, and model serving pipeline

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🧠 Backend (AI & ML)

1. Hierarchical Classification System

• Two-stage prediction pipeline:
  • Model A: Predicts broad career path
  • Model B: Predicts specialization within chosen path
• Algorithm: Random Forest Classifier
• Approach: Cascade classification for better accuracy
• Benefits: Reduces complexity by breaking down the prediction problem

2. Ensemble Learning

• Random Forest used because it:
  • Handles mixed data types efficiently
  • Robust to outliers and noise
  • Provides feature importance scores
  • Reduces overfitting compared to single decision trees
  • Performs well in high-dimensional spaces

3. Advanced Feature Engineering

• Categorical encoding
• Feature combination
• Text processing
• Domain knowledge integration

4. Class Imbalance Handling

• Class weighting
• Data augmentation
• Consolidation of categories
• Stratified sampling

5. Special Feature for Confused Students

• Default career recommendation based on chosen specialization
• Motivational messages and initial steps for each career

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💻 Frontend (User Interface)

• Technologies: HTML, CSS3, JavaScript (Vanilla)
• Key Features:
  • Dynamic Questionnaire: 3 questions per page with progress tracking
  • Real-time Validation: Client-side form validation
  • Smooth Animations: CSS transitions and micro-interactions
  • Dark/Light Mode: Theme switching with LocalStorage

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⚙️ Backend (Web Server & API)

• Flask Microframework
• RESTful API design
• Session management
• Template rendering
• Model serving pipeline for real-time recommendations

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📈 Benefits of the System

• Personalized career guidance for students and professionals
• Improved accuracy via two-stage hierarchical classification
• Motivational messages and actionable first steps for each recommended career
• Supports both detailed questionnaires and quick default recommendations
• Inclusive design with robust ML backend

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🛠 Technology Stack

• Frontend: HTML, CSS3, JavaScript
• Backend: Python, Flask
• Machine Learning: Random Forest, Hierarchical Classification, Ensemble Learning
• Dataset: diverse_Career_dataset.csv (self-curated)

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🔧 Future Improvements

• Expand and diversify the dataset for better coverage
• Experiment with other ML models (XGBoost, Neural Networks)
• Enhance feature engineering and domain knowledge integration
• Improve UI/UX with more interactivity and accessibility
• Add analytics to track user progress over time

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✨ Built with ❤️ by Snehal