Career Recommendation Engine - AI/ML Components

A comprehensive machine learning system for personalized career recommendations based on user skills, interests, and personality traits.

🎯 Project Overview

This project implements a multi-label classification system that predicts suitable careers with confidence scores, featuring:

• Advanced feature engineering with skill clustering
• Multi-label classification with Random Forest and XGBoost
• Intelligent confidence scoring with validation framework
• Production-ready FastAPI deployment

📁 Project Structure

assig-ai-tut/
├── data/
│   └── synthetic_user_profiles_large.csv
├── notebooks/
│   ├── 01_data_engineering.ipynb
│   └── 02_model_development.ipynb
├── src/
│   ├── preprocessing.py
│   ├── feature_engineering.py
│   ├── model_trainer.py
│   ├── confidence_scorer.py
│   └── api.py
├── models/
│   └── career_recommender_v1.pkl
├── tests/
│   └── test_api.py
├── requirements.txt
└── README.md

🚀 Quick Start

Installation

pip install -r requirements.txt

Run Data Preprocessing & Model Training

jupyter notebook notebooks/01_data_engineering.ipynb
jupyter notebook notebooks/02_model_development.ipynb

Start API Server

uvicorn src.api:app --reload --port 8000

Access API Documentation

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Run Tests

pytest tests/ -v

📊 Model Performance

• Hamming Loss: < 0.15
• Precision@3: > 0.80
• Label Ranking Average Precision: > 0.85

🔧 API Usage

import requests

response = requests.post(
    "http://localhost:8000/predict",
    json={
        "skills": ["Python", "Communication"],
        "interests": ["Technology", "Management"],
        "personality": {
            "analytical": 0.8,
            "creative": 0.4,
            "social": 0.7
        },
        "education": "Bachelor",
        "experience": 3
    }
)

print(response.json())

📈 Key Features

Task 1: Data Engineering

• Skill clustering (technical/soft skills)
• Interest profiling
• Personality trait normalization
• Class imbalance handling
• Feature importance analysis

Task 2: Model Development

• Multi-label classification
• Hyperparameter optimization
• Probability calibration
• Comprehensive evaluation metrics
• Error analysis

Task 3: Confidence Scoring

• Hybrid scoring (model + heuristics)
• Rule-based adjustments
• Validation framework
• Quality assurance metrics

Task 4: API Deployment

• FastAPI implementation
• Input validation
• Model versioning
• Comprehensive testing
• OpenAPI documentation

👨‍💻 Author

Built as part of AI/ML Practice assignment demonstrating end-to-end ML system development.