anurag = {
    "name"       : "Anurag Chowdhury",
    "role"       : ["Data Scientist", "ML Engineer", "AI Architect"],
    "location"   : "Bangalore / Kolkata, India",
    "current"    : "ML Engineer Intern @ Iolite Technologies",
    "education"  : [
        "MSc AI & ML   — Christ University (2025–27)  · CGPA 3.55/4.0",
        "MSc Data Sci  — Christ University (2021–23)  · CGPA 3.55/4.0",
        "B.Tech CSE    — Christ University (2015–19)  · CGPA 9.45/10.0",
    ],
    "stack"      : ["Python", "PyTorch", "LangChain", "GNN", "FastAPI", "Docker", "CUDA"],
    "interests"  : ["Graph AI", "NLP/LLMs", "Football Analytics", "Computer Vision"],
    "hobbies"    : ["⚽ Football", "📸 Photography", "🔭 Stargazing"],
    "status"     : "██████████████████░░  MISSION ACTIVE",
}

class PlayerPulse:
    """Gaming Analytics Intelligence Platform"""
    monte_carlo  = "1M+ spins processed in < 2 seconds"
    churn_model  = "XGBoost → 68.5% prediction accuracy"
    ab_testing   = "40% lower churn via experimentation framework"
    domains      = ["RTP", "Hit Frequency", "Volatility", "ARPU", "LTV"]
    stack        = ["Python", "XGBoost", "Monte Carlo", "Streamlit", "FastAPI"]

class SentinelX:
    """Graph Neural Network Fraud Detection System"""
    f1_score          = "98%"
    latency           = "0.5 ms per prediction"
    architecture      = ["GAT (Graph Attention Network)", "XGBoost", "Anomaly Detection"]
    player_coverage   = "1,500+ players profiled"
    revenue_protected = "$25,000+ weekly"
    stack             = ["PyTorch Geometric", "GNN", "XGBoost", "Streamlit", "Docker"]