# advanced_implementation_guide.py
import polars as pl

def create_advanced_implementation_guide():
    """Create practical implementation guide for advanced analyses"""
    
    print("🚀 ADVANCED ANALYSIS IMPLEMENTATION GUIDE")
    print("=" * 60)
    
    guide = [
        "📋 QUICK START IMPLEMENTATION PLAN:",
        "",
        "1. 📈 TIME SERIES ANALYSIS (Week 1-2):",
        "   TOOLS: Polars, Matplotlib, Pandas",
        "   STEPS:",
        "   • Convert timestamps to datetime objects",
        "   • Aggregate data by day/week/month",
        "   • Calculate moving averages and growth rates",
        "   • Identify seasonal patterns and trends",
        "   • Create time-based content scheduling",
        "",
        "2. 💬 SENTIMENT ANALYSIS (Week 3-4):",
        "   TOOLS: TextBlob, NLTK, Transformers",
        "   STEPS:",
        "   • Clean and preprocess text data",
        "   • Implement sentiment classification",
        "   • Analyze emotion and intent detection",
        "   • Correlate sentiment with engagement",
        "   • Build sentiment-aware content guidelines",
        "",
        "3. 🔗 NETWORK ANALYSIS (Week 5-6):",
        "   TOOLS: NetworkX, Gephi, Plotly",
        "   STEPS:",
        "   • Extract creator mentions and collaborations",
        "   • Build creator relationship graph",
        "   • Calculate network centrality metrics",
        "   • Identify influencer clusters",
        "   • Develop collaboration recommendations",
        "",
        "4. 🔮 PREDICTIVE MODELING (Week 7-8):",
        "   TOOLS: Scikit-learn, XGBoost, TensorFlow",
        "   STEPS:",
        "   • Feature engineering and selection",
        "   • Train classification/regression models",
        "   • Validate model performance",
        "   • Deploy prediction API",
        "   • Create content scoring system",
        "",
        "5. 🧪 A/B TESTING FRAMEWORK (Week 9-12):",
        "   TOOLS: StatsModels, SciPy, Custom Platform",
        "   STEPS:",
        "   • Define hypotheses and success metrics",
        "   • Calculate sample sizes and duration",
        "   • Implement randomization and tracking",
        "   • Analyze results with statistical tests",
        "   • Scale successful variants",
        "",
        "🎯 SUCCESS METRICS FOR EACH ANALYSIS:",
        "",
        "Time Series:",
        "• 90%+ accuracy in engagement forecasting",
        "• Identification of 3+ seasonal patterns",
        "• 20%+ improvement in posting timing",
        "",
        "Sentiment Analysis:",
        "• 85%+ sentiment classification accuracy", 
        "• 25%+ engagement improvement with emotional content",
        "• 50%+ increase in comment engagement",
        "",
        "Network Analysis:",
        "• Identification of 10+ collaboration opportunities",
        "• 30%+ growth in cross-creator engagement",
        "• Mapping of 3+ distinct creator clusters",
        "",
        "Predictive Modeling:",
        "• 80%+ viral content prediction accuracy",
        "• 40%+ improvement in content performance",
        "• Reduction of 50%+ in poor-performing content",
        "",
        "A/B Testing:",
        "• 5+ completed experiments per quarter",
        "• 25%+ average performance improvement",
        "• 95%+ statistical significance in results",
        "",
        "🔧 TECHNICAL INFRASTRUCTURE REQUIREMENTS:",
        "",
        "Data Layer:",
        "• Real-time data ingestion pipeline",
        "• Scalable data storage (1TB+ capacity)",
        "• Data processing cluster (Spark/Dask)",
        "",
        "Analysis Layer:",
        "• ML model training infrastructure",
        "• A/B testing platform",
        "• Real-time analytics dashboard",
        "",
        "Application Layer:",
        "• Creator analytics interface",
        "• Content recommendation API",
        "• Automated reporting system",
        "",
        "💰 EXPECTED ROI:",
        "• Content performance: 68-142% improvement",
        "• Creator retention: 25-40% increase", 
        "• Platform engagement: 30-50% growth",
        "• Revenue impact: $2-5M annual increase"
    ]
    
    for item in guide:
        print(item)

if __name__ == "__main__":
    create_advanced_implementation_guide()