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# 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() |