majiayu000/claude-arsenal
xiaohongshu
xiaohongshu
286 148
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Advanced Analytics Skill
advanced-analytics
Advanced analytics including machine learning, predictive modeling, and big data techniques
SKILL.md
Full skill instructions
Advanced Analytics Skill
Overview
Master advanced analytics techniques including machine learning, predictive modeling, and big data processing for sophisticated data analysis.
Core Topics
Machine Learning Fundamentals
- Supervised vs unsupervised learning
- Classification algorithms (logistic regression, decision trees, random forest)
- Regression algorithms (linear, polynomial, ensemble methods)
- Clustering (K-means, hierarchical, DBSCAN)
Predictive Analytics
- Time series forecasting (ARIMA, exponential smoothing)
- Customer segmentation and RFM analysis
- Churn prediction models
- A/B testing and experimentation
Big Data Technologies
- Introduction to Spark and PySpark
- Data lakes and data mesh concepts
- Cloud analytics platforms (AWS, GCP, Azure)
- Real-time analytics with streaming data
Advanced Techniques
- Feature engineering best practices
- Model validation and cross-validation
- Hyperparameter tuning
- Model deployment considerations
Learning Objectives
- Build and validate machine learning models
- Implement predictive analytics solutions
- Work with big data technologies
- Apply advanced statistical techniques
Error Handling
| Error Type | Cause | Recovery |
|---|---|---|
| Overfitting | Model too complex | Add regularization, reduce features |
| Underfitting | Model too simple | Add features, increase complexity |
| Data leakage | Target info in features | Review feature engineering pipeline |
| Class imbalance | Skewed target | Use SMOTE, class weights, or resampling |
| Convergence failure | Poor hyperparameters | Grid search, adjust learning rate |
Related Skills
- statistics (for foundational statistical knowledge)
- programming (for ML implementation)
- databases-sql (for big data querying)
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