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# KDD Study Guide - Quick Reference
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## 📁 File Organization Summary

Your KDD documentation has been successfully organized into **13 focused files** (plus README):

### Core Foundations (Start Here)
- **01_data_preparation.txt** (66 lines)
  Variable types, IID assumption, train/test splits, data leakage, curse of dimensionality, bias-variance tradeoff

- **02_statistics_distributions.txt** (61 lines)  
  Mean/median/variance, skewness/kurtosis, CLT, normality tests (Shapiro-Wilk, KS, Anderson-Darling)

- **03_hypothesis_testing.txt** (152 lines)
  Z-tests, t-tests, Mann-Whitney, Wilcoxon, ANOVA, Chi-square, Fisher exact, permutation tests, bootstrap

### Advanced Statistical Concepts  
- **04_causality_features.txt** (200 lines)
  Causality vs correlation, confounders, Simpson's paradox, Granger causality, feature selection (Pearson, Spearman, MI, VIF, RFE)

- **05_outliers_robust.txt** (100 lines)
  Z-score, IQR, MAD, LOF, Isolation Forest, robust scaling, influence on estimators

### Machine Learning Models
- **06_supervised_learning.txt** (100 lines)
  Linear/logistic regression, Ridge, Lasso, KNN, Naive Bayes, SVM, Decision Trees, Random Forest, Gradient Boosting

- **07_model_evaluation.txt** (249 lines)
  Regression metrics (RMSE, MAE, R², MAPE), classification metrics (accuracy, precision, recall, F1, ROC-AUC, PR-AUC, MCC), cross-validation, model comparison tests

### Special Topics
- **08_imbalanced_missing.txt** (160 lines)
  Class imbalance (SMOTE, sampling), missing data theory (MCAR/MAR/MNAR), imputation methods

- **09_explainability_viz.txt** (466 lines)
  SHAP, LIME, feature importance, all visualization techniques (histograms, KDE, scatter, ROC, PR, confusion matrices, learning curves, diagnostic plots)

- **10_dimensionality_clustering.txt** (207 lines)
  PCA, Factor Analysis, t-SNE, K-Means, Hierarchical, DBSCAN, Spectral clustering, cluster validation

### Advanced & Comprehensive
- **11_advanced_topics.txt** (559 lines)
  Time series (ARIMA, stationarity), association rules, concept drift, information theory, probability foundations, sampling theory, distance metrics, kernel methods, optimization

- **12_encoding_validation.txt** (408 lines)
  All encoding techniques (one-hot, ordinal, target, WoE, frequency), validation strategies, data quality checks, scaling, categorical processing

### Critical for Exam Success ⚠️
- **13_exam_traps.txt** (215 lines)
  Common mistakes, data leakage examples, invalid tests, p-value misinterpretation, preprocessing errors, Simpson's paradox, p-hacking

## 🎯 Recommended Study Path

### Week 1: Foundations
1. README.txt (overview)
2. 01_data_preparation.txt
3. 02_statistics_distributions.txt  
4. 03_hypothesis_testing.txt

### Week 2: Statistical Depth
5. 04_causality_features.txt
6. 05_outliers_robust.txt
7. 07_model_evaluation.txt

### Week 3: Machine Learning
8. 06_supervised_learning.txt
9. 08_imbalanced_missing.txt
10. 10_dimensionality_clustering.txt

### Week 4: Advanced & Practice
11. 09_explainability_viz.txt
12. 11_advanced_topics.txt
13. 12_encoding_validation.txt

### Final Week: Exam Prep
14. **13_exam_traps.txt** (read 3-4 times!)
15. Review all "TYPICAL EXAM TRAP" sections
16. Practice all code examples

## 💡 Quick Tips

- **Search for "EXAM TRAP"** across files to find common pitfalls
- **Practice code** - all examples are executable
- **Focus on assumptions** - know when each method is valid/invalid
- **Understand "why"** - not just "what" or "how"
- Files 13 is your safety net before the exam!

## 📊 Statistics

- **Total Lines**: ~2918 (original doc1.txt)
- **Organized Files**: 13 focused files + README + this guide
- **Topics Covered**: 74 major topics
- **Code Examples**: 200+ executable examples
- **Exam Traps Highlighted**: 50+ common mistakes

Good luck with your exam! 🎓
