Standard syllabus
Data mining · Undergraduate · Statistics
Topics
Data preparation
- Train/validation/test splits
- Feature engineering and encoding
- Handling missing values and outliers
- Dimensionality reduction: PCA for mining
- Class imbalance strategies
Supervised learning
- Classification and regression trees
- Ensemble methods: bagging and random forests
- Boosting (AdaBoost, gradient boosting intro)
- k-nearest neighbors and naive Bayes
- Model evaluation: ROC, AUC, and confusion matrices
Unsupervised learning
- Cluster analysis for market segmentation
- Association rules and market basket analysis
- Anomaly detection (introduction)
- Text mining basics (optional)
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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