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Technical Library
Statistical Methods & Analysis
A curated collection of technical case studies demonstrating proficiency in fundamental and advanced statistical algorithms, regression modeling, and machine learning techniques using R.
Model Selection & Validation
Advanced techniques for feature selection and robust error estimation.
Variable Selection & Regularization
LassoRidgeBest SubsetBIC
High-dimensional regression using Lasso, Ridge, and Best Subset Selection.
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Logistic Regression & Cross-Validation
Cross-ValidationLogistic RegressionLOOCV
Robust error estimation using LOOCV, k-Fold, and Validation Set approaches.
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Classification & Machine Learning
Categorizing data (e.g., Expensive vs Cheap) using probabilistic and tree-based models.
Tree-Based Methods (Decision Trees)
Decision TreesPruningCost-Complexity
Growing and pruning Classification and Regression Trees (CART).
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Classification Boundaries & ROC Analysis
ROC / AUCLDA vs QDAStandardization
Benchmarking LDA, QDA, Logistic Regression, and Standardized KNN via ROC/AUC.
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Regression Analysis
Predicting continuous outcomes (e.g., Prices) using Linear and Non-Linear methods.
Taipei Real Estate Valuation (Comprehensive)
Multiple RegressionKNN OptimizationReal Estate
A comprehensive end-to-end analysis comparing Parametric vs Non-Parametric methods.
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Linear Regression & KNN Analysis
Linear RegressionKNNMSE Analysis
Improving housing price predictions using Multiple Linear Regression and KNN.
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Foundational Statistics
Core statistical concepts and simulations.
Descriptive Statistics & Normal Distribution
Descriptive StatsSimulationMatrix Operations
Foundational analysis of variance, histograms, and standard normal simulations.
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