UsefulLinks
Computer Science
Data Science
Data Mining and Knowledge Discovery
1. Introduction to Data Mining and Knowledge Discovery
2. Data Types and Sources
3. Data Preprocessing Fundamentals
4. Classification Methods
5. Regression Analysis
6. Clustering Analysis
7. Association Rule Mining
8. Advanced Mining Techniques
9. Model Evaluation and Validation
10. Model Interpretation and Explainability
11. Deployment and Production Systems
12. Ethics, Privacy, and Security
5.
Regression Analysis
5.1.
Regression Fundamentals
5.1.1.
Relationship Modeling
5.1.2.
Dependent and Independent Variables
5.1.3.
Parametric vs Non-Parametric Methods
5.1.4.
Model Assumptions
5.2.
Linear Regression Models
5.2.1.
Simple Linear Regression
5.2.1.1.
Least Squares Estimation
5.2.1.2.
Model Interpretation
5.2.1.3.
Residual Analysis
5.2.2.
Multiple Linear Regression
5.2.2.1.
Multiple Predictors
5.2.2.2.
Multicollinearity Issues
5.2.2.3.
Variable Selection
5.2.3.
Polynomial Regression
5.2.4.
Regularized Linear Models
5.2.4.1.
Ridge Regression
5.2.4.2.
Lasso Regression
5.2.4.3.
Elastic Net
5.3.
Non-Linear Regression
5.3.1.
Non-Parametric Regression
5.3.2.
Kernel Regression
5.3.3.
Spline Methods
5.3.4.
Local Regression
5.4.
Logistic Regression
5.4.1.
Binary Logistic Regression
5.4.2.
Multinomial Logistic Regression
5.4.3.
Ordinal Logistic Regression
5.4.4.
Model Diagnostics
5.5.
Regression Evaluation
5.5.1.
Residual Analysis
5.5.2.
Goodness of Fit Measures
5.5.3.
Cross-Validation
5.5.4.
Prediction Intervals
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4. Classification Methods
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6. Clustering Analysis