Data Mining
1
A Brief Introduction
2
Tidyverse tools
2.1
Select
2.1.1
Original Data Set
2.1.2
Other select functions:
2.2
Filter
2.2.1
Logical Tests in R
2.3
Mutate
2.3.1
Useful mutate functions:
2.4
Summarise / Summarize
2.4.1
Useful summary functions
2.5
Arrange()
2.6
Group_by()
3
Linear Regresssion
3.1
Non linear data
3.1.1
Interactions + subtracting variables
3.2
Last trap, ording of the data
3.2.1
Missing
3.2.2
GLM
4
Classification
4.1
Music dataset
4.2
Logistic Regression Review
4.3
Resampling
4.3.1
Up-sampling
4.3.2
Bootstrapping
4.3.3
Cross Validation
4.4
Initial Work
4.5
A basic model with upsampling
5
Linear Model Selection
5.1
Regression requires more data than features
5.2
Lasso
5.3
simulating collinearity + sparsity
5.4
Demo on glmnet functionalities
5.5
Principal Component Analysis
5.5.1
Principle Component Analysis
5.6
Typical machine learning approach
5.7
What we would do in data mining
5.8
Principal Component Analysis Applied!
5.8.1
Other example
5.8.2
Explore the data
5.9
PCA on weather data example
5.10
Different noramlizations
5.10.1
No normalization
5.10.2
Centering and scaling
6
Comparing different models
6.1
Question 0
6.2
Question 1
6.3
Question 2
6.4
Question 3
6.5
Question 4
6.6
Question 5
6.7
Question 6
7
Homework 3 Project
7.1
HW1
7.1.1
Q1
7.1.2
Q2
7.1.3
Q3
7.1.4
Q4
7.2
R Markdown
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HW1
HW1
Noah Love
1/18/2021
Chapter 1
A Brief Introduction