*Practical Machine Learning in R* is a book that introduces machine learning concepts and algorithms using the R programming language. Below is a summary of the concepts I learned from each chapter and the code for the book exercises that I completed.

_{Please click on the blue links below for the GitHub code of the book’s examples and the end of chapter exercises.}

### CHAPTER 3: Managing Data

_{This chapter focuses on how to prepare data prior to modeling using the tidyverse package. }

### CHAPTER 4: Linear Regression

_{This chapter focuses on how to build, evaluate, interpret, improve upon, and apply the results of a multiple linear regression model using R.}

### CHAPTER 5: Logistic Regression

_{This chapter focuses on how to build, evaluate, interpret, improve upon, and apply the results of a logistic regression model using R.}

### CHAPTER 6: k-Nearest Neighbors

_{This chapter focuses on how to use the k-NN classifier to assign labels to new data in R.}

### CHAPTER 7: Naive Bayes

_{This chapter focuses on how to build a naive Bayes classifier in R and how to use it to predict the class values of previously unseen data.}

### CHAPTER 8: Decision Trees

_{This chapter focuses on how to build a decision tree classifier in R and how to use it to predict the class values of previously unseen data.}

### CHAPTER 9: Evaluating Performance

_{This chapter focuses on how to evaluate model performance with metrics other than accuracy.}

### CHAPTER 10: Improving Performance

_{This chapter focuses on how to improve performance by tuning the parameters of a model to make it better and how to improve performance by combining several weak models together to achieve better results.}

### CHAPTER 11: Discovering Patterns with Association Rules

_{This chapter focuses on different ways to evaluate and quantify the strength of association rules.}

### CHAPTER 12: Grouping Data with Clustering

_{This chapter focuses on how to segment data using the k-means algorithm in R.}