#### Overview of this book

With huge amounts of data being generated every moment, businesses need applications that apply complex mathematical calculations to data repeatedly and at speed. With machine learning techniques and R, you can easily develop these kinds of applications in an efficient way. Practical Machine Learning with R begins by helping you grasp the basics of machine learning methods, while also highlighting how and why they work. You will understand how to get these algorithms to work in practice, rather than focusing on mathematical derivations. As you progress from one chapter to another, you will gain hands-on experience of building a machine learning solution in R. Next, using R packages such as rpart, random forest, and multiple imputation by chained equations (MICE), you will learn to implement algorithms including neural net classifier, decision trees, and linear and non-linear regression. As you progress through the book, you’ll delve into various machine learning techniques for both supervised and unsupervised learning approaches. In addition to this, you’ll gain insights into partitioning the datasets and mechanisms to evaluate the results from each model and be able to compare them. By the end of this book, you will have gained expertise in solving your business problems, starting by forming a good problem statement, selecting the most appropriate model to solve your problem, and then ensuring that you do not overtrain it.
Free Chapter
An Introduction to Machine Learning
Data Cleaning and Pre-processing
Feature Engineering
Introduction to neuralnet and Evaluation Methods
Linear and Logistic Regression Models
Unsupervised Learning

## Chapter 2: Data Cleaning and Pre-processing

### Activity 6: Pre-processing using Center and Scale

Solution:

In this exercise, we will perform the center and scale pre-processing operations.

1. Load the mlbench library and the PimaIndiansDiabetes dataset:

library(caret)

library(mlbench)

data(PimaIndiansDiabetes)

View the summary:

# view the data

summary(PimaIndiansDiabetes [,1:2])

The output is as follows:

pregnant         glucose

Min.   : 0.000   Min.   :  0.0

1st Qu.: 1.000   1st Qu.: 99.0

Median : 3.000   Median :117.0

Mean   : 3.845   Mean   :120.9

3rd Qu.: 6.000   3rd Qu.:140.2

Max.   :17.000   Max.   :199.0

2. User preProcess() to pre-process the data to center and scale:

# to standardise we will scale and center

params <- preProcess(PimaIndiansDiabetes [,1:2], method=c("center", "scale"))

3. Transform the dataset using predict():

# transform the dataset

new_dataset <- predict(params, PimaIndiansDiabetes [,1:2])

4. Print the summary of the new dataset:

# summarize the transformed dataset

summary(new_dataset)

The output is as follows:

pregnant          glucose

Min.   :-1.1411   Min.   :-3.7812

1st Qu.:-0.8443   1st Qu.:-0.6848

Median :-0.2508   Median :-0.1218

Mean   : 0.0000   Mean   : 0.0000

3rd Qu.: 0.6395   3rd Qu.: 0.6054

Max.   : 3.9040   Max.   : 2.4429

We will notice that the values are now mean centering values.

### Activity 7: Identifying Outliers

Solution:

2. Load the outlier package and use the outlier function to display the outliers:

library(outliers)

3. Detect outliers in the dataset using the outlier() function:

#Detect outliers

outlier(mtcars)

The output is as follows:

mpg     cyl    disp      hp    drat      wt    qsec      vs      am

gear    carb

33.900   4.000 472.000 335.000   4.930   5.424  22.900

1.000   1.000   5.000   8.000

4. Display the other side of the outlier values:

#This detects outliers from the other side

outlier(mtcars,opposite=TRUE)

The output is as follows:

mpg    cyl   disp     hp   drat     wt   qsec     vs     am

gear   carb

10.400  8.000 71.100 52.000  2.760  1.513 14.500  0.000  0.000

3.000  1.000

5. Plot a box plot:

#View the outliers

boxplot(Mushroom)

The output is as follows:

###### Figure 2.36: Outliers in the mtcars dataset.

The circle marks are the outliers.

### Activity 8: Oversampling and Undersampling

Solution:

The detailed solution is as follows:

1. Read the mushroom CSV file:

summary(ms\$bruises)

The output is as follows:

f    t

4748 3376

2. Perform downsampling:

set.seed(9560)

undersampling <- downSample(x = ms[, -ncol(ms)], y = ms\$bruises)

table(undersampling\$bruises)

The output is as follows:

f    t

3376 3376

3. Perform oversampling:

set.seed(9560)

oversampling <- upSample(x = ms[, -ncol(ms)],y = ms\$bruises)

table(oversampling\$bruises)

The output is as follows:

f    t

4748 4748

In this activity, we learned to use downSample() and upSample() from the caret package to perform downsampling and oversampling.

### Solution:

The detailed solution is as follows:

1. Load the German credit dataset:

library(caret)

library(ROSE)

data(GermanCredit)

2. View the samples in the German credit dataset:

#View samples

str(GermanCredit)

3. Check the number of unbalanced data in the German credit dataset using the summary() method:

#View the imbalanced data

summary(GermanCredit\$Class)

The output is as follows:

300  700

4. Use ROSE to balance the numbers:

balanced_data <- ROSE(Class ~ ., data  = stagec,seed=3)\$data

table(balanced_data\$Class)

The output is as follows: