#### 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 1: An Introduction to Machine Learning

### Activity 1: Finding the Distribution of Diabetic Patients in the PimaIndiansDiabetes Dataset

Solution:

2. Create a variable PimaIndiansDiabetesData for further use.

#Assign it to a local variable for further use

PimaIndiansDiabetesData<- PimaIndiansDiabetes

3. Use the head() function to view the first five rows of the dataset.

#Display the first five rows

The output is as follows:

pregnant glucose pressure triceps insulin mass pedigree age diabetes

1        6     148       72      35       0 33.6    0.627  50      pos

2        1      85       66      29       0 26.6    0.351  31      neg

3        8     183       64       0       0 23.3    0.672  32      pos

4        1      89       66      23      94 28.1    0.167  21      neg

5        0     137       40      35     168 43.1    2.288  33      pos

6        5     116       74       0       0 25.6    0.201  30      neg

From the preceding data, identify the input features and find the column that is the predictor variable. The output variable is diabetes.

4. Display the different categories of the output variable:

levels(PimaIndiansDiabetesData\$diabetes)

The output is as follows:

[1] "neg" "pos"

5. Load the required library for plotting graphs.

library(ggplot2)

6. Create a bar plot to view the output variables.

barplot <- ggplot(data= PimaIndiansDiabetesData, aes(x=age))

barplot + geom_histogram(binwidth=0.2, color="black", aes(fill=diabetes))  + ggtitle("Bar plot of Age")

The output is as follows:

###### Figure 1.36: Bar plot output for diabetes

We can conclude that we have the most data for the age group of 20-30. Graphical representation thus allows us to understand the data.

### Activity 2: Grouping the PimaIndiansDiabetes Data

Solution :

1. View the structure of the PimaIndiansDiabetes dataset.

#View the structure of the data

str(PimaIndiansDiabetesData)

The output is as follows:

'data.frame':768 obs. of  9 variables:

\$ pregnant: num  6 1 8 1 0 5 3 10 2 8 ...

\$ glucose : num  148 85 183 89 137 116 78 115 197 125 ...

\$ pressure: num  72 66 64 66 40 74 50 0 70 96 ...

\$ triceps : num  35 29 0 23 35 0 32 0 45 0 ...

\$ insulin : num  0 0 0 94 168 0 88 0 543 0 ...

\$ mass    : num  33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...

\$ pedigree: num  0.627 0.351 0.672 0.167 2.288 ...

\$ age     : num  50 31 32 21 33 30 26 29 53 54 ...

\$ diabetes: Factor w/ 2 levels "neg","pos": 2 1 2 1 2 1 2 1 2 2 ...

2. View the summary of the PimaIndiansDiabetes dataset.

#View the Summary of the data

summary(PimaIndiansDiabetesData)

The output is as follows:

###### Figure 1.37: Summary of PimaIndiansDiabetes data
3. View the statistics of the columns of PimaIndiansDiabetes dataset grouped by the diabetes column.

#Perform Group by and view statistics for the columns

#Install the package

install.packages("psych")

library(psych) #Load package psych to use function describeBy

Use describeby with pregnancy and diabetes columns.

describeBy(PimaIndiansDiabetesData\$pregnant, PimaIndiansDiabetesData\$diabetes)

The output is as follows:

Descriptive statistics by group

group: neg

vars   n mean   sd median trimmed  mad min max range skew kurtosis   se

X1    1 500  3.3 3.02      2    2.88 2.97   0  13    13 1.11     0.65 0.13

----------------------------------------------------------------------------------------------

group: pos

vars   n mean   sd median trimmed  mad min max range skew kurtosis   se

X1    1 268 4.87 3.74      4     4.6 4.45   0  17    17  0.5    -0.47 0.23

We can view the mean, median, min, and max of the number of times pregnant attribute in the group of people who have diabetes (pos) and who do not have diabetes (neg).

4. Use describeby with pressure and diabetes.

describeBy(PimaIndiansDiabetesData\$pressure, PimaIndiansDiabetesData\$diabetes)

The output is as follows:

Descriptive statistics by group

group: neg

vars   n  mean    sd median trimmed   mad min max range skew kurtosis   se

X1    1 500 68.18 18.06     70   69.97 11.86   0 122   122 -1.8     5.58 0.81

----------------------------------------------------------------------------------------------

group: pos

vars   n  mean    sd median trimmed   mad min max range  skew kurtosis   se

X1    1 268 70.82 21.49     74   73.99 11.86   0 114   114 -1.92     4.53 1.31

We can view the mean, median, min, and max of the pressure in the group of people who have diabetes (pos) and who do not have diabetes (neg).

We have learned how to view the structure of any dataset and print the statistics about the range of every column using summary().

### Activity 3: Performing EDA on the PimaIndiansDiabetes Dataset

Solution:

2. View the correlation among the features of the PimaIndiansDiabetes dataset.

#Calculate correlations

correlation <- cor(PimaIndiansDiabetesData[,1:4])

3. Round it to the second nearest digit.

#Round the values to the nearest 2 digit

round(correlation,2)

The output is as follows:

pregnant glucose pressure triceps

pregnant     1.00    0.13     0.14   -0.08

glucose      0.13    1.00     0.15    0.06

pressure     0.14    0.15     1.00    0.21

triceps     -0.08    0.06     0.21    1.00

4. Pair them on a plot.

#Plot the pairs on a plot

pairs(PimaIndiansDiabetesData[,1:4])

The output is as follows:

###### Figure 1.38: A pair plot for the diabetes data
5. Create a box plot to view the data distribution for the pregnant column and color by diabetes.

library(ggplot2)

boxplot <- ggplot(data=PimaIndiansDiabetesData, aes(x=diabetes, y=pregnant))

boxplot + geom_boxplot(aes(fill=diabetes)) +

ylab("Pregnant") + ggtitle("Diabetes Data Boxplot") +

stat_summary(fun.y=mean, geom="point", shape=5, size=4)

The output is as follows:

###### Figure 1.39: The box plot output using ggplot

In the preceding graph, we can see the distribution of "number of times pregnant" in people who do not have diabetes (neg) and in people who have diabetes (pos).

### Activity 4: Building Linear Models for the GermanCredit Dataset

Solution:

2. Subset the data.

GermanCredit_Subset=GermanCredit[,1:10]

3. Fit a linear model using lm().

# fit model

fit <- lm(Duration~., GermanCredit_Subset)

4. Summarize the results using the summary() function.

# summarize the fit

summary(fit)

The output is as follows:

Call:

lm(formula = Duration ~ ., data = GermanCredit_Subset)

Residuals:

Min      1Q  Median      3Q     Max

-44.722  -5.524  -1.187   4.431  44.287

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept)                2.0325685  2.3612128   0.861  0.38955

Amount                     0.0029344  0.0001093  26.845  < 2e-16 ***

InstallmentRatePercentage  2.7171134  0.2640590  10.290  < 2e-16 ***

ResidenceDuration          0.2068781  0.2625670   0.788  0.43094

Age                       -0.0689299  0.0260365  -2.647  0.00824 **

NumberExistingCredits     -0.3810765  0.4903225  -0.777  0.43723

NumberPeopleMaintenance   -0.0999072  0.7815578  -0.128  0.89831

Telephone                  0.6354927  0.6035906   1.053  0.29266

ForeignWorker              4.9141998  1.4969592   3.283  0.00106 **

ClassGood                 -2.0068114  0.6260298  -3.206  0.00139 **

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 8.784 on 990 degrees of freedom

Multiple R-squared:  0.4742,    Adjusted R-squared:  0.4694

F-statistic:  99.2 on 9 and 990 DF,  p-value: < 2.2e-16

5. Use predict() to make the predictions.

# make predictions

predictions <- predict(fit, GermanCredit_Subset)

6. Calculate the RMSE for the predictions.

# summarize accuracy

rmse <- sqrt(mean((GermanCredit_Subset\$Duration - predictions)^2))

print(rmse)

The output is as follows:

[1] 76.3849

In this activity, we have learned to build a linear model, make predictions on new data, and evaluate performance using RMSE.

### Activity 5: Using Multiple Variables for a Regression Model for the Boston Housing Dataset

Solution:

2. Build a regression model using multiple variables.

#Build multi variable regression

regression <- lm(medv~crim + indus+rad , data = BostonHousing)

3. View the summary of the built regression model.

#View the summary

summary(regression)

The output is as follows:

Call:

lm(formula = medv ~ crim + indus + rad, data = BostonHousing)

Residuals:

Min      1Q  Median      3Q     Max

-12.047  -4.860  -1.736   3.081  32.596

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 29.27515    0.68220  42.913  < 2e-16 ***

crim        -0.23952    0.05205  -4.602 5.31e-06 ***

indus       -0.51671    0.06336  -8.155 2.81e-15 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.838 on 502 degrees of freedom

Multiple R-squared:  0.2781, Adjusted R-squared:  0.2737

F-statistic: 64.45 on 3 and 502 DF,  p-value: < 2.2e-16

4. Plot the regression model using the plot() function.

#Plot the fit

plot(regression)

The output is as follows:

###### Figure 1.40: Residual versus fitted values

The preceding plot compares the predicted values and the residual values.

Hit <Return> to see the next plot:

###### Figure 1.41: Normal QQ

The preceding plot shows the distribution of error. It is a normal probability plot. A normal distribution of error will display a straight line.

Hit <Return> to see the next plot:

###### Figure 1.42: Scale location plot

The preceding plot compares the spread and the predicted values. We can see how the spread is with respect to the predicted values.

Hit <Return> to see the next plot:

###### Figure 1.43: Cook's distance plot

This plot helps to identify which data points are influential to the regression model, that is, which of our model results would be affected if we included or excluded them.

We have now explored the datasets with one or more variables.