Summarizing dataset to apply machine learning – exercises

INTRODUCTION

Dear reader,

If you are a newbie in the world of machine learning, then this tutorial is exactly what you need in order to introduce yourself to this exciting new part of the data science world.

This post includes a full machine learning project that will guide you step by step to create a “template,” which you can use later on other datasets.

Before proceeding, please follow our short tutorial.

Look at the examples given and try to understand the logic behind them. Then try to solve the exercises below using R and without looking at the answers. Then check the solutions.
to check your answers.

Exercise 1

Create a list of 80% of the rows in the original dataset to use for training. HINT: Use createDataPartition().

Exercise 2

Select 20% of the data for validation.

Exercise 3

Use the remaining 80% of data to train and test the models.

Exercise 4

Find the dimensions of the “iris” dataset. HINT: Use dim().

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• Create a machine learning algorithm from a beginner point of view
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• And much more

This course shows a complete workflow start to finish. It is a great introduction and fallback when you have some experience.

Exercise 5

Find the type of each attribute in your dataset. HINT: Use sapply().

Exercise 6

Take a look at the first 5 rows of your dataset. HINT: Use head().

Exercise 7

Find the levels of the variable “Species.” HINT: Use levels().

Exercise 8

Find the percentages of rows that belong to the labels you found in Exercise 7. HINT: Use prop.table() and table().

Exercise 9

Display the absolute count of instances for each class as well as its percentage. HINT: Use cbind().

Exercise 10

Display the summary of the “iris” dataset. HINT: Use summary().