#### Overview of this book

Learning Data Mining with Python
Credits
www.PacktPub.com
Preface
Free Chapter
Getting Started with Data Mining
Classifying with scikit-learn Estimators
Predicting Sports Winners with Decision Trees
Recommending Movies Using Affinity Analysis
Extracting Features with Transformers
Social Media Insight Using Naive Bayes
Discovering Accounts to Follow Using Graph Mining
Clustering News Articles
Classifying Objects in Images Using Deep Learning
Working with Big Data
Index

## Introducing data mining

Data mining provides a way for a computer to learn how to make decisions with data. This decision could be predicting tomorrow's weather, blocking a spam email from entering your inbox, detecting the language of a website, or finding a new romance on a dating site. There are many different applications of data mining, with new applications being discovered all the time.

Data mining is part of algorithms, statistics, engineering, optimization, and computer science. We also use concepts and knowledge from other fields such as linguistics, neuroscience, or town planning. Applying it effectively usually requires this domain-specific knowledge to be integrated with the algorithms.

Most data mining applications work with the same high-level view, although the details often change quite considerably. We start our data mining process by creating a dataset, describing an aspect of the real world. Datasets comprise of two aspects:

• Samples that are objects in the real world. This can be a book, photograph, animal, person, or any other object.

• Features that are descriptions of the samples in our dataset. Features could be the length, frequency of a given word, number of legs, date it was created, and so on.

The next step is tuning the data mining algorithm. Each data mining algorithm has parameters, either within the algorithm or supplied by the user. This tuning allows the algorithm to learn how to make decisions about the data.

As a simple example, we may wish the computer to be able to categorize people as "short" or "tall". We start by collecting our dataset, which includes the heights of different people and whether they are considered short or tall:

Person

Height

Short or tall?

1

155cm

Short

2

165cm

Short

3

175cm

Tall

4

185cm

Tall

The next step involves tuning our algorithm. As a simple algorithm; if the height is more than x, the person is tall, otherwise they are short. Our training algorithm will then look at the data and decide on a good value for x. For the preceding dataset, a reasonable value would be 170 cm. Anyone taller than 170 cm is considered tall by the algorithm. Anyone else is considered short.

In the preceding dataset, we had an obvious feature type. We wanted to know if people are short or tall, so we collected their heights. This engineering feature is an important problem in data mining. In later chapters, we will discuss methods for choosing good features to collect in your dataset. Ultimately, this step often requires some expert domain knowledge or at least some trial and error.

### Note

In this book, we will introduce data mining through Python. In some cases, we choose clarity of code and workflows, rather than the most optimized way to do this. This sometimes involves skipping some details that can improve the algorithm's speed or effectiveness.