#### Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Classification Using K-Nearest Neighbors
Time Series Analysis
Python Reference
Statistics
Glossary of Algorithms and Methods in Data Science
Other Books You May Enjoy
Index

## Chapter 7. Time Series Analysis

Time series analysis is the analysis of time-dependent data. Given data for a certain period, the aim is to predict data for a different period, usually in the future. For example, time series analysis is used to predict financial markets, earthquakes, and weather.

In this chapter, we are mostly concerned with predicting the numerical values of certain quantities, for example, the human population in 2030.

The main elements of time-based prediction are as follows:

• Trends: Does the variable tend to rise or fall as time passes? For example, does the human population grow or shrink?
• Seasonality: How is the data dependent on certain regular events in time? For example, are restaurant profits greater on Fridays than on Tuesdays?

Combining these two elements of time series analysis equips us with a powerful method to make time-dependent predictions.

In this chapter, you will learn about the following topics:

• Analyzing data trends using regression through a business profits...