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Data Science Algorithms in a Week

You're reading from   Data Science Algorithms in a Week Top 7 algorithms for scientific computing, data analysis, and machine learning

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789806076
Length 214 pages
Edition 2nd Edition
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Authors (2):
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David Toth David Toth
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David Toth
David Natingga David Natingga
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David Natingga
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Toc

Table of Contents (12) Chapters Close

Preface 1. Classification Using K-Nearest Neighbors 2. Naive Bayes FREE CHAPTER 3. Decision Trees 4. Random Forests 5. Clustering into K Clusters 6. Regression 7. Time Series Analysis 8. Python Reference 9. Statistics 10. Glossary of Algorithms and Methods in Data Science
11. Other Books You May Enjoy

Business profits – analyzing trends


We are interested in predicting the profits of a business for the year 2018 given its profits for previous years:

Year

Profit in USD

2011

$40,000

2012

$43,000

2013

$45,000

2014

$50,000

2015

$54,000

2016

$57,000

2017

$59,000

2018

?

 

Analysis

In this example, the profit is always increasing, so we can think of representing the profit as a growing function that's dependent on the time variable, which is represented by years. The variations in profit between the subsequent years are $3,000, $2,000, $5,000, $4,000, $3,000, and $2,000. These differences do not seem to be affected by time, and the variation between them is relatively low. Therefore, we may try to predict the profit for the coming years by performing linear regression. We express profit, p, in terms of the year, y, in a linear equation, also called a trend line:

We can find the constants, a and b, using linear regression.

Analyzing trends using the least squares method in Python

Input:

We store the data from the preceding...

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