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Practical Machine Learning Cookbook

You're reading from   Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785280511
Length 570 pages
Edition 1st Edition
Languages
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Author (1):
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Atul Tripathi Atul Tripathi
Author Profile Icon Atul Tripathi
Atul Tripathi
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Toc

Table of Contents (15) Chapters Close

Preface 1. Introduction to Machine Learning FREE CHAPTER 2. Classification 3. Clustering 4. Model Selection and Regularization 5. Nonlinearity 6. Supervised Learning 7. Unsupervised Learning 8. Reinforcement Learning 9. Structured Prediction 10. Neural Networks 11. Deep Learning 12. Case Study - Exploring World Bank Data 13. Case Study - Pricing Reinsurance Contracts 14. Case Study - Forecast of Electricity Consumption

Naive Bayes - predicting the direction of stock movement

Stock trading is one of the most challenging problems statisticians are trying to solve. There are multiple technical indicators, such as trend direction, momentum or lack of momentum in the market, volatility for profit potential, and volume measures to monitor the popularity in the market, to name a few. These indicators can be used to create strategy to high-probability trading opportunities. Days/weeks/months can be spent discovering the relationships between technical indicators. An efficient and less time-consuming tool, such as a decision tree, can be used. The main advantage of a decision tree is that it is a powerful and easily interpretable algorithm, which gives a good head start.

Getting ready

In order to perform naive Bayes, we will be using a dataset collected from the stock markets dataset.

Step 1 - collecting and describing the data

The dataset to be used is the Apple Inc. daily closing stock value between January...

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