Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
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 1, 2012...