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

Random forest - currency trading strategy


The goal of forecasting future price trends for forex markets can be scientifically achieved after carrying out technical analysis. Forex traders develop strategies based on multiple technical analyses such as market trend, volume, range, support and resistance levels, chart patterns and indicators, as well as conducting a multiple time frame analysis using different time-frame charts. Based on statistics of past market action, such as past prices and past volume, a technical analysis strategy is created for evaluating the assets. The main goal for analysis is not to measure an asset's underlying value but to calculate future performance of markets indicated by the historical performance.

Getting ready

In order to perform random forest, we will be using a dataset collected from the US Dollar and GB Pound dataset.

Step 1 - collecting and describing the data

The dataset titled PoundDollar.csv will be used. The dataset is in standard format. There are 5...