Book Image

Practical Data Analysis

By : Hector Cuesta
Book Image

Practical Data Analysis

By: Hector Cuesta

Overview of this book

Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB. Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.
Table of Contents (24 chapters)
Practical Data Analysis
About the Author
About the Reviewers

Getting started with support vector machine

The SVM is a supervised classification method based in a kernel geometrical construction as is shown in the following figure. SVM can be applied either for classification or regression. SVM will look for the best decision boundary that split the points into the class that they belong. To accomplish this SVM, we will look for the largest margin (space that is free of training samples parallel to the decision boundary). In the following figure, we can see the margin as the space between the dividing line and dotted lines. SVM will always look for a global solution due to the algorithm only care about the vectors close to the decision boundary. Those points in the edge of the margin are the support vectors. However, this is only for two-dimensional spaces, when we have high-dimensional spaces the decision boundaries turn into hyperplane (maximum decision margin) and the SVMs will look for the maximum-margin hyperplanes. In this chapter we will only...