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

Implementing DTW

In this example, we will look for similarity in 684 images from 8 categories. We will use four imports of PIL, numpy, mlpy, and collections:

from PIL import Image
from numpy import array
import mlpy
from collections import OrderedDict

First, we need to obtain the time series representation of the images and store it in a dictionary (data) with the number of the image and its time series as data[fn] = list.


The performance of this process will lie in the number of images processed, so beware of the use of memory with large datasets.

data = {}

for fn in range(1,685):
  img ="ImgFolder\\{0}.jpg".format(fn))
  arr = array(img)
  list = []
  for n in arr: list.append(n[0][0])
  for n in arr: list.append(n[0][1])
  for n in arr: list.append(n[0][2])
  data[fn] = list

Then, we need to select an image for the reference, which will be compared with all the other images in the data dictionary:

reference = data[31]

Now, we need to apply the mlpy.dtw_std function to all the...