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Sayan Mukhopadhyay - Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples

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Book:
Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples
Author:
Sayan Mukhopadhyay
Publisher:
Apress
Genre:
Books / Computer
Year:
2018
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Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. Youll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.

After reading this book you will have experience of every technical aspect of an analytics project. Youll get to know the concepts using Python code, giving you samples to use in your own projects.

What You Will Learn

Work with data analysis techniques such as classification, clustering, regression, and forecasting
Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
Examine the different big data frameworks, including Hadoop and Spark
Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP

Who This Book Is For

Data scientists and software developers interested in the field of data analytics.

Sayan Mukhopadhyay: author's other books

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Sayan Mukhopadhyay 2018

Sayan Mukhopadhyay Advanced Data Analytics Using Python

1. Introduction

Sayan Mukhopadhyay 1

(1)

Kolkata, West Bengal, India

In this book, I assume that you are familiar with Python programming. In this introductory chapter, I explain why a data scientist should choose Python as a programming language. Then I highlight some situations where Python is not a good choice. Finally, I describe some good practices in application development and give some coding examples that a data scientist needs in their day-to-day job.

Why Python?

So, why should you choose Python?

It has versatile libraries. You always have a ready-made library in Python for any kind of application. From statistical programming to deep learning to network application to web crawling to embedded systems, you will always have a ready-made library in Python. If you learn this language, you do not have to stick to a specific use case. R has a rich set of analytics libraries, but if you are working on an Internet of Things ( IoT ) application and need to code in a device-side embedded system, it will be difficult in R.
It is very high performance. Java is also a versatile language and has lots of libraries, but Java code runs on a Java virtual machine, which adds an extra layer of latency. Python uses high-performance libraries built in other languages. For example, SciPy uses LAPACK, which is a Fortran library for linear algebra applications. TensorFlow uses CUDA, which is a C library for parallel GPU processing.
It is simple and gives you a lot of freedom to code. Python syntax is just like a natural language. It is easy to remember, and it does not have constraints in variables (like constants or public / private ).

When to Avoid Using Python

Python has some downsides too.

When you are writing very specific code, Python may not always be the best choice. For example, if you are writing code that deals only with statistics, R is a better choice. If you are writing MapReduce code only, Java is a better choice than Python.
Python gives you a lot of freedom in coding. So, when many developers are working on a large application, Java/C++ is a better choice so that one developer/architect can put constraints on another developers code using public / private and constant keywords.
For extremely high-performance applications , there is no alternative to C/C++.

OOP in Python

Before proceeding, I will explain some features of object-oriented programming ( OOP ) in a Python context.

The most basic element of any modern application is an object. To a programmer or architect, the world is a collection of objects. Objects consist of two types of members: attributes and methods. Members can be private, public, or protected. Classes are data types of objects. Every object is an instance of a class. A class can be inherited in child classes. Two classes can be associated using composition.

In a Python context, Python has no keywords for public, private, or protected, so encapsulation (hiding a member from the outside world) is not implicit in Python. Like C++, it supports multilevel and multiple inheritance. Like Java, it has an abstract keyword. Classes and methods both can be abstract.

The following code is an example of a generic web crawler that is implemented as an airlines web crawler on the Skytrax site and as a retail crawler for the Mouthshut.com site. Ill return to the topic of web crawling in Chapter .

from abc import ABCMeta, abstractmethod

import BeautifulSoup

import urllib

import sys

import bleach

#################### Root Class (Abstract) ####################

class SkyThoughtCollector(object):

__metaclass__ = ABCMeta

baseURLString = "base_url"

airlinesString = "air_lines"

limitString = "limits"

baseURl = ""

airlines = []

limit = 10

@ abstractmethod

def collectThoughts(self):

print "Something Wrong!! You're calling an abstract method"

@classmethod

def getConfig(self, configpath):

#print "In get Config"

config = {}

conf = open(configpath)

for line in conf:

if ("#" not in line):

words = line.strip().split('=')

config[words[0].strip()] = words[1].strip()

#print config

self.baseURl = config[self.baseURLString]

if config.has_key(self.airlinesString):

self.airlines = config[self.airlinesString].split(',')

if config.has_key(self.limitString):

self.limit = int(config[self.limitString])

#print self.airlines

def downloadURL(self, url):

#print "downloading url"

pageFile = urllib.urlopen(url)

if pageFile.getcode() != 200:

return "Problem in URL"

pageHtml = pageFile.read()

pageFile.close()

return "".join(pageHtml)

def remove_junk(self, arg):

f = open('junk.txt')

for line in f:

arg.replace(line.strip(),'')

return arg

def print_args(self, args):

out =''

last = 0

for arg in args:

if args.index(arg) == len(args) -1:

last = 1

reload(sys)

sys.setdefaultencoding("utf-8")

arg = arg.decode('utf8','ignore').encode('ascii','ignore').strip()

arg = arg.replace('\n',' ')

arg = arg.replace('\r','')

arg = self.remove_junk(arg)

if last == 0:

out = out + arg + '\t'

else:

out = out + arg

print out

####################### Airlines Chield #######################

class AirLineReviewCollector(SkyThoughtCollector):

months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December' ]

def __init__(self, configpath):

#print "In Config"

super(AirLineReviewCollector,self).getConfig( configpath )

def parseSoupHeader(self, header):

#print "parsing header"

name = surname = year = month = date = country =''

txt = header.find("h9")

words = str(txt).strip().split(' ')

for j in range(len(words)-1):

if words[j] in self.months:

date = words[j-1]

month= words[j]

year = words[j+1]

name = words[j+3]

surname = words[j+4]

if ")" in words[-1]:

country = words[-1].split(')')[0]

if "(" in country:

country = country.split('(')[1]

else:

country = words[-2].split('(')[1] + country

return (name, surname, year, month, date, country)

def parseSoupTable(self, table):

#print "parsing table"

images = table.findAll("img")

over_all = str(images[0]).split("grn_bar_")[1].split(".gif")[0]

money_value = str(images[1]).split("SCORE_")[1].split(".gif")[0]

seat_comfort = str(images[2]).split("SCORE_")[1].split(".gif")[0]

staff_service = str(images[3]).split("SCORE_")[1].split(".gif")[0]

catering = str(images[4]).split("SCORE_")[1].split(".gif")[0]

entertainment = str(images[4]).split("SCORE_")[1].split(".gif")[0]

if 'YES' in str(images[6]):

recommend = 'YES'

else :

recommend = 'NO'

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