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Top 32 Pr Consultants on 23 Jul 2019 on Toogit. Pr Consultants on Toogit are highly skilled and talented. Hiring Pr Consultants on Toogit is quite affordable as compared to a full-time employee and you can save upto 50% in business cost by hiring Pr Consultants on Toogit. Hiring Pr Consultants on Toogit is 100% safe as the money is released to the Freelancer only after you are 100% satisfied with the work.

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Kanhu Charan M.Bioinformatician data analyst, Brazil
$1 /hr
4 Years Exp.
0 Followers
I am doing my phD in bioinformatics. In bioinformatics analysis I analyze large data sets using in-house scripts written in Perl, R, Python and shell...Read More
Sathish R.crawling ,scraping, India
$0 /hr
1 Years Exp.
0 Followers
i am professional web crawler
Kiran M.Backend Developer, India
$10 /hr
8 Years Exp.
0 Followers
An engineer who passionate about scripting in Perl/Python. Worked as automation engineer and backend developer under Banking and storage domains for v...Read More
Abhit R.Freelancer | student | diligent | Keen learner , India
$3 /hr
3 Years Exp.
1 Followers
Student | Diligent | Ethical Hacker Currently Pursuing my graduation in Bachelor of computer and applications with having interests in Cyber security...Read More
Bryan Software Engineer , United States
$18 /hr
0 Years Exp.
0 Followers
I primarily write Perl, Oracle PL/SQL, and complex Excel VBA, and I have experience in other various languages. Quick learner, nothing is impossible,...Read More
Phil M.Education Consultant, United States
$100 /hr
10 Years Exp.
0 Followers
I am a Director, Author, and Consultant with 10+ years of experience implementing Academic Models in Elementary schools - Universities. In addition, I...Read More
Animesh J.Senior Software Engineer, India
$3 /hr
4 Years Exp.
0 Followers
I am 4.2 years experienced in Web development as a full stack developer and worked in technical support to provide optimum solutions within time.
Nityanand S.Software Developer, India
$10 /hr
9 Years Exp.
0 Followers
I am a software engineer having 9+ years of work experience.
Santhosh K.Software Engineer, India
$10 /hr
4 Years Exp.
0 Followers
I am having 4.4 years of experience in web scraping, web crawling, data extraction and script automation.
Gajender web scraping Expert , India
$12 /hr
7 Years Exp.
0 Followers
Hello, my name is Gajender. I have been working as a professional perl/python developer for the last 7 years. I have acquired experience in the field...Read More
Madan K.Software developer, India
$35 /hr
3 Years Exp.
0 Followers
I am a MEAN stack developer who worked on various projects like IOT, Digital marketing, Automation
Hi, I am a Ph.D. scholar working in bioinformatics. I have expertise in digital paintings. Would love to help with tasks which requires expertise in A...Read More
Mehdi M.Broadcasting & Streaming Expert / Enginner, Azerbaijan
$15 /hr
15 Years Exp.
0 Followers
I'm Streaming enginner with more than 15 years experience and strong programming, system & network engineering skills.
Zuhairiya Translator,data entry specialist, United Arab Emirates
$35 /hr
4 Years Exp.
0 Followers
I am a native from TamilNadu with proficiency in English and Tamil Languages .I have a Bachelor degree in Mathematics .Also,I have worked for four ye...Read More
A learning and evolving programmer with a constant zeal to improvise. Have recently started with machine learning and breaking down complex task into...Read More
Sangeeth K.senior software engineer, India
$50 /hr
6 Years Exp.
0 Followers
I have total 6+ in voip product development .I had developed a programmable Unified communication platform from strach using freeswitch,lua,php cass...Read More
Gaurav P.Full stack developer, India
$10 /hr
6 Years Exp.
0 Followers
I am python perl and django developer.
Karthigai Deepan senior software developer, India
$18 /hr
6 Years Exp.
0 Followers
Highly organized and efficient in fast-paced multitasking environments, able to prioritize effectively to accomplish objectives with creativity and en...Read More
Senjam Asterisk VOIP SIP IVR , India
$15 /hr
7 Years Exp.
0 Followers
I have 7 years experience in building voice communication systems and integrated the use of several applications including technologies like SIP, VOIP...Read More
Sateesh B.Senior DevOps Enginer, India
$17 /hr
7 Years Exp.
0 Followers
I am senior DevOps Engineer having 7 years of experience and strong in projects deployments and automating the CI and CD flow.
Martin Software Engineer and Reasearcher, United Kingdom
$40 /hr
25 Years Exp.
0 Followers
I have been working with perl since the days of perl 4 (in the early 1990's) and have implemented a large number of complex perl applications:...Read More
Perl CAD 
Nicky P.Senior developer and architect, Israel
$52 /hr
20 Years Exp.
0 Followers
Many years of experience in many software environments. Will find the solution. Always. I see the big picture, since I had in the past my own ventur...Read More
Anirban Developer, India
$4 /hr
0 Years Exp.
0 Followers
I am a certified JSP developer and alsohave knowledge over PHP,android,wordpress and many other which i have learned over my personal interest
James (.Web Development Expert, United States
$65 /hr
12 Years Exp.
0 Followers
I am an expert automation programmer with a guarantee for quality, speed and honesty. In that context my past and current projects have spanned a w...Read More
Ram R.API Integrations & Web Scraping Expert, India
$1 /hr
8 Years Exp.
0 Followers
I'm an API and Online Integrations Expert who enjoys solving data automation and analytics problems. When an API isn't available for the dat...Read More
Dipali A.AWS / Mobile Development Expert, India
$9 /hr
6 Years Exp.
6 Followers
My name is Dipali, a Mobile/Back-end Developer with 6+ years experience from Pune. Professional Skills : [AWS - iOS - Android - React - Xamarin - Kotl...Read More
Serviceberry A.ITSM PROCESS AND WEB DEV, India
$1 /hr
0 Years Exp.
0 Followers
I am certified ITSM and web developer
Shital S.QA Engineer, India
$109 /hr
5 Years Exp.
11 Followers
I have 5+ years of experience as Web and Mobile Testing. I am familiar with Testing Life Cycle as well tools like QTP, Load Runner, Selenium etc. I ca...Read More
Manaal Ahlam Ziyard I.Software Developer, Italy
$0 /hr
0 Years Exp.
0 Followers
I am a certified Software Developer, having completed my B.E. Computer Science & Engineering degree, with a First Class result. I am looking to bu...Read More
Dmitri Experienced web-developer 12+ years, Ukraine
$27 /hr
12 Years Exp.
0 Followers
I am professional full stack backend/frontend web-developer having 12+ years experience
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What is customer service?

Customer service is the support you offer your customers before and after they purchase and use your products or services that helps them have an easy and enjoyable experience with you. Offering amazing customer service is important if you want to retain customers and grow your business. Today’s customer service goes far beyond the traditional telephone support agent. It’s available via email, web, text message, and social media. Many companies additionally provide self-service support, so customers can find their own answers at any time day or night. Customer support is more than just providing answers, it’s an important part of the promise your brand makes to its customers.

How to hire customer service agent?

Whether you’re hiring a lot of customer service representative to scale your customer support team to satisfy new business demands or need specialists in different time zones to handle calls or requests that come through outside of your local peak hours, it’s imperative your new talent understands your company and your customer after all, they’ll be the voice of your company. The success of your representative starts with how well you write the project description and ends with asking all the right questions in an interview.

Why are you hiring a customer service agents?

It’s important to provide the most effective customer service to ensure that your customers receive the optimal experience. In many cases, customer service reps are the face of the brand. They are the people your customers will have the most contact with, so you need to hire the most effective possible talent. Customer Service Agents on Toogit are highly skilled and talented. Hiring freelance Customer Service Agents on Toogit is quite affordable as compared to a full-time employee and you can save up to 50% in business cost by hiring Freelance Customer Service Agents on Toogit.

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The importance of extracting information from the web is becoming increasingly loud and clear. Every few weeks, I realize myself in a situation where we need to extract information from the web to create a machine learning model. We have to pull or extract a large amount of information from websites and we would like to do it as quickly as possible. How would we do it without manually going to every web site and getting the data? Web Scraping simply makes this job easier and faster.

Why is web scraping needed?

Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? Let’s look at the applications of web scraping: 

  1. Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products.
  2. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending.
  3. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails.
  4. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc.) from websites, which are analyzed and used to carry out Surveys or for R&D.
  5. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the user.

 

Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code.

Why Python is best for Web Scraping

Features of Python which makes it more suitable for web scraping:

  1. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use.
  2. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data.
  3. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster.
  4. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code.
  5. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code.
  6. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help from.

How does web scraping work

To extract data using web scraping with python, you need to follow these basic steps:

  1. Find the URL that you want to scrape
  2. Inspecting the Page
  3. Find the data you want to extract
  4. Write the code
  5. Run the code and extract the data
  6. Store the data in the required format

Example: Scraping a website to get product details

Pre-requisite:

  • Python 2.x or Python 3.x
  • Selenium Library
  • BeautifulSoup Library
  • Pandas Library
  1. We are going scrape online shopping website to extract the Price, Name, and rating of products, go to products URL
  2. The data is usually nested in tags. So, we inspect the page to examine, under which tag the information we would like to scrape is nested. To inspect the page, just right click on the element and click on “Inspect”. When you click on the “Inspect” tab, you will see a “Browser Inspector Box” open.
  3. Let’s extract the Price, Name, and Rating which is nested in the “div” tag respectively.
  4. Write code:

#Let us import all the necessary libraries

from selenium import webdriver

from BeautifulSoup import BeautifulSoup

import pandas as pd

driver = webdriver.Chrome("/usr/lib/chromium-browser/chromedriver")

products=[] #List to store name of the product

prices=[] #List to store price of the product

ratings=[] #List to store rating of the product

driver.get("Product_URL")

content = driver.page_source

soup = BeautifulSoup(content)

for a in soup.findAll('a',href=True, attrs={'class':'.…'}):

name=a.find('div', attrs={'class': '….'})

price=a.find('div', attrs={'class':'….'})

rating=a.find('div', attrs={'class':'….'})

products.append(name.text)

ratings.append(rating.text)

df = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings})

df.to_csv('products.csv', index=False, encoding='utf-8')

 

To run the code, a file name “products.csv” is created and this file contains the extracted data.

NLP is a branch of data science that consists of systematic processes for analyzing, understanding, and deriving information from the text information in a smart and efficient manner. By utilizing NLP and its parts, one can organize the massive chunks of text information, perform various automated tasks and solve a wide range of issues like – automatic summarization, machine translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation etc.

 

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to lexical resources like WordNet, along with a collection of text processing libraries for classification, tokenization, stemming, and tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

 

NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.”

 

Downloading and installing NLTK

  1. Install NLTK: run pip install nltk
  2. Test installation: run python then type import nltk and run nltk.download() and download all packages.

 

Pre-Processing with NLTK

The main issue with text data is that it's all in text format. However, the Machine learning algorithms need some variety of numerical feature vector so as to perform the task. Thus before we have a tendency to begin with any NLP project we'd like to pre-process it to form it ideal for working. Basic text pre-processing includes:

 

  • Converting the whole text into uppercase or lowercase, in order that the algorithm doesn't treat the same words completely different in several cases.
  • Tokenization: Process of converting the normal text strings into a list of tokens i.e. words that we actually want. The NLTK data package includes a pre-trained Punkt tokenizer for English.

 

           import nltk

           from nltk.tokenize import word_tokenize

           text = "God is Great! I won a lottery."

           print(word_tokenize(text))

           Output: ['God', 'is', 'Great', '!', 'I', 'won', 'a', 'lottery', '.']

 

  • Noise removal: Process of removing everything that isn’t in a standard number or letter.
  • Stop word removal: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”). We would not want these words or taking up valuable processing time. For this, we can remove them easily, by storing a list of words that you consider to be stop words. NLTK (Natural Language Toolkit) in python has a list of stopwords stored in sixteen different languages. You can find them in the nltk_data directory.  home/Saad/nltk_data/corpora/stopwords is the directory address.

           import nltk

           from nltk.corpus import stopwords

           set(stopwords.words('english'))

 

  • Stemming: Stemming is the process of reducing the words to its root form. Example if we were to stem the following words: “Connects”, “Connecting”, “Connected”, “and Connection”, the result would be a single word “Connect”.

           # import these modules

           from nltk.stem import PorterStemmer

           from nltk.tokenize import word_tokenize   

           ps = PorterStemmer()  

           # choose some words to be stemmed

           words = ["Connect", "Connects", “Connected”, "Connecting", "Connection", "Connections"]

 

           for w in words:

           print(w, " : ", ps.stem(w)) 

 

  • Lemmatization: Lemmatization is the process of grouping along the various inflected forms of a word in order that they may be analyzed as a single item. Lemmatization is similar to stemming but it brings context to the words. Therefore it links words with similar meaning to one word.

           # import these modules

           from nltk.stem import WordNetLemmatizer  

           lemmatizer = WordNetLemmatizer()  

           print("rocks :", lemmatizer.lemmatize("rocks"))

           print("corpora :", lemmatizer.lemmatize("corpora"))  

           # a denotes adjective in "pos"

          print("better :", lemmatizer.lemmatize("better", pos ="a"))

 

          -> rocks : rock

          -> corpora : corpus

          -> better : good

 

Now we need to transform text into a meaningful vector array. This vector array is a representation of text that describes the occurrence of words within a document. For example, if our dictionary contains the words {Learning, is, the, not, great}, and we want to vectorize the text “Learning is great”, we would have the following vector: (1, 1, 0, 0, 1). A problem is that extremely frequent words begin to dominate within the document (e.g. larger score), however might not contain as much informational content. Also, it will offer additional weight to longer documents than shorter documents.

 

One approach is to rescale the frequency of words or the scores for frequent words called Term Frequency-Inverse Document Frequency.

 

  • Term Frequency: is a scoring of the frequency of the word in the current document.

           TF = (Number of times term t appears in a document)/ (Number of terms in the document)

 

  • Inverse Document Frequency: It is a scoring of how rare the word is across documents.

           IDF = 1+log(N/n), where, N is the number of documents and n is the number of documents a term t has appeared in.

 

           Tf-idf weight is a weight often used in information retrieval and text mining.

           Tf-IDF can be implemented in scikit learn as:

 

           from sklearn.feature_extraction.text import TfidfVectorizer

           corpus = [

           ...     'This is the first document.’

           ...     'This document is the second document.’

           ...     'And this is the third one.’

           ...     'Is this the first document?',]

           >>> vectorizer = TfidfVectorizer()

           >>> X = vectorizer.fit_transform(corpus)

           >>> print(vectorizer.get_feature_names())

           ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']

           >>> print(X.shape)

           (4, 9)

 

  • Cosine similarity: TF-IDF is a transformation applied to texts to get two real-valued vectors in vector space. We can then obtain the Cosine similarity of any pair of vectors by taking their dot product and dividing that by the product of their norms. That yields the cosine of the angle between the vectors. Cosine similarity is a measure of similarity between two non-zero vectors.

           Cosine Similarity (d1, d2) =  Dot product(d1, d2) / ||d1|| * ||d2||

 

          import numpy as np

          from sklearn.metrics.pairwise import cosine_similarity

          # vectors

          a = np.array([1,2,3])

          b = np.array([1,1,4])

          # manually compute cosine similarity

          dot = np.dot(a, b)

          norma = np.linalg.norm(a)

          normb = np.linalg.norm(b)

          cos = dot / (norma * normb)

 

After completion of cosine similarity matric we perform algorithmic operation on it for Document similarity calculation, sentiment analysis, topic segmentation etc.

 

I have done my best to make the article simple and interesting for you, hope you found it useful and interesting too.

PHP development began in 1994 when Rasmus Lerdorf wrote several Common Gateway Interface (CGI) programs in C, which he used to maintain his personal homepage. He extended them to work with web forms and to communicate with databases, and called this implementation "Personal Home Page/Forms Interpreter" or PHP/FI.

 

PHP/FI could be used to build simple, dynamic web applications. To accelerate bug reporting and improve the code, Lerdorf initially announced the release of PHP/FI as "Personal Home Page Tools (PHP Tools) version 1.0" on the Usenet discussion group comp.infosystems.www.authoring.cgi on June 8, 1995. This release already had the basic functionality that PHP has today. This included Perl-like variables, form handling, and the ability to embed HTML. The syntax resembled that of Perl, but was simpler, more limited and less consistent.

 

Early PHP was not intended to be a new programming language, and grew organically, with Lerdorf noting in retrospect: "I don't know how to stop it, there was never any intent to write a programming language. I have absolutely no idea how to write a programming language, I just kept adding the next logical step on the way." A development team began to form and, after months of work and beta testing, officially released PHP/FI 2 in November 1997.

 

The fact that PHP was not originally designed, but instead was developed organically has led to inconsistent naming of functions and inconsistent ordering of their parameters. In some cases, the function names were chosen to match the lower-level libraries which PHP was "wrapping", while in some very early versions of PHP the length of the function names was used internally as a hash function, so names were chosen to improve the distribution of hash values.

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