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Scraping Website: How to Write a Script in Python

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Here's a python script that you can use as a model to scrape a website. Python script The below logic uses BeautifulSoup Package for web scraping. import requests from bs4 import BeautifulSoup url = 'https://www.example.com' response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') # Print the title of the webpage print(soup.title.text) # Print all the links in the webpage for link in soup.find_all('a'):     print(link.get('href')) In this script, we first import the Requests and Beautiful Soup libraries. We then define the URL we want to scrape and use the Requests library to send a GET request to that URL. We then pass the response text to Beautiful Soup to parse the HTML contents of the webpage. We then use Beautiful Soup to extract the title of the webpage and print it to the console. We also use a for loop to find all the links in the webpage and print their href attributes to the console. This is just a basic example, but
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Predictive Analytics - A Case Study

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Nishad Sharma, a Delhi-based entrepreneur, is a typical online shopper who keeps a tab on the various sales and promotional deals run by e-commerce companies from time to time. On the cool Delhi evening of February 4, he opened online fashion company Myntra's app on his smartphone to see if there were any deals on trousers. That day, Myntra was running its Rush Hour sale in which customers could avail up to 50 per cent discount on select products. After filtering his search, Sharma decided to add a pair of UCB trousers to his shopping cart.


Predictive Analytics

But then he changed his mind. Perhaps he could get a better deal if he logged on into a sale on a weekend. To his surprise, Sharma received a mail from Myntra next morning, telling him what he presumably lost by abandoning his cart the previous day. The same product was now available at a 100 per cent mark-up. To close the sale, the company sent another mail to Sharma a couple of days later, offering a smaller discount of 20 per cent. Sharma couldn't let it go waste a second time round.

The systematic and gentle hounding of Sharma points at a big shift in the way e-commerce players target customers. Says Myntra's chief strategy officer Prasad Kompalli, "Gone are the days of sending irrelevant mail shots to one and all. 


Today we are in a position to identify and reach out to our customers." To make this possible on a large scale, e-commerce firms are sprucing up their predictive analytics skills and the attendant infrastructure to understand who their most valuable customers are.

Predictive analytics factors in all possible variables that help the marketer devise the right strategy to generate the desired engagement with customers - from providing timely and accurate sales forecasting insights, to equipping them with opportunities to improve diagnosis and the design of their websites to accommodate the pressures of any shopping blitzkrieg. 


These insights can range from what time of the day your website can witness maximum traffic, what products/pages will receive high impressions, which region you can expect the bulk of the orders to come from (to help in the planning of logistics and cash on delivery options) and the like.

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