Web Scraping Projects Using Python | How to Scrape Data from a Web


Web Scraping is the process of drawing out information from a website on the world wide web (www) and transforming this information on a web page into structured data for additional analysis.

Web Scraping is also labeled as web data extraction or web harvesting, with an enormous amount of data being generated daily on the internet nowadays web scraping is a proven strategy to aggregate big data sets.

Web Scraping (also called web harvesting, or web data extraction) is an automated process of collecting data from targeted websites. Instead of gathering data manually, web scraping tools are used to automatically acquire a vast amount of information, making the process much faster.

   In this backdrop web, scraping projects are essential for your portfolio. These projects are helpful for fine-tuning essential skills such as your programming insight, mathematical ability, python libraries master, and knowledge of web scraping and algorithms.

Here is the list of five (5) web scraping projects using Python;

  1. Get Financial Data

   Financial data helps an investor to analyze the company’s performance. In this project, you can put your knowledge of web scraping to good use in the finance sector. You have several options to proceed, start by scraping the web for data on the performance of a company’s stock over a specific time frame.

  This data will be useful for the investor who is trying to figure out how different events affect that company’s stock price, based on this, investors will understand the factors that affect the company’s stock price as well as ones they don’t.


  1. Scrape Data of Sports Players

   You will love this project if you are a sports fan. Scrape the data on your favorite sports players and find interesting insights. You can choose any player you like from any popular sport, for instance, basketball, football, baseball, tennis, etc.

  Pick your favorite player and scrape popular websites for a date on them, the best place to start would be the organization that handles their sports, for instance, if you are a tennis fan you can use the website atptour.com after you have scraped all the information needed on your favorite player you can expand this project add more players in your collection.


  1. Analyze Competitors

Competitors analysis is an acritical aspect of marketing, companies need to know what their competitors are doing to remain in business and sustain business growth.

In this project, you will pick an industry of your liking first, for instance, you can start a car company next pick a specific model and analyze the competitors for this model. Begin the model with a small market share as it will have few competitors, a model with a large market share will have several competitors.

Build a scraping tool that analyzes the price of the competing models and also includes the data on the dealer’s offers on the competing models. Analyze at least four competing models.


  1. Automate Repetitive Tasks

In this project, you will use beautiful soap, the simplest python library for web scraping, the goal is to retrieve the title and body paragraph from a published article on any website for example blog posts, news articles, video transcripts, etc.

You will then export all the contents into a txt file, the name of the file can be the same as the title of the article.  One of the chief applications of web scraping is to automate your analysis strategy.

Web scraping bots help to automate data collection once you automate the data collection and you can also automate your analysis strategy hence automation serves as a mainstay of the entire web scarping concept.


  1. Perform Consumer Research

  Consumer research helps companies to understand what their target audience wants whether or not they like their products and the general perception of their product or service.

   Researching potential buyers helps companies greatly they will get to know the products their consumers use and the ones they avoid you start consumer research by gathering data from social media sites and customer review websites, YELP, BBB, TRUSTPILOT, and GRIPEO are the most popular review sites and you can get the necessary data on these sites, Facebook is also a great source of customer reviews.

Pick specific products or brands and research its review online.

Other Recommended for you

.What is Machine Learning in Python? Types of Machine Learning, Steps in Machine Learning

.Pagination Python Flask MySQL

.Creating Table In Database with Flask-SQLAlchemy in a Python.

.Creating Simple Gallery Using Python Flask.