Abstract :

In The Era Of Digital Transformation, Vast Amounts Of Data Are Generated Continuously Across The Internet, Making Web Scraping A Powerful Technique For Extracting Useful Information From Websites. This Project Presents A Comprehensive System For Performing Data Analysis Through Web Scraping Using Python. The Proposed System Leverages Pythonbased Tools Such As BeautifulSoup And Requests To Extract Structured And Unstructured Data From Web Pages, Particularly Focusing On Job Portals And E-commerce Platforms. The Extracted Data Is Further Processed, Stored, And Analyzed To Derive Meaningful Insights.The System Is Implemented Using The Django Framework, Which Provides A Robust Backend For Managing User Interactions, Authentication, And Database Operations. The Application Allows Users To Register, Log In, And Perform Web Scraping Tasks Dynamically. The Scraped Data, Such As Job Listings Or Product Details, Is Stored In A Structured Format Using Models And Can Be Exported Into CSV Files For Further Analysis. The System Also Supports Uploading CSV Datasets And Storing Them Into A Database, Enabling Users To Perform Data-driven Operations Efficiently.One Of The Key Features Of The System Is Its Ability To Automate The Data Extraction Process. By Using HTTP Requests And HTML Parsing, The System Collects Relevant Information Such As Job Titles, Company Names, Ratings, Reviews, And Pricing Details. This Automation Reduces Manual Effort And Improves Data Accuracy. Additionally, The System Incorporates Filtering And Search Functionalities That Allow Users To Query Specific Data Based On Their Requirements.The Proposed Framework Also Supports Data Visualization And Analysis Using Python Libraries Such As Pandas. This Enables Users To Interpret Trends And Patterns From The Collected Data. The Integration Of Web Scraping With Data Analytics Provides A Powerful Tool For Decisionmaking In Domains Such As Recruitment, Market Analysis, And Business Intelligence.Overall, The System Demonstrates How Web Scraping Combined With Data Analysis Techniques Can Be Used To Build Intelligent Applications. It Provides A Scalable And Efficient Solution For Extracting And Analyzing Large Volumes Of Web Data. The Proposed Approach Is Cost-effective, Flexible, And Can Be Extended To Various Domains Where Real-time Data Extraction And Analysis Are Required.

Published:

07-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1535-1543

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite