Technical SEO Project

SEO Broken Link Checker

A Python automation tool that checks internal links before publishing, helping identify broken pages and improve SEO hygiene while reducing manual quality assurance.

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Technical SEO Project

SEO Broken Link Checker

Automating Internal Link Validation Before Publishing

Python Beautiful Soup Requests Pandas HTML CSV Reporting
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Project Overview

Before publishing new pages, I wanted a simple way to verify that every internal link was working correctly. Manually checking dozens of links is repetitive and easy to miss, especially as websites grow.

I built this Python automation tool to scan a local HTML page, validate each internal link, identify broken pages, and generate a CSV report that can be reviewed before publication.

Questions I Wanted to Explore

  • Which internal links return successful responses?
  • Which pages are broken or missing?
  • Can link validation be automated before publishing?
  • Can a simple report reduce manual quality assurance?

Dataset

The project analyses the HTML source of a local webpage and extracts all internal hyperlinks.

Each link is validated using HTTP requests, and the results are compiled into a CSV report containing the page URL, HTTP status code, and validation result.

Approach

  1. Load a local HTML page using Beautiful Soup.
  2. Extract all hyperlinks from the document.
  3. Filter internal links.
  4. Build complete URLs for validation.
  5. Send HTTP requests to each page.
  6. Record the HTTP status code and validation result.
  7. Export the results as a CSV report.

Key Insights

Automated Link Validation

The tool automatically validates every internal link on the page, removing the need to manually open each destination before publishing.

Improved SEO Hygiene

Broken internal links can negatively affect user experience and search engine crawling. Identifying these issues before publication helps maintain healthier website navigation.

Reusable Marketing Workflow

The project demonstrates how Python can automate repetitive marketing quality assurance tasks and generate reports that are easy to review before content goes live.

What I Learned

This project showed me how Python can automate repetitive SEO tasks that are normally completed manually. Building the tool also strengthened my understanding of HTML parsing, HTTP status codes, and working with external Python libraries.

More importantly, it reinforced how small automation tools can improve marketing workflows by saving time, reducing human error, and creating repeatable quality assurance processes.

Explore the Project

The source code, documentation, and sample output are available on GitHub.

View on GitHub
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