I automate SEO data extraction and analysis using Python on VS Code to monitor performance evolution across multiple websites of Karavel. Each week, I collect and compare key metrics, clicks, impressions, CTR, and average position ; for 2024 and 2025 directly from Google Search Console. 

Main tasks : 

• Retrieve and structure weekly GSC data via Python scripts.

• Store all metrics in a SQLite database to build a historical dataset.

• Compare 2025 vs 2024 week by week and month by month.

• Generate automated Excel and Google Sheets dashboards including : interactive charts and filters, conditional formatting for variations and “Top 10 pages” comparisons for each year.

Objectives : 

• Detect early traffic or ranking drops.

• Identify pages with high growth potential.

• Provide managers with clear, data-driven visual insights.

Result :

A precise, year-over-year SEO vision enabling faster reaction and strategic prioritization.

Using SEMrush and Python automation, I collect and analyze both our site data and competitor data to evaluate visibility and keyword positioning within the travel market.

Main tasks :

• Extract keyword metrics (position, volume, competition) Karavel’s websites and their competitors.

• Consolidate all information in a structured database.

• Build multi-sheet analytical reports : my site performance (ranking evolution, volume, CTR), competitor performance (keyword overlaps, strengths / weaknesses) and comparison dashboard.

Objectives :

• Understand keyword opportunities and content gaps.

• Monitor visibility evolution across the market.

• Define targeted SEO actions (quick wins, defensive keywords, long-term growth).

Result : 

A clear competitive map positioning Karavel’s websites against its main rivals and guiding high-impact optimization efforts.

I developed a Predictive Ranking system in Python, integrating SERP API, AI recommendations, and historical tracking to anticipate ranking changes and improve on-page SEO quality. 

Main tasks : 

• Retrieve the Top 100 Google search results for each keyword using the SERP API.

• For every URL, extract key elements : current Google rank, displayed title & meta description, real on-page elements (title, meta description, h1– h6) parsed directly from HTML.

• Compare displayed vs real metadata to detect automatic rewrites made by Google.

• Store all daily SERP data in a SQLite database for historical comparison.

• Analyze daily changes to identify : Websites gaining or losing visibility, key improvement factors (content, structure, keywords).

• Use an integrated AI module to :  recommend optimized titles and meta descriptions, suggest ideal lengths and keyword usage, propose rewritten metadata to improve CTR and relevance.

Objectives : 

• Anticipate algorithmic updates and competitive movements before they impact rankings.

• Maintain full alignment between real page content and what Google displays in the SERP.

• Continuously improve metadata quality through AI-driven optimization.

Result : 

A predictive and intelligent SEO framework that transforms static performance tracking into a proactive, insight-driven system, capable of identifying ranking shifts and improvement opportunities in real time.