Fragmented foreign trade data problem
Foreign trade data often lives in scattered sources, inconsistent formats, and structures that are not directly comparable. That slows research down and makes reporting too manual.
Foreign Trade Data Analytics & Web Scraping Project
Research and product-style work that turns fragmented foreign trade sources into one structured pipeline for analysis and reporting.
Last updated: April 30, 2026
End-to-end analytics pipeline for collecting, cleaning, and visualizing foreign trade data.
Python scraping and analytics pipeline
I created a Python-based collection and cleaning layer, a SQL model for queryable analysis, and a Streamlit interface for decision-support output. The goal was not only scraping, but turning data into a reusable analytical system.
Python, SQL and Streamlit analytics stack
- Python for data collection and transformation
- SQL for queryable structure and filtering logic
- Streamlit for fast reporting interfaces
- Web scraping to standardize fragmented sources
Collection, cleaning and reporting workflow
- Collect data from source systems
- Normalize and clean the raw dataset
- Prepare it for analytical querying in SQL
- Generate reporting and visualization outputs
Decision-support reporting outcome
The repeatable workflow shortens the path from raw data to decision-support reporting. It also establishes a base that can be extended to additional datasets later.
Why repeatable trade analytics matters
This project combines data engineering, analytical thinking, and economic context in one piece of work. It is one of the clearest examples of the site’s data-driven positioning.
Evidence snapshot
The case is positioned around a repeatable data workflow: collection, cleaning, SQL-backed analysis, Streamlit reporting, and a final decision-support output. That makes the page more defensible than a generic project card.
Analytical scope
The value of the project is not only in collecting the data, but in reducing multiple source formats into one reusable logic and making the same model applicable across different periods, sectors, or country slices. That moves the work from a one-off scraping task to a durable analysis foundation.
Sources and methodology note
This case describes the pipeline logic for turning foreign trade data into decision-support output. Official data context should be validated against primary sources such as the Turkish Statistical Institute data portal, and scraped outputs should be rechecked whenever source schemas change.