Problem

Our team was tasked with the automation of scraping and analysis of export and import data. The problem was that the process was manual, time-consuming, and prone to errors.

We had to find a way to automate the process while ensuring accuracy and efficiency.

Solution

To solve this problem, we developed a web scraper that could extract data from various sources. We used Python for the programming language and BeautifulSoup for the web scraping library.

The extracted data was then analyzed using Pandas, which helped us identify trends, patterns, and insights. We also created visualizations using Matplotlib to help stakeholders understand the data better.

Impact

Reduced manual effort by X%
Increased accuracy by X%
Provided real-time insights into import/export trends

Quote of how incredible of an impact this had.

Team

Team Member #1 - Data Scientist
- Built the web scraper using Python and BeautifulSoup
- Analyzed the data using Pandas and created visualizations using Matplotlib
Team Member #2 - Project Manager
- Oversaw project planning, execution, and delivery within budget and timeline constraints
- Liaised with stakeholders to ensure requirements were met and expectations were managed

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Automating Import/Export Data Scraping & Analysis