This project focuses on analyzing global tax regulations and agricultural product pricing. By collecting historical data on taxes and local prices from various countries, the project aims to understand how tax laws influence buyer decisions in the agricultural industry. The goal is to empower companies to make data-driven decisions on whether to prioritize local or imported agricultural products based on financial impacts.

What You'll Do:

• Collect and analyze tax data on agricultural products across different countries, focusing on historical tax data.
• Set up workflows and automated scripts to streamline data collection, while ensuring data quality and consistency through validation scripts.
• Create a dashboard to visualize the pricing trends of local agricultural products versus imported ones, including costs added at incoterms.
• Work within a team to interpret the data findings and continuously improve data validation and analysis workflows.

Technologies:

• Python & Dagster: For data scraping, data processing, and building the predictive model (using libraries like pandas, numpy, and scikit-learn).
• SQL: For managing and querying the necessary data.
• Power BI & Streamlit: For creating dashboards and visualizations to present the analysis results.