This project began with a chaotic cost dataset containing over one hundred construction projects. The raw data presented numerous challenges including multiple currency formats, text based numbers with "USD" and "EUR" prefixes, comma separated values, estimated figures labeled "est.", negative material costs, and completely blank fields. Some entries had costs in different units without conversion, while others contained explanatory notes mixed with numerical data.

The cleaning process transformed this inconsistent information into a structured analysis ready format. All currency values were standardized to numeric US dollar amounts, text entries were converted to proper numbers, and missing values were addressed. New calculated fields were created including total project cost, gross profit, and profit margin percentage. A variance flag was added to identify projects with margins below 15 percent or negative returns.

The resulting analysis revealed critical business insights. While the median profit margin stood at a healthy 22 percent, nearly half of all projects triggered the variance flag requiring review. Electrical work showed troubling inconsistency delivering both the highest and lowest margins. Material and labor costs dominated expenses comprising over 92 percent of total costs combined, highlighting where focused cost control could dramatically improve overall profitability.

This project transformed thousands of messy transaction records with inconsistent dates, currencies, and misspellings into a clean, analysis ready dataset. After standardizing formats and correcting errors, a comprehensive dashboard was built to monitor key performance metrics. The dashboard reveals balanced revenue across all regions, Ground shipping as the most used method, and a healthy mix of top selling products. A negative profit order was also identified, highlighting the importance of data quality for operational decision making.