• This project involves analyzing data from Embers, a non-profit temp staffing agency that works with marginalized individuals. The goal is to improve the organization's dispatch and assignment process using data-driven insights.
• The project uses a Microsoft SQL database, which is accessed through TempsPlus, a legacy program that lacks analytical capabilities.
• The six tables analyzed include applicants, customers, history, summary, placements, and orders.
• A hypothesis is proposed that profitability can be predicted by analyzing customer attributes.
• A predictive machine learning model that allowed us to predict with significant certainty, the percentage profitability of a specific customer given his address, and historical billing rate is built.