Top Agent Performance - Fluid AI

PythonSQLFrontier WorkbenchIBM Watson

This project's goal is to forecast agent performance for the next quarter so that the company may make data-driven decisions to meet future sales targets. The company will be able to identify probable defaulters or overperformers and allocate additional resources accordingly.

I was working in collaboration with my team lead Yash Vedi on this project. In addition to development, there were a lot of back to back calls with clients on understanding the sort of data and their outcome expectation.

Some of the key development phases that were there are :

  • ETL tasks with data from multiple sources
  • Data exploration
  • Building a timeline series of data
  • Different scaler implementation for handling the outlier in data
  • Model building with a lot of hyperparameter tuning
  • Model Validation with out of time data
  • Pipelines to push data to client system
  • Reports for clients to view at Frontier Workbench