Siemens PLM


  • Research Name Sales Prediction Project
  • Client Siemens PLM

Project overview

Siemens PLM aims to maintain its position as a software industry leader by leveraging customer and business data through data science projects. The projects are set to achieve four goals: opportunity outcome prediction (Win/Loss), revenue forecasting, market analysis and market expansion.

In this project, the team was provided with three datasets for analysis (1) the opportunity dataset, (2) revenue and (3) the expansion candidate dataset. EDA was done on some of the data feature columns (Company Size Segment, Zones, Physical Country, etc.) to understand the trends and business scope. The EDA results were used in the modeling to help choose and engineer important feature columns. The analysis was performed with two methods : XGBoost and neural network. Insights from XGBoost and neural network models were extracted using SHAP and LIME respectively and the two approaches were generally in agreement with the insights produced.

Student team

Harjeet Singh Monga, Jamal Warida, Nicholas Carruthers, Randeep Singh, and Shruti Singh