After careful analysis of the current systems and practices our team of data experts designed a plan to build a predictive model which requires less information intake and give accurate predictions within a few minutes.
- The team first connected the data sources and performed exploratory data analysis to identify the projects on which the machine learning algorithm needs to be trained.
- Next, our data scientists consulted with the stakeholders and identified and finalized the project parameters that could be the best drivers for the forecasts.
- After extracting and finalizing the data, the team then built a prediction algorithm along with the ETL pipeline to establish the data connection.
- The model is then trained on the data such that it produced accurate forecasts with less granularity by capturing the variance associated with specific complexity of a project, rather than a particular molecule or business category.
- The team then deployed the model and compared the model’s predictions with the actuals, and were able to achieve 80% accuracy, and the results were out within a few minutes.