Financial Services Company

Development and deployment of a customized forecasting model.

The goal, to conduct a feasibility study to support investment decisions by utilizing a forecasting model. The question raised: can you draw conclusions on the success of companies today by using different KPI’s from the past.

The solution entails applying Microsoft Office techniques. A supplementary tool for Excel and VBA was required based on the specifications drafted. The forecasting model needs to be built using machine learning techniques. In addition, optimization of the models needs to be possible by applying automated tests from different model parameters. Based on the requirements the decision was made to use Python, as the whole process involved data cleansing, analyzing, building the forecasting model and using the tool for visualization of the results.

Two built-in Python extensions proved to be invaluable during the project: Pandas and Scikit-learn:

1. The Pandas extension delivers pre-defined data structures and functions for data cleansing and analysis
2. The Scikit-learn extension was needed for the forecasting model and included an optimization functionality

Development and deployment of the forecasting tool realized in phase 2 of the project.