The next stage of the data analytics workflow is to use your data to make inferences and to build predictive models. Some problems require a mathematical approach, where a theoretical model is calibrated using historical data. Others warrant a data-driven approach, where machine learning techniques are used to help a standard model learn from data without being explicitly programmed. Common tasks in this stage of the workflow include:
From the single environment of MATLAB, engineers and analysts have the flexibility to experiment with multiple built-in statistical and mathematical techniques, ranging from hypothesis testing to clustering to predictive modelling. The algorithms are accessible through interactive “apps” or high-level functions, making it easy to test multiple approaches before choosing the best one. Our consulting team has experience with the building of predictive models for forecasting and classification purposes, and can get you started with your model.