These days “machine learning” is a common phrase across a wide range of industries. The need to manage, analyse and report on large scale datasets is no longer restricted to tech companies - sectors from retail to manufacturing to healthcare rely on data scientists to make sense of the huge volumes of data gathered in the execution of their business, and attempt to use it to plan for the future, optimise processes and predict risks and opportunities.
Feature Engineering is a crucial step in the process of building a Machine Learning pipeline, as the features will be used by the algorithm as predictors.
Therefore, it’s advisable to prioritise building and optimizing our features to make sure that we start with a robust data model - which will result in our machine learning model achieving good results.
During the implementation of Data Science Projects, we always face cases where we have to decide on the best method of implementation in order for it to be integrated with the pipeline smoothly. The goal is to achieve the most simplistic implementation as the overall design is always complex. We focus on to simplifying our approaches as much as possible so we can keep track of all the steps and modify them easily with minimum implementation/modification time.
Our journey in Sports Data Analytics started back in 2015 and what a ride it has been! Back in 2015, in the offices of Aspire Academy, we were invited to brainstorm on a performance platform that could serve professional football on one side and individual sports on the other. Thanks to the trust of Aspire Academy an amazing project was brought to life, a new experience for the Clariba team was initiated and my personal journey in sports performance analytics began!
In today’s post, we will narrate you our journey navigating through the ocean of NULLs. This is the story of how we moved forward from the mystical, the initial expectations and assumption, to the practical, an actual problem-solving methodology that became an integral and reusable approach of our data science framework.
Machine learning has become a central topic of interest in the media, thanks to its recent successful applications in creating value in a variety of business scenarios. At Clariba, as experts in predictive analytics, we are active agents of its adoption and democratization, since we have been applying ML in our predictive solutions for a long time. When used wisely and with the proper methodology, Machine Learning techniques can offer an increase in performance to businesses and organizations of all types.