Four Common Mistakes in Machine Learning Projects

As machine learning and data analytics experts, we have considerable experience in developing and implementing machine learning projects with businesses across different industries, including manufacturing, telecoms, financial services and retail. As such, we're all too familiar with the types of mistakes that are typically made during machine learning projects – and not only by beginners. It is essential for businesses to learn what not to do in order to focus their efforts on solving real issues and build solutions capable of delivering ROI. Drawing on our own examples, as well as others that we have come across, we are able to highlight the costly mistakes that could have otherwise been avoided. Here are the top four typical mistakes made by businesses during machine learning projects.