Machine learning is fast-tracking the way that pattern recognition and item matching is performed. However, even machine learning techniques are still befuddled by dirty data. In fact, dirty data is an unfortunate inevitability with big datasets. Current solutions for cleaning data usually involve a lot of hours spent scripting, repeating the same steps over and over again on new cohorts, and coming up with solutions that too are specific to apply to other areas of your dataset. Self-serve, code free, autonomous data prep for machine learning can easily solve all these issues.
In this on-demand video, we use Rapid Insight’s Veera Construct to walk through the ways that this easy and affordable data prep solution can cut through the fog of dirty data, effectively increasing the cleanliness of your training data so your machine learning algorithm is more accurate. Specific data cleansing techniques that will be covered are: handling outliers, duplicate records, rule violations, and pattern violations. We’ll wrap up the data prep for machine learning discussion by showing the effectiveness of clean data on a predictive model using Rapid Insight’s Veera Predict.