By following a series of tactics as you apply EDA, you’re able to build predictive models you can trust. Along the way, you begin to understand your data in the context of the problem you’re trying solve. You also improve the predictive capacity of your models and generate insights that strengthen your business. When your EDA is done well, there are no surprises.
Knowing and understanding each piece of data you have is key to producing meaningful results that have practical application. Exploratory data analysis, often the first step in data analysis and modeling, is an investigative process that gives you a feel for data sets, enabling you to see patterns, spot anomalies, test hypotheses, and check assumptions.
When an information services company discovered its model monitoring process was fraught with inefficiencies and roadblocks, they needed to redesign the platform to support automation, provide confidence in results, and facilitate widespread adoption throughout the organization.
Beyond our credit-meets-technology expertise, part of what differentiates Flying Phase from our competitors is how we attack clients’ problems. Yes, the results stand on their own, but the way in which we get there – iteratively and with a focus on incremental value – plays a huge role in shaping the end result.
Understanding polling and election modeling: How the behind data science and models that were seemingly off-target in 2016 are different in 2020.
Highly regulated industries can better leverage machine learning tools (and create trust in their findings) by developing monitoring systems that are holistic, insightful and actionable.
Summary Solution: Organizations often shortcut internal tech products because they don’t provide direct value to the customer. This post explores where and how to invest your time and efforts. Read a book or article on the topic of building a great product, and the process seems simple enough: Identify a customer base with an underserved […]
Automated machine learning solutions drive better modeling results and a more controlled process in loss forecasting.