Zest Insights

6 Methods for Dealing with Missing Data

Posted by Bojan Tunguz on Mar 5, 2018 10:19:37 AM

Most datasets in the real world contain missing data, incorrectly encoded data, or other data that cannot be used for modeling. Sometimes missing data is just that — missing. There is no actual value in a given field; for example: an empty string in a csv file. Other times data is encoded with a special keyword or a string. Some common encodings are NA, N/A, None, and -1. Before you can use data with missing data fields, you need to transform those fields so they can be used for analysis and modeling. There are machine learning algorithms and packages that can automatically detect and deal with missing data, but it’s still a good practice to transform that data manually.

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AI Will Take Over Credit Decisions… And That’s Good

Posted by Douglas Merrill on Feb 22, 2018 1:46:36 PM

When the first Model T drove out of the factory in 1908, it was pretty clear something big was happening. If you had asked someone then whether cars would replace horses for transportation by 2010, even without the benefit of hindsight the answer would be "Duhhh." Like the assembly line before it, artificial intelligence (AI) will transform nearly every industry by making powerful technology ubiquitous. Yet the talking heads are still asking: will AI take over heavily regulated high stakes tasks like credit underwriting? (Spoiler: yes, it will, and it already is.)

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Machine Learning in Credit. Today.

Machine learning can be explainable, useable, incredible.

Zest Insights brings you the latest in machine learning and credit underwriting. We believe in a world where everyone has access to fair and transparent credit, and machine learning is the way to get us there. Machine learning is changing every industry, and now is the time for lenders to bring this new predictive power into their businesses. 

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