From 1b666b6fe8d3537162b6e353e75136e23c9ddd57 Mon Sep 17 00:00:00 2001
From: vim-kakali <1799225723@qq.com>
Date: Mon, 1 Aug 2016 21:33:36 +0800
Subject: [PATCH] vim-kakali translating

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 ...ing a data science portfolio - Machine learning project.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/sources/team_test/part 4 - Building a data science portfolio - Machine learning project.md b/sources/team_test/part 4 - Building a data science portfolio - Machine learning project.md
index 66db19bc54..a9af49b188 100644
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@@ -1,3 +1,7 @@
+vim-kakali translating
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 ### Computing values from the performance data
 
 The next step we’ll take is to calculate some values from processed/Performance.txt. All we want to do is to predict whether or not a property is foreclosed on. To figure this out, we just need to check if the performance data associated with a loan ever has a foreclosure_date. If foreclosure_date is None, then the property was never foreclosed on. In order to avoid including loans with little performance history in our sample, we’ll also want to count up how many rows exist in the performance file for each loan. This will let us filter loans without much performance history from our training data.