From ec739c187c3c1126899e2060bb36250681453c95 Mon Sep 17 00:00:00 2001
From: Adrian Silich <97989721+asilich123@users.noreply.github.com>
Date: Fri, 28 Apr 2023 19:09:46 -0700
Subject: [PATCH] Update README.md grammar and wording

Updated the grammar of a few words in the paragraph found on line 111.
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 2-Regression/4-Logistic/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 01790853..b3149b51 100644
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@@ -108,7 +108,7 @@ Seaborn offers some neat ways to visualize your data. For example, you can compa
     ✅ Given this categorical plot, what are some interesting explorations you can envision?
 
 ### Data pre-processing: feature and label encoding
-Our pumpkins dataset contains string values for all its columns. Working with categorical data is intuitive for humans but not for machines. Machine learning algorithms work well with numbers. There's why encoding is a very important step in the data pre-processing phase, since it enables to turn categorical data into numerical data, without losing any information. A good encoding leads to build a good model.
+Our pumpkins dataset contains string values for all its columns. Working with categorical data is intuitive for humans but not for machines. Machine learning algorithms work well with numbers. That's why encoding is a very important step in the data pre-processing phase, since it enables us to turn categorical data into numerical data, without losing any information. Good encoding leads to building a good model.
 
 For feature encoding there are two main types of encoders: