From 2fab713c2d5c2020f9310dee37353a0d4184464e Mon Sep 17 00:00:00 2001
From: Dasani Madipalli <55562013+dasani-madipalli@users.noreply.github.com>
Date: Tue, 27 Apr 2021 22:10:18 -0700
Subject: [PATCH] Update README.md

adding the multinomial vs ordinal infographic
---
 2-Regression/4-Logistic/README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index e0f819b04..6269228b4 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -31,7 +31,8 @@ Logistic Regression does not offer the same features as Linear Regression. The f
 
 There are other types of Logistic Regression, including Multinomial and Ordinal. Multinomial involves having more than one categories - "Orange, White, and Striped". Ordinal involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
 
-> Infographic on the difference between multinomial vs. ordinal logistic regression in the context of our pumpkin dataset: there are images here for multinomial https://www.codespeedy.com/multinomial-logistic-regression-in-python/ and for ordinal check this out: http://fa.bianp.net/blog/static/images/2013/ordinal_1.png - you can show the pumpkin sizes in a line - the smaller, the more expensive by the bushel, for example.
+![Multinomial vs Ordinal](https://github.com/jlooper/ml-for-beginners/blob/main/2-Regression/4-Logistic/images/Multinomial_Vs_Ordinal.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ### It's Still Linear