From a6a746d7b6761e8df515fb1fbbf4baf078acd761 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nu=C3=B1o=20Sempere?= Date: Sat, 12 Oct 2019 20:01:20 +0200 Subject: [PATCH] Update readme.md --- maths-prog/MachineLearningDemystified/readme.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/maths-prog/MachineLearningDemystified/readme.md b/maths-prog/MachineLearningDemystified/readme.md index 9d89ee3..3462615 100644 --- a/maths-prog/MachineLearningDemystified/readme.md +++ b/maths-prog/MachineLearningDemystified/readme.md @@ -22,7 +22,20 @@ The exercise proved highly, highly instructive, because sklearn is really easy t It came as a surprise to me that understanding and implementing the algorithm were two completely different steps. -## Some visualizations and three curious findings about the dataset. +### All KMeans plots produced by the code. + +![](KMeans-age.png) +![](KMeans-bmi.png) +![](KMeans-charges.png) +![](KMeans-children.png) +![](KMeans-cluster.png) +![](KMeans-region_numeric.png) +![](KMeans-sex_numeric.png) +![](KMeans-smoking_numeric.png) + +## Three highlights findings about the dataset. + +The code produces the above visualizations for all algorithms. Here are three highlights. - Those who have 4+ children get charged less by insurance, and smoke less. ![](children-charge-smoking.png)