From 8778d18d47453040fe7a6dd4df39982e0c447e18 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nu=C3=B1o=20Sempere?= Date: Wed, 9 Oct 2019 21:08:04 +0200 Subject: [PATCH] Update readme.md --- maths-prog/MachineLearningDemystified/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/maths-prog/MachineLearningDemystified/readme.md b/maths-prog/MachineLearningDemystified/readme.md index c86e8ee..c8d9e6b 100644 --- a/maths-prog/MachineLearningDemystified/readme.md +++ b/maths-prog/MachineLearningDemystified/readme.md @@ -7,7 +7,7 @@ I used a dataset from Kaggle: [Health Care Cost Analysis](https://www.kaggle.com Otherwise, the current files in this directory are: - [CleaningUpData.py](https://github.com/NunoSempere/nunosempere.github.io/blob/master/maths-prog/MachineLearningDemystified/CleaningUpData.py). I couldn't work with the dataset directly, so I tweaked it somewhat. -- [AlgorithmsClassification.py](https://github.com/NunoSempere/nunosempere.github.io/blob/master/maths-prog/MachineLearningDemystified/AlgorithmsClassification.py). As a first exercise, I try to predict whether the medical bills of a particular individual are higher than the mean of the dataset. Some algorithms, like Naïve Bayes, are not really suitable for regression, but are great for predicting classes. +- [AlgorithmsClassification.py](https://github.com/NunoSempere/nunosempere.github.io/blob/master/maths-prog/MachineLearningDemystified/AlgorithmsClassification.py). As a first exercise, I try to predict whether the medical bills of a particular individual are higher than the mean of the dataset. Some algorithms, like Naïve Bayes, are not really suitable for regression, but are great for predicting classes. After the first couple of examples, I wrapp everything in a function. - [AlgorithmsRegression,py](https://github.com/NunoSempere/nunosempere.github.io/blob/master/maths-prog/MachineLearningDemystified/AlgorithmsRegression,py). I try to predict the healthcare costs of a particular individual, using all the features in the dataset. ## Thoughts on sklearn