From 5e4041502c9c97bed1b703b13c6ad70ce3ff26d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nu=C3=B1o=20Sempere?= Date: Wed, 9 Oct 2019 20:53:29 +0200 Subject: [PATCH] Create readme.md --- maths-prog/MachineLearningDemystified/readme.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 maths-prog/MachineLearningDemystified/readme.md diff --git a/maths-prog/MachineLearningDemystified/readme.md b/maths-prog/MachineLearningDemystified/readme.md new file mode 100644 index 0000000..7e2bbc7 --- /dev/null +++ b/maths-prog/MachineLearningDemystified/readme.md @@ -0,0 +1,14 @@ +# Machine Learning Demystified + +Several friends encouraged me to apply to a Data Scientist position at ID Insights, an organization I greatly admire, and for a position which I would be passionate about. + +Unfortunately, they require Python, and I'm more of a R programmer. I decided to apply anyways, but before, I familiarized myself throrougly with numpy, pandas and sklearn, three of the most important libraries for machine learning in Python. + +I used a dataset from Kaggle [Health Care Cost Analysis](https://www.kaggle.com/flagma/health-care-cost-analysys-prediction-python/data), referenced as insurance.csv thoughout the code. The reader will also have to change the variable directory to fit their needs. + +Otherwise, the current files in this directory are: + +- [CleaningUpData.py](https://nunosempere.github.io/maths-prog/MachineLearningDemystified/CleaningUpData.py). I couldn't work with the dataset directly, so I tweaked it somewhat. +- [AlgorithmsClassification.py](https://nunosempere.github.io/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. +- [AlgorithmsRegression,py](https://nunosempere.github.io/maths-prog/MachineLearningDemystified/AlgorithmsRegression,py). I try to predict the healthcare costs of a particular individual, using all the features in the dataset. +