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maths-prog/MachineLearningDemystified/readme.md
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@ -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: 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. - [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. - [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 ## Thoughts on sklearn