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ML-Blood Sugar Level Example

You can find the source for the example here:

Here we used CLS-Luigi to create a ML Pipeline to predict the blood sugar level of some patients. The Steps are very easy to understand. We start by loading the dataset from Scikit-Learn, then we split it into 2 subsets for training and testing.

The first variation point is the scaling method. We introduce 2 concrete implementation, namely `RobustScaler` & `MinMaxScaler`. After scaling we have our second variation point which is the regression model. Here we have also 2 concrete implementation, namely `LinearRegression` & `LassoLars`.

Lastly we evaluate each regression model by predicting the testing target and calculating the root mean squared error.

Requirements

The example contains a requirements.txt file. To experiment with the example, you can set up your environment by executing the following command:

# cd into the ml blood sugar level example folder
pip install -r requirements.txt

Static Visualization

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Dynamic Visualization

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