Practical Machine Learning Tutorial: Part.1 (Exploratory Data Analysis) | by Ryan A. Mardani | Oct, 2020

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Predicted PE in effectively ALEXANDER D reveals the traditional vary and variation. Prediction accuracy is 77%.

1–2–2 Characteristic Extraction

Having a restricted set of options on this dataset can lead us to consider extracting some information from the present dataset. First, we are able to convert the formation categorical information into numeric information. Our background data will help us to guess that some facies are presumably current extra in a selected formation slightly than others. We are able to use the LabelEncoder operate:

We transformed formation class information into numeric to make use of as a predictor and added 1 to start out predictor from 1 as an alternative of zero. To see if new function extraction would help prediction enchancment, we should always outline a baseline mannequin then evaluate it with the extracted function mannequin.

Baseline Mannequin Efficiency

For simplicity, we are going to use a logistic regression classifier as a baseline mannequin and can study mannequin efficiency with a cross-validation idea. Knowledge can be cut up into 10 subgroups and the method can be repeated Three occasions.

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