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Statistics for Machine Learning

You're reading from   Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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Pratap Dangeti Pratap Dangeti
Author Profile Icon Pratap Dangeti
Pratap Dangeti
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Table of Contents (10) Chapters Close

Preface 1. Journey from Statistics to Machine Learning FREE CHAPTER 2. Parallelism of Statistics and Machine Learning 3. Logistic Regression Versus Random Forest 4. Tree-Based Machine Learning Models 5. K-Nearest Neighbors and Naive Bayes 6. Support Vector Machines and Neural Networks 7. Recommendation Engines 8. Unsupervised Learning 9. Reinforcement Learning

HR attrition data example


In this section, we will be using IBM Watson's HR Attrition data (the data has been utilized in the book after taking prior permission from the data administrator) shared in Kaggle datasets under open source license agreement https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset to predict whether employees would attrite or not based on independent explanatory variables:

>>> import pandas as pd 
>>> hrattr_data = pd.read_csv("WA_Fn-UseC_-HR-Employee-Attrition.csv") 
 
>>> print (hrattr_data.head())

There are about 1470 observations and 35 variables in this data, the top five rows are shown here for a quick glance of the variables:

The following code is used to convert Yes or No categories into 1 and 0 for modeling purposes, as scikit-learn does not fit the model on character/categorical variables directly, hence dummy coding is required to be performed for utilizing the variables in models:

>>> hrattr_data['Attrition_ind...
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