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Mastering pandas. - Second Edition

You're reading from  Mastering pandas. - Second Edition

Product type Book
Published in Oct 2019
Publisher
ISBN-13 9781789343236
Pages 674 pages
Edition 2nd Edition
Languages
Author (1):
Ashish Kumar Ashish Kumar
Profile icon Ashish Kumar

Table of Contents (21) Chapters

Preface Section 1: Overview of Data Analysis and pandas
Introduction to pandas and Data Analysis Installation of pandas and Supporting Software Section 2: Data Structures and I/O in pandas
Using NumPy and Data Structures with pandas I/Os of Different Data Formats with pandas Section 3: Mastering Different Data Operations in pandas
Indexing and Selecting in pandas Grouping, Merging, and Reshaping Data in pandas Special Data Operations in pandas Time Series and Plotting Using Matplotlib Section 4: Going a Step Beyond with pandas
Making Powerful Reports In Jupyter Using pandas A Tour of Statistics with pandas and NumPy A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates Data Case Studies Using pandas The pandas Library Architecture pandas Compared with Other Tools A Brief Tour of Machine Learning Other Books You May Enjoy

The scikit-learn ML/classifier interface

We'll be diving into the basic principles of machine learning and demonstrate the use of these principles via the scikit-learn basic API.

The scikit-learn library has an estimator interface. We illustrate it by using a linear regression model. For example, consider the following:

    In [3]: from sklearn.linear_model import LinearRegression
  

The estimator interface is instantiated to create a model, which is a linear regression model in this case:

    In [4]: model = LinearRegression(normalize=True)   
    In [6]: print model
        LinearRegression(copy_X=True, fit_intercept=True, normalize=True)
  

Here, we specify normalize=True, indicating that the x-values will be normalized before regression. Hyperparameters (estimator parameters) are passed on as arguments in the model creation. This is an example of creating a model with...

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