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Data Science Projects with Python

You're reading from  Data Science Projects with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781838551025
Pages 374 pages
Edition 1st Edition
Languages
Author (1):
Stephen Klosterman Stephen Klosterman
Profile icon Stephen Klosterman
Toc

Table of Contents (9) Chapters close

Data Science Projects with Python
Preface
1. Data Exploration and Cleaning 2. Introduction toScikit-Learn and Model Evaluation 3. Details of Logistic Regression and Feature Exploration 4. The Bias-Variance Trade-off 5. Decision Trees and Random Forests 6. Imputation of Missing Data, Financial Analysis, and Delivery to Client Appendix

Introduction to Scikit-Learn


While pandas will save you a lot of time in loading, examining, and cleaning data, the machine learning algorithms that will enable you to do predictive modeling are located in other packages. We consider scikit-learn to be the premier machine learning package for Python, outside of deep learning. While it's impossible for any one package to offer "everything," scikit-learn comes pretty close in terms of accommodating a wide range of approaches for classification and regression, and unsupervised learning. That being said, a few other packages you should also be aware of are as follows:

SciPy:

  • Most of the packages we've used so far are actually part of the SciPy ecosystem.

  • SciPy itself offers lightweight functions for classical approaches such as linear regression and linear programming.

StatsModels:

  • More oriented toward statistics and more comfortable for users familiar with R

  • Can get p-values and confidence intervals on regression coefficients

  • Capability for time series...

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