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Data Science Projects with Python

Data Science Projects with Python

By : Stephen Klosterman
4.3 (17)
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Data Science Projects with Python

Data Science Projects with Python

4.3 (17)
By: Stephen Klosterman

Overview of this book

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions. By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.
Table of Contents (9 chapters)
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Data Science Projects with Python
Preface

Chapter 3. Details of Logistic Regression and Feature Exploration

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Write list comprehensions in Python

  • Describe the workings of logistic regression

  • Formulate the sigmoid and logit versions of logistic regression

  • Utilize univariate feature selection to find important features

  • Customize plots with the Matplotlib API

  • Characterize the linear decision boundary of a logistic regression

Note

This chapter presents the basics of logistic regression along with various other methods for examining the relationship between features and a response variable.

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