Book Image

Data Science Projects with Python

By : Stephen Klosterman
Book Image

Data Science Projects with Python

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)
Data Science Projects with Python
Preface

Univariate Feature Selection: What It Does and Doesn't Do


In this chapter, we have learned techniques for going through features one by one to see whether they have predictive power. This is a good first step, and if you already have features that are very predictive of the outcome variable, you may not need to spend much more time considering features before modeling. However, there are drawbacks to univariate feature selection. In particular, it does not consider the interactions between features. For example, what if the credit default rate is very high specifically for people with a certain education level and a certain range of credit limit?

Also, with the methods we used here, only the linear effects of features are captured. If a feature is more predictive when it's undergone some type of transformation, such as a polynomial or logarithmic transformation, or binning (discretization), linear techniques of univariate feature selection may not be effective. Interactions and transformations...