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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

Summary


In this chapter, we finished the initial exploration of the case study data by examining the response variable. Once we became confident in the completeness and correctness of the data set, we became prepared to explore the relation between features and response and build models.

We spent much of this chapter getting used to model fitting in scikit-learn at a technical, coding level, and learning about metrics we could use with the binary classification problem of the case study. When trying different feature sets and different kinds of models, you will need some way to tell if one approach is working better than another. Consequently, you'll need to use model performance metrics.

While accuracy is a familiar and intuitive metric, we learned why it may not give a useful assessment of the performance of a classifier. We learned how to use a majority class null model to tell whether an accuracy rate is truly good, or no better than what would result from prediction of simply the most...

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