Data Science Projects with Python - Info and Reading Options
A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn
By Stephen Klosterman
"Data Science Projects with Python" was published by de Gruyter GmbH, Walter in 2019 - Place of publication not identified, it has 1 pages and the language of the book is English.
“Data Science Projects with Python” Metadata:
- Title: ➤ Data Science Projects with Python
- Author: Stephen Klosterman
- Language: English
- Number of Pages: 1
- Publisher: de Gruyter GmbH, Walter
- Publish Date: 2019
- Publish Location: ➤ Place of publication not identified
“Data Science Projects with Python” Subjects and Themes:
- Subjects: ➤ Mathematics - Python (Computer program language) - Machine learning - Data mining - Information visualization - Data Mining - Python (Langage de programmation) - Apprentissage automatique - Exploration de données (Informatique) - Visualisation de l'information
Edition Specifications:
- Pagination: 374
Edition Identifiers:
- The Open Library ID: OL51790210M - OL25343784W
- ISBN-13: 9781838552602 - 9781838551025
- All ISBNs: 9781838552602 - 9781838551025
AI-generated Review of “Data Science Projects with Python”:
"Data Science Projects with Python" Description:
Open Data:
Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You'll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you'll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data
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