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Agile Machine Learning with DataRobot

You're reading from   Agile Machine Learning with DataRobot Automate each step of the machine learning life cycle, from understanding problems to delivering value

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801076807
Length 344 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Bipin Chadha Bipin Chadha
Author Profile Icon Bipin Chadha
Bipin Chadha
Sylvester Juwe Sylvester Juwe
Author Profile Icon Sylvester Juwe
Sylvester Juwe
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Foundations
2. Chapter 1: What Is DataRobot and Why You Need It? FREE CHAPTER 3. Chapter 2: Machine Learning Basics 4. Chapter 3: Understanding and Defining Business Problems 5. Section 2: Full ML Life Cycle with DataRobot: Concept to Value
6. Chapter 4: Preparing Data for DataRobot 7. Chapter 5: Exploratory Data Analysis with DataRobot 8. Chapter 6: Model Building with DataRobot 9. Chapter 7: Model Understanding and Explainability 10. Chapter 8: Model Scoring and Deployment 11. Section 3: Advanced Topics
12. Chapter 9: Forecasting and Time Series Modeling 13. Chapter 10: Recommender Systems 14. Chapter 11: Working with Geospatial Data, NLP, and Image Processing 15. Chapter 12: DataRobot Python API 16. Chapter 13: Model Governance and MLOps 17. Chapter 14: Conclusion 18. Other Books You May Enjoy

Technical requirements

Most of the analysis and modeling carried out in this chapter requires access to the DataRobot software. Some manipulations were carried out using other tools, including MS Excel. The dataset utilized in this chapter is the House Dataset.

House Dataset

The House Dataset can be accessed at Eman Hamed Ahmed's GitHub account (https://github.com/emanhamed). Each row in this dataset represents a specific house. The initial feature set describes its characteristics, price, zip code, images of the bedroom, bathroom, kitchen, and frontal view. There was no missing data. We went on to develop text descriptions for each house, based on the number of bedrooms, bathrooms, city, country, state, and actual size of the property. Elsewhere, the ZIP codes were converted into latitude and longitude, which were added to the dataset as columns. More information on the base features is provided at the GitHub link and the data is provided in .csv format.

Dataset Citation...

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