You're reading from The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800566927

Length 824 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Science

Authors (5):

Robert Thas John

Thomas Joseph

Anthony So

Dr. Samuel Asare

Andrew Worsley

+1 more

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Table of Contents (16) Chapters

Preface

1. Introduction to Data Science in Python

2. Regression FREE CHAPTER

3. Binary Classification

4. Multiclass Classification with RandomForest

5. Performing Your First Cluster Analysis

6. How to Assess Performance

7. The Generalization of Machine Learning Models

8. Hyperparameter Tuning

9. Interpreting a Machine Learning Model

10. Analyzing a Dataset

11. Data Preparation

12. Feature Engineering

Introduction

13. Imbalanced Datasets

14. Dimensionality Reduction

15. Ensemble Learning

10. Analyzing a Dataset

Overview

By the end of this chapter, you will be able to explain the key steps involved in performing exploratory data analysis; identify the types of data contained in the dataset; summarize the dataset and at a detailed level for each variable; visualize the data distribution in each column; find relationships between variables and analyze missing values and outliers for each variable

This chapter will introduce you to the art of performing exploratory data analysis and visualizing the data in order to identify quality issues, potential data transformations, and interesting patterns.

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You're reading from The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

Table of Contents (16) Chapters

10. Analyzing a Dataset

Authors (5)

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You're reading from The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

Table of Contents (16) Chapters

10. Analyzing a Dataset

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Authors (5)

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