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The Supervised Learning Workshop - Second Edition

By Blaine Bateman , Ashish Ranjan Jha , Benjamin Johnston and 1 more
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About this book
Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Publication date:
February 2020
Publisher
Packt
Pages
532
ISBN
9781800209046

 

2. Exploratory Data Analysis and Visualization

Overview

This chapter takes us through how to perform exploration and analysis on a new dataset. By the end of this chapter, you will be able to explain the importance of data exploration and communicate the summary statistics of a dataset. You will visualize patterns in missing values in data and be able to replace null values appropriately. You will be equipped to identify continuous features, categorical features and visualize distributions of values across individual variables. You will also be able to describe and analyze relationships between different types of variables using correlation and visualizations.

 

Introduction

Say we have a problem statement that involves predicting whether a particular earthquake caused a tsunami. How do we decide what model to use? What do we know about the data we have? Nothing! But if we don't know and understand our data, chances are we'll end up building a model that's not very interpretable or reliable. When it comes to data science, it's important to have a thorough understanding of the data we're dealing with, in order to generate features that are highly informative and, consequently, to build accurate and powerful models. To acquire this understanding, we perform an exploratory analysis of the data to see what the data can tell us about the relationships between the features and the target variable (the value that you are trying to predict using the other variables). Getting to know our data will even help us interpret the model we build and identify ways we can improve its accuracy. The approach we take to achieve this is...

About the Authors
  • Blaine Bateman

    Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry.

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  • Ashish Ranjan Jha

    Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.

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  • Benjamin Johnston

    Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.

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  • Ishita Mathur

    Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.

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