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Applied Supervised Learning with Python
Applied Supervised Learning with Python

Applied Supervised Learning with Python: Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

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Profile Icon Benjamin Johnston Profile Icon Mathur
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Arrow left icon
Profile Icon Benjamin Johnston Profile Icon Mathur
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$43.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Apr 2019 404 pages 1st Edition
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Paperback
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Applied Supervised Learning with Python

Chapter 2. Exploratory Data Analysis and Visualization

Note

Learning Objectives

By the end of the chapter, you will be able to:

  • Explain the importance of data exploration and communicate the summary statistics of a dataset

  • Visualize patterns in missing values in data and be able to replace null values appropriately

  • Identify continuous features and categorical features

  • Visualize distributions of values across individual variables

  • Describe and analyze relationships between different types of variables using correlation and visualizations

Note

This chapter takes us through how to perform exploration and analysis on a new dataset.

Introduction


Say we have a problem statement that involves predicting whether a particular earthquake caused a tsunami or not. 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.

In order to gain this understanding, we perform an exploratory analysis on the data to see what the data can tell us about the relationships between the features and the target variable. 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 to allow the data to reveal its structure or model, which helps gain some new, often unsuspected...

Summary Statistics and Central Values


In order to find out what our data really looks like, we use a technique known as data profiling. This is defined as the process of examining the data available from an existing information source (for example, a database or a file) and collecting statistics or informative summaries about that data. The goal is to make sure that you understand your data well and are able to identify any challenges that the data may pose early on in the project, which is done by summarizing the dataset and assessing its structure, content, and quality.

Data profiling includes collecting descriptive statistics and data types. Here are a few commands that are commonly used to get a summary of a dataset:

  • data.info(): This command tells us how many non-null values there are there in each column, along with the data type of the values (non-numeric types are represented as object types).

  • data.describe(): This gives us basic summary statistics for all the numerical columns in the...

Missing Values


When there is no value (that is, a null value) recorded for a particular feature in a data point, we say the data is missing. Having missing values in a real dataset is inevitable; no dataset is ever perfect. However, it is important to understand why the data is missing, and if there is a factor that has affected the loss of data. Appreciating and recognizing this allows us to handle the remaining data in an appropriate manner. For example, if the data is missing randomly, then it's highly likely that the remaining data is still representative of the population. However, if the missing data is not random in nature and we assume that it is, it could bias our analysis and subsequent modeling.

Let's look at the common reasons (or mechanisms) for missing data:

  • Missing Completely at Random (MCAR): Values in a dataset are said to be MCAR if there is no correlation whatsoever between the value missing and any other recorded variable or external parameter. This means that the remaining...

Distribution of Values


In this section, we'll look at how individual variables behave—what kind of values they take, what the distribution across those values is, and how those distributions can be represented visually.

Target Variable

The target variable can either have values that are continuous (in the case of a regression problem) or discrete (as in the case of a classification problem). The problem statement we're looking at in this chapter involves predicting whether or not an earthquake caused a tsunami, that is, the flag_tsunami variable, which takes on two discrete values only—making it a classification problem.

One way of visualizing how many earthquakes resulted in tsunamis and how many didn't is a bar chart, where each bar represents a single discrete value of the variable, and the height of the bars is equal to the count of the data points having the corresponding discrete value. This gives us a good comparison of the absolute counts of each category.

Exercise 16: Plotting a Bar...

Relationships within the Data


There are two reasons why it is important to find relationships between variables in the data:

  • Finding which features are potentially important can be deemed essential, since finding ones that have a strong relationship with the target variable will aid in the feature selection process.

  • Finding relationships between different features themselves can be useful, since variables in the dataset are usually never completely independent of every other variable and this can affect our modeling in a number of ways.

Now, there are a number of ways we can visualize these relationships, and this really depends on the types of variable we are trying to find the relationship between, and how many we are considering as part of the equation or comparison.

Relationship between Two Continuous Variables

To find a relationship between two continuous variables is basically to see how one varies as the value of the other is increased. The most common way to visualize this would be using...

Summary


In this chapter, we started by talking about why data exploration is an important part of the modeling process and how it can help in not only preprocessing the dataset for the modeling process, but also help us engineer informative features and improve model accuracy. This chapter focused on not only gaining a basic overview of the dataset and its features, but also gaining insights by creating visualizations that combine several features.

We looked at how to find the summary statistics of a dataset using core functionality from pandas. We looked at how to find missing values and talked about why they're important, while learning how to use the Missingno library to analyze them and the pandas and scikit-learn libraries to impute the missing values.

Then, we looked at how to study the univariate distributions of variables in the dataset and visualize them for both categorical and continuous variables using bar charts, pie charts, and histograms. Lastly, we learned how to explore relationships...

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Key benefits

  • Understand various machine learning concepts with real-world examples
  • Implement a supervised machine learning pipeline from data ingestion to validation
  • Gain insights into how you can use machine learning in everyday life

Description

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Who is this book for?

Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.

What you will learn

  • Understand the concept of supervised learning and its applications
  • Implement common supervised learning algorithms using machine learning Python libraries
  • Validate models using the k-fold technique
  • Build your models with decision trees to get results effortlessly
  • Use ensemble modeling techniques to improve the performance of your model
  • Apply a variety of metrics to compare machine learning models
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Table of Contents

6 Chapters
Python Machine Learning Toolkit Chevron down icon Chevron up icon
Exploratory Data Analysis and Visualization Chevron down icon Chevron up icon
Regression Analysis Chevron down icon Chevron up icon
Classification Chevron down icon Chevron up icon
Ensemble Modeling Chevron down icon Chevron up icon
Model Evaluation Chevron down icon Chevron up icon

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AS Sep 11, 2019
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Wonderful introduction to supervised machine learning. The code provided was also super helpful!
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