Unsupervised machine learning deals with unlabeled data. This type of learning can discover all kinds of unknown patterns in the data and can facilitate useful categorization. Consider a scenario where patients use an online web application to learn about a disease, learn about their symptoms, and manage their illness. Such web applications that provide psychoeducation about certain diseases are referred to as Internet-Delivered Treatments (IDT). Imagine several thousand patients accessing the website at different timestamps of the day, learning about their illness, and all their activities are being logged into our database. When we analyze these log files and plot them using a scatter plot, we find a large group of patients who are accessing the website in the afternoon and a large chunk accessing the website in the evening. Some other patients...
Hands-On Exploratory Data Analysis with Python
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Hands-On Exploratory Data Analysis with Python
By:
Overview of this book
Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization.
You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence.
By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.
Table of Contents (17 chapters)
Preface
Section 1: The Fundamentals of EDA
Free Chapter
Exploratory Data Analysis Fundamentals
Visual Aids for EDA
EDA with Personal Email
Data Transformation
Section 2: Descriptive Statistics
Descriptive Statistics
Grouping Datasets
Correlation
Time Series Analysis
Section 3: Model Development and Evaluation
Hypothesis Testing and Regression
Model Development and Evaluation
EDA on Wine Quality Data Analysis
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