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Hands-On Exploratory Data Analysis with Python

Hands-On Exploratory Data Analysis with Python

By : Suresh Kumar Mukhiya, Usman Ahmed
2.5 (2)
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Hands-On Exploratory Data Analysis with Python

Hands-On Exploratory Data Analysis with Python

2.5 (2)
By: Suresh Kumar Mukhiya, Usman Ahmed

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)
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1
Section 1: The Fundamentals of EDA
6
Section 2: Descriptive Statistics
11
Section 3: Model Development and Evaluation

Understanding regression

We use correlation in statistical terms to denote the association between two quantitative variables. Note that we have used the term quantitative variables. This should be meaningful to you. If not, we suggest you pause here and go through Chapter 1, Exploratory Data Analysis Fundamentals.

When it comes to quantitative variables and correlation, we also assume that the relationship is linear, that is, one variable increases or decreases by a fixed amount when there is an increase or decrease in another variable. To determine a similar relationship, there is the other method that's often used in these situations, regression, which includes determining the best straight line for the relationship. A simple equation, called the regression equation, can represent the relation:

Let's examine this formula:

  • Y = The dependent variable (the variable...
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Hands-On Exploratory Data Analysis with Python
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