Book Image

Hands-On Exploratory Data Analysis with Python

By : Suresh Kumar Mukhiya, Usman Ahmed
Book Image

Hands-On Exploratory Data Analysis with Python

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

EDA on Wine Quality Data Analysis

We have discussed a plethora of tools and techniques regarding Exploratory Data Analysis (EDA) so far, including how we can import datasets from different sources and how to remove outliers from the dataset, perform data analysis on the dataset, and generate illustrative visualization from such a dataset. In addition to this, we have discussed how we can apply advanced data analysis such as the correlation between variables, regression analysis, and time series analysis, and build advanced models based on such datasets. In this chapter, we are going to apply all of these techniques to the Wine Quality dataset.

The main topics discussed in this chapter include the following:

  • Disclosing the wine quality dataset
  • Analyzing red wine
  • Analyzing white wine
  • Model development and evaluation
  • Further reading
...