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

Model development and evaluation

In this section, we are going to develop different types of classical ML models and evaluate their performances. We have already discussed in detail the development of models and their evaluation in Chapter 9, Hypothesis Testing and Regression and Chapter 10, Model Development and Evaluation. Here, we will dive directly into implementation.

We are going to use different types of following algorithms and evaluate their performances:

  • Logistic regression
  • Support vector machine
  • K-nearest neighbor classifier
  • Random forest classifier
  • Decision tree classifier
  • Gradient boosting classifier
  • Gaussian Naive Bayes classifier

While going over each classifier in depth is out of the scope of this chapter and book, our aim here is to present how we can continue developing ML algorithms after performing EDA operations on certain databases:

  1. Let's first...