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Applied Multivariate Analysis with Python

Step by Step Guide to Perform Multivariate Analysis with Python

A.J. García

This book takes you step by step through the process of preparing the data and producing multivariate analysis on it with Python.

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About

About

About the Book

In today's world, Data is everywhere and it is getting easier to produce it , collect it and perform multiple analysis.

This book is designed as a step by step guide on how to perform multivariate analysis with Python. It focuses on PCA (Principal Components Analysis) and LDA (Linear Discriminant Analysis).

The book's main idea is to focus on the step by step implementation. It is not necessary to have an advanced knowledge of Python but it is recommended to be familiar with the basics of programming, basics of Python, Statistics, Math and some Multivariate Methods.

The chapters in the book cover the following:

  • Setting up the python environment.
  • Reading and Plotting Multivariate Data.
  • Calculating Summary Statistics for Multivariate Data
  • Principal Component Analysis.
  • Linear Discriminant Analysis.

Check out other books from the author:

Applied Multivariate Analysis with R

Data Science Workflow for Beginners

DevOPs

Javascript Snippets

Appwrite Up and Running

Front End Developer Interview Questions

ReactJS Documentation

Backend Developer Interview Questions

VueJS Documentation

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Author

About the Author

A.J. García

I started my coding career back in 2003. Lately I've been involved a lot in Javascript for frontend and the backend.

I would love passing along to you some of the experiences and challenges I've faced over the years.

Contents

Table of Contents

Other Books by Alejandro

  1. Recommended Resources

Introduction

  1. Who is this book for ?
  2. What this book covers ?

Chapter 1: Setting up the python environment

  1. Install Python
  2. Libraries
  3. Importing the libraries
  4. Python console

Chapter 2: Reading and Plotting Multivariate Data

  1. Plotting Multivariate Data

Chapter 3: Calculating Summary Statistics for Multivariate Data

  1. Means and Variances Per Group
  2. Between-groups Variance and Within-groups Variance for a Variable
  3. Between-groups Covariance and Within-groups Covariance for Two Variables
  4. Calculating Correlations for Multivariate Data
  5. Standardising Variables

Chapter 4: Principal Component Analysis

  1. Deciding How Many Principal Components to Retain
  2. Loadings for the Principal Components
  3. Scatterplots of the Principal Components

Chapter 5: Linear Discriminant Analysis

  1. Loadings for the Discriminant Functions
  2. Separation Achieved by the Discriminant Functions
  3. A Stacked Histogram of the LDA Values
  4. Scatterplots of the Discriminant Functions
  5. Allocation Rules and Misclassification Rate

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