Applied Multivariate Analysis with Python
Applied Multivariate Analysis with Python
Step by Step Guide to Perform Multivariate Analysis with Python
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
Front End Developer Interview Questions
About the Author
Bundles that include this book
Table of Contents
-
Other Books by Alejandro
- Recommended Resources
-
Introduction
- Who is this book for ?
- What this book covers ?
-
Chapter 1: Setting up the python environment
- Install Python
- Libraries
- Importing the libraries
- Python console
-
Chapter 2: Reading and Plotting Multivariate Data
- Plotting Multivariate Data
-
Chapter 3: Calculating Summary Statistics for Multivariate Data
- Means and Variances Per Group
- Between-groups Variance and Within-groups Variance for a Variable
- Between-groups Covariance and Within-groups Covariance for Two Variables
- Calculating Correlations for Multivariate Data
- Standardising Variables
-
Chapter 4: Principal Component Analysis
- Deciding How Many Principal Components to Retain
- Loadings for the Principal Components
- Scatterplots of the Principal Components
-
Chapter 5: Linear Discriminant Analysis
- Loadings for the Discriminant Functions
- Separation Achieved by the Discriminant Functions
- A Stacked Histogram of the LDA Values
- Scatterplots of the Discriminant Functions
- Allocation Rules and Misclassification Rate
- Keep developing your Data Science skills
- About the Author
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