Principal component analysis example in Python
Principal Component Analysis Example in Python
Principal Component Analysis (PCA) is a method commonly used for data dimensionality reduction. It can reduce the dimensionality of high-dimensional data to low dimensions, retaining all the data. Possibly more data variation information. Python provides many libraries and tools for implementing PCA. This article uses an example to introduce how to use the sklearn library in Python to implement PCA.
First, we need to prepare a data set. This article will use the Iris data set, which contains 150 sample data. Each sample has 4 feature values (the length and width of the calyx, the length and width of the petals), and a label (the type of iris flower). Our goal is to reduce the dimensionality of these four features and find the most important principal components.
First, we need to import the necessary libraries and data sets.
from sklearn.datasets import load_iris from sklearn.decomposition import PCA import matplotlib.pyplot as plt iris = load_iris() X = iris.data y = iris.target
Now we can create a PCA object and apply it.
pca = PCA(n_components=2) X_pca = pca.fit_transform(X)
The PCA object here sets n_components=2, which means that we only want to display our processed data on a two-dimensional plane. We apply fit_transform to the original data X and obtain the processed data set X_pca.
Now we can plot the results.
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y) plt.xlabel('Component 1') plt.ylabel('Component 2') plt.show()
In this figure, we can see the distribution of the Iris data set in the two-dimensional space after dimensionality reduction. Each dot represents a sample of an iris flower, and the color indicates the type of iris flower.
Now let’s see what the principal components should be.
print(pca.components_)
This will output two vectors called "Component 1" and "Component 2".
[[ 0.36158968 -0.08226889 0.85657211 0.35884393]
[-0.65653988 -0.72971237 0.1757674 0.07470647]]
Each element represents the weight of a feature in the original data. In other words, we can think of principal components as vectors used to linearly combine the original features. Each vector in the result is a unit vector.
We can also look at the amount of variance in the data explained by each component.
print(pca.explained_variance_ratio_)
This output will show the proportion of the variance in the data explained by each component.
[0.92461621 0.05301557]
We can see that these two components explain a total of 94% of the variance in the data. This means we can capture the characteristics of the data very accurately.
One thing to note is that PCA will remove all features from the original data. Therefore, if we need to retain certain features, we need to remove them manually before applying PCA.
This is an example of how to implement PCA using the sklearn library in Python. PCA can be applied to all types of data and helps us discover the most important components from high-dimensional data. If you can understand the code in this article, you will also be able to apply PCA on your own data sets.
The above is the detailed content of Principal component analysis example in Python. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

PhpStorm Mac version
The latest (2018.2.1) professional PHP integrated development tool

SublimeText3 English version
Recommended: Win version, supports code prompts!

Atom editor mac version download
The most popular open source editor