How to reduce dimensionality using PCA in Python

Hits: 56

How to reduce dimensionality using PCA in Python

Principal Component Analysis (PCA) is a technique for dimensionality reduction that is commonly used in machine learning and data analysis. It works by identifying the directions (principal components) in the data that have the most variation and projecting the data onto these directions. By doing so, it reduces the dimensionality of the data while preserving as much of the variation as possible.

In Python, we can use the PCA class from the sklearn.decomposition library to perform PCA on a dataset. Here’s an example:

from sklearn.decomposition import PCA
import numpy as np


// Create a sample dataset
data = np.random.rand(100, 10)


// Initialize the PCA model
pca = PCA(n_components=5)


// Fit the model to the data
pca.fit(data)


// Transform the data
reduced_data = pca.transform(data)

In this example, we first create a sample dataset with 10 features and 100 samples. Then, we initialize the PCA model and set the number of components to 5, which means we want to reduce the dimensionality of the data from 10 to 5. Next, we fit the model to the data and use the transform method to project the data onto the first 5 principal components. The resulting reduced_data matrix will have 5 columns and 100 rows, representing the reduced dimensionality of the original dataset.

It’s worth noting that, we can set the number of components parameter as n_components=None to keep all the principal components and later check the explained variance ratio to select the number of components we want to keep.

Additionally, it’s also important to scale the data before performing PCA to make sure that each feature of the data contributes equally to the principal components.

In simple words, Principal Component Analysis (PCA) is a technique for dimensionality reduction that is used to identify the directions (principal components) in the data that have the most variation and to project the data onto these directions. In Python, we can use the PCA class from the sklearn.decomposition library to perform PCA on a dataset and set the number of components to reduce the dimensionality. It’s important to scale the data before performing PCA and check the explained variance ratio to select the number of components we want to keep.

In this Learn through Codes example, you will learn: How to reduce dimensionality using PCA in Python.



 

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners