fb-pixelDemystifying PCA (Principal Component Analysis) in Data Science - Avalith

Demystifying PCA (Principal Component Analysis) in Data Science

Digital Graphics

By Avalith Editorial Team ♦ 1 min read



Data Science is a world full of paths, realities, and myths, making it a temptation for those who are lovers of knowledge and its intricacies.

Data Science had its origins in 1962 when the American statistician John W. Tukey, renowned for developing complex algorithms and the famous box-and-whisker plot (Box Plot), wrote and questioned the future of statistics as an empirical science.

In these statements, the evolution of mathematical statistics into Data Science is mentioned for the first time. However, it wasn't until later in 1974 when Peter Naur, a Danish scientist known for his work in computer science and the winner of the Turing Award in 2005, coined the term as we know it today.

There are many fields involved in this discipline, and one of the most well-known is PCA or Principal Component Analysis.

Data scientists are accustomed to cleaning and preparing the databases they work with, with the data processing step (or Data Preparation) being crucial in any data world process.

But let's start with the basics…

What is Data Science and what is it used for?

The simplest definition of Data Science is the extraction of actionable information from raw data. Moreover, this multidisciplinary field aims primarily to identify trends, concepts, reasons, practices, connections, and correlations in large datasets.

On the other hand, it encompasses a wide variety of tools and techniques such as programming, predictive analysis, mathematics, statistics, and artificial intelligence. Even Data Science includes Machine Learning algorithms. You can learn more about the power of I.A here!

As you can imagine, in an increasingly digitized environment, companies have large amounts of data. Well-organized and analyzed, these data become a significant competitive advantage in their operations.

This data encapsulates the reality of user behavior across the communications and platforms of a company; where they come from, how long they stay, when they leave, what they purchase, and much more.

Therefore, with data science, one can, among other things, adjust the company's communication and organizational strategy, as well as modify the architecture of the website or app to enhance and make it more functional based on the analyzed data.

Now, once we have defined what Data Science is, we move on to step two: 

The definition of PCA

Principal Component Analysis (PCA) is a statistical method whose utility lies in reducing the dimensionality of the database (DB) we are working with.

This technique is used when we want to simplify the database, either to choose a smaller number of predictors to forecast a target variable or to understand a database more simply.

Machine Learning techniques require large volumes of data to create efficient and quality models. However, training datasets often contain a large amount of irrelevant data or data that provides little information.

Feature selection algorithms analyze input data, classify it into different subsets, and define a metric to assess the relevance of the information provided by each. They then discard the least informative features or fields from the working dataset, allowing for data storage savings and runtime, resulting in a more efficient model.

Principal Component Analysis (PCA) is one of the most common feature selection algorithms.

It is a specific feature selection technique to convert a set of observations of possibly correlated variables into a smaller set of variables that no longer exhibit correlation, known as principal components.

Benefits of PCA

PCA offers many advantages that make it one of the most chosen methods for freeing up space in databases. The most important ones include:


Dimensionality reduction without significant information loss: Particularly useful in AI applications, where high-dimensional data can lead to increased computational complexity and overfitting. By reducing dimensions, PCA can help alleviate these issues and improve the generalization performance of AI algorithms. Learn more about cyber security here!

Preprocessing step in various AI tasks: PCA can be used as a preprocessing step in tasks such as image recognition, natural language processing, and recommendation systems. 


For example, in image recognition, PCA can reduce the dimensionality of raw pixel data, which can then be fed into a neural network or other machine learning algorithms for classification or object detection. 

Similarly, in natural language processing, PCA can identify the most important features of a large text dataset, allowing for more efficient text classification or sentiment analysis.

Data visualization: Representing high-dimensional data can be challenging, as it is difficult to depict multiple dimensions in a two- or three-dimensional space. 

By applying PCA, researchers can project data into a lower-dimensional space, making it easier to visualize and interpret relationships between variables and observations. This can be particularly useful in exploratory data analysis, where the goal is to gain insights and identify patterns in the data before building predictive models.

So, as we can see, does using this technique provide infinite advantages? Yes and no.

Despite having numerous benefits, it also has its downside. Like everything, PCA also has some limitations.

Disadvantages of PCA

One of the main drawbacks is that it assumes that principal components are linear combinations of the original variables, which may not always be the case, especially in complex datasets with nonlinear relationships.

Additionally, PCA is sensitive to the scale of variables, meaning that variables with larger scales can dominate the principal components, leading to biased results. To overcome this problem, it is often recommended to standardize variables before applying PCA.

In conclusion, Principal Component Analysis is a powerful tool for dimensionality reduction in AI, with numerous applications in unsupervised learning, data preprocessing, and visualization.

By identifying the most significant patterns in data and transforming them into a lower-dimensional space, PCA can help improve the performance of AI algorithms and facilitate the interpretation of complex datasets.

As the field of AI continues to evolve and the amount of generated and collected data grows, PCA will undoubtedly remain an essential technique for researchers and professionals alike.