#Pca In Data Visualization

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#Pca In Data Visualization Reel by @aibutsimple - Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance a
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@aibutsimple
Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset. It transforms the original correlated variables into a new set of uncorrelated variables called principal components. The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables. Eigenvectors and their corresponding eigenvalues are then calculated from the covariance matrix. The eigenvectors represent the directions/principal components of the highest variance, while the eigenvalues quantify the amount of variance in each direction. By selecting the top “k” eigenvectors with the highest eigenvalues, PCA projects the data into a lower dimensional space, simplifying analysis and visualization while retaining the most important information. C: deepia Join our AI community for more posts like this @aibutsimple 🤖
#Pca In Data Visualization Reel by @infusewithai - Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance a
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@infusewithai
Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset. It transforms the original correlated variables into a new set of uncorrelated variables called principal components. The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables. Eigenvectors and their corresponding eigenvalues are then calculated from the covariance matrix. The eigenvectors represent the directions/principal components of the highest variance, while the eigenvalues quantify the amount of variance in each direction. By selecting the top “k” eigenvectors with the highest eigenvalues, PCA projects the data into a lower dimensional space, simplifying analysis and visualization while retaining the most important information. C: deepia
#Pca In Data Visualization Reel by @getintoai (verified account) - Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance a
41.2K
GE
@getintoai
Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset. It transforms the original correlated variables into a new set of uncorrelated variables called principal components. The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables. Eigenvectors and their corresponding eigenvalues are then calculated from the covariance matrix. The eigenvectors represent the directions/principal components of the highest variance, while the eigenvalues quantify the amount of variance in each direction. By selecting the top “k” eigenvectors with the highest eigenvalues, PCA projects the data into a lower dimensional space, simplifying analysis and visualization while retaining the most important information. C: deepia
#Pca In Data Visualization Reel by @quantmaxxing - In quant finance, PCA is used to (1) decompose risk (e.g., yield-curve level/slope/curvature in rates, or market/sector factors in equities), (2) stab
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@quantmaxxing
In quant finance, PCA is used to (1) decompose risk (e.g., yield-curve level/slope/curvature in rates, or market/sector factors in equities), (2) stabilize covariance estimates for portfolio optimization, (3) identify common drivers across assets for factor models and hedging, and (4) stress-test portfolios by shocking dominant components. By working in the eigenbasis, quants manage exposure to the true underlying risk factors rather than to thousands of correlated securities. This is needed because financial data is highly correlated and noisy. PCA reduces dimensionality, improves numerical stability, and separates signal from noise—critical for modeling, optimization, and risk control. In Principal Component Analysis (PCA), the covariance matrix captures how variables move together, and its eigenvectors identify the dominant directions of joint variation. Projecting the data onto the top components compresses high-dimensional, correlated data into a few orthogonal factors that retain most of the information
#Pca In Data Visualization Reel by @insightforge.ai - Principal Component Analysis (PCA) is a dimensionality reduction method that reprojects data into a new coordinate system, where each axis - called a
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@insightforge.ai
Principal Component Analysis (PCA) is a dimensionality reduction method that reprojects data into a new coordinate system, where each axis - called a principal component - captures the maximum possible variance, preserving the most important information in the dataset. To compute PCA, we first calculate the covariance matrix of the data, which measures how features vary together. Then, we perform an eigenvalue decomposition on this matrix. Each eigenvalue indicates how much variance a particular principal component explains, while the corresponding eigenvector defines the direction of that component in the new space. By sorting the eigenvalues in descending order and keeping only the top components, we can reduce the dataset’s dimensionality while retaining the majority of its meaningful variance and structure. C: Deepia #machinelearning #deeplearning #datascience #AI #dataanalytics #computerscience #python #programming #data #datascientist #neuralnetworks #computervision #statistics #robotics #ML
#Pca In Data Visualization Reel by @datascience.swat - Principal Component Analysis, or PCA, is a method used to simplify complex data by transforming it into a smaller number of new variables called princ
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@datascience.swat
Principal Component Analysis, or PCA, is a method used to simplify complex data by transforming it into a smaller number of new variables called principal components. These components are arranged in a way that they are independent from each other and capture the most meaningful structure in the data. The idea is to retain the directions that hold the most variation while removing less important details. By focusing only on the top components, large and complicated datasets can be reduced into a simpler form without losing the key patterns that matter most. Follow @datascience.swat for more daily videos like this Credits; Deepia Shared under fair use for commentary and inspiration. No copyright infringement intended. If you are the copyright holder and would prefer this removed, please DM me. I will take it down respectfully. ©️ All rights remain with the original creator (s)
#Pca In Data Visualization Reel by @kaylena_rose - Here's a more lecture-style breakdown of PCA for y'all :) #pca #measurement #statistics #psychometrics #datascience
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@kaylena_rose
Here’s a more lecture-style breakdown of PCA for y’all :) #pca #measurement #statistics #psychometrics #datascience
#Pca In Data Visualization Reel by @mathswithmuza - Principal Component Analysis is a dimensionality-reduction technique that transforms a high-dimensional dataset into a smaller set of new variables ca
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@mathswithmuza
Principal Component Analysis is a dimensionality-reduction technique that transforms a high-dimensional dataset into a smaller set of new variables called principal components. These components are constructed to capture as much of the original variation in the data as possible while remaining uncorrelated with one another. PCA works by identifying directions in the data where the spread is largest, meaning those directions explain the most meaningful structure. Instead of trying to interpret dozens of correlated features, PCA allows you to rotate the coordinate system and focus on just a few axes that summarize most of the information. Once the principal components are found, the data can be projected onto them, making it easier to visualize patterns, clusters, and trends that may be hidden in the original space. This is especially useful in fields like image processing, genetics, or marketing analytics where datasets can have hundreds or thousands of variables. PCA also helps reduce noise by filtering out directions with very little variance, which often correspond to measurement error rather than true structure. Overall, PCA simplifies complex datasets without losing their essential relationships, helping analysts uncover clearer insights and build more efficient models. Like this video and follow @mathswithmuza for more! #math #maths #mathematics #learn #learning #study #studying #coding #ai #chatgpt #foryou #fyp #reels #education #stem #physics #statistics #new #animation #manim #school #university #highschool #college
#Pca In Data Visualization Reel by @chartosaur (verified account) - Just because you can make your chart look unique, doesn't mean you should. A good data visualization is about clarity, not just creativity. When every
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@chartosaur
Just because you can make your chart look unique, doesn't mean you should. A good data visualization is about clarity, not just creativity. When every element - color, font, and shape - works against readability, you're not improving the story, you're burying it. Radial bar charts, for example, may look visually compelling, but they aren't always the best tool. While they do okay in displaying cyclical data, such as seasonal trends or annual sales patterns, their complexity can make your point harder to understand in other contexts. #Charts #Presentation #Viz #PPT #Excel #Graph #Consulting #Mckinsey #Bain #BCG #Vizualization #Slides #Chart #Graphs #Deck #GoogleSlides #StackedBarChart #BarChart #Education #Data #Anchoring #Likert #RadialChart
#Pca In Data Visualization Reel by @thephdstudent (verified account) - Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to
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@thephdstudent
Data visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻 ✨ Teaches you how to move from spreadsheets to web-based visualisations ✨ Covers tools like Google Sheets, Datawrapper, Tableau Public, Chart.js & Leaflet ✨ Perfect if you want to communicate data clearly — even without heavy coding ✨Open-source so freely available online 📌 Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code — Jack Dougherty & Ilya Ilyankou 💭 Summary: This book shows you how to clean, analyse, and visualise data using practical tools — starting with spreadsheets and moving into customisable web-based charts and maps. It’s especially useful if you want to share your work online and make your data interactive, not just informative. If you’re learning data science, bioinformatics, or just want to present your work better, this is a great place to start 🤍 📌 Save this for later — I’ll be sharing more recommendations soon. #womeninstem #datavisualization #datascience #bioinformatics #tech
#Pca In Data Visualization Reel by @phdwithanjali - Stop suffering in silence. These tools will level up your analysis game! Which one's your fav? Comment down👇🏻 #dataanalysis #phdlife #statsmadeeasy
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@phdwithanjali
Stop suffering in silence. These tools will level up your analysis game! Which one’s your fav? Comment down👇🏻 #dataanalysis #phdlife #statsmadeeasy #dataanalysis #rstats #prism #researchtools #scientificreels #academiaa #phd #phdwithanjali #juliusai @try_julius.ai
#Pca In Data Visualization Reel by @softwarewithnick (verified account) - Top 3 data visualization packages 🤔 1️⃣ https://matplotlib.org 2️⃣ https://seaborn.pydata.org 3️⃣ https://ggplot2.tidyverse.org Being able to vis
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@softwarewithnick
Top 3 data visualization packages 🤔 1️⃣ https://matplotlib.org 2️⃣ https://seaborn.pydata.org 3️⃣ https://ggplot2.tidyverse.org Being able to visualize and tell the story is a key component of being a data scientist. With these 3 packages you can pretty much create any plot you can think of! Not only that, but these packages aren’t actually that bad to learn! There are many other packages out there for data visualization as well, but these are my 3 favorites! Drop a follow for more coding tips 🎯 #code #coding #datascience #tech #python

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