PCA Visualizer

Principal Component Analysis - dimensionality reduction and visualization

120 points

Original Data

120 pts

Projected (apply PCA)

PCA Results

Eigenvalues

?1

13.6654

?2

5.9488

Explained Variance

PC169.7%
PC230.3%

Covariance Matrix

[9.6045, 3.8530]

[3.8530, 10.0097]

How it works

Generate: Creates 3 clusters of 2D points with Gaussian noise.

Covariance: Computes the covariance matrix of the data.

PCA: Finds eigenvectors (principal components) and projects data onto them.

What is PCA Visualizer?

PCA Visualizer is a free online tool for students and professionals — handy for everyday study and work tasks. It runs fully in your browser: fast, private and free forever, with no account needed.

How to use PCA Visualizer?

Step 1: Use the live controls in the tool above — it runs on this page. Step 2: Add your data or settings as the screen prompts. Step 3: Run the tool, then copy or download the result. Everything happens locally in your browser, so large inputs stay private.

Frequently Asked Questions — PCA Visualizer

Is PCA Visualizer free to use?

Yes — PCA Visualizer is a free online tool by DSWallah. No signup, no install, no limits for normal use. It runs entirely in your browser.

Do I need to install anything for PCA Visualizer?

No. PCA Visualizer works online in Chrome, Edge, Firefox or Safari on mobile and desktop. Your data never leaves your device.

Who should use PCA Visualizer?

PCA Visualizer is built for students and professionals — useful for everyday study and work tasks. DSWallah students use it daily in our Python Course and Data Science Course batches.

Learn the skill behind the tool: Python Course in Lucknow · Data Science Course

Related free tools: Daily Homework Generator · Decision Tree Visualizer · Docker Compose Generator · Dockerfile Generator

About PCA Visualizer

100 features ko 2 me samjhao — wahi PCA hai. DSWallah visualizer data points ko compress hote dikhata hai, variance ke saath. Dimensionality reduction ab dar nahi, khel hai. Free.

How to use (3 steps)

  1. Feature dataset lo.
  2. Components 2 par set karo.
  3. Scatter me clusters dekho.

FAQs

PCA kya karta hai?

Zyada features ko kam dimensions me badalta hai, bina info khoye (jitna ho sake).

Variance kya hoti hai?

Kitni information bachi — pehle 2 components me 90%+ hona achha sign hai.

Ye free hai?

Haan, fully free.

Popular courses: Data Science Course in Lucknow · AI Course in Lucknow · Python Course in Lucknow · Power BI Course · Data Analytics Course