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Machine Learning

Iris Clustering via PCA

Reducing dimensionality of flower datasets using Principal Component Analysis for optimized clustering.

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Project Overview

Using linear dimensionality reduction to project 4D Iris data into a 2D space for visualization.

Technologies Used

PythonPCAK-Means

Methodology

  1. 1Standardizing the feature space.
  2. 2Calculating covariance matrices and eigenvectors.
  3. 3Determining explained variance ratios.
  4. 4Visualizing distinct clusters in reduced dimensions.

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