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k-means

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This project aims to cluster various cryptocurrencies based on their market performance using machine learning techniques. The analysis involves several key steps: normalizing the data, reducing its dimensionality with Principal Component Analysis (PCA), and using K-Means clustering to identify distinct groups.

  • Updated May 28, 2024
  • Jupyter Notebook

This project classifies images from the Flower102 dataset using k-means clustering followed by K-Nearest Neighbors (KNN) classification. It optimizes KNN parameters to achieve high accuracy, with the best results obtained using 7 clusters and 5 nearest neighbors.

  • Updated May 25, 2024
  • Jupyter Notebook

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