This repository contains introductory notebooks for principal component analysis.
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Updated
Nov 17, 2022 - Jupyter Notebook
This repository contains introductory notebooks for principal component analysis.
This repository contains introductory notebook for clustering techniques like k-means, hierarchical and DB SCAN
This repository contains a Jupyter Notebook that explores various clustering techniques applied to the Fashion MNIST dataset like K-Means, Hierarchical,etc.
All-in-1 notebook which applies different clustering (K-means, hierarchical, fuzzy, optics) and classification (AdaBoost, RandomForest, XGBoost, Custom) techniques for the best model.
Analyzed the Silhouette score to determine the optimal number of clusters for K-Means clustering using the IRIS dataset. The notebook includes data preprocessing, clustering, and Silhouette score evaluation, providing insights into cluster quality and optimal cluster count for effective data segmentation.
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