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rochitasundar / Stock-clustering-using-ML

Star 12

The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

principal-component-analysis k-means-clustering agglomerative-clustering elbow-plot dendrogram sklearn-library standardscaler linkage-analysis silhouette-score scipy-library cophenetic-distance
  • Updated Jan 20, 2022
  • Jupyter Notebook

vaitybharati / Assignment-08-PCA-Data-Mining-Wine-

Star 2

Assignment-08-PCA-Data-Mining-Wine data. Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we h…

python scale sklearn pca-analysis decomposition scipy scatter-plot preprocessing data-mining-algorithms clustering-algorithm kmeans-clustering covariance-matrix hierarchical-clustering agglomerative-clustering elbow-plot dendrogram wcss variance-plot
  • Updated Jul 3, 2021
  • Jupyter Notebook

Abhik35 / Assignment-K-Means-Clustering-Airlines-

Star 1

Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage histo…

python numpy sklearn pandas scatter-plot clustering-algorithm kmeans-clustering normalization elbow-plot matplotlib-pyplot cluster-centers wcss standard-scaler sumofasequence
  • Updated Feb 13, 2022
  • Jupyter Notebook

Abhik35 / Assignment-PCA-Data-Mining-Wine-

Star 1

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it …

python scale clustering sklearn pca-analysis decomposition pca scipy scatter-plot preprocessing data-mining-algorithms kmeans-clustering covariance-matrix hierarchical-clustering agglomerative-clustering elbow-plot dendrogram wcss variance-plot
  • Updated Jan 5, 2022
  • Jupyter Notebook

vaitybharati / Assignment-07-K-Means-Clustering-Airlines-

Star 1

Assignment-07-K-Means-Clustering-Airlines. Perform clustering (K means clustering) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include informati…

python numpy sklearn pandas scatter-plot normalize clustering-algorithm kmeans-clustering sum-of-squares elbow-plot matplotlib-pyplot cluster-centers wcss standard-scaler
  • Updated Jun 26, 2021
  • Jupyter Notebook

vaitybharati / P30.-Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ.-

Star 1

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of c…

python numpy sklearn pandas scatter-plot normalize clustering-algorithm kmeans-clustering sum-of-squares kmeans-algorithm elbow-plot cluster-centroids matplotlib-pyplot wcss standard-scaler
  • Updated Jun 27, 2021
  • Jupyter Notebook

y656 / Weather-data-clustering

Star 1

This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset

pandas-dataframe pca outlier-detection clustering-algorithm kmeans-clustering hierarchical-clustering unsupervised-machine-learning elbow-plot dendrogram truncated-svd minmaxscaling matplotlib-pyplot seaborn-plots kernelpca robust-scaling outliers-detection silhouette-score standard-scaler davies-bouldin
  • Updated Jul 2, 2022
  • Jupyter Notebook

Rudraroy21 / Rudra-Roy

Star 0

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the beginning who shows it…

python data-mining pca scipy kmeans-clustering hierarchical-clustering elbow-plot
  • Updated Nov 2, 2021
  • Jupyter Notebook

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