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Assignment-07-Clustering-Hierarchical-Airlines. Perform clustering (hierarchical) 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 history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.
Introduction to Unsupervised Machine Learning, number of approaches to unsupervised learning such as K-means clustering, hierarchical agglomerative Clustering and its applications.
Unsupervised-ML---Hierarchical-Clustering-University Data. Import libraries, Import dataset, Create Normalized data frame (considering only the numerical part of data), Create dendrograms, Create Clusters, Plot Clusters.
In this project, unsupervised learning methods, particularly clustering, are employed to determine the optimal algorithm for predicting whether a company has achieved net profit or incurred a net loss.
Used libraries and functions as follows:
Hierarchical Clustering
Hierarchical Clustering using Scikit-Learn
Conduct hierarchical clustering on the classic Iris dataset