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This course teaches you how to calculate distance metrics, form and identify clusters in a dataset, implement k-means clustering from scratch and analyze clustering performance by calculating the silhouette score
The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. How can the wholesale distributor use the customer segments to determine which customers, if any, would reach positively…
The objective of this project is to categorise the countries using some socio-economic and health factors that determine the overall development of the country and then accordingly suggest the NGO the country which is in dire need of help.
This project demonstrates a Clustering Model using Python. An international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during the time of disasters and natural calamities. It has been able to raise around $ 10 million. The model is needed to help decide ho…
This repository contains all program files and datasets used in implementation of Masters Thesis Research Work for the topic - "Efficient Clustering via Kernel Principal Component Analysis and Optimal One Dimensional Clustering".
Clustered customers into distinct groups based on similarity among demographical and geographical parameters. Applied PCA to dispose insignificant and multi correlated variances. Defined optimal number of clusters for K-Means algorithm. Used Euclidian distance as a measure between centroids.
This machine learning model makes, grouping a set of objects in such a way that objects in the same group(cluster) are more similar to each other than other groups(clusters).
Implemented KMeans from scratch and trained it on Fashion-MNIST dataset by experimenting with initializaion methods like forgy, random partitions, kmeans++ and found the optimal number of clusters by implementing elbow & silhouette algorithms from scratch