jtemporal/kmeans_e_cotovelo

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stefmolin/ml-utils

Machine learning utility functions and classes.

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arvkevi/ikneed

Interactive knee point detection using kneed!

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rochitasundar/Stock-clustering-using-ML

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.

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rhettadam/Optimal-K

Function to find the optimal number of clusters for k-means analysis using the Elbow Method

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manmolecular/http-response-clustering

:chart_with_downwards_trend: Clustering of HTTP responses using k-means++ and the elbow method

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

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 have ignored at the begining who shows it has 3 clusters)

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y656/Weather-data-clustering

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

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smh997/Audiobook-Customer-Segmentation-and-Purchase-Prediction

Segmenting customers of an audiobook platform and predicting their future purchase.

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jose-perth/Cryptocurrencies

Used Unsupervised Machine Learning to create an analysis of cryptocurrencies on the trading market and how they could be grouped to create a classification system.

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

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 clusters)

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

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 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.

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HotuRam/Credit-engine

Credit engine majorly based on Unsupervised learning

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srilakshmi-thota/K-MEANS-CLUSTERING-CLASSIFIER

K-means Clustering algorithm is used to classify,experimenting with different values of K to find the elbow point in the plot error vs K

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SurgoHealth/segmentation Fork of petersmittenaar/segmentation

Source code for examples of k-means and hierarchical clustering

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SaadARazzaq/Imtiaz-Remastered

Data Science for Supermarket Customer Retention

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abednarz210/Machine_Learning_Unsupervised

Create an unsupervised Machine Learning Model to determine actively traded cryptocurrencies for new investment.

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SadeTosin/E-Commerce-Sales-Customer-Segmentation

By aligning marketing efforts with customer preferences and desires, this approach promises to enhance market presence and drive substantial sales growth.

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manjugovindarajan/Trade-Ahead-StockClustering-using-ML

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 faces lower risk by tempering potential losses when the market is down.

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Rajivjha003/Netflix-Movies-TV-Shows-Clustering-Project

Netflix Movies & TV Shows Clustering

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S-CHAN11/Bank-Customer-Segmentation

The objective of this project is to analyze the customers of a bank, categorize them with K-Means and Hierarchical Clustering and evaluate their distinct characteristics

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zillur-av/k-means-algorithm

This is a Python implementation of k-means algorithm including elbow method and silhouette method for selecting optimal K

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priya-explorer/Customer_spend_behaviour_using_Clustering

Clustering Project

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PatilSukanya/Assignment-08-PCA

Used libraries and functions as follows:

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PatilSukanya/Assignment-07-Clustering-Q1-Airline-

Used libraries and functions as follows:

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shohaha/ML-myopia-clusters

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imane-ayouni/Chicago-Public-School-Students-Clustering

Unsupervised learning algorithms to cluster students of a public school

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RiteshopShrivastava/Hierarchical_Clustering

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 clusters)

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Aysenuryilmazz/ClusteringAnuranCallsMFCCs

Clustering for Anuran Calls with 4 different families

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SmartNamDevoloper/Clustering_Countries

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 how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid. The model is used to categorize the countries using some socio-economic and health factors that determine the overall development of the country.

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sumitkutty/Sparks-Foundation-Internship

The projects are a part of the internship by The Sparks Foundation

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krishcy25/K-Means-Clustering-Unsupervised-Learning

This repository focuses on building K-Means Clustering (Unsupervised Learning algorithm) that builds the effective number of cluster grouping/segmentation based on Elbow method.

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sakusuma/FundPrioritzation

HELP International is 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 runs a lot of operational projects from time to time along with advocacy drives to raise awareness as well as for funding purposes. After the recent funding programmes, they have been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. The significant issues that come while making this decision are mostly related to choosing the countries that are in the direst need of aid. And this is where you come in as a data analyst. Your job is to categorise the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries which the CEO needs to focus on the most. The datasets containing those socio-economic factors and the corresponding data dictionary are provided below.

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Vineeta12345/KMeans

Performing kmeans clustering and also providing elbow plot

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