There are 0 repository under dendogram topic.
Chart.js Graph-like Charts (tree, force directed)
Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.
learn about indonesian text classification and topics modeling
Perform Clustering (Hierarchical, K Means Clustering and DBSCAN) for the airlines and crime data to obtain optimum number of clusters. Draw the inferences from the clusters obtained.
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.
This Repo Consists of some of the Tasks for The Sparks Foundation-Machine Learning and Data Science Internship, containing Supervised and Unsupervised Machine Learning Techniques to solve A ML Problem in a Systematic Way.
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 ID --Unique ID Balance--Number of miles eligible for award travel Qual_mile--Number of miles counted as qualifying for Topflight status cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months: cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months: cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months: 1 = under 5,000 2 = 5,000 - 10,000 3 = 10,001 - 25,000 4 = 25,001 - 50,000 5 = over 50,000 Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months Bonus_trans--Number of non-flight bonus transactions in the past 12 months Flight_miles_12mo--Number of flight miles in the past 12 months Flight_trans_12--Number of flight transactions in the past 12 months Days_since_enrolled--Number of days since enrolled in flier program Award--whether that person had award flight (free flight) or not
Superimpose a set of protein structures and report a RSMD matrix, in CSV and Mega-compatible formats.
Consensus Recommendation
This project is a step towards building an Artificial General Intelligence. The main goal is to discover an individual's biasses getting his/her field of interests from Instagram ad interests.
Hierarchical clustering analysis on Credit Card customers dataset.
Trabalho Final de Graduação em Arquitetura e Urbanismo Apresentado ao Centro Universitário Belas Artes de São Paulo sobre a complexidade morfológica
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.
Perform Principal component analysis and perform clustering using first 3 principal component scores both Heirarchial and K Means Clustering and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data.
Hierarchical-Clustering
Utilized hierarchical clustering to identify the most similar cryptocurrency clusters and determine which currencies had the most significant impact on each other. Constructed a portfolio based on these findings.
This project focuses on network anomaly detection due to the exponential growth of network traffic and the rise of various anomalies such as cyber attacks, network failures, and hardware malfunctions. This project implement clustering algorithms from scratch, including K-means, Spectral Clustering, Hierarchical Clustering, and DBSCAN
Data prepration and preprocessing for predictive modeling with SAS and Python
ExcelR_Assignment---Clustering---Assignment---7
Using Hierarchial clustering to categories the spending of customers into groups based on their spending habit and other features
Classification Model of Potential Credit Card Customers
This clustering analysis aims to provide valuable insights into the viability of introducing an original language cinema in Milan, Italy.
This is a R repository of studies that I made on some data sets. There are linear models, predicition models (boosting - bagging - RandomFlorest), clustering and dendograms.
Implemented K - means and Hierarchical clustering to cluster the retail customers into different segments, based on their spending habits. Employed RFM metrics and assigned labels to each customer based on RFM score.
This project explores and analyzes financial data of a number of securities, applies Hierarchical and K-means clustering to group securities and create cluster profiles to develop personalized portfolios and investment strategies for clients
Explore a comprehensive analysis of Netflix's extensive collection of movies and TV shows, clustering them into distinct categories. This GitHub repository contains all the details, code, and insights into how we've organized and grouped the vast content library into meaningful clusters.
Data Science - Clustering Work
This repo explores KMeans and Agglomerative Clustering effectiveness in simplifying large datasets for ML. Goals include dataset download, finding optimal clusters via Elbow and Silhouette methods, comparing clustering techniques, validating optimal clusters, tuning hyperparameters. Detailed explanations and analysis are provided.
Mall Customer Segmentation Data
This repository contains a Jupyter Notebook that explores various clustering techniques applied to the Fashion MNIST dataset like K-Means, Hierarchical,etc.
NETFLIX MOVIES AND TV SHOWS CLUSTERING is a project that aims to cluster the available movies and TV shows on Netflix based on their attributes such as genre, release year, and country of production.
This project aims to practice the steps of Crisp Data Mining ( CRISP-DM ). The repository includes 3 phases, data understanding, supervised learning, and unsupervised learning.
Agglomerative Clustering from scratch without using built-in library with different hyper-parameters using Python and evaluated the cluster quality using intrinsic and extrinsic scores
Used libraries and functions as follows:
Data Science - PCA (Principal Component Analysis)