swalusimbi/K-means-crime-clustering

This repository is a machine learning project entailing clustering of regions/districts based on crime types features. Application of k-means simplifies this clustering as you can easily tell districts with similar crime patterns, know regions of high risk due to the diversity of crimes committed.

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tgchacko/Customer-Segmentation---Purchasing-Behavior

Customer-Segmentation---Purchasing-Behavior

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nancyp321/MiningMastodonsSilentUsers

Mining Mastodon for silent users

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hugohiraoka/Credit_Card_Customer_Segmentation

Classification Model of Potential Credit Card Customers

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raviatkumar/Netflix-Movies-and-TV-Shows-Clustering

Based on a user's preferred movie or TV show, Unsupervised Machine Learning-Netflix Recommender suggests Netflix movies and TV shows. These suggestions are based on a K-Means Clustering model. These algorithms base their recommendations on details about movies and tv shows, such as their genres and description.

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Akashash01/data_clustering

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).

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JMartinArocha/ClusteringBankClients

This repository contains a practical exercise focused on clustering techniques, designed to train and enhance skills in data analysis and machine learning.

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LinaYorda/Spotify-songs-clustering

Spotify song clustering involves grouping similar songs together based on characteristics like genre, tempo, and mood to enhance music recommendation and discovery for users

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El-Giovanni92/RFM-customer-profiling

A customer profiling project based on RFM (Recency, Frequency, Monetary) analysis using a dataset from an online retail company in the United Kingdom. The aim is to identify customer habits and create personalized marketing strategies for targeted advertising.

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MohammadYasinKarbasian/Machine-Learning-Homeworks

This repository contains my solutions and implementations for assignments assigned during the Machine Learning course.

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haniye6776/clustering-countries

clustering with optimal number of clusters

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haniye6776/fraud-detection-using-clustering

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incheonQ/imgColorDetection3D

detect unique colors from an image and express it in 3D

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giuliano-macedo/clusopt_core

Clustream, Streamkm++ and metrics utilities C/C++ bindings for python

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aesthetic176/Application-of-Unsupervised-Learning-in-Detecting-Behavioral-Patterns-in-E-commerce-Customers

Machine Learning Project

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aisyahaini/klastering-kmeans

Clustering K-Means with streamlit app deployment

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shreyas-bk/OptimalCluster

OptimalCluster is the Python implementation of various algorithms to find the optimal number of clusters. The algorithms include elbow, elbow-k_factor, silhouette, gap statistics, gap statistics with standard error, and gap statistics without log. Various types of visualizations are also supported.

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Aysenuryilmazz/ClusteringAnuranCallsMFCCs

Clustering for Anuran Calls with 4 different families

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RimTouny/Advanced-NLP-Powered-Sentiment-Analysis-for-E-commerce-Enhancement

Using NLP and a smart chatbot, this project gauges customer sentiments online, offering customization and real-time feedback. Employing TF-BOW-LDA and ML models, it empowers e-commerce decisions, culminating in an NLP course at uOttawa in 2023.

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RimTouny/Enhancing-Gutenberg-Book-Clustering-using-Advanced-NLP-Techniques

Text clustering, an unsupervised ML technique in NLP, groups similar texts based on content. Techniques like hierarchical, k-means, or density-based clustering categorize unstructured data, unveiling insights and patterns in diverse datasets. This exploration was part of the NLP course in my University of Ottawa master's program in 2023.

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saikrishnabudi/PCA-Principal-Component-Analysis

Data Science - PCA (Principal Component Analysis)

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mrafifrbbn/airline-customer-segmentation

An end-to-end project on clustering (unsupervised ML)

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saikrishnabudi/Clustering

Data Science - Clustering Work

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jojo142/LendingClubAnalysis

Lending Club's loan data analysis using data cleaning/wrangling to predictive modeling

Size: 173 KB - Last synced: 5 months ago - Pushed: 5 months ago - Stars: 0 - Forks: 0

glendawur/indices_kmeans

Language: Python - Size: 6.55 MB - Last synced: 5 months ago - Pushed: 5 months ago - Stars: 0 - Forks: 0

parissashahabi/Behavioral-Data-Clustering-and-Gender-Correlation-Analysis

Clustered behavioral data into two groups, regardless of gender, and evaluated cluster consistency with gender division using silhouette and Davies-Bouldin scores. Additionally, identified the optimal cluster count using the elbow method and re-evaluated clustering efficacy.

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PacktWorkshops/The-Machine-Learning-Workshop

An interactive approach to understanding Machine Learning using scikit-learn

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apoorvar5/Deaths-in-United-States-Analysis

Used Jupyter along with python libraries like numpy, panda, matplotlib, etc to understand and visualize cause of deaths in the united states and formed clusters based on each diseases and their respective cause of death.

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MariaDimopoulou/Churn-Prediction-Customer-Segmentation-in-E-Commerce

This project focuses on predicting customer churn in an e-commerce setting using machine learning techniques.

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evgenygrobov/Customer_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.

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mansi-k/KMeans_on_imgs

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

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khlinh2512/Predict_Customer_Segmentation

A model for predicting customer segmentation using K-Means Clustering and Support Vector Machine Classification

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SINGHxTUSHAR/Assignment-77

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venkatesh-eranti/clustering_milestone-project-Wholesale-customers

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 to the change in delivery service?

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ashishyadav24092000/Hierarchical-Clustering

Performing Hierarchical clustering.

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Yunanouv/Airlines-Passengers-Clustering

ML Unsupervised -Clustering : Airlines Passengers Value Clustering

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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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NaumanAnwar97/kMeans_Silhoulttee_Score

K Means Clustering in R (Silhouttee Score)

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Cintia0528/Project-5.-Unsupervised-Machine-Learning

The project aims to assess the potential of utilizing Machine Learning to automate playlist creation for Moosic, a startup known for manual curation, while addressing skepticism from music experts regarding the ability of audio features to capture the subjective "mood" of playlists.

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atwyburde/East-West-Airlines-Analysis

East West Airlines Analysis by Clustering.

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Nastiiasaenko/Russian_ecology_project

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akeelrashid/Netflix_Movies_TV_Show_Clustering

This project explores Netflix's content evolution, analyzes TV shows and movies, and builds a recommendation system. Discover insights from a dataset of 7,787 titles as of 2019 and learn how we clustered content based on textual features.

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Apaulgithub/Netflix_Movies_And_TV_Shows_Clustering

Unsupervised Machine Learning project for Netflix Movies and TV Shows Clustering. The main goal of this project is to create a content-based recommender system that recommends top 10 shows to users based on their viewing history.

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pagoma3/UnSupervised_Clustering

Sprint 15, Task 1

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SaadTariq01DataAnalyst/Employee-Segmentation-on-Absenteesim

The goal of this project is to use clustering techniques to segment employees based on their absenteeism patterns and provide insights that can help organizations to reduce absenteeism and improve employee productivity.

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g4lius/rfm-customer-profiling

A customer profiling project based on RFM (Recency, Frequency, Monetary) analysis using a dataset from an online retail company in the United Kingdom. The aim is to identify customer habits and create personalized marketing strategies for targeted advertising.

Language: Python - Size: 7.11 MB - Last synced: about 2 months ago - Pushed: 10 months ago - Stars: 2 - Forks: 0

masendhy/predict_customer_personality_to_boost_marketing_campaign_by_using_machine_learning

Generate a cluster prediction model using K-Means Clustering with RFMLC Method and PCA to boost marketing campaign.

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

Netflix Movies & TV Shows Clustering

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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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exelero565/DS_Project_6

create models customer clustering by solvency

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Navneet2409/netflix-movies-and-tv-shows-clustering

The goal of this project is to use Netflix data (7787,12) to classify and group movies and shows into specific clusters. We will utilize techniques such as K-means clustering, Agglomerative clustering and content-based recommendation systems to analyze the data and provide personalized suggestions to consumers based on their preferences.

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Sanky18/Optimal-Location-Selection-for-a-New-Restaurant-in-Chennai

This project aims to assist stakeholders in selecting an optimal location for a new restaurant in Chennai, Tamil Nadu, India.

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Preetirai-tech/Online-Retail-Customer-Segmentation-Project

This project aims to analyze a transnational dataset from a UK-based online retail company and identify major customer segments. By categorizing customers into distinct groups based on their characteristics, businesses can gain valuable insights and tailor their strategies to better serve each segment.

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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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matheusventurads/loyalty-clustering-programm

Clustering Clients for Insiders Loyalty Program.

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shreyansh-2003/Clustering-Analysis-KMeans-vs-Agglomerative-Clustering-for-Large-Datasets

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.

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sakshibabbar2019/Applying-Evaluating-Clustering-algorithms

This is a small tutorial project that demonstrates application and evaluation methods of popular clustering algorithms namely, K-means, DBSCAN and Agglomerative.

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ajaywanekar/E-Commerce_Customer_Segmentation

Unsupervised Learning - Using K Means algorithm to Cluster the customers.

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vbhsharma7/Netflix-movies-and-tv-show-clustering-capstone

Unsupervised learning project (Clustering)

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jolual2747/unsupervised_learning_workshop2

Unsupervised machine learning methods built from scratch, KMeans, KMedoids

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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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shu-nya/Netflix-movies-and-TV-shows-Clustering

Unsupervised Learning model to cluster movies and TV shows on Netflix.

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shanuhalli/Assignment-Clustering

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.

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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.

Language: Jupyter Notebook - Size: 9.16 MB - Last synced: about 1 year ago - Pushed: over 2 years ago - Stars: 3 - Forks: 3

sidharth178/Mall-Customers-Segmentation

This case requires to develop a customer segmentation to understand customer's behaviour and separate them in different groups according to their preferences, and once the division is done, this information can be given to marketing team so they can plan the strategy accordingly.

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prajwalDU/customer-segmentation

To Identify Major Customer Segments On Transnational Dataset Using Unsupervised ML Clustering Algorithms

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Krystkowiakk/Argentinian-Tango-Lyrics-Sentiment-Topics-NLP

Metis project 5/7

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maxschelski/pytorch-cluster-metrics

Pytorch implementation of standard metrics for clustering

Language: Python - Size: 33.2 KB - Last synced: about 1 year ago - Pushed: almost 2 years ago - Stars: 4 - Forks: 0

marayyy/E-Commerce-Customer-Segmentation

The purpose of this project is to create customer segmentations by using similarity between products purchased between the users by using Natural Language Processing techniques and Clustering

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sanketmaneDS/Clustering

This repository contains introductory notebook for clustering techniques like k-means, hierarchical and DB SCAN

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ksharma67/K-Means-Algorithm-On-The-Iris-Dataset

Applied the K-Means algorithm on the Iris dataset, and utilized the Silhouette Score method to find the best value of K

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rohithaug/Battle-of-Neighborhoods-Chennai

Capstone Project for the IBM Professional Certificate on Coursera

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omarja12/Insurance_Company_Customers_Clustering

Using several clustering algorithm to segment an insurance company customers

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chugunova24/kMEANS

Language: Jupyter Notebook - Size: 1.33 MB - Last synced: about 1 year ago - Pushed: almost 2 years ago - Stars: 0 - Forks: 0

ShiouLu/Customer-Segmentation-of-Retail-Industry

To perform customer segmentation using Python unsupervised learning model

Language: Jupyter Notebook - Size: 15.3 MB - Last synced: over 1 year ago - Pushed: over 1 year ago - Stars: 2 - Forks: 0

jermynyeo/Predict-Automobile-Insurance-Prices

All-in-1 notebook which applies different clustering (K-means, hierarchical, fuzzy, optics) and classification (AdaBoost, RandomForest, XGBoost, Custom) techniques for the best model.

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Neel7317/Machine_learning-Unsupervised-

Unsupervised learning with different types clustering algorithms..

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berksudan/PySpark-Auto-Clustering

Implemented an auto-clustering tool with seed and number of clusters finder. Optimizing algorithms: Silhouette, Elbow. Clustering algorithms: k-Means, Bisecting k-Means, Gaussian Mixture. Module includes micro-macro pivoting, and dashboards displaying radius, centroids, and inertia of clusters. Used: Python, Pyspark, Matplotlib, Spark MLlib.

Language: Python - Size: 73.2 KB - Last synced: about 1 year ago - Pushed: about 2 years ago - Stars: 0 - Forks: 0

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

Unsupervised learning algorithms to cluster students of a public school

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nafisa-samia/Unsupervised-and-Semi-Supervised-Methods-on-Breast-Cancer-Dataset

In this project, we apply different Unsupervised and Semi-supervised techniques on a Supervised Dataset which is Breast Cancer Dataset and evaluate the model accuracy

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nafisa-samia/Face-Recognition-using-Unsupervised-Semi-supervised-ML Fork of kuntala-c/Face-Recognition-using-Unsupervised-Semi-supervised-ML

Face Recognition Algorithm using Unsupervised and Semi-supervised techniques using Olivetti faces dataset

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kuntala-c/Face-Recognition-using-Unsupervised-Semi-supervised-ML

Face Recognition Algorithm using Unsupervised and Semi-supervised techniques

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Walid-khaled/Network-Intrusions-Clustering-ML

This repository contains an implementation for network intrusions clustering. In this task, unsupervised approach is used to cluster network intrusions. It is apart of Assignment2 in Machine Learning course for ROCV master's program at Innopolis University.

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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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rmerzouki/ml

Best Clustering using silhouette_score

Language: Jupyter Notebook - Size: 658 KB - Last synced: about 1 year ago - Pushed: about 3 years ago - Stars: 2 - Forks: 0

Develop-Packt/Introduction-to-Clustering

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

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