shreyaa-1702/Customer_segmentation

Customer segmentation using K-Means clustering helps businesses group customers based on factors like Annual Income and Spending Score to understand their behavior and optimize marketing strategies.

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srafay/Machine_Learning_A-Z

Learning to create Machine Learning Algorithms

Language: Python - Size: 10.8 MB - Last synced at: 3 days ago - Pushed at: almost 4 years ago - Stars: 386 - Forks: 195

IBM/ibm-developer-watsonx

Storing code used in Generative AI Developer Guides on the IBM Developer Website

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tugot17/K-Means-Algorithm-From-Scratch

The K-Means algorithm, written from scratch using the Python programming language

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AninditaGuha98/Learning-Management-System-serverless-application

This repository is a collaborative work towards creating a serverless application called Learning Management System. This application follows multi-cloud deployment and will implement backend-as-a service architecture.

Language: Python - Size: 21.7 MB - Last synced at: 19 days ago - Pushed at: over 2 years ago - Stars: 7 - Forks: 1

darenr/wordnet-clusters

Clustering a set of word/tags using K-Means with word2vec or wordnet distance

Language: Python - Size: 216 KB - Last synced at: 1 day ago - Pushed at: about 6 years ago - Stars: 26 - Forks: 5

nivgartzi/k-means-clustering

k means algorithm implementation in python.

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aryanGupta-09/Kmeans-using-MapReduce

K-means clustering algorithm using MapReduce.

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RAHAMNIabdelkaderseifelislem/PRODIGY_ML_02

Task 2 of the Prodigy InfoTech ML internship which involves Creation of K-means clustering algorithm to group customers of a retail store based on their purchase history.

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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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yexhin/Predictive-Marketing

Predictive Marketing based on Consumer Behavior.

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sushantnair/K-Means-Clustering

A simple implementation of the K-Means Clustering Algorithm

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nano-bot01/Customer-Segmentation-using-Clustering-

Customer Segmentation using Clustering (Machine Learning)

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pragyapranati/K-Means-Image-Compression

K-Means Image Compression is a Python-based project that compresses an image by reducing the number of colors used. This technique is implemented using the K-Means clustering algorithm, making it ideal for those looking to understand and apply machine learning concepts in image processing.

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bhattbhavesh91/k_means_iris_dataset

KMeans Clustering for IRIS Dataset Classification

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NidhiU-24/Urban-spatial-order-street-network-orientation-configuration-and-entropy

This project provides a comprehensive overview of the principles underlying K-Means and Hierarchical clustering techniques, focusing on key concepts such as Clustering, the Elbow Method, the Silhouette Method, and Dendrogram analysis.

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ndvarga/They-means-they-nearest-neighbors

This repository includes my final project for EECE2140, a ground-up implementation of the k-means and k-nearest neighbors algorithm. I collaborated with Wendy Quizhpi coding this project.

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Adioosin/image-compression-using-k-mean

This notebook consist of implementation of K-Mean clustering algorithm on an image to compress it from scratch using only numpy

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robail-yasrab/RGB_GT_Encoding

Language: Python - Size: 10.7 KB - Last synced at: 12 months ago - Pushed at: almost 5 years ago - Stars: 1 - Forks: 0

NimishNagapure/Problems_on_K-means_Clustering

➕ Solving Problems Using ➗ (✔ K-Means Clustering Algorithm✔)

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mattburnham/Personal_Kernels

Data science projects worked on by Matt Burnham

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Aalaa4444/Hamming_Network

Hamming Network implementation using PCA implementation from scratch

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janhavi-giri/Clustering

Explanation and implementation of Machine Learning Algorithms in Data Science: Clustering

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Samahussien7/Hamming-Network

Hamming Network implementation using pca implementation for reduction all from scratch

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Neill-Erasmus/customer-personality-analysis

Leveraging K-Means clustering for insightful customer segmentation, enabling businesses to tailor products to specific customer types.

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rtimbro185/syr_mads_mar653_marketing_analytics

Syracuse University, Masters of Applied Data Science - MAR 653 Marketing Analytics

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adityaknegi/python-for-data-science-and-machine-learning-bootcamp1

machine learning bootcamp

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soheilabadifard/k_means_algorithm_vs_agglomerative_hierarchical_clustering

Clustering

Language: Python - Size: 116 KB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

ZdrzalikPrzemyslaw/Machine-Learning

Implementations of Machine Learning algorithms in Python

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vaitybharati/K-means

K-means

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prathmachowksey/Clustering

Implementation of K-means, Hierarchical and DBSCAN clustering algorithms in python

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R-Mussabayev/flakylib

Flaky Clustering Library (Minimum Sum-Of-Squares Clustering)

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cristiansgonzalez/Software-Clasificador-de-Heliconias

Colombia es el país con el mayor número de especies vegetales en el mundo. Dentro de ellas, las heliconias desempeñan un importante papel ecológico dentro de los ecosistemas, pues son componentes frecuentes del interior y límites de los bosques, así como de ambientes abiertos como potreros, bordes de carretera y orillas de ríos. En algunos ecosistemas actúan como pioneras en el proceso de regeneración natural de la vegetación y restauración del suelo degradado. Además, mantienen importantes relaciones coevolutivas con otras especies animales y vegetales, constituyéndose en un elemento importante dentro del complejo armazón de la vida en el trópico. La clasificación de especies de plantas es crucial para la protección y conservación de la biodiversidad. La clasificación manual requiere mucho tiempo, es costosa y se necesitan expertos que a menudo tienen disponibilidad limitada. Para hacer frente a estos problemas, en este trabajo se utilizaron tres métodos de clasificación de imágenes SVM (Máquina de Vector de Soporte), ANN (Redes neuronales), KNN (Vecinos más próximos) con distancia euclidiana y de intersección, las cuales entregaron buenos resultados en la clasificación de cuatro especies de heliconias encontradas en la Universidad de Ibagué. Los datos empleados para el entrenamiento, prueba y validación de los métodos fueron imágenes RGB tomadas en el hábitat natural de las heliconias, con el fin de tener información desde su germinación hasta su momento óptimo de corte. Las imágenes fueron preprocesadas, haciendo un ajuste de balance de blancos, contraste y temperatura del color. Para separar las heliconias del fondo se utilizó una técnica de segmentación por grafos mediante SPG. Los descriptores se obtuvieron empleando la técnica conocida como BoW (Bag of Words), encontrando que el número de palabras visuales más adecuadas para la clasificación estaba entre 20 a 40. El método con el que se obtuvieron los mejores resultados fue el KNN; empleando los tres vecinos más cercanos, con una precisión del 97%.

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MuhammadHammad-git/PCA_and_K_Means_Clustering

Here in TASK 1 we apply PCA on a sample video provided in the folder and in TASK 2 we use K-means clustering on IRIS Data Set. **Use the link for best possible experience.

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Abir0810/COVID-19-unsupervised-learning

Unsupervised learning algorithms are used here. agglomerative algorithms and k-means clustering are used here.

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gaurangmhatre/K-Means-implementation-in-python

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suyashdamle/ScalableDataMining

Assignments from the course of Scalable Data Mining

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ExcViral/K-Means-Clustering

Implementation of K-Means clustering algorithm in python

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suubh/Machine-Learning-in-Python

My learnings on different algorithms of Machine Learning with Python .

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SukhmeetSingh2002/Image-Classification-using-bag-of-visual-words

Implemented a Bag-of-visual-words based classifier for classification of MNIST fashion dataset

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rohansun/Image-compression-with-K-means

Language: Jupyter Notebook - Size: 8.44 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

chlaudiah/ClusteringK-Means

An implementation of K-Means for Data Clustering without libraries

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Flow-Glow/K-means

K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

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DayoSamuel/Experimentation-on-Online-Retail-Data-using-two-clustering-Algorithms-to-discover-distinct-groups.-

Experimentation on Online Retail Data using two clustering Algorithms to discover distinct groups.

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Albion007/AnalyzingAndProcessingSMDataTwitterusingPython

Analyzing and processing social media data on Twitter using Python

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Mubarakhussain1/Capstone-project--Retail-Analysis

Online Retail Analysis requirments for business to value drived from the customer.RFM is used to analyze customer value. K-means clustring is used to seggragated the clusters of customers from most valued to least valued customer

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temmyfioye/ClusteringWineDataSet

Applying K-Means and Agglomerative hierarchical clustering to dataset

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PhuongBui712/Image-Compression

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hexsujan0264/Book-Recommendation-System

A recommender system collects the data to improve the user experience on recommending appropriate material to users.

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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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anshul1004/TweetsClustering

Clustering similar tweets using K-means clustering algorithm and Jaccard distance metric

Language: Python - Size: 3.32 MB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 7 - Forks: 4

mahesh147/KMeans-Clustering

A simple K-Means Clustering model implemented in python

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bamtak/machine-learning-implemetation-python

Basic Machine Learning implementation with python

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navreeetkaur/machine-learning-algorithms

Implementation of Machine Learning algorithms from scratch

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mbidu/Machine-Learning-Projects

Machine Learning Projects

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gouravaich/k-means-clustering-movie-ratings

Explore the similarities and differences in people's tastes in movies based on how they rate different movies. Can understanding these ratings contribute to a movie recommendation system for users? Let's dig into the data and see.

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peijin0405/What-Are-The-Commonalities-of-Successful-Social-Enterprises

This project aims to answer the question of the common features of successful social enterprises by applying unsupervised learning on 5,210 B corporations impact data.

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RoundofThree/python-scripts

A collection of scripts by me.

Language: Python - Size: 7.05 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

csgn/data-mining-alg

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icodeitnl/Unsupervised-Machine-Learning-Algorithm-K-Means-for-RFM

Client segmentation. Recency, frequency, and monetary value.

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OfficialBishal/itonics-hackathon

K-means Clustering in Python without using any libraries

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WISSAL-MN/K-Means-Clustering

K-Means-Clustering (ANS)

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Amirhossein-Rajabpour/Information-Retrieval-Project

Information retrieval course project

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deepakrana47/K-mean-clustering

K-mean clustering

Language: Python - Size: 25.4 KB - Last synced at: almost 2 years ago - Pushed at: about 6 years ago - Stars: 5 - Forks: 3

SameetAsadullah/K-Means-Clustering-on-Abalone-Dataset

Implemented K-Means Clustering on the given Abalone Dataset using Python Language

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SameetAsadullah/K-Means-Clustering-on-Iris-Dataset

Implemented K-Means Clustering on Iris Dataset using Python Language

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alexander-lee/k-means-image-compression

Using K-means Clustering for Image Compression

Language: Python - Size: 346 KB - Last synced at: 18 days ago - Pushed at: over 6 years ago - Stars: 9 - Forks: 3

berkerol/tum-social-gaming-projects

Projects for Social Gaming course in Technical University of Munich

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roshancyriacmathew/Unsupervised-machine-learning-on-iris-dataset

This project demonstrates how to implement k-means clustering on unsupervised data. The dataset used here is the famous iris dataset. To see a complete video explanation on this topic, check out the attached link.

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kivancgunduz/fraud_detection_enron

Language: HTML - Size: 1.12 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 0 - Forks: 0

gurkandemir/Color-Quantizer

Color quantization is the process of reducing number of colors used in an image while trying to maintain the visual appearance of the original image. In general, it is a form of cluster analysis, if each RGB color value is considered as a coordinate triple in the 3D colorspace.

Language: Python - Size: 9.42 MB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 3 - Forks: 3

chllrisll/Neural_Network_Charity_Analysis

Binary Classification in Neural Network using TensorFlow

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chllrisll/Cryptocurrencies

Compare ML models K-means algorithm vs hierarchal-clustering in crypto

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dzvlfi/K-Means

K-means with error while creating some centroid haha

Language: Python - Size: 135 KB - Last synced at: about 2 years ago - Pushed at: about 7 years ago - Stars: 1 - Forks: 0

prathmachowksey/NFHS-4-Analysis

This project explores factors affecting child stunting and influence of contraceptive awareness in women on other health indicators using NFHS-4 (National Family Health Survey - 4, India) data.

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JediRhymeTrix/COSC-6339-HW1

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easonlai/customer_segmentation_with_rfm_k-means

This is a code sample repository to leverage the famous Online Retail dataset by UCI Machine Learning Library to perform Customer Segmentation with RFM Modelling and perform clustering by K-Means cluster algorithm.

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KelvinHong/k-mean-clustering

K-means Clustering, an unsupervised machine learning method for detecting clusters among a large dataset.

Language: Python - Size: 45.9 KB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 0

TkhiienLok/Clustering-in-Numpy-Pandas

Jupyter Notebook showing clustering with K-means algorithm.

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alainiliho/K-means-From-Scratch

This implementation is done for the purpose of understanding and getting an overview of clustering and K-means especially. Any suggestions for improvement are welcome or something the same way is warmly welcome.

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AumkarG/Data-Mining-and-BDA

Language: Jupyter Notebook - Size: 438 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 1

agx01/iris_kmeans

Implementing K-Means Clustering on the Iris data set using Euclidean and Manhattan metrics

Language: Python - Size: 1.48 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

neemiasbsilva/kmeans-image-segmentation

Apply K-means technique to plant segmentations

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Antika-Roy/kMeansClusteringWithMNISTDigitDataset

A demo of K-Means clustering on the handwritten digits data

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mkamran37/CSE-601-Data-mining

CSE 601 Data mining and bioinformatics

Language: Python - Size: 4.06 MB - Last synced at: about 2 years ago - Pushed at: over 5 years ago - Stars: 3 - Forks: 0

HxnDev/K-Means-KNN-and-Davies-Bouldin-Index-on-Multi-Class-Dataset

Calculating DB index, KNN and K-Means from the given dataset

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splAcharya/K_Means_Clustering

An implementation of K Means Clustering Algorithm from scratch. Includes implementation K Means Clustering with Smart Initialization.

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Sidl419/k-means_experiments

Реализация алгоритма "K средних" и исследование его границ применимости и базовых свойств.

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aroraayush/k-means-clustering-implementation-sklearn

Custom implementation for k-means in python, comparison with the one in sklearn.

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SergioNoivak/K_means

Language: Python - Size: 2.93 KB - Last synced at: 2 months ago - Pushed at: almost 6 years ago - Stars: 2 - Forks: 0

Ruchikamodgil/Clustering-Algorithms

Language: Jupyter Notebook - Size: 275 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0

nachiketdhamankar/ml-assignments

Machine Learning Assignments CS 6140 at Northeastern University Summer 2019.

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BharatMaheshwari96/IRIS-K_Means-PCA

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sarthak815/Clustering_Neighborhoods_Toronto

In this notebook I have gathered data on neighborhoods of Toronto using geopy and clustered them using K-means. I have used folium to plot these points on the map to improve the visualization.

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sadimanna/kmeansplusplusclustering

K-Means++ Clustering using Gap Statistic for determining optimal value of K in Python

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Harshpatel44/K-means_algorithm

This repository uses K-means algorithm on MNIST dataset and evaluate the results

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ThomasKranz/GeoDataScience

Introduction to Geospatial Data in Python using Google API and GeoPandas

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saminens/Machine-Learning-Models

Machine Learning models on Anomaly detection, Recommender system on movies based on IMDB dataset, Digit Identification using Logistic regression, Neural network based facial feature recognition, PCA, SVM based Spam filter, Logistic Regression - Nelder Mead

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modee00/Data-Exercise

Describe the data set. Create predictive models, describe, and assess their accuracy. Visualize. Determine source(s) of data. Assign to source(s).

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akshay326/k-means-clustering-api

Sample Python API using flask, uses PyTorch to cluster image vectors

Language: Python - Size: 46 MB - Last synced at: 6 days ago - Pushed at: almost 7 years ago - Stars: 4 - Forks: 1