lilyxgates/queen_of_pop

Using Spotify data and K-Means clustering, this project analyzes top-streamed female artists—like Taylor Swift, Beyoncé, and Ariana Grande—by comparing their popularity, follower counts, and musical genres to uncover patterns among today’s queens of pop.

Language: Python - Size: 4 MB - Last synced at: 3 days ago - Pushed at: 3 days ago - Stars: 0 - Forks: 0

aelmah/Data-Mining

Data Mining Projects

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anastasius21/CustomerSegmentation

Customer Segmentation Model using KMeans Clustering

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arvkevi/kneed

Knee point detection in Python :chart_with_upwards_trend:

Language: Python - Size: 12.2 MB - Last synced at: 30 days ago - Pushed at: 2 months ago - Stars: 764 - Forks: 75

GiatrasKon/Clustering-Countries-Socioeconomic-Health-Analysis

Exploration and analysis of socio-economic and health data from 167 countries using MATLAB. Application of clustering algorithms to identify development patterns, visualize disparities, and understand global trends.

Language: MATLAB - Size: 2.72 MB - Last synced at: 5 days ago - Pushed at: 6 months ago - Stars: 2 - Forks: 0

benjaminjvdm/Retail_Cluster_Analysis

This project uses K-means clustering to identify distinct customer segments based on their income and spending patterns. The insights gained can be used to create more effective, personalized marketing campaigns.

Language: Python - Size: 76.2 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 0

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: 64.5 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

UdaySrivastava/SCT_ML_2

This project implements a K-Means clustering algorithm to group customers of a retail store based on their purchase history. Customer segmentation is a crucial task in retail analytics, helping businesses understand customer behavior, personalize marketing strategies, and improve customer engagement.

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dorianDraper/KMeans-project

Exploring Unsupervised ML models. For this project we'll classify houses according to their region and median income from the well-known California Housing dataset.

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aryansk/Customer-Segmentation-Analysis

Advanced customer segmentation project using K-Means clustering to analyze customer behavior based on annual income, spending score, and age.

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d-kleine/kneed_visualizations

Visualizations for kneed article

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zaw-may/Customer-Behavior-Analysis

Customer Behavior Analysis

Language: Jupyter Notebook - Size: 111 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

rhettadam/Optimal-K

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

Language: R - Size: 11.7 KB - Last synced at: about 1 month ago - Pushed at: 5 months ago - Stars: 2 - Forks: 0

deliprofesor/K-Means-Clustering-for-Retail-Data-Analysis

This project uses K-Means clustering to segment wholesale customers based on their spending habits. The data is preprocessed, scaled, and clustered into four groups. The Elbow and Silhouette methods determine the optimal number of clusters, and results are visualized using boxplots and scatter plots to uncover spending patterns.

Language: R - Size: 28.3 KB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

eceyy/Data_Glacier_Intership_2023

Data Glacier Internship Program

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pavankethavath/Music_Recommendation_Engine

An advanced Music Recommendation System leveraging a Spotify dataset to deliver personalized song suggestions. The project applies KMeans clustering, PCA, t-SNE, and cosine similarity for precise recommendations. Built with a user-friendly Streamlit interface, it showcases data preprocessing, unsupervised learning, and insightful visualizations.

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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.

Language: Python - Size: 1.38 MB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 0

Fedesgh/CreditCard_Customer_Segmentation

Brief Customer Segmentation with Kmeans

Language: Python - Size: 44.7 MB - Last synced at: 3 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

aristotle-malichetty/customer-segmentation-product-recommendation-engine

Customer Segmentation and Product Recommendation for ACME Innovations - School Project

Language: R - Size: 15.5 MB - Last synced at: 7 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

Fahrettinsolak/AI-Map-Based-Geographic-Clustering-Project

This project focuses on clustering crime incidents in San Francisco using the K-Means algorithm. The dataset is obtained from Kaggle and contains information about crime types, geographical coordinates, and other relevant features. The goal is to identify crime hotspots through geographic clustering and visualize the clusters on an interactive map.

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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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abrhame/Telecom-Data-Analysis

This repository contains a comprehensive analysis of telecom user behavior and engagement. It includes: - User Overview Analysis: Identifies top handsets and manufacturers, explores user behavior across various applications, and performs dimensionality reduction for deeper insights. - User Engagement Analysis: Evaluates user engagement

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ceenaa/Topic-extraction

Text classification and topic extraction from COVID-19 articles

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srosalino/Clustering_Antartic_Penguin_Species

Using penguin dataset collected from Palmer Station, Antarctica, this project applies data science techniques to identify and group penguins based on physical traits in the absence of species labels

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shimazahabi/Rayan-AI-Course

RAYAN AI international competition training course : Homeworks and Projects

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Sherryyy00/GRIP-TheSparksFoundation

This repository features three data science tasks from GRIP October'23: Linear Regression on student scores, K-Means Clustering on the Iris dataset, and Exploratory Data Analysis on a retail dataset.

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Sherryyy00/KMeans-Clustering

This project demonstrates the use of the K-Means clustering algorithm on the Iris dataset, a classic dataset in machine learning. It includes code for loading the dataset, determining the optimal number of clusters using the Elbow Method, applying K-Means clustering, and visualizing the resulting clusters and centroids.

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fardinabbasi/PCA

Applying Principal Component Analysis for image compression, exploring how varying numbers of principal components affect image quality and compression ratio.

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melodiw82/Rayan_AI

Vault of variety of topics taught for Rayan Contest

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ConradKleykamp/Wine-Analysis-Clustering

Employing unsupervised learning techniques to cluster Italian wines grown by three different cultivars

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Tobi1018/Cryptocurrencies

Cryptocurrencies Analysis using Unsupervised Machine Learning.

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chaitanya-chafale/KMeans-Elbow-Method

This project uses the CGPA.csv file as the dataset (provides CGPA of the students) and uses the K-means algorithm to cluster the points using the elbow point method.

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AmirMotefaker/Customer-Segmentation-using-R

Customer Segmentation using R

Language: R - Size: 29.2 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0

dhwabqryh/Data-Mining-I

Tugas praktikum Data Mining I

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hugohiraoka/Credit_Card_Customer_Segmentation

Classification Model of Potential Credit Card Customers

Language: HTML - Size: 12.7 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 0

RimpleDabas/Customer-Segmentation

The aim for this project is to segment customers. The segmentation was done based on RFM as well as K-means clustering using SQL and Python programming language.

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TejaswiniKatale/New-Approaches-to-Robust-Homogeneous-And-Clearly-Identifiable-Cluster-Creation

A new clustering technique is proposed that incorporates outliers during clustering. The proposed approach involves using a variable, (λ > 0), to define the cluster radius. Weighted an

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AnshikaBansal2004/senior-living-segmentation-analysis

Analysis to optimize services & resident satisfaction in senior living facilities by segmenting population based on characteristics & behaviors.

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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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Goww-99/TSF-GRIP_Task-2_Sol2-by-GV

Task-2 Completed as a DSBA Intern @ The Sparks Foundation

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KaranSharma18/Customer-Segmentation-

This Repository uses K-Means Clustering Algorithms , Silhouette Analysis and Elbow method in order to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly

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educarrascov/Diplo_BigData

Repositorio creado para almacenar archivos, script y el informe final del curso de modelamiento estadístico del Diplomado en Big Data de la Pontificia Universidad Católica de Chile.

Language: R - Size: 11.7 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

AbbasPak/Pattern-recognition-by-using-principal-component-analysis-PCA-

Analysing practical examples by using principal component analysis (PCA) and Clustring

Language: R - Size: 794 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 1

chasenuzum/naz_data_clustering_pricing

Beer data clustering and pricing, evidence based pricing with Random Forest.

Language: Python - Size: 11.6 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

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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devanisdwi/skripsi

Learning Styles Segmentation using K-Prototypes

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Ninad077/Machine_Learning-K_means_clustering

Content: Unsupervised ML, Clustering, Customer Segmentation, WCSS, elbow method

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cavallon/CryptoClustering

This project applies Python and unsupervised learning to predict cryptocurrency price changes over 24 hours or 7 days. It involves data preparation, clustering using K-means, and visualizing results to understand the impact of using fewer features in clustering.

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juliaobenauer/Customer-segmentation-with-ML

Udemy Machine Learning project

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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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mattburnham/Personal_Kernels

Data science projects worked on by Matt Burnham

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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.

Language: Python - Size: 7.38 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

orestasdulinskas/song_recommendation_system

The project creates a robust song recommendation system using K-means clustering with Spotify data. By grouping songs based on musical attributes like danceability, energy, and acousticness, personalized recommendations will be generated, enhancing user satisfaction and engagement in music discovery.

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kaylah176/Unsupervised_Learning

Objective: Utilize unsupervised learning to cluster crptocurrencies by their performance in different time periods.

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haniye6776/clustering-countries

clustering with optimal number of clusters

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orestasdulinskas/customer_segmentation

The project uses KMeans clustering on the Global Superstore dataset to categorize customers based on their buying habits, aiming to help retailers make better business decisions by tailoring their marketing strategies and improving their inventory management.

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Saurabh620/Mental_Health_Analysis_Machine-_Learning

Analyzing a dataset to understand mental health factors, this project employs Python tools for preprocessing, exploration, segmentation, trend analysis, and modeling.

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singh-pawan/Netflix-Movies_TV-Shows-Clustering__Unsupervised-ML-

About Unsupervised Machine Learning-Netflix Recommender recommends Netflix movies and TV shows based on a user's favorite movie or TV show. It uses a a K-Means Clustering model to make these recommendations. These models use information about movies and TV shows such as their plot descriptions and genres to make suggestions.

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SaadARazzaq/Cluster-Analysis-Elbow-Method

implements the elbow method to determine the optimal number of clusters (k) for a given dataset using the K-means clustering algorithm.

Language: Python - Size: 3.91 KB - Last synced at: about 2 months ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

deva-246/K-Means-Clustering-using-Python-on-Employees-Income-and-Age

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Wolverine-Shiva/Netflix-Movies_TV-Shows-Clustering__Unsupervised-ML-

Unsupervised Machine Learning-Netflix Recommender recommends Netflix movies and TV shows based on a user's favorite movie or TV show. It uses a a K-Means Clustering model to make these recommendations. These models use information about movies and TV shows such as their plot descriptions and genres to make suggestions.

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Cintia0528/Data_Science-Unsupervised_Machine_Learning

I aim to automate playlist creation for Moosic, a startup known for manual curation, using Machine Learning, while addressing skepticism about the ability of audio features to capture playlist "mood."

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sumitkutty/Sparks-Foundation-Internship

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

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damaniayesh/Customer_Segmentation

This project offers the K-means clustering algorithm to identify potential customers for the manager to target with calls, streamlining the process by focusing on specific customer groups.

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janhavi-giri/Clustering

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

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labrijisaad/Optimal-K-in-K-Means-Clustering

Using the Elbow Method and Silhouette Analysis to find the optimal K in K-Means Clustering.

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eeshwarib23/GRIP-THESPARKSFOUNDATION_-VirtualInternship

**Projects in this Repo:** Basic Regression Model for predicting student scores, Unsupervised Learning with K-Means clustering to find optimal K value using the Elbow method, and an introduction to Decision Trees with visualization.

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Aysenuryilmazz/ClusteringAnuranCallsMFCCs

Clustering for Anuran Calls with 4 different families

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pablosolanoc/k-MeansClusteringResearch

Implementing K-Means clustering for research about environmental awareness and environmental practices of Ecuadorian households regarding the enviroment

Language: Python - Size: 13.9 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 1

mishika12/Clustering-Segmentation_of_Survey_Respondents

Deploying clustering machine learning algorithms to segment survey respondents

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mrafifrbbn/airline-customer-segmentation

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

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tinabl/world-KMeans

application of ML K-means algorithm on world population and economy dataset

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saikrishnabudi/Clustering

Data Science - Clustering Work

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Richard-Vuong/Movie-Recommender-Engine

This is a movie recommender engine I created for my capstone project, that utilises text vectorization, and K-means clustering with the elbow and silhouette method to evaluate the optimal K value.

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glendawur/indices_kmeans

Language: Python - Size: 6.55 MB - Last synced at: over 1 year ago - Pushed at: over 1 year 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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kamilalfian/Clustering

This project aims at clustering flowers using iris dataset, and tested how good a model can predict the flower's species based on the clusters made.

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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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VinayVirraj/Customer-segmentation

An analysis and approach to customer segmentation

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anishdulal/clustering

I have performed district clustering using 3 clustering algorithms(k-means, dbscan and gmm).

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VMD7/K-Means-Clustering-of-Iris-Dataset

This is task 2 of The Sparks Foundation GRIPNOV20. This repository is basically focused on Unsupervised Machine Learning. I used K-Means Clustering Algorithm to make clusters of Iris dataset.

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ReneDCDSL/Cuisine_Cultural_Diffusion

Repository with code and images for my Cultural Diffusion through Cuisine project.

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kennedyCzar/NLP-PROJECT-BOOK-INSIGHTS-WITH-PLOTLY

Plotly-Dash NLP project. Document similarity measure using Latent Dirichlet Allocation, principal component analysis and finally follow with KMeans clustering. Project is completed with dynamic visual interaction.

Language: Python - Size: 171 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 10 - Forks: 5

kennedyCzar/EIGEN-FREQUENCY-CLUSTERING-USING-KMEANS-DBSCAN-PCA-HDBSCAN

EIGEN FREQUENCY CLUSTERING USING [KMEANS] [KMEANS & PCA ] [DBSCAN] [HDBSCAN]

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sohilsshah91/nyhais-tcga-series-kmeans

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venkatesh-eranti/Clustering_Project_online-retail_case-study

For an UK based non-store online retail for which we need to cluster it's customers in to different groups so that we can run targeted campaign for each group

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Shashank9830/avantari_tech

AutoEncoder model for finding N similar images to a given input image and partitioning the entire image dataset into K groups.

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sharmaroshan/Clustering-of-Mall-Customers

Clustering Analysis Performed on the Customers of a Mall based on some common attributes such as salary, buying habits, age and purchasing power etc, using Machine Learning Algorithms.

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aparajitad60/The-Sparks-Foundation---Machine-Learning-Data-Science

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.

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venkatesh-eranti/clustering_cricket-data

Analyze the data of batting figures of batsmen in ODI matches by Choosing strike rate and average as the two factors on which clustering the data

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badhonparvej481/K-Means-Clustering_ML

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ZhenyuWangg/Mall-Customers-Segmentation--Classification-using-Machine-Learning-

Utilized Python-based unsupervised machine learning algorithms, including K-Means and DBSCAN, to effectively segment the mall customer market.

Size: 780 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

chiarasaini/Supervised-and-unsupervised-analysis

Supervised and unsupervised analysis

Language: R - Size: 8.98 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

GesangPJ/kmeans_kopi

K-Means Project For Coffe Plant

Language: PHP - Size: 16 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

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.

Language: Jupyter Notebook - Size: 1.21 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Nastiiasaenko/Russian_ecology_project

Language: Jupyter Notebook - Size: 871 KB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 19.8 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

Chandrakant817/KNN-Classifiers-Implementation-

KNN Classifiers Implementation using anonymized data.

Language: Jupyter Notebook - Size: 499 KB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 815 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

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 at: 3 months ago - Pushed at: almost 2 years ago - Stars: 2 - Forks: 0