unsupervised-machine-learning | Topic

Topic: "unsupervised-machine-learning"

dachosen1/Frameworks-Final-Project

Analyzing ratings and reviews for restaurants across 31 European cities

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sharmaroshan/Understanding-PCA

Principal Component Analysis is One of the Most Popular Dimensionality Reduction Algorithms used in Machine Learning Which comes under Unsupervised Way of Learning. It is also Used as a way of Feature Extraction where, More Information is Extracted from all the Existing Attributes, in just some 3-4 Attributes using the Concepts of Eigen Values and Eigen Vectors.

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BATspock/Image-Segementation-using-LM-filters

Segments image using LM filters and K Means

Language: Python - Size: 5.86 KB - Last synced at: over 2 years ago - Pushed at: over 6 years ago - Stars: 2 - Forks: 2

utk09/MLBootcampJanuary

Getting-Started Examples of Supervised and Unsupervised Machine Learning.

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SimplifyData/Customer-Segments-Arvato

Analyze customers using unsupervised learning, PCA and K-Mean Clustering of Arvato dataset

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bnriiitb/MLND

This repository contains all the Machine Learning and Deep Learning projects that I worked on, spans across the two sub domains of Artificial Intelligence i.e., Computer Vision and Text Processing as a part of Machine Learning Nano Degree program at Udacity.

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varunnandu/plagiarism-detection-nlp

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sozelfist/handson-ml3 Fork of ageron/handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

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danial-hosseinpour/Machine-Learning-Projects

This repository showcases a collection of machine learning projects that I have worked on, applying various algorithms and techniques to solve real-world problems.

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GGasset/CudaNeatNetwork

Easy to use, Neural Network framework made from scratch in CUDA/C++, featuring LSTMs, able to train using Supervised learning and Evolution methods

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daniau23/customer_segment

Segmentation of customers using Agglomerative clustering and analysis using PowerBI

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udaycodespace/Unsupervised-Anime-Recommendation-System

A content-based anime recommendation system ⚡using unsupervised learning (K-Means). Suggests similar anime using synopsis & genre—no user history needed. Built during Edunet AI + Azure Internship.

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amirabaskanov/valorant-team-optimizer

Clustering Player Archetypes to Optimize Team Composition in Valorant Esports

Language: Python - Size: 1.65 MB - Last synced at: 13 days ago - Pushed at: 13 days ago - Stars: 1 - Forks: 0

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

Clustering similar content by matching text-based features and building a recommendation system

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PhenomSG/ml-notebook

This project is designed for personal learning and exploration of fundamental machine learning concepts.

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aomdahl/gleanr_workflow

Sparse factorization framework to integrate GWAS studies and identify shared latent genetic components

Language: R - Size: 54.7 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

nullHawk/k-means

Implementation of K-means algorithm from scratch with adaptive distance metrics

Language: Python - Size: 57.6 KB - Last synced at: 27 days ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

zenklinov/Clustering_K-Means_Metrics_PCA

Comparing Euclidean Distance, Manhattan Distance, Cosine Distance, with PCA in K-Means Clustering

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Shailesh-Padhariya/Online-Retail-Clustering

This project applies the K-Means clustering algorithm to segment customers based on their purchasing behavior. The dataset used contains transaction data for an online retail store, and the project aims to group customers using three key metrics: Recency, Frequency, and Monetary Value (RFM).

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codeasarjun/_easy_machine_learning

This repo contains a comprehensive tutorial on machine learning with practical implementations and examples using Python.

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jzhangc/git_RBioArray

Simple to use package for OMICS data analysis (featuring limma, WGCNA, GSEA)

Language: R - Size: 42.7 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 1 - Forks: 0

KyriakosPsa/BreastCancer-SingleCell

Replication and extension of the single cell RNA-seq analysis paper "Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer"

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ankushmallick1100/Task-2-The-sparks-Foundation-Data-Science-and-Business-Analytics-Internship

Task 02: Clustering using Unsupervised Machine Learning

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virajbhutada/ML-projects

A collection of machine learning mini-projects and analyses developed using Jupyter Notebook. Each project demonstrates practical applications of machine learning algorithms on a variety of datasets, covering techniques from exploratory data analysis (EDA) to model training and evaluation.

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DrStef/MIMII-Unsupervised-classification-of-valve-sounds

Malfunctioning Industrial Machine Investigation and Inspection

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iPoetDev/ibm-skills-ai-colab-sessions

PORTFOLIO: IBM Skills Build Programme for Artificial Intelligence - CoLab - Live Sessions & Final Project

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srijan-Git1247/ML.Net-Application-FileTypeClassifier

A File Type Classifier that predicts whether a file is a document, executable or a script based on a given set of attributes statically extracted from a file using K-means clustering trainer based on the Yinyang Method.

Language: C# - Size: 20.5 KB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0

SarahAmiraslani/solar-wind-coronal-origin-ml

This project uses in-situ measurements and unsupervised learning to cluster solar wind with similar properties and map it to its coronal origins, aiming to enhance predictions of heliospheric phenomena through machine learning.

Language: Jupyter Notebook - Size: 132 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0

AmeenUrRehman/Machine-Learning-Algorithms-with-examples

This repository includes Machine Learning Algorithms

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Abhi-37/heart-attack-prediction

This script performs KMeans clustering and trains a neural network to predict heart disease, including data preprocessing, clustering visualization, and model evaluation.

Language: Python - Size: 541 KB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 1 - Forks: 1

diodz/chile-state-of-the-union-LDA

Topic modeling of state of the union speeches given by Chilean presidents by a Latent-Dirichlet-Allocation (LDA).

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danieladam7/writing-patterns-and-changing-detection

Stylometry approach detecting writing patterns and changings using NLTK, XML-roBERTa, Gensim topic modelling and unsupervised-PCA learning

Language: Python - Size: 9.24 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

tohid-yousefi/Unsupervised_Learning_in_Machine_Learning_Algorithms

In this section, we will examine unsupervised learning algorithms in machine learning.

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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.

Language: Jupyter Notebook - Size: 8.11 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

canayter/unsupervised-machine-learning

Utilizing Python and unsupervised learning to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

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A-sad-ali/Machine-Learning-Specialization

Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG

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MSnellgrove/Springboard

Projects and case studies from my time studying Data Science with USF

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abhi227070/Customer-Segmentation-Using-KMeans

Utilizing KMeans clustering, this project segments customers for targeted marketing and analysis. Developed on Google Colab, it imports datasets from Kaggle, performs data analysis, preprocessing, and model building, providing actionable insights for businesses.

Language: Jupyter Notebook - Size: 82 KB - Last synced at: 20 days ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

zeppehpt1/treeclust

My code for my master thesis - includes a automatic pipeline to evaluate the clustering capabilities of single tree crown images.

Language: Python - Size: 27.6 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

snikumbh/seqArchR

seqArchR: Identifying (promoter) sequence architectures de novo using NMF

Language: R - Size: 11.7 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 1

neelsoumya/visualization_lecture

visualization lecture

Language: R - Size: 155 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 1

CtrlAltFly/AIML-Projects

these are my projects that i submitted for AIML course with great lakes & some good notebooks with great explaination of the topics

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brian-kipkoech-tanui/clustering

Clustering Analysis

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UzoigweC/Trade-Ahead

Unsupervised Learning: Analyze the stocks data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group.

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NandanKumar07/ML_CODE_REVISIT

The code in this repository corresponds to exercises, projects, and examples covered in the respective courses of the Machine Learning Specialization. The goal is to review and reinforce the concepts learned during the specialization.

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eeshwarib23/DataScienceInsights-Regression-Unsupervised-Learning-EDA-Statistics

**Projects in this Repo:** Regression on Advertising Sales, Unsupervised Learning on Credit Card Transactions, Mushroom Classification (Decision Tree vs. RandomForest), EDA on Retail Data, Exploration of EDA Techniques, Statistical Concepts Exploration, Tennis Analysis SLC - KNN and Python

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huseyincenik/machine_learning

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YashJain251198/Machine_Learning_Projects

I am Applying My Whole Machine Learning Concepts On Several Datasets and Solve Their Problems in a Relevant Manner.

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Gallillio/Unsupervised_Clustering_Music_Recommendation_System

Music Recommendation System using Unsupervised Machine Learning Clustering Methods using K-Means, Fuzzy C Mean DBSCAN, Gaussian Mixture Model, BIRCH and Agglomerative Clustering

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KhushiBhadange/IBM-MachineLearning-ProfessionalCertificate

Here are the learnings from IBM Machine Learning Professional Certification. This repository serves as a comprehensive resource and path for the Machine Learning. The repository delves into various facets of machine learning, equipping learners with skills essential for the data-driven future.

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Aditya-modi20/unsupervsied_learning

repository for the unsupervised learning university project

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MohammedAhdal/Football-Analysis-and-Clustring

Streamlit App for The Analysis of team tactics and player performances

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MohammedAhdal/Football-Team-Player-Performances-Analysis

The analysis of team tactics and player performances using clustring modeling

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ajitmane36/Netflix-Movies-and-Tv-Shows-Clustering-ML-Unsupervised

The Netflix Movies and TV Shows Clustering Project aims to cluster similar movies and TV shows available on Netflix into different clusters based on their content. The project uses Natural Language Processing (NLP) and unsupervised machine learning techniques to analyze the dataset, including K-Means, Hierarchical clustering, and DBSCAN algorithms.

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nikhilbordekar/Book-Recommender-System

The "Book Recommender System" project aims to provide personalized book recommendations using advanced algorithms and user data. It enhances the reading experience by suggesting books that match users' preferences and interests.

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yassmin1/machine_Learning_projects

Machine Learning Projects

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SadeTosin/M-B-Insurance-Customer-Segmentation

This repo contains Customer Segmentation via Unsupervised Learning for business insights and targeted marketing strategies.

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arashshams/Anomaly_Detection_with_PCA

This repo hosts an Unsupervised Machine Learning model for Anomaly Detection of credit card transactions.

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slothengineer/MachineLearning-concepts

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VaishnaviSh14/AnomalyDetection-and-TimeSeries

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raj-pulapakura/Basketball-Players-Analysis

This repo features an analysis on various basketball players, using unsupervised learning techniques.

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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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TheKidPadra/Machine-Learning-Stanford-University-Coursera

This Repository contains Solutions to Lab Assignments/slides and my personal Notes of the Machine Learning (2022) from Stanford University on Coursera taught by Andrew Ng.

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singh-jagjot/K-means-Clustering-From-Scratch

Implementation of K-means Clustering from scratch using mainly NumPy and using it for PNG image compression.

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ChiaraVanderputten/Real-Time-Domain-Adaptation-in-Image-Segmentation

The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.

Language: Python - Size: 2.68 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

secil-carver/KMeans-Cluster-Analysis

Identifying specific groups in customer base with K-Means clustering

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M-Adil-AS/K-Means-Cluster

AI Unsupervised Algorithm

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Scrayil/k-means

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear omparison between the sequential and parallel execution of the clustering steps.

Language: C++ - Size: 29.1 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

treelunar/2023_Capstone_BSTN

ReelWhisperer: Story-Driven Movie Recommendation System

Language: Python - Size: 177 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

huzaifa-57/Anomaly-Detection-using-Unsupervised-Machine-Learning

This project is developed to detect anomalies on the recorded data. It is deployed using Flask

Language: Python - Size: 1.71 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 1

3rd-Son/web-scrap-of-books.toscrap

Language: Jupyter Notebook - Size: 128 KB - Last synced at: 4 days ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 0

allyssonmacedo/ds-naive-bees

Working with image data, building classifiers using traditional techniques, and leveraging the power of deep learning for computer vision.

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SRIDHAR3131/E-commerce-Customer-Segmentation

Analyzing the existing customer data and getting valuable insights about the purchase pattern

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SAhmadrezaAnaami/basics-of-AI-and-ML

This presentation provides an overview of Machine Learning, including its history, types, algorithms, and applications. It covers topics such as supervised and unsupervised learning,linear regression, classification, clustering, model selection, evaluation, and deployment.

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samchak18/Capstone-Project-4_Netflix-Movies-and-TV-Shows-Clustering

AlmaBetter Capstone Project - Unsupervised ML: Netflix Movies And Tv Shows Clustering This project is a part of the Almabetter Pro Program Curriculum(Full Stack Data Science)

Language: Jupyter Notebook - Size: 10.9 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

isksamiul/Machine_Learning_Coursera_AndrewNg

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG

Language: Jupyter Notebook - Size: 328 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 2

Mertdil/IBM_machine_learning

IBM Machine Learning Professional Certificate with Coursera

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bharatkulmani/Customer-Data-Sorting

This Program is for Clustering Customer Data On the Basis of their Spending, Income,Family and Children.

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Chandrakant817/Mall-Customer-Segmentation-using-K-Means

Mall Customer Segmentation using K-Means Algorithm

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micolrosini/Real-Time-Domain-Adaptation-in-Image-Segmentation

The main objective of this repository is to become familiar with the task of Domain Adaptation applied to the Real-time Semantic Segmentation networks.

Language: Python - Size: 5.14 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

diamidiaan/Customer-Segmentation-Clustering-and-Analysis-in-Python

This repository contains my data analysis of Mall Customers using some Unsupervised Machine Learning in python.

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SandroMartens/ds2l-som

A Local Density-based Simultaneous Two-level Algorithm for Topographic Clustering

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Gandhasiri-Rahul-Mohan/Association-Rules-Q1-Books

Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots

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mahin-arvind/NETFLIX-MOVIES-AND-TV-SHOWS-CLUSTERING.

This project aims to cluster the video content available on Netflix based on the company’s site data. Apart from aiding in the development of an efficient recommendation system, clustering the video content would also provide information about the type of content the company is interested in listing on its site. Thus giving an insight to content creators and filmmakers on the type of video content in demand.

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renewollny/Spotify-Playlist

MachineLearning-Project (Clustering)

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Rl16193/Cryptocurrencies

Accountability Accounting, a prominent investment bank, is interested in offering a new cryptocurrency investment portfolio for its customers. The company, however, is lost in the vast universe of cryptocurrencies. We create a report that includes what cryptocurrencies are on the trading market and how they could be grouped

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aifenaike/Market-Basket-Analysis-InstaCart-Orders

Can we use association mining and machine learning to understand groceries purchase? Can we predict products that a user will buy again, try for the first time or add to cart next during a session? Can we segment our customer base into several cohorts based on their preferred products and purchase behaviour?

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