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Developed a Windows tool using PyQt5, integrating K-means clustering for data analysis. The application recommends optimal cluster numbers, identifies cluster members, and allows exporting results to Excel.
An interactive approach to understanding Machine Learning using scikit-learn
Clustream, Streamkm++ and metrics utilities C/C++ bindings for python
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.
Capstone Project for the IBM Professional Certificate on Coursera
Pytorch implementation of standard metrics for clustering
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.
This repository contains introductory notebooks for principal component analysis.
K-means is a least-squares optimization problem, so is PCA. k-means tries to find the least-squares partition of the data. PCA finds the least-squares cluster membership vector.
It's the HAC algorithm that Im using to sort newspaper articles by news. You can adapt it to pretty much any type of text.
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.
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.
Clustering K-Means with Streamlit App Deployment
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.
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.
Cryptocurrency classification system using dimensionality reduction with PCA & t-SNE and cluster analysis with K-Means
This repository contains introductory notebook for clustering techniques like k-means, hierarchical and DB SCAN
This project aims to assist stakeholders in selecting an optimal location for a new restaurant in Chennai, Tamil Nadu, India.
To perform customer segmentation using Python unsupervised learning model
Given the e-commerce data, k-means clustering algorithm is used to cluster customers with similar interest. The data was collected from a well known e-commerce website over a period of time based on the customer’s search profile.
Iris dataset
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."
It's the continuation of my kleanee_ClusteringAnalysis project, in which I include the Silhouette Method for KMeans Clustering.
кластеризация клиентов на основе их покупательской способности
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
Clustering Clients for Insiders Loyalty Program.
An end-to-end project on clustering (unsupervised ML)
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.
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.
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
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.
Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)
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.
Leverage unsupervised machine-learning techniques (K-means) to segment mall customers
This repository contains code for creating ml model for clustering