Knee point detection in Python 📈

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.

Machine learning utility functions and classes.

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.

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

Implementation of hierarchical clustering on small n-sample dataset with very high dimension. Together with the visualization results implemented in R and python

This repository contains introductory notebooks for principal component analysis.

全球新冠肺炎的数据分析，包括基础知识有：kmeans算法设计，SSE算法设计，分级聚类算法设计，cophenetic distance 算法设计。

Project on hyperspectral-image clustering for the Μ402 - Clustering Algorithms course, NKUA, Fall 2022.

🗽🚕 Performance of data analysis in taxi trips in NYC and creation of a Random Forest Regressor in order to predict the duration of taxi trips.

This project clusters white wines based on their chemical properties to understand their relationship with quality ratings, using techniques like k-means and PCA.

Use unsupervised machine learning, PCA algorithm, and K-Means clustering to analyze and classify a database of cryptocurrencies.

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.

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

Problem Statement: This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form.You are owing a supermarket mall and through membership cards , you have some basic …

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.

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.

Text classification and topic extraction from COVID-19 articles

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.