Knee point detection in Python 📈

Machine learning utility functions and classes.

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.

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.

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

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.

This repository contains introductory notebooks for principal component analysis.

🗽🚕 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.

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 …

Segment airline customers, analyze the characteristics of different customer categories, compare the value of customers from different customer categories, provide personalized services for categories of customers with different values, and formulate the right marketing strategy.

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.

Implementation of robust knee/elbow finding algorithm 'Kneedle' in c#

📉 Clustering of HTTP responses using k-means++ and the elbow method

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

Apply KNN algorithm to classify whether cancer is present or not. Implemented Pipeline, GridSearchCV, Elbow method to fit the best model.

Comparing the Elbow Method and Silhouette Method for choosing the optimal number of clusters in K-Means algorithm

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.

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.