Clustering with Agglomerative and DBSCAN algorithm Machine Learning

A customer segmentation prediction and analysis project with the implementation of RFM analysis and DBSCAN clustering algorithm

A gem to read GTFS stops data and create clusters based on coordinates and stop names' similarities.

Weather Stations Clustering using DBSCAN ML algo.

EXCELR ALL ASSIGNMENTS

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见机器学习算法原理与实现

In this repository i have performed Hierarchical clustering on car clus dataset, DBSCAN on penguins datset, K-means clustering on mall customer dataset.

Investment Analysis and Asset Mgmt, Time Series Analysis & Forecasting, Machine Learning in Finance & Causal Inference Methods

DBSCAN in Python

🌏 Colorful app to compute dbscan clusters with TurfJS powered by the almighty HERE Maps Places API.

Problem Statement Perform clustering (Hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Content This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas

In this notebook, i have tried to appy KMeans, Hierarchical and DBSCAN clustering along PCA. The dataset used is Mall_Customers. In DBSCAN, certain type of Heatmaps are used to find the Epsilon and min_samples value which have performed quite well in identifying the correct number of clusters.

Year 1 Data Science (HVE) course assignment (2022): cluster the data, make a dashboard with some exploratory plots

Customer segmentation is essential for enhancing marketing efficiency and satisfaction. By categorizing customers based on demographics, interests, and purchasing behavior, companies tailor messages to engage each segment effectively. Our app utilizes advanced clustering algos like KMeans, DBSCAN, and AGNES to extract insights from data

Explore a comprehensive analysis of Netflix's extensive collection of movies and TV shows, clustering them into distinct categories. This GitHub repository contains all the details, code, and insights into how we've organized and grouped the vast content library into meaningful clusters.

Python implementation of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for unsupervised learning. Identifies clusters of varying shapes and sizes in data, robust to noise. Useful for data exploration and anomaly detection.

Customer segmentation through their behavior, their habits and their personal data.

Welcome to my Classical Learning Projects repository, where I showcase my work in the fields of supervised and unsupervised learning. Here, you'll find code and datasets for various projects, such as classification and clustering tasks, implemented using popular algorithms like decision trees, neural networks, and k-means.

Machine learning Mini Projects

An attempt at the network anomaly detection task using manually implemented k-means, spectral clustering and DBSCAN algorithms, with manually implemented evaluation metrics (precision, recall, f1-score and conditional entropy) used to evaluate these algorithms.