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