A Music Player with a Clustering based Recommendation Engine utilizing Spotify API
кластеризация клиентов на основе их покупательской способности
The goal of this project is to compare different clustering algorithms, executed on the MNIST dataset.
Sklearn, K-means Clustering, Hierarchical Clustering, DBSCAN, Mean Shift Clustering, Gaussian Mixture Models (GMM), Spectral Clustering, Affinity Propagation, OPTICS (Ordering Points to Identify the Clustering Structure), Birch (Balanced Iterative Reducing and Clustering using Hierarchies), marketing_campaign
Clustering a low-level event log of a process involves creating groups of related events based on their commonalities. In this case, the centrality score of a process's graph can be used to determine the similarity between events and to guide the clustering process.
Gaussian Mixture Model Classifier
This project analyzes Amazon product data to understand pricing strategies, customer satisfaction, and market segmentation. Using data visualization, regression analysis, and clustering techniques, it uncovers relationships between product features, ratings, and prices to provide insights into e-commerce trends.
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