Analysing practical examples by using principal component analysis (PCA) and Clustring
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Updated
May 14, 2024 - R
Analysing practical examples by using principal component analysis (PCA) and Clustring
This project clusters white wines based on their chemical properties to understand their relationship with quality ratings, using techniques like k-means and PCA.
Function to find the optimal number of clusters for k-means analysis using the Elbow Method
K-means as an unsupervised machine learning technique. Customer Segmentation Case.
Clusterização dos dados presentes no dataset de câncer de mama, implementando os algoritmos K-means, algoritmo do cotovelo (elbow method) e da silhueta média (Silhouette).
Avaliação do estado de aplicativos Android entre os anos de 2010 e 2018 presentes na Google Play Store por técnicas de visualização de dados. Identificação de parâmetros potencialmente importantes para o número de instalações de um aplicativo e seu envolvimento com a precificação (gratuito ou pago), através do uso de técnicas de machine learning.
This is my first attempt at a KNN model, where I attempt to classify the purchase of caravan insurance in the Caravan data set (ISLR package).
Customer Segmentation using R
This project uses K-Means clustering to segment wholesale customers based on their spending habits. The data is preprocessed, scaled, and clustered into four groups. The Elbow and Silhouette methods determine the optimal number of clusters, and results are visualized using boxplots and scatter plots to uncover spending patterns.
Repositorio creado para almacenar archivos, script y el informe final del curso de modelamiento estadístico del Diplomado en Big Data de la Pontificia Universidad Católica de Chile.
Grouping pupils according to the performance at two intermediate examinations
Customer Segmentation and Product Recommendation for ACME Innovations - School Project
Classify the subsidy eligible users based on their electricity usage patterns
Clustering wedding guests.
Supervised and unsupervised analysis
Clustering personalities and labelling based on sampled survey responses (Hierarchical and K-Means).
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