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silhouette-score

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In this project, unsupervised methods were employed to form clusters of similar vehicles based on sales data from Italy between 2003 and 2005. Through detailed analysis of monthly sales volumes, vehicles were grouped to reveal competitive relationships. This approach aids in understanding market dynamics and identifying key competitors.

  • Updated Aug 6, 2024
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Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)

  • Updated Jul 13, 2024
  • Jupyter Notebook

Based on a user's preferred movie or TV show, Unsupervised Machine Learning-Netflix Recommender suggests Netflix movies and TV shows. These suggestions are based on a K-Means Clustering model. These algorithms base their recommendations on details about movies and tv shows, such as their genres and description.

  • Updated May 7, 2024
  • Jupyter Notebook

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