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Feature Description
Data Preprocessing: to clean and prepare the data. Feature Engineering: to create features for the clustering algorithms. KMeans Clustering: to apply KMeans clustering on the dataset. DBSCAN Clustering: to apply DBSCAN clustering on the dataset. Model Evaluation: to evaluate the performance of the clustering models.
Use Case
KMeans: A centroid-based clustering algorithm that partitions the data into K clusters. DBSCAN: A density-based clustering algorithm that identifies clusters based on the density of data points.
Benefits
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Priority
High
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