This Repositories gonna explain about the difference between classification and clustering.
Classification is a machine learning method for predicting the class or category of data. As part of Supervised Learning, it aims to assign correct labels based on model predictions.
Models are trained on labeled datasets to learn data patterns and structures. After training, they are tested on new data to assess accuracy.
In essence, classification groups data into categories based on learned patterns, allowing for accurate label predictions on new data.
Clustering is a machine learning method for grouping similar objects into clusters. As part of Unsupervised Learning, it aims to find natural groupings within data without using predefined labels.
Algorithms are applied to unlabeled data to discover patterns and structures. The quality of clustering can be evaluated with specific metrics.
In essence, clustering organizes data into groups based on similarity, uncovering natural patterns useful for data analysis or preprocessing.