Machine Learning, Artificial Intelligence, Technology, Python, Data Science.
all data points are assigned, the centroids are recalculated based on the mean of the points within each cluster. This process continues until convergence is reached. Strengths of K-Means Clustering: - Efficient and scalable for large datasets. - Well-suited for numerical data. - Intuitive and easy