Clustering text documents is a typical issue in natural language processing (NLP). Based on their content, related documents are to be grouped. The k-means Clustering technique is a well-liked solution to this issue. In this article, we’ll demonstrate how to cluster text documents using k-means using Scikit Learn.The k-means algorithm is a well-liked unsupervised learning algorithm that organizes data points into groups based on similarities. The algorithm operates by iteratively assigning each data point to its nearest cluster centroid and then recalculating the centroids based on the newly formed clusters.Preprocessing describes the procedures used to get data ready for machine learning or analysis. It frequently involves transforming, reformatting, and cleaning raw data and vectorization into a format appropriate for additional analysis or modeling.Output:Text clustering using KMeans