Hands-on label prediction using K-means clustering

K-means clustering is a powerful unsupervised machine learning technique used for grouping similar data points into clusters. In the context of text data, K-means clustering can be employed to predict labels or categories for the given text based on their similarity. The provided code showcases how to utilize K-Means clustering to predict labels for movie reviews, breaking down the process into several key steps.

Step 1: Importing libraries and downloading data.

The following code begins by importing essential libraries such as scikit-learn and NLTK. It then downloads the necessary NLTK data, including the movie reviews dataset:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from nltk.corpus import movie_reviews
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import nltk
import re
# Download the necessary NLTK data
nltk.download('movie_reviews&apos...