unsupervised learning python tutorial

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unsupervised learning is a branch of machine learning where the algorithm is trained on unlabeled data without explicit guidance. instead of predicting an output variable, the algorithm discovers patterns or structures within the data. in this tutorial, we'll explore unsupervised learning in python using popular libraries such as scikit-learn.
unsupervised learning is often used when the task at hand involves finding hidden patterns, grouping similar data points, or reducing the dimensionality of the dataset. unlike supervised learning, there are no labeled target variables in unsupervised learning.
clustering involves grouping similar data points together. a common algorithm for clustering is k-means, which partitions the dataset into k clusters.
dimensionality reduction is the process of reducing the number of input variables in a dataset. principal component analysis (pca) is a popular technique for achieving this.
before starting, make sure you have scikit-learn installed. if not, you can install it using:
in this example, we use the k-means algorithm to cluster synthetic data into four groups. the resulting clusters and centroids are visualized using matplotlib.
this example demonstrates the use of pca to reduce the dimensionality of the famous iris dataset. the reduced data is visualized in a 2d space.
unsupervised learning is a powerful tool for exploring and analyzing data without labeled target variables. in this tutorial, we covered two common techniques, clustering and dimensionality reduction, using scikit-learn in python. to deepen your understanding, consider exploring other unsupervised learning algorithms, such as hierarchical clustering, t

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