scikit-learn

scikit-learn (imported as sklearn) is an open-source Python library for traditional machine learning. It bundles dozens of algorithms behind one consistent interface, along with tools for preparing data and checking how well a model performs. It is the library behind most MLforSEO clustering and classification notebooks.

How it works

Every scikit-learn model follows the same pattern, which makes it easy to swap one algorithm for another:

• Create the model, for example KMeans(n_clusters=5).
• Call fit() on your data so the model learns from it.
• Call predict() (or fit_predict() for clustering) to get labels for new data.
• Score the result with functions from sklearn.metrics, such as accuracy, F1 or silhouette score.

Its main areas are clustering (K-Means, DBSCAN, hierarchical), classification (logistic regression, SVM, random forests), regression, Dimensionality Reduction such as PCA, and text feature extraction such as TF-IDF.

How it’s used in SEO and marketing

• Customer segmentation with K-Means on behavioural data.
• Clustering images by colour or pages by content.
• TF-IDF vectors for keyword and title similarity, and for fuzzy matching beyond character edits.
• Training simple intent or page-type classifiers on labelled examples.
• Spotting outliers in traffic data with DBSCAN or Isolation Forest.
• Evaluating any model with train/test splits, cross-validation and metrics.

Example

In the Introduction to Machine Learning for SEO customer segmentation practical, customers are described by recency, frequency and monetary value, scaled so no feature dominates, and grouped with K-Means. The resulting segments, such as “Champions” and “Promising”, then shape landing pages and campaigns for each group.

Limitations and things to watch

• scikit-learn is built for classic machine learning on tabular data. For transformers and large language models, use Hugging Face or an API.
• Results depend on preprocessing: unscaled numbers or poor text cleaning produce poor clusters.
• Defaults are a starting point. Tune parameters such as the number of clusters and check them with metrics and common sense.
• Most algorithms run on a single machine in memory, which is fine for most SEO datasets but not for billions of rows.

Learn it on MLforSEO

• What Machine Learning Clustering Is, and Where It Fits in SEO
• Customer Segmentation with Machine Learning Using Three Clustering Approaches
• Image Clustering by Color with Three Machine Learning Approaches for Visual Content Organization
• Introduction to Machine Learning for SEO