unsupervised-learning

## What I do - Find patterns in unlabeled data - Cluster similar data points - Reduce data dimensionality - Detect anomalies - Learn representations - Segment data populations ## When to use me Use me when: - Data has no labels - Exploratory analysis needed - Dimensionality reduction required - Anomaly detection needed - Customer segmentation - Data compression ## Key Concepts ### Clustering Methods ```python import numpy as np from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering from sklearn.mixture import GaussianMixture from sklearn.metrics import silhouette_score # K-Means kmeans = KMeans(n_clusters=3, random_state=42) labels = kmeans.fit_predict(X) centers = kmeans.cluster_centers_ # Find optimal k silhouette_scores = [] for k in range(2, 10): kmeans = KMeans(n_clusters=k) score = silhouette_score(X, kmeans.fit_predict(X)) silhouette_scores.append(score) # DBSCAN (density-based) dbscan = DBSCAN(eps=0.5, min_samples=5) labels = dbscan.fit_predict(X) # Hierarchical hierarchical = AgglomerativeClustering(n_clusters=3) labels = hierarchical.fit_predict(X) # Gaussian Mixture (probabilistic) gmm = GaussianMixture(n_components=3) gmm.fit(X) labels = gmm.predict(X) probs = gmm.predict_proba(X) ``` ### Dimensionality Reduction ```python from sklearn.decomposition import PCA, TruncatedSVD from sklearn.manifold import TSNE # PCA pca = PCA(n_components=0.95) # 95% variance X_pca = pca.fit_transform(X) # t-SNE (visualization) tsne = TSNE(n_components=2, perplexity=30) X_tsne = tsne.fit_transform(X) # SVD (sparse data) svd = TruncatedSVD(n_components=100) X_svd = svd.fit_transform(X) ``` ### Anomaly Detection ```python from sklearn.ensemble import IsolationForest from sklearn.neighbors import LocalOutlierFactor # Isolation Forest iso = IsolationForest(contamination=0.1) outliers = iso.fit_predict(X) # LOF lof = LocalOutlierFactor(n_neighbors=20, contamination=0.1) outliers = lof.fit_predict(X) ```