kmeans-session1.hashnode.devKmeansClustering Grouping data into similar groups based on their features- without knowing the labels in advance. example 100 customer records (age, income, location) You want to group similar customers together → That’s clustering Application of clu...Jul 29, 2025·5 min read
kmeans-session2.hashnode.devK means session 2Silhouette Score Cohesion Definition:Cohesion measures how closely related or similar the data points within the same cluster are. Goal:We want points within a cluster to be as close together as possible — this means the cluster is compact and well-f...Jul 29, 2025·3 min read
kmeanshyperparameter.hashnode.devK means session 3 hyperparameterHyper-parameters The hyper-parameters are from Scikit’s KMeans: class sklearn.cluster.KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=None, alg...Jul 29, 2025·7 min read
pca.hashnode.devPrincipal Component AnalysisPCA: PCA (Principal Component Analysis) is used for feature extraction, especially when feature selection isn't helpful — for example, when the features are not strongly correlated with the target or when we can't remove any features without losing i...Jul 29, 2025·3 min read