This analysis uses the American Companies Bankruptcy Prediction dataset to build a two-cluster k-means model and evaluate its ability to separate surviving firms from failed firms. The workflow follows the standard exploratory data analysis pipeline: data ingestion, type verification, missing value assessment, outlier handling, transformation, splitting, feature engineering, modeling, and evaluation. The dataset contains 18 financial variables along with a company identifier, year, and survival status label. The table below documents the original column codes, the renamed variables used throughout this analysis, and a short description of each measure.

Es el analisis economico de Nuevo laredo

Lightning talk draft

Analisis ini membandingkan performa metode K-Means, Hierarchical Clustering, DBSCAN, dan Fuzzy C-Means pada Rice Dataset untuk menentukan metode clustering terbaik berdasarkan evaluasi Silhouette Score dan kesesuaian terhadap struktur alami data.