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.

This is my final project updated to include feedback received from the peer review.