Dividend Investing Dataset 2024-2025 and K-Means Clustering Simulation

This dataset constitutes a compilation of quantitative data from a number of publicly listed companies on the Indonesia Stock Exchange (IDX) during the 2024–2025 period. The data were collected from various publicly available market reports and publications, then organized into a structured format to support machine-learning–based analytical processes, particularly unsupervised learning techniques employing the K-Means Clustering algorithm. The selection of the 2024–2025 timeframe aims to provide a contemporary representation of the performance and fundamental characteristics of publicly listed companies in Indonesia. The variables included in this dataset consist of financial and operational indicators commonly used in corporate analysis, such as asset size, profitability levels, liquidity, capital structure, and business activity parameters. The construction of these variables was carried out with attention to cross-company consistency to ensure their usability for comparative clustering purposes. In addition, the dataset has undergone basic preprocessing procedures, including data cleaning, simple normalization, and missing-value inspection, thus making it ready for use as input data for various analytical experiments. This repository also includes a Python script used to perform the K-Means Clustering simulation. The script covers several stages, ranging from dataset loading, variable transformation, K-Means model initialization, visualization of clustering results, to an initial interpretation of the emerging patterns. By providing this script, users may replicate the analytical process end-to-end, adjust parameters as needed, or use the dataset as a foundation for further research. Both the dataset and the script are prepared for educational, research, and methodological demonstration purposes. The content is not intended to serve as a basis for investment decision-making or professional financial analysis. Accordingly, this repository functions as an open resource that may be utilized by academics, students, and practitioners interested in exploring the application of K-Means within the context of Indonesian corporate data.