An Exploratory Data Analysis (EDA) Approach for Analyzing Financial Statements in Pharmaceutical Companies Using Machine Learning

This research investigates the use of Exploratory Data Analysis (EDA) and machine learning techniques to analyze financial statements (FSs) of pharmaceutical companies. The study focuses on three major Indonesian pharmaceutical companies: Kimia Farma, Kalbe Farma, and IndoFarma. By leveraging EDA, this study aims to uncover hidden patterns and insights within financial data, such as earnings per share (EPS), return on capital employed (ROCE), net profit margin, and inventory turnover ratio. Additionally, the study employs machine learning models, including Linear Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree, to predict financial performance metrics and trends. The performance of these models is evaluated using metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Among the models tested, the Decision Tree model demonstrated the highest performance, indicating high accuracy and a strong fit to the data. These results highlight the potential of data-driven approaches in improving the operational efficiency and financial stability of healthcare organizations.