Machine Learning–Based Prediction of Oil Palm Plantation Yield Using Random Forest Regression - Mayang Modelina Cynthia; Sigit Prabowo; Jheki Pranta Singarimbun; Muhammad Akbar Firdaus; Hafizh Al-Ghifari Rangkuti Rangkuti; Rido Favorit Saronitehe Waruwu; Muhammad Amin (CV. AFDIFAL MAJU BERKAH, 2026)
The rapid development of digital technology has led to a significant increase in the volume and diversity of customer transaction data, making big data a crucial asset for organizations in designing business strategies. However, abundant data will not provide meaningful value if it is not analyzed appropriately. This study aims to implement data science techniques to extract insights from big data of customer transactions using the Python programming language. The research adopts a descriptive–exploratory quantitative approach by utilizing customer transaction datasets as secondary data. The analysis stages include data preprocessing, exploratory data analysis (EDA), and the application of data science algorithms such as clustering and predictive analysis using Python libraries including pandas, numpy, matplotlib, and scikit-learn. The results show that the data science approach is capable of identifying customer behavior patterns based on spending value, transaction frequency, and purchasing habits over a specific period. Furthermore, the clustering model successfully groups customers into several segments with distinct characteristics, providing valuable insights that can be used as a basis for more effective and personalized marketing decision-making. Therefore, this study confirms that the implementation of data science using Python can assist companies in transforming big data of customer transactions into high-value information that supports improved business strategies and customer retention.