Predicting Potential Car Buyers using Logistic Regression Algorithm - Lapatta, Nouval Trezandy; Husin, Abdullah (Sistemasi: Jurnal Sistem Informasi, 2024)
This research aims to develop a predictive model to identify individuals with a high potential to become car buyers, employing logistic regression algorithm. The primary objective is to support the automotive industry in devising more efficient and focused marketing strategies. The choice of logistic regression is based on its superiority in handling categorical dependent variables and its practicality in result interpretation. The data processed in this study derive from demographic information, consumption habits, brand preferences, and various other factors that influence car buying decisions. The main data source is the outcome of online surveys participated in by individuals predicted to have the potential to buy a car within the next 12 months. The analysis results indicate that factors such as income, age, previous vehicle ownership status, gender and marriage status play significant roles in predicting the likelihood of someone becoming a car buyer. The developed model achieved an accuracy and precision of 95%, proving its significant capability in identifying potential car buyers with a high success rate. These findings provide valuable insights for the automotive industry in formulating more targeted and efficient marketing strategies, as well as contributing to the academic literature on the application of logistic regression in consumer behavior prediction.