Predicting car purchase intent using data mining approach

Abstract

Data mining involves the exploration and analysis of large databases to find patterns and valuable information that can aid in decision making. This paper illustrates the use of data mining approach to build predictive models for predicting customer's intent of car purchase after booking a car. Records show that a customer who has booked a car has the tendency to cancel their booking. Three data mining predictive models: Logistic Regression (LR), Decision Tree (DT) and Neural Network (NN) were used to model the intent of purchase (IOP). The sample for this study has 1935 cases. The data was partitioned into training (70%) and validation (30%) samples. Comparisons of the performance of these three predictive models were based on the validation accuracy rate, sensitivity and specificity. Results show that all three models validation accuracy rate are quite similar (LR= 91.79%, CART=91.17%, NN=91.17%) while LR has the highest sensitivity (LR=87.77%, CART=85.47%, NN=85.89%). Important customer characteristics were also revealed from these models.