Predicting Car Purchase Intent Using Data Mining Approach
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 customers 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.
logistic regression decision tree data mining, classification predictive modeling
Yap Bee Wah Nor Huwaina Ismail Simon Fong
Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA 40450 Shah Alam, Selangor, M Department of Computer and Information Science,University of Macau, China
国际会议
上海
英文
2049-2054
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）