NDLI: Predicting car purchase intent using data mining approach

Content Provider	IEEE Xplore Digital Library
Author	Yap Bee Wah Ismail, N.H. Fong, S.
Copyright Year	2011
Description	Author affiliation: Department of Computer and Information Science, University of Macau, China (Fong, S.) \|\| Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia (Yap Bee Wah; Ismail, N.H.)
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.
Starting Page	1994
Ending Page	1999
File Size	260392
Page Count	6
File Format	PDF
ISBN	9781612841809
e-ISBN	9781612841816
DOI	10.1109/FSKD.2011.6019863
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Accuracy decision tree predictive modeling data mining Predictive models Data models Data mining Decision trees logistic regression classification Logistics
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Using data mining predictive models to classify credit card applicants

The Study on Classification and Prediction for Data Mining

Using data mining techniques for predicting individual tree mortality in tropical rain forest: logistic regression and decision trees approach

Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients

Predicting player disengagement and first purchase with event-frequency based data representation

Mining human failure dynamics from accident data using logistic regression and decision trees

Accurate and interpretable regression trees using oracle coaching

Predicting car insurance policies using random forest

PPS — Placement prediction system using logistic regression

Predicting car purchase intent using data mining approach

Similar Documents

Using data mining predictive models to classify credit card applicants

The Study on Classification and Prediction for Data Mining

Using data mining techniques for predicting individual tree mortality in tropical rain forest: logistic regression and decision trees approach

Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients

Predicting player disengagement and first purchase with event-frequency based data representation

Mining human failure dynamics from accident data using logistic regression and decision trees

Accurate and interpretable regression trees using oracle coaching

Predicting car insurance policies using random forest

PPS — Placement prediction system using logistic regression

Predicting car purchase intent using data mining approach