Paper ID:
IJSDIP_2025_VOLUME 13_ISSUE 3_32-37
Author's Name:
B.Shathya
Volume:
13
Issue:
Issue 3
Year:
2025
Pages:
32-37
Abstract
Data mining is also known as knowledge discovery is the process of uncovering patterns and extracting knowledge from large dataset. Data mining offers many applications in business. It helps a company to increase sales from the behaviour and practices of its customers. It pulls out information from data sets and compares it to help the business to make decisions. Classification is one of the data mining techniques used to assign a class label to each instance in a dataset. There are many classification techniques available. The performance of each model will vary based on the dataset and data mining techniques used. The proposed work is evaluated using Online Shoppers Purchasing Intention Dataset from UCI Machine Learning Repository website. This binary classification dataset is imbalanced with an unequal class distribution and many data points that fall on outside the pattern. The imbalance dataset and outliers will create bias in the result. To overcome this problem the outliers in the dataset are removed using Z-Score method. The important features are selected using Recursive Feature Elimination method and the imbalanced dataset is balanced using the oversampling technique SVM-SMOTE. The online shopper’s intention is predicted using Machine Learning algorithms. The individual models like K-Nearest Neighbor, Support Vector Machines, Random Forest, Naïve Bayes have been built. The popular ensemble methods like boosting, bagging and stacking have been built. The proposed stacking model has been built using K-Nearest Neighbor, Support Vector Machines, Random Forest, Naive Bayes as base model. By using the output of the base models, the meta model is trained. The Performance measures of all the models are found. The analysis results show that Proposed stacking ensemble classification leads improved performance.
References
[1] H.Ozen and N.Engizek, “Shopping online without thinking: being emotional or rational?”, Asia Pacific Journal of Marketing and Logistics, 2014, pp.78-93.
[2] Chien Chin Chen, Meng-Chieh Chung, “Predicting the success of group buying auctions via classification”, Knowledge-Based Systems, Volume 89, 2015, pp.627-640.
[3] Rish, Irina,” An Empirical Study of the Naïve Bayes Classifier”,IJCAI 2001,Work Empir Methods Artif Intell, pp41-46, Jan 2001.
[4] A. Abdul Rasheed,” Feature Selection: An Assessment of Some Evolving Methodologies”, Turkish Journal of Computer and Mathematics Education,Vol.12,2021.
[5] Bissan Ghaddar, Joe Naoum-Sawaya, “High Dimensional Data Classification and Feature Selection using Support Vector Machines”, European Journal of Operational Research,2017.
[6] Neha Sharma, “Classification Using Naive Bayes- A Survey”, International Journal of Engineering Science Invention Research & Development, Vol. 2,February 2016.
[7] Srivastava, Durgesh & Bhambhu, Lekha, “Data classification using support vector machine”,Journal of Theoretical and Applied Information Technology,2010,pp.1-7.
[8] Marta Baldomero-Naranjo, Luisa I. Martínez-Merino, Antonio M. Rodríguez-Chía, “A robust SVM-based approach with feature selection and outliers detection for classification problems”, Expert Systems with Applications, Volume 178, 2021.
[9] Sondos Jameel Mukhyber, Dhahir Abdulhade Abdulah, Amer D. Majeed, “Effect Z-Score Normalization on Accuracy of classification of liver disease”, Turkish Journal of Computer and Mathematics Education, Vol.12, 2021, pp.658- 662.
[10] Bowen, Y. , Buyang, C. , “Research on Ensemble Learning-based Housing Price Prediction Model”, Big Geospatial Data and Data Science,2018.
[11] Mohd Khalid Awang, Mokhairi Makhtar, Norlina Udin and Nur Farraliza Mansor, “Improving Customer Churn Classification with Ensemble Stacking Method”, International Journal of Advanced Computer Science and Applications, 2021.
[12] Taunk, Kashvi & De, Sanjukta & Verma, Srishti & Swetapadma, Aleena, ”A Brief Review of Nearest Neighbor Algorithm for Learning and Classification”, 2019, pp. 1255-1260.
[13] M. Mohamad, M. Y. M. Saman, and N. A. Hamid, “Complexity Approximation of Classification Task for Large Dataset Ensemble Artificial Neural Networks”, Lect. Notes Electr. Eng., vol. 520, April 2019, pp. 195–2019.
[14] Sakar.C.O., Polat.S.O., Katircioglu.M, et al., “Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks”, Neural Computing & Applications, 2018, pp.6893-6908.
