• TEENA RATHORE
• Dr. Manish Tiwari
• Dr. Mayank Patel

Abstract Online purchases are phenomena that are growing rapidly nowadays. We present a neural network to predict purchase intent in an electronic commerce environment. We use trainable vector spaces to model semi-structured and varied input data that comprise categorical and unique instances. In this paper, we compare the traditional machine learning technique with the more advanced deep learning approaches. The convenience of online shopping makes it an emerging trend among consumers. The prevalence of online shopping has sparked the interest of retailers to focus on this area. Therefore, this study determined the relationship between the subjective norm, perceived utility and online shopping behavior while mediated by purchase intention. Purchase intentions are often measured and used by marketing managers as an input to decisions about new and existing products and services. An exploration of the design decision of the model, which includes the sharing of parameters and the omission connections, further increases the accuracy of the model. The results in reference datasets provide classification accuracy within 98% of the state of the art in one and exceed the state of the art in the second without the need for any domain-specific feature engineering / data set in events short and long sequences It is interesting to note that perceived utility also negligibly influences the behavior of online purchases. The discovery also revealed that significant purchase intent positively influences online shopping behavior. In this paper Naïve Bayes (NB), BayesNet, Lazy.LWL and Lazy.IBK algorithms are applied in Weka for predicting purchasing intent online shopping behavior model. There are 18 attributes are used in different algorithms to show terms and error rates with accuracy. Lazy.LWL algorithm gives best results in all algo with minimum error rate.

Keywords : Recurrent Neural Network, WEKA Algorithm, e-commerce, Machine learning etc.

"Predicting Purchase Intent: Online Shopping Behavior Learning Through Recurrent Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.513-521, June 2019, Available :http://www.jetir.org/papers/JETIR1907I63.pdf

"Predicting Purchase Intent: Online Shopping Behavior Learning Through Recurrent Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp513-521, June 2019, Available at : http://www.jetir.org/papers/JETIR1907I63.pdf