Paper Title : Implementation of Customer Purchase Prediction using Machine Learning Techniques
ISSN : 2394-2231
Year of Publication : 2021
MLA Style: Ms. N. Zahira Jahan M.C.A., M.Phil.,M. Gokul "Implementation of Customer Purchase Prediction using Machine Learning Techniques " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Ms. N. Zahira Jahan M.C.A., M.Phil.,M. Gokul "Implementation of Customer Purchase Prediction using Machine Learning Techniques " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is to help make predictions on online shopping data. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform delivery time reduction with higher accuracy by using different machine learning techniques. The main motivation of doing this project is to present a delivery time reduction model for the sales company. Further, this research work is aimed towards identifying the best classification algorithm for identifying the reduction time in delivery. Online retailers still struggle with the disadvantage of delivery times compared to traditional brick and mortar stores. With the emergence of big data analytics, it has become possible to extract meaningful knowledge from the volume of data that online retailers collect on their website. Nevertheless, limited research exists that investigates how this data can be used to optimize delivery times for customers. Different forecasting methods in combination with k-means clustering are applied to test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The proposed prediction model can easily be implemented and used to predict purchases, which can also be leveraged for other areas of application besides anticipatory shipping Model development is based on Means clustering and categorization using Support Vector Machine algorithms along KNearest Neighbour also. It is found that proposed machine learning algorithm performs better when compared to other algorithms for delivery time reduction. The project is designed using R Language 3.4.4 with R Studio.
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————Machine Learning K-Means,KNN Algorithms , SVM Algorithms.