Predicting customer behavior in online shopping using SVM classifier

Maheswari, K.; Priya, P.

doi:10.1109/itcosp.2017.8303085

Cited by 25 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data mining algorithms are regularly used such as KNN [21], decision trees [22] or SVM [23]. Multi-Layer Perceptron (MLP) has also been successfully used to perform data mining for years and continues to yield good performances in several domains [24], [25].…”

Section: Data Mining and Anonymous Data Representationsmentioning

confidence: 99%

Proportional representation to increase data utility in k-anonymous tables

Viton

Mauger

Dequen

et al. 2021

2021 IEEE Symposium on Computers and Communications (ISCC)

View full text Add to dashboard Cite

Section: Data Mining and Anonymous Data Representationsmentioning

confidence: 99%

Proportional representation to increase data utility in k-anonymous tables

Viton

Mauger

Dequen

et al. 2021

2021 IEEE Symposium on Computers and Communications (ISCC)

View full text Add to dashboard Cite

“…DS dapat membantu perusahaan menavigasi lautan luas data yang tersedia untuk membuat keputusan bisnis yang terinformasi dan tepat waktu. Tidak hanya bisnis besar, tetapi hampir setiap pengecer online yang menggunakan platform e-commerce mapan seperti Shopify atau solusi e-commerce lainnya memiliki banyak data [5], tetapi biasanya tidak memiliki keterampilan dan orang untuk menganalisisnya dan menggunakannya secara efisien [6]. Perusahaan-perusahaan yang dapat melakukannya memiliki keunggulan kompetitif yang jelas terutama dalam hal pemasaran dan penjualan [7], karena sekitar 90% data yang dikumpulkan perusahaan berkaitan dengan tindakan pelanggan dalam konteks aktivitas pemasaran.…”

Section: Pendahuluanunclassified

Data Science bidang Pemasaran : Analisis Prilaku Pelanggan

Harahap

Lubis

Situmorang

2021

dsi

View full text Add to dashboard Cite

Dalam kegiatan pemasaran digital, data Science (DS) memiliki peran penting dalam memahami kinerja industri pemasaran sebelum menerapkan teknik pemasaran digital pada pemasaran produk. Hal ini dikarenakan setiap pelanggan merespons secara berbeda setiap penawaran. Perilaku pelanggan juga berubah berdasarkan waktu karena mereka mungkin memiliki kebutuhan yang berbeda pada situasi yang berbeda. Pada makalah ini fokus menyajikan analisis bisnis dengan penerapan DS untuk mengeksplorasi pola perilaku dan juga memprediksi bagaimana pelanggan akan merespons penawaran yang berbeda. Penerapan analisis data eksplorasi juga diterapkan untuk menjawab beberapa pertanyaan bisnis, dari hasil pengamatan menghasilkan lima kelompok pelanggan yang disajikan dalam bentuk visualisasi dan model Random Forest Classifier memiliki skor akurasi prediksi terbaik sebesar 91%, kemudian K neighbors Classifier dan Logistic Regression.

show abstract

“…This margin give whether it is a positive or negative classifier [12], it also give the distance between the training data and the plane. The data has to forecast properly even if new data is introduced to generate the optimal plane.…”

Section:  Primal Formmentioning

confidence: 99%

Use of API’s for Comparison of Different Product Information under one Roof: Analysis Using SVM

Desai¹,

Kulkarni²

2018

IJITCS

View full text Add to dashboard Cite

Abstract-The internet has grown in leaps and bounds and hence all the data is now available online; be it shopping, banking, private and public institutes or universities, private public sectors are all making their presence felt online. The online data is just a click away thanks to ubiquitous systems today. The browser does not require any specific program set up hence easier for the end user. Earlier the data online was static used HTML now it's dynamic uses ASP, ASP.NET, Servlet, JSP and other operational tools therefore the internet operation is broken down into many categories. The problem arises with the customer while trying to buy something online. There are lots of online stores sometimes it's difficult to browse through all products to get a better deal. The pricing of products are different on different sites, this is the first gap at the customer end. The second problem arises at the provider end. The second gap here is to understand the customer need. How can the variation in prices be checked? ; The existing prices available on sites cannot be changed but the customer can be provided with options to select the best deal of the same product. For the first problem the paper deals with an API implementation wherein the information of at least some products is compared under one roof. How can the provider know the genuine customer? ; The second problem is resolved by the use of SVM. Last problem is in detecting if a customer visiting a site will actually buy the product being compared.The paper focuses on the selection of ASP.NET to deal with the implementation problems stated and find solution to the forecasting problem using SVM. SVM and C4.5 are used for comparison.

show abstract

Predicting customer behavior in online shopping using SVM classifier

Cited by 25 publications

References 6 publications

Proportional representation to increase data utility in k-anonymous tables

Proportional representation to increase data utility in k-anonymous tables

Data Science bidang Pemasaran : Analisis Prilaku Pelanggan

Use of API’s for Comparison of Different Product Information under one Roof: Analysis Using SVM

Contact Info

Product

Resources

About