scholarly journals Consumer recommendation dynamics in online retail business under logistic regression and naïve Bayes analyses

2020 ◽  
Vol 2 (1) ◽  
pp. 152-160
Author(s):  
Irina Georgescu ◽  
Jani Kinnunen

Abstract Competitive businesses need to study the behavior of their current and potential customer base. Relevant data on the behavior can be obtained from online, where the purchase decisions are increasingly made and often based on product reviews, ratings and recommendations available in social media networks. The original data consists of 23486 customer reviews with ten variables/features of the reviewing customers, the products under review and the feedback to their reviews from online retail clothing business, and about half of the dataset is analyzed after cleaning the data. To find out, which features are the most important factors leading to a recommendation, the naïve Bayes and logistic regression methods are applied. Earlier research has shown that the sentiment of textual reviews and the given numerical ratings are key factors for the decision to recommend or not recommend products. The focus of this paper is to identify and rank-order the most relevant (numerical) factors affecting the review process leading to a recommendation. After applying the logistic regression classifier, we have found that rating, positive feedback count and age are statistically significant factors, in that order. The results support online retailers and manufacturers, as well, in adjusting their product portfolios and marketing efforts optimally to obtain recommendations for their products, reach potential customers and expose them to the given recommendations leading to positive purchase decisions. Further, the results indicate some future research opportunities.

2020 ◽  
Vol 1641 ◽  
pp. 012061
Author(s):  
Harsih Rianto ◽  
Amrin ◽  
Rudianto ◽  
Omar Pahlevi ◽  
Paramita Kusumawardhani ◽  
...  

2020 ◽  
Vol 19 ◽  
pp. 153303382090982
Author(s):  
Melek Akcay ◽  
Durmus Etiz ◽  
Ozer Celik ◽  
Alaattin Ozen

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.


2021 ◽  
Vol 6 (2) ◽  
pp. 96-104
Author(s):  
Yulia Resti ◽  
Endang Sri Kresnawati ◽  
Novi Rustiana Dewi ◽  
Des Alwine Zayanti ◽  
Ning Eliyati

Diabetes is a chronic disease that can cause serious illness. Women are four times more likely to develop heart problems caused by diabetes. Women are also more prone to experience complications due to diabetes, such as kidney problems, depression, and decreased vision quality. Nearly 200 million women worldwide are affected by diabetes, with two out of five affected by the disease being women of reproductive age. This paper aims to predict women with at least 21 years of age having diabetes based on eight diagnostic measurements using the statistical learning methods; Multinomial Naive Bayes, Fisher Discriminant Analysis, and Logistic Regression. Model validation is built based on dividing the data into training data and test data based on 5-fold cross-validation. The model validation performance shows that the Gaussian Naïve Bayes is the best method in predicting diabetes diagnosis. This paper’s contribution is that all performance measures of the Multinomial Naïve Bayes method have a value greater than 93 %. These results are beneficial in predicting diabetes status with the same explanatory variables.


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