Product Review Classification using Machine Learning and Statistical Data Analysis

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Kajal Singh

Abstract

The aim of the paper is to implement and analyze the machine learning models for product review dataset. The project focuses on binary classification, multi-class classification, and clustering approaches to analyze and categorize product reviews. The performance of the models over each of the five classification tasks is measured by the 5-fold cross-validation scores over the training data.

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How to Cite
[1]
Kajal Singh , Tran., “Product Review Classification using Machine Learning and Statistical Data Analysis”, IJRTE, vol. 12, no. 2, pp. 91–96, Jul. 2023, doi: 10.35940/ijrte.A7530.0712223.
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How to Cite

[1]
Kajal Singh , Tran., “Product Review Classification using Machine Learning and Statistical Data Analysis”, IJRTE, vol. 12, no. 2, pp. 91–96, Jul. 2023, doi: 10.35940/ijrte.A7530.0712223.
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References

https://www.researchgate.net/publication/342890321_Mac hine_Learning_A_Review_of_Learning_Types

https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.L ogisticRegression.html

https://machinelearningmastery.com/types-of-classification-in-machine-learning/

https://en.wikipedia.org/wiki/Binary_classification

https://scikitlearn.org/stable/auto_examples/model_selectio n/plot_roc.html

https://www.educative.io/answers/classification-vs-clustering

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