A Machine Learning Model for Effective Consumer Behaviour Prediction
5th International Conference on Information Systems and Computer Networks, ISCON 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1759113
ABSTRACT
Effective consumer behavior prediction can play a crucial role in online marketing, especially in the COVID19 scenario. In this work, we have analyzed consumer behavior to understand consumer needs and predict future requirements. For the same, we have applied the machine learning models on an amazon dataset collected from Kaggle. The dataset consists of reviewers' comments, ratings, many other parameters for the product. The model's outcome indicates that the proposed Random Forest model performs exceptionally well, and its Accuracy is approx. 98.73%. A comparative study has been done to show the efficacy of the work, and it has been observed that the performance of the proposed model is quite remarkable, and it can be a competent model for effective consumer behavior prediction. © 2021 IEEE.
Behaviour Prediction; Data Visualization; Machine Learning; Natural Language Processing; Consumer behavior; Decision trees; Forecasting; Learning algorithms; Natural language processing systems; Behavior prediction; Comparatives studies; Consumer's needs; Machine learning models; Machine-learning; Online marketing; Performance; Random forest modeling
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
5th International Conference on Information Systems and Computer Networks, ISCON 2021
Year:
2021
Document Type:
Article
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