E-Commerce Product Recommendation System based on ML Algorithms
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913453448364032 |
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| author | Haque, Md. Zahurul |
| author_facet | Haque, Md. Zahurul |
| contents | Algorithms are used in eCommerce product recommendation systems. These systems just recently began utilizing machine learning algorithms due to the development and growth of the artificial intelligence research community. This project aspires to transform how eCommerce platforms communicate with their users. We have created a model that can customize product recommendations and offers for each unique customer using cutting-edge machine learning techniques, we used PCA to reduce features and four machine learning algorithms like Gaussian Naive Bayes (GNB), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), the Random Forest algorithms achieve the highest accuracy of 99.6% with a 96.99 r square score, 1.92% MSE score, and 0.087 MAE score. The outcome is advantageous for both the client and the business. In this research, we will examine the model's development and training in detail and show how well it performs using actual data. Learning from machines can change of eCommerce world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | E-Commerce Product Recommendation System based on ML Algorithms Haque, Md. Zahurul Information Retrieval Networking and Internet Architecture Algorithms are used in eCommerce product recommendation systems. These systems just recently began utilizing machine learning algorithms due to the development and growth of the artificial intelligence research community. This project aspires to transform how eCommerce platforms communicate with their users. We have created a model that can customize product recommendations and offers for each unique customer using cutting-edge machine learning techniques, we used PCA to reduce features and four machine learning algorithms like Gaussian Naive Bayes (GNB), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), the Random Forest algorithms achieve the highest accuracy of 99.6% with a 96.99 r square score, 1.92% MSE score, and 0.087 MAE score. The outcome is advantageous for both the client and the business. In this research, we will examine the model's development and training in detail and show how well it performs using actual data. Learning from machines can change of eCommerce world. |
| title | E-Commerce Product Recommendation System based on ML Algorithms |
| topic | Information Retrieval Networking and Internet Architecture |
| url | https://arxiv.org/abs/2407.21026 |