Dive into the world of culinary exploration with our Amazon Food Review Dataset. This comprehensive collection captures the essence of diverse gastronomic experiences, offering insights into the myriad flavors and preferences of online consumers. As we sift through this data, anticipate a journey through taste, quality, and consumer satisfaction. From trending products to hidden gems, our dataset unravels the tapestry of Amazon's vast food offerings. Whether you're a researcher, marketer, or simply a food enthusiast, this review compilation provides a valuable resource to understand and analyze the dynamic landscape of online food reviews on Amazon in a concise and informative manner.
Make sure you have the following prerequisites installed:
- Python (version >= 3.6)
- Jupyter Notebook (optional, for exploring and running the notebooks)
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Clone the repository:
git clone https://github.com/DeveloperRedoy/ML-amazon-food-review.git
| Apply Algorithm List | Kaggle Link |
|---|---|
| Linear Regression | https://www.kaggle.com/code/mdredoysarder/amazon-food-review/edit |
| Logistic Regression | |
| Decision Tree | |
| Random Forest | |
| AdaBoost (Adaptive Boosting) | |
| Gradient Boosting Machines (GBM) | |
| Support Vector Machines(SVM) | |
| K-Nearest Neighbors (KNN) | |
| Naive Bayes | |
| Principal Component Analysis (PCA) |
🏆 Machine learning project🏆
You can use this service for free. I'm looking for sponsors to help us keep up with this service❤️
| Social accounts | Link |
|---|---|
| https://twitter.com/FreelancerRedoy | |
| https://www.linkedin.com/in/redoytime/ | |
| https://www.facebook.com/redoy.sarder.714 | |
| Kaggle | https://www.kaggle.com/mdredoysarder |
| Profile | https://www.hackerrank.com/profile/syber_redoy_php |
| Github | https://github.com/Redoy365 |