README.md

---
dataset_info:
  features:
  - name: title
    dtype: string
  - name: name
    dtype: string
  - name: product_url
    dtype: string
  - name: price
    dtype: float32
  - name: original_price
    dtype: float32
  - name: unit
    dtype: string
  - name: overall_rating
    dtype: float32
  - name: rating_count
    dtype: int32
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 1762857315.375
    num_examples: 113677
  download_size: 1118819788
  dataset_size: 1762857315.375
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Product Thai Dataset

## Dataset Description

The Product Thai Dataset is a comprehensive collection of Thai product information, including images and ratings, designed for e-commerce and product analysis tasks specific to the Thai market. This dataset contains 113,677 examples, providing a rich source of information for researchers and developers working on product recommendation systems, image-based product search, and e-commerce analytics in the context of Thai products.

### Dataset Summary

- **Number of examples:** 113,677
- **Dataset size:** 1.76 GB
- **Download size:** 1.12 GB

## Features

The dataset includes the following features for each product:

1. **title** (string): The title of the product in Thai.
2. **name** (string): The name of the product (may differ from title) in Thai.
3. **product_url** (string): URL link to the product page.
4. **price** (float32): Current price of the product in Thai Baht (฿).
5. **original_price** (float32): Original price of the product in Thai Baht (฿) (before any discounts).
6. **unit** (string): Unit of measurement or sale for the product in Thai.
7. **overall_rating** (float32): Average rating of the product.
8. **rating_count** (int32): Number of ratings received by the product.
9. **image** (image): Product image.

## Usage

This dataset can be used for various tasks, including but not limited to:

- Thai product image classification
- Price prediction for Thai market
- Rating prediction for Thai products
- Product recommendation systems for Thai e-commerce
- Visual search and similarity for Thai products
- E-commerce trend analysis in Thailand

To load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("Porameht/product-thai-114k")
```

## Data Splits

The dataset is provided as a single train split.

## Dataset Creation

### Source

The data was collected from various Thai e-commerce platforms and product listings. The specific sources and collection methodology are not provided to maintain privacy and data integrity.

### Preprocessing

The dataset has undergone cleaning and preprocessing to ensure consistency and usability:

- Missing values have been handled appropriately for each feature type.
- Image data has been standardized and preprocessed for machine learning applications.
- Numerical values (price, rating, etc.) have been normalized and checked for consistency.
- Thai text has been preserved and standardized where necessary.

## Considerations for Use

When using this dataset, please consider the following:

- The dataset may contain biases inherent in Thai e-commerce data, such as popularity bias or category imbalance.
- Product information and pricing data may become outdated over time.
- Images and product details should be used in compliance with copyright and fair use guidelines.
- The dataset is specific to the Thai market and may not be generalizable to other regions without adaptation.

## Citation

If you use this dataset in your research or applications, please cite it as follows:

```
@dataset{product_thai_dataset,
  author = {Porameht},
  title = {Product Thai Dataset},
  year = {2023},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/Porameht/product-thai-114k}
}
```

## Feedback and Contributions

For any questions, feedback, or contributions to improve this dataset, please open an issue on the dataset's GitHub repository or contact the dataset maintainer through Hugging Face.