smartcat/Amazon_All_Beauty_2023

---
dataset_info:
features:
- name: title
dtype: string
- name: average_rating
dtype: float64
- name: rating_number
dtype: int64
- name: features
dtype: string
- name: description
dtype: string
- name: price
dtype: float64
- name: images
list:
- name: thumb
dtype: string
- name: large
dtype: string
- name: variant
dtype: string
- name: hi_res
dtype: string
- name: videos
list:
- name: title
dtype: string
- name: url
dtype: string
- name: user_id
dtype: string
- name: store
dtype: string
- name: categories
sequence: 'null'
- name: parent_asin
dtype: string
- name: upc
dtype: string
- name: brand
dtype: string
- name: manufacturer
dtype: string
- name: product_dimensions
dtype: string
- name: color
dtype: string
- name: material
dtype: string
- name: is_discontinued_by_manufacturer
dtype: string
- name: item_form
dtype: string
- name: item_model_number
dtype: string
- name: age_range_(description)
dtype: string
- name: skin_type
dtype: string
- name: scent
dtype: string
- name: package_dimensions
dtype: string
- name: hair_type
dtype: string
- name: unit_count
dtype: string
- name: number_of_items
dtype: string
splits:
- name: train
num_bytes: 35604373
num_examples: 18956
download_size: 17113574
dataset_size: 35604373
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---

# Dataset Card for Dataset Name

Original dataset can be found on: https://amazon-reviews-2023.github.io/

## Dataset Details
This dataset is downloaded from the link above, the category Amazon All Beauty meta dataset.
### Dataset Description

This dataset is a refined version of the Amazon All Beauty 2023 meta dataset, which originally contained product metadata for beauty products sold on Amazon. The dataset includes detailed information about products such as their descriptions, ratings, prices, images, and features. The primary focus of this modification was to ensure the completeness of key fields while simplifying the dataset by removing irrelevant or empty columns.
The table below represents the original structure of the dataset.

<table border="1" cellpadding="5" cellspacing="0">
<tr>
<th>Field</th>
<th>Type</th>
<th>Explanation</th>
</tr>
<tr>
<td>main_category</td>
<td>str</td>
<td>Main category (i.e., domain) of the product.</td>
</tr>
<tr>
<td>title</td>
<td>str</td>
<td>Name of the product.</td>
</tr>
<tr>
<td>average_rating</td>
<td>float</td>
<td>Rating of the product shown on the product page.</td>
</tr>
<tr>
<td>rating_number</td>
<td>int</td>
<td>Number of ratings in the product.</td>
</tr>
<tr>
<td>features</td>
<td>list</td>
<td>Bullet-point format features of the product.</td>
</tr>
<tr>
<td>description</td>
<td>list</td>
<td>Description of the product.</td>
</tr>
<tr>
<td>price</td>
<td>float</td>
<td>Price in US dollars (at time of crawling).</td>
</tr>
<tr>
<td>images</td>
<td>list</td>
<td>Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image.</td>
</tr>
<tr>
<td>videos</td>
<td>list</td>
<td>Videos of the product including title and url.</td>
</tr>
<tr>
<td>store</td>
<td>str</td>
<td>Store name of the product.</td>
</tr>
<tr>
<td>categories</td>
<td>list</td>
<td>Hierarchical categories of the product.</td>
</tr>
<tr>
<td>details</td>
<td>dict</td>
<td>Product details, including materials, brand, sizes, etc.</td>
</tr>
<tr>
<td>parent_asin</td>
<td>str</td>
<td>Parent ID of the product.</td>
</tr>
<tr>
<td>bought_together</td>
<td>list</td>
<td>Recommended bundles from the websites.</td>
</tr>
</table>

### Modifications Made
<ul>
<li>Products without a description, title, or images were removed.</li>
<li>The column main_category was dropped due to lack of informative content. The column bought_together was entirely empty, so it was also removed.</li>
<li>For the details column, only the top 16 most frequent detail types were kept. Products with empty details fields were excluded from the final dataset. The details column was then split into these new 16 columns based on the detail types kept.</li>
</ul>

### Dataset Size
<ul>
<li>Total entries: 18,956</li>
<li>Total columns: 27</li>
</ul>

### Final structure

<table border="1" cellpadding="5" cellspacing="0">
<tr>
<th>Field</th>
<th>Type</th>
<th>Explanation</th>
</tr>
<tr>
<td>title</td>
<td>str</td>
<td>Name of the product.</td>
</tr>
<tr>
<td>average_rating</td>
<td>float</td>
<td>Rating of the product shown on the product page.</td>
</tr>
<tr>
<td>rating_number</td>
<td>int</td>
<td>Number of ratings in the product.</td>
</tr>
<tr>
<td>features</td>
<td>list</td>
<td>Bullet-point format features of the product.</td>
</tr>
<tr>
<td>description</td>
<td>list</td>
<td>Description of the product.</td>
</tr>
<tr>
<td>price</td>
<td>float</td>
<td>Price in US dollars (at time of crawling).</td>
</tr>
<tr>
<td>images</td>
<td>list</td>
<td>Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image.</td>
</tr>
<tr>
<td>videos</td>
<td>list</td>
<td>Videos of the product including title and url.</td>
</tr>
<tr>
<td>store</td>
<td>str</td>
<td>Store name of the product.</td>
</tr>
<tr>
<td>details</td>
<td>dict</td>
<td>Product details, including materials, brand, sizes, etc.</td>
</tr>
<tr>
<td>parent_asin</td>
<td>str</td>
<td>Parent ID of the product.</td>
</tr>
<tr>
<td>upc</td>
<td>str</td>
<td>Universal Product Code (UPC), a barcode for uniquely identifying the product.</td>
</tr>
<tr>
<td>brand</td>
<td>str</td>
<td>Brand name associated with the product.</td>
</tr>
<tr>
<td>manufacturer</td>
<td>str</td>
<td>Name of the company or manufacturer responsible for producing the product.</td>
</tr>
<tr>
<td>product_dimension</td>
<td>str</td>
<td>Dimensions of the product, typically including length, width, and height.</td>
</tr>
<tr>
<td>color</td>
<td>str</td>
<td>Primary color or color variants of the product.</td>
</tr>
<tr>
<td>material</td>
<td>str</td>
<td>Main materials used in the product’s construction.</td>
</tr>
<tr>
<td>is_discontinued_by_manufacturer</td>
<td>str</td>
<td>Indicates whether the product has been discontinued by the manufacturer.</td>
</tr>
<tr>
<td>item_form</td>
<td>str</td>
<td>Form of the item (e.g., liquid, solid, gel).</td>
</tr>
<tr>
<td>item_model_number</td>
<td>str</td>
<td>Model number of the product as assigned by the manufacturer.</td>
</tr>
<tr>
<td>age_range_(description)</td>
<td>str</td>
<td>Recommended age range for the product, often used for toys or children’s products.</td>
</tr>
<tr>
<td>skin_type</td>
<td>str</td>
<td>Suitable skin types for the product (e.g., oily, dry, sensitive).</td>
</tr>
<tr>
<td>scent</td>
<td>str</td>
<td>Fragrance or scent associated with the product.</td>
</tr>
<tr>
<td>package_dimensions</td>
<td>str</td>
<td>Dimensions of the product’s packaging.</td>
</tr>
<tr>
<td>hair_type</td>
<td>str</td>
<td>Suitable hair types for the product (e.g., curly, straight, fine).</td>
</tr>
<tr>
<td>unit_count</td>
<td>str</td>
<td>Total quantity or units contained in the product package.</td>
</tr>
<tr>
<td>number_of_items</td>
<td>str</td>
<td>Total number of individual items in the product package.</td>
</tr>
</table>