README.md

---
dataset_info:
  features:
  - name: main_category
    dtype: string
  - 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: string
  - name: parent_asin
    dtype: string
  - name: date_first_available
    dtype: int64
  - name: department
    dtype: string
  - name: manufacturer
    dtype: string
  - name: item_model_number
    dtype: string
  - name: product_dimensions
    dtype: string
  - name: item_weight
    dtype: string
  - name: color
    dtype: string
  - name: brand
    dtype: string
  - name: size
    dtype: string
  - name: best_sellers_rank
    dtype: string
  - name: package_dimensions
    dtype: string
  - name: material
    dtype: string
  - name: style
    dtype: string
  - name: age_range_(description)
    dtype: string
  - name: country_of_origin
    dtype: string
  splits:
  - name: train
    num_bytes: 6001681637
    num_examples: 2539634
  download_size: 2552518590
  dataset_size: 6001681637
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Amazon Clothing Shoes and Jewelry 2023 Dataset

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

## Dataset Details
This dataset is downloaded from the link above, the category Clothing Shoes and Jewelry meta dataset.

### Dataset Description

The Amazon Clothing Shoes and Jewelry 2023 dataset provides information on products from a diverse ranger of categories, including main attributes like ratings, price, and desciptions.
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, images or details were removed.</li>
<li>Lists in features and description are transformed into strings concatinated with a newline</li>
<li>For the details column, only the top 16 most frequent detail types were kept. The details column was then split into these new 16 columns based on the detail types kept.</li>
<li>Products with date first available before the year 2015 are dropped.</li>
<li>Products with is_discontinued_by_manufacturer set to 'true' or 'yes' are dropped.</li>
<li>Column bought_together is dropped due to missing values.</li>
</ul>

### Dataset Size
<ul>
<li>Total entries: 2 539 634</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>main_category</td>
    <td>string</td>
    <td>Primary product category</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[str]</td>
    <td>Subcategories of the product.</td>
  </tr>
  <tr>
    <td>parent_asin</td>
    <td>str</td>
    <td>Parent ID of the product.</td>
  </tr>
  <tr>
    <td>date_first_available</td>
    <td>timestamp</td>
    <td>First date when the product was available.</td>
  </tr>
  <tr>
    <td>department</td>
    <td>string</td>
    <td>Department of the product. (E.g. womens, mens)</td>
  </tr>
  <tr>
    <td>country_of_origin</td>
    <td>string</td>
    <td>Name of the country of origin</td>
  </tr>
  <tr>
    <td>item_weight</td>
    <td>string</td>
    <td>Weight of the product in ounces or pounds.</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_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>size</td>
    <td>str</td>
    <td>Size of product</td>
  </tr>
  <tr>
    <td>best_sellers_rank</td>
    <td>string</td>
    <td>Amazon best-sellers rank</td>
  </tr>
  <tr>
    <td>package_dimensions</td>
    <td>string</td>
    <td>Package dimensions</td>
  </tr>
  <tr>
    <td>style</td>
    <td>string</td>
    <td>Style</td>
  </tr>
</table>

### Usage
## Download the dataset
```ruby
dataset = load_dataset("smartcat/Amazon_Clothing_Shoes_and_Jewelry_2023", split="train")
```