Peter14352/Amazon_Sports_and_Outdoors_2023

---
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: manufacturer
dtype: string
- name: brand_name
dtype: string
- name: color
dtype: string
- name: package_weight
dtype: string
- name: item_package_dimensions_l_x_w_x_h
dtype: string
- name: part_number
dtype: string
- name: material
dtype: string
- name: best_sellers_rank
dtype: string
- name: size
dtype: string
- name: style
dtype: string
- name: brand
dtype: string
- name: suggested_users
dtype: string
- name: item_weight
dtype: string
- name: item_dimensions__lxwxh
dtype: string
- name: department
dtype: string
- name: sport_type
dtype: string
splits:
- name: train
num_bytes: 1293209275
num_examples: 535206
download_size: 607190296
dataset_size: 1293209275
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 Sports and Outdoors meta dataset.

### Dataset Description

This dataset is a refined version of the Amazon Sports and Outdoors 2023 meta dataset, which originally contained product metadata for sports and outdoors products that are 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, 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. Then that column was dropped.</li>
<li>Column bought_together is dropped due to missing values.</li>
</ul>

### Dataset Size
<ul>
<li>Total entries: 535,206</li>
<li>Total columns: 29</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>str</td>
<td>Main 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>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>date_first_available</td>
<td>int64</td>
<td>Date first time the product was available</td>
</tr>
<tr>
<td>manufacturer</td>
<td>str</td>
<td>Manufacturer of the product</td>
</tr>
<tr>
<td>brand_name</td>
<td>str</td>
<td>Brand name</td>
</tr>
<tr>
<td>color</td>
<td>str</td>
<td>color</td>
</tr>
<tr>
<td>package_weight</td>
<td>str</td>
<td>Package weight</td>
</tr>
<tr>
<td>item_package_dimensions_l_x_w_x_h</td>
<td>str</td>
<td>Dimensions of the package item LxWxH</td>
</tr>
<tr>
<td>part_number</td>
<td>str</td>
<td>Part number</td>
</tr>
<tr>
<td>material</td>
<td>str</td>
<td>Material</td>
</tr>
<tr>
<td>best_sellers_rank</td>
<td>str</td>
<td>Best seller rank</td>
</tr>
<tr>
<td>size</td>
<td>str</td>
<td>Size</td>
</tr>
<tr>
<td>style</td>
<td>str</td>
<td>Style</td>
</tr>
<tr>
<td>brand</td>
<td>str</td>
<td>Brand</td>
</tr>
<tr>
<td>suggested_users</td>
<td>str</td>
<td>Suggested users</td>
</tr>
<tr>
<td>item_weight</td>
<td>str</td>
<td>Weight of the item</td>
</tr>
<tr>
<td>item_dimensions__lxwxh</td>
<td>str</td>
<td>Item dimensions LxWxH</td>
</tr>
<tr>
<td>department</td>
<td>str</td>
<td>Department</td>
</tr>
<tr>
<td>sport_type</td>
<td>str</td>
<td>Sport type</td>
</tr>
</table>