Applied machine learning group project on H&M dataset
https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data
articles.csv: table
transactions.csv: images
customers.csv: images
Transactions.csv summary:
- Rows: each row represents a transaction of the purchases in H&M group.
- Columns:
- t_dat: date of the transaction
- customer_id: a foreign key that maps to each customer entry in the customer_id column in customers.csv, which contains customers' metadata
- article_id: a foreign key that maps to each customer entry in the article_id column in articles.csv, which contains articles' metadata
- price: price that the customer paid for the transaction, there are multiple prices for the same article, even on the same day/channel. According to data's contributor, the unit of price is not any "currency/unit" since they chose to not disclose the real values
- sales_channel_id: it determines how the customer purchases the article, 2 is online and 1 store
The article.csv contains all h&m articles with details such as a type of product, a color, a product group and other features.
Article data description:
article_id: A unique identifier of every article.
product_code,prod_name: A unique identifier of every product and its name (not the same).
product_type,product_type_name: The group of product_code and its name
graphical_appearance_no,graphical_appearance_name: The group of graphics and its name
colour_group_code,colour_group_name: The group of color and its name
perceived_colour_value_id,perceived_colour_value_name,perceived_colour_master_id,perceived_colour_master_name: The added color info
department_no,department_name: : A unique identifier of every dep and its name
index_code,index_name: : A unique identifier of every index and its name
index_group_no,index_group_name: : A group of indeces and its name
section_no,section_name: : A unique identifier of every section and its name
garment_group_no,garment_group_name: : A unique identifier of every garment and its name
detail_desc: : Details