Datasetpublic

imambru/superkart-sales-forecast

SuperKart Sales Forecast (Tabular) This dataset contains product/store-level attributes with the target Product_Store_Sales_Total for supervised learning and forecasting. Files data/SuperKart.csv Schema Product_Id — Unique identifier of each product (AA… pattern) Product_Weight — Weight (kg) Product_Sugar_Content — low sugar / regular / no sugar Product_Allocated_Area — Ratio of display area allocated to the product Product_Type — Category (meat… See the full description on the dataset page: https://huggingface.co/datasets/imambru/superkart-sales-forecast.

sourceHugging Facemitupdated 1y agoView on Hugging Face

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Dataset Card

SuperKart Sales Forecast (Tabular)

This dataset contains product/store-level attributes with the target Product_Store_Sales_Total for supervised learning and forecasting.

Files

—data/SuperKart.csv

Schema

—Product_Id — Unique identifier of each product (AA… pattern)
—Product_Weight — Weight (kg)
—ProductSugarContent — low sugar / regular / no sugar
—ProductAllocatedArea — Ratio of display area allocated to the product
—Product_Type — Category (meat, snacks, dairy, canned, soft drinks, etc.)
—Product_MRP — Maximum Retail Price
—Store_Id — Unique store identifier
—StoreEstablishmentYear — Year store established
—Store_Size — high / medium / low
—StoreLocationCity_Type — Tier 1 / Tier 2 / Tier 3
—Store_Type — Departmental, Supermarket Type 1/2, Food Mart
—ProductStoreSales_Total — Target: revenue for that product-store pair

Load with 🤗 Datasets

from datasets import loaddataset ds = loaddataset("imambru/superkart-sales-forecast") print(ds)

License

MIT