ποΈ Fashion Retail Sales Dataset
- kaggle.com
Updated Apr 1, 2025
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EmailClick to copy linkLink copiedCiteAtharva Soundankar (2025). ποΈ Fashion Retail Sales Dataset [Dataset]. https://www.kaggle.com/datasets/atharvasoundankar/fashion-retail-salesCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedApr 1, 2025AuthorsAtharva SoundankarLicenseApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automaticallyDescriptionπ Dataset Overview
This dataset contains 3,400 records of fashion retail sales, capturing various details about customer purchases, including item details, purchase amounts, ratings, and payment methods. It is useful for analyzing customer buying behavior, product popularity, and payment preferences.
π Dataset Details
Column Name Data Type Non-Null Count Description Customer Reference IDInteger 3,400 A unique identifier for each customer. Item PurchasedString 3,400 The name of the fashion item purchased. Purchase Amount (USD)Float 2,750 The purchase price of the item in USD (650 missing values). Date PurchaseString 3,400 The date on which the purchase was made (format: DD-MM-YYYY). Review RatingFloat 3,076 The customer review rating (scale: 1 to 5, 324 missing values). Payment MethodString 3,400 The payment method used (e.g., Credit Card, Cash). π Key Insights
- The dataset contains 3,400 transactions.
- Missing values are present in:
Purchase Amount (USD): 650 missing valuesReview Rating: 324 missing values
Payment Methodincludes multiple categories, allowing analysis of payment trends.Date Purchaseis in DD-MM-YYYY format, which can be useful for time-series analysis.- The dataset can help analyze sales trends, customer preferences, and payment behaviors in the fashion retail industry.
π Potential Use Cases
- Sales Analysis: Understanding which fashion items are selling the most.
- Customer Insights: Analyzing purchase behaviors and spending patterns.
- Trend Forecasting: Identifying seasonal trends in fashion retail.
- Payment Method Preferences: Understanding how customers prefer to pay.
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- paperswithcode.com
Updated Jun 19, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCite(2025). All-in-One Crypto Help β Crypto.com Support Number +1-888-416-9087 Dataset [Dataset]. https://paperswithcode.com/dataset/all-in-one-crypto-help-crypto-com-supportDataset updatedJun 19, 2025DescriptionCrypto.com offers an entire financial ecosystemβfrom a crypto wallet and exchange to its popular Visa debit card. But with so many integrated features, users sometimes encounter issues. Whether itβs a failed transaction or technical glitch, the Crypto.com support number +1-888-416-9087 is your first stop for assistance.
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FacebookTwitterEmailποΈ Fashion Retail Sales Dataset
A detailed dataset capturing fashion sales trends, customer reviews, and payment
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
π Dataset Overview
This dataset contains 3,400 records of fashion retail sales, capturing various details about customer purchases, including item details, purchase amounts, ratings, and payment methods. It is useful for analyzing customer buying behavior, product popularity, and payment preferences.
π Dataset Details
| Column Name | Data Type | Non-Null Count | Description |
|---|---|---|---|
Customer Reference ID | Integer | 3,400 | A unique identifier for each customer. |
Item Purchased | String | 3,400 | The name of the fashion item purchased. |
Purchase Amount (USD) | Float | 2,750 | The purchase price of the item in USD (650 missing values). |
Date Purchase | String | 3,400 | The date on which the purchase was made (format: DD-MM-YYYY). |
Review Rating | Float | 3,076 | The customer review rating (scale: 1 to 5, 324 missing values). |
Payment Method | String | 3,400 | The payment method used (e.g., Credit Card, Cash). |
π Key Insights
- The dataset contains 3,400 transactions.
- Missing values are present in:
Purchase Amount (USD): 650 missing valuesReview Rating: 324 missing values
Payment Methodincludes multiple categories, allowing analysis of payment trends.Date Purchaseis in DD-MM-YYYY format, which can be useful for time-series analysis.- The dataset can help analyze sales trends, customer preferences, and payment behaviors in the fashion retail industry.
π Potential Use Cases
- Sales Analysis: Understanding which fashion items are selling the most.
- Customer Insights: Analyzing purchase behaviors and spending patterns.
- Trend Forecasting: Identifying seasonal trends in fashion retail.
- Payment Method Preferences: Understanding how customers prefer to pay.