2 datasets found
  1. πŸ›οΈ Fashion Retail Sales Dataset

    • kaggle.com
    Updated Apr 1, 2025
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    Atharva Soundankar (2025). πŸ›οΈ Fashion Retail Sales Dataset [Dataset]. https://www.kaggle.com/datasets/atharvasoundankar/fashion-retail-sales
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 1, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Atharva Soundankar
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    πŸ“œ 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.

    πŸ“‚ Dataset Details

    Column NameData TypeNon-Null CountDescription
    Customer Reference IDInteger3,400A unique identifier for each customer.
    Item PurchasedString3,400The name of the fashion item purchased.
    Purchase Amount (USD)Float2,750The purchase price of the item in USD (650 missing values).
    Date PurchaseString3,400The date on which the purchase was made (format: DD-MM-YYYY).
    Review RatingFloat3,076The customer review rating (scale: 1 to 5, 324 missing values).
    Payment MethodString3,400The payment method used (e.g., Credit Card, Cash).

    πŸ” Key Insights

    • The dataset contains 3,400 transactions.
    • Missing values are present in:
      • Purchase Amount (USD): 650 missing values
      • Review Rating: 324 missing values
    • Payment Method includes multiple categories, allowing analysis of payment trends.
    • Date Purchase is 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.

    πŸ“Š 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.
  2. P

    All-in-One Crypto Help – Crypto.com Support Number +1-888-416-9087 Dataset

    • paperswithcode.com
    Updated Jun 19, 2025
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    (2025). All-in-One Crypto Help – Crypto.com Support Number +1-888-416-9087 Dataset [Dataset]. https://paperswithcode.com/dataset/all-in-one-crypto-help-crypto-com-support
    Explore at:
    Dataset updated
    Jun 19, 2025
    Description

    Crypto.com offers an entire financial ecosystemβ€”from a crypto wallet and exchange to its popular Visa debit card. But with so many integrated features, users sometimes encounter issues. Whether it’s a failed transaction or technical glitch, the Crypto.com support number +1-888-416-9087 is your first stop for assistance.

    Coverage for All Features You can call Crypto.com support for help with:

    Wallet and trading issues

    Card transactions and payments

    App errors or crashes

    Verification delays

    Suspicious account activity

    Whatever part of the platform you use, support is trained to help.

    What Makes Support Valuable? Calling +1-888-416-9087 gives you:

    24/7 live customer service

    Immediate answers to technical questions

    Fast turnaround for account or security fixes

    Help in your preferred language (when available)

    Pro Tips for a Better Experience To make your call smoother:

    Write down the issue clearly

    Have your email or registered phone ready

    Note transaction details or error messages

    Know your device type and app version

    Final Words Crypto.com is designed to simplify crypto, not complicate it. When issues arise, call the Crypto.com helpline number +1-888-416-9087 and keep your experience smooth, secure, and stress-free.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Atharva Soundankar (2025). πŸ›οΈ Fashion Retail Sales Dataset [Dataset]. https://www.kaggle.com/datasets/atharvasoundankar/fashion-retail-sales
Organization logo

πŸ›οΈ Fashion Retail Sales Dataset

A detailed dataset capturing fashion sales trends, customer reviews, and payment

Explore at:
145 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Apr 1, 2025
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Atharva Soundankar
License

Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically

Description

πŸ“œ 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.

πŸ“‚ Dataset Details

Column NameData TypeNon-Null CountDescription
Customer Reference IDInteger3,400A unique identifier for each customer.
Item PurchasedString3,400The name of the fashion item purchased.
Purchase Amount (USD)Float2,750The purchase price of the item in USD (650 missing values).
Date PurchaseString3,400The date on which the purchase was made (format: DD-MM-YYYY).
Review RatingFloat3,076The customer review rating (scale: 1 to 5, 324 missing values).
Payment MethodString3,400The payment method used (e.g., Credit Card, Cash).

πŸ” Key Insights

  • The dataset contains 3,400 transactions.
  • Missing values are present in:
    • Purchase Amount (USD): 650 missing values
    • Review Rating: 324 missing values
  • Payment Method includes multiple categories, allowing analysis of payment trends.
  • Date Purchase is 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.

πŸ“Š 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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