This dataset captures retail transaction data during a Black Friday sale. It includes anonymized details about customer demographics, product preferences, and purchasing behavior. The dataset is valuable for understanding consumer purchasing patterns, analyzing trends, and developing predictive models for the retail sector. Potential Uses - Analyzing customer behavior during sales events. - Exploring purchase patterns by demographics (age, gender, location). - Understanding the distribution of product categories. - Building recommendation systems or forecasting models.

The Black Friday Sales dataset is a comprehensive collection of sales transaction data from a major retail store during a Black Friday event. This dataset includes over 550,000 observations and 12 key variables, offering valuable insights into customer purchasing behavior during one of the biggest shopping days of the year.

-> Key Features: - User ID: Unique ID for each customer. - Product ID: Unique ID for each product. - Gender: Gender of the customer, either male or female. - Age: The age group of the customer, represented in categories (e.g., 18-25, 26-35, etc.). - Occupation: Occupation category code of the customer. - City_Category: The category of the city where the customer resides, classified as A, B, or C. - Stay_In_Current_City_Years: Number of years the customer has lived in the current city. - Marital_Status: Indicates whether the customer is married (1) or not (0). - Product_Category 1, 2, 3: Product categories associated with the purchased item. - Purchase: The amount spent by the customer on the product.

This dataset can be utilized for analyzing patterns in consumer behavior, demographic-based purchasing tendencies, and predicting future sales trends. It's widely used in data science projects for regression, classification, and recommendation systems, making it ideal for feature engineering, model building, and data visualization.

Discover the booming Black Friday sales market! Our analysis reveals key trends, growth drivers, and challenges impacting retailers from Amazon to Zara. Explore market size projections, regional breakdowns, and top companies dominating this global shopping event.

Unlock the secrets of the booming Black Friday market! Discover key trends, CAGR projections, and regional insights from our comprehensive analysis. Learn how top retailers like Amazon and Walmart are capitalizing on this massive shopping event. Explore the future of Black Friday sales and plan your strategy for 2025 and beyond.

Dataset History

A retail company, “ABC Private Limited” wants to understand the customer purchase behavior (specifically, purchase amount) against various products of different categories. They have shared purchase summaries of various customers for selected high-volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stay in the current city), product details (product ID and product category), and total purchase amount from last month.

Now, they want to build a model to predict the purchase amount of customers against various products, which will help them to create a personalized offer for customers against different products.

Tasks to perform

The purchase column is the target variable; perform Univariate Analysis and Bivariate Analysis w.r.t. the Purchase.

Masked in the column description means already converted from categorical value to numerical column.

The below-mentioned points are just given to get you started with the dataset; it is not mandatory to follow the same sequence.

DATA PREPROCESSING

Check the basic statistics of the dataset Check for missing values in the data Check for unique values in data Perform EDA Purchase Distribution Check for outliers Analysis by Gender, Marital Status, occupation, occupation vs purchase, purchase by city, purchase by age group, etc Drop unnecessary fields Convert categorical data into integer using map function (e.g 'Gender' column) Missing value treatment Rename columns Fill NaN values map range variables into integers (e.g 'Age' column)

Data Visualisation

visualize individual column Age vs Purchased Occupation vs Purchased Productcategory1 vs Purchased Productcategory2 vs Purchased Productcategory3 vs Purchased City category pie chart check for more possible plots

All the Best!!

Compared to sales that took place in the preceding eight weeks, retail sales during Black Friday grew by varying degrees in the United Kingdom in 2020. On Black Friday, the highest increase was observed in the apparel and accessories category, with a sales increase of *** percent. Cyber Monday, which immediately follows holiday season’s most anticipated retail event, led to increased activity as well.

UK: where the biggest Black Friday happens In Europe, Black Friday stimulates the highest sales in the United Kingdom. In 2020, the total value of sales during Black Friday and Cyber Monday amounted to *** billion British pounds. Online purchases made up the majority of the projected value of Black Friday and Cyber Weekend sales in the UK.

Black Friday 2021: what are UK consumers buying?
In 2021, consumers in the older age brackets were preparing to allocate higher budgets for their Black Friday purchases, over *** British pounds each. In comparison, Gen Z and millennials were not planning to splash their money on Black Friday purchases. London was projected to be the region where consumers were intending to spend the highest amount during this retail event.

Comprehensive dataset tracking Black Friday online sales percentages, growth rates, and market trends from 2019 to 2024, including mobile commerce adoption and payment method statistics.

Historical Black Friday revenue data showing 11-year trends in U.S. online and retail sales with year-over-year growth analysis and market insights.

Dataset History

A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summaries of various customers for selected high-volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stay in the current city), product details (productid and product category) and Total purchase amount from last month.

Now, they want to build a model to predict the purchase amount of customers against various products which will help them to create a personalized offer for customers against different products.

Tasks to perform

The purchase column is the Target Variable, perform Univariate Analysis and Bivariate Analysis w.r.t the Purchase.

Masked in the column description means already converted from categorical value to numerical column.

Below mentioned points are just given to get you started with the dataset, not mandatory to follow the same sequence.

DATA PREPROCESSING

• Check the basic statistics of the dataset

• Check for missing values in the data

• Check for unique values in data

• Perform EDA

• Purchase Distribution

• Check for outliers

• Analysis by Gender, Marital Status, occupation, occupation vs purchase, purchase by city, purchase by age group, etc

• Drop unnecessary fields

• Convert categorical data into integer using map function (e.g 'Gender' column)

• Missing value treatment

• Rename columns

• Fill nan values

• map range variables into integers (e.g 'Age' column)

Data Visualisation

• visualize individual column
• Age vs Purchased
• Occupation vs Purchased
• Productcategory1 vs Purchased
• Productcategory2 vs Purchased
• Productcategory3 vs Purchased
• City category pie chart
• check for more possible plots

All the Best!!

After the digital transition that occurred due to COVID-19, more consumers got comfortable with shopping online. Compared to the previous year's Black Friday revenue the pet supplies segment's revenue grew **** percent since the previous Black Friday edition, followed by fashion and accessories by *** percent.

Dataset History

A retail company “ABC Private Limited” wants to understand the customer purchase behaviour (specifically, purchase amount) against various products of different categories. They have shared purchase summaries of various customers for selected high-volume products from last month. The data set also contains customer demographics (age, gender, marital status, city type, stay in the current city), product details (productid and product category) and Total purchase amount from last month.

Now, they want to build a model to predict the purchase amount of customers against various products which will help them to create a personalized offer for customers against different products.

Tasks to perform

The purchase column is the Target Variable, perform Univariate Analysis and Bivariate Analysis w.r.t the Purchase.

Masked in the column description means already converted from categorical value to numerical column.

Below mentioned points are just given to get you started with the dataset, not mandatory to follow the same sequence.

DATA PREPROCESSING

• Check the basic statistics of the dataset

• Check for missing values in the data

• Check for unique values in data

• Perform EDA

• Purchase Distribution

• Check for outliers

• Analysis by Gender, Marital Status, occupation, occupation vs purchase, purchase by city, purchase by age group, etc

• Drop unnecessary fields

• Convert categorical data into integer using map function (e.g 'Gender' column)

• Missing value treatment

• Rename columns

• Fill nan values

• map range variables into integers (e.g 'Age' column)

Data Visualisation

• visualize individual column
• Age vs Purchased
• Occupation vs Purchased
• Productcategory1 vs Purchased
• Productcategory2 vs Purchased
• Productcategory3 vs Purchased
• City category pie chart
• check for more possible plots

All the Best!!

The goal of this dataset is to perform a data analysis project to investigate customer purchase behaviour during Black Friday at Walmart, specifically focusing on understanding if there are differences in spending habits between male and female customers. The analysis aims to provide insights to assist Walmart's management team in making informed business decisions.

Roughly ************* U.S. consumers surveyed in a recent study said they might go shop in-store for the Black Friday sales in 2024. This made it the most popular response. About the same percent of respondents said that they will definitely not do their Black Friday sales shopping in store.

Black Friday Deals Srl recorded an import turnover of USD 74,818.19 million and an export turnover of USD 0 between December 2024 and November 2025. Explore detailed trade value insights, supply chain analytics, HS code-wise data, shipment history, partner countries, customs trade values, top import and export commodities with pricing, buyers, suppliers, ports, and key competitors in Costa Rica.

In 2025, online retail sales during Black Friday and Cyber Monday were forecast to reach *** billion euros in Italy. This represented a ***** percent increase from the previous year.

Dataset

This dataset was created by Ritik Kesharwani

Contents

Black Friday spending was forecast to be the higher in the United Kingdom (UK) than any other European nation in 2020, although total figures are expected crash in 2020 due to the impact of coronavirus restrictions and lockdown measures in place in many European countries. Following the UK, the second biggest Black Friday spender was Germany, where Black Friday weekend and Cyber Monday sales value reached *** billion in 2018 and *** billion British pounds in 2019. In 2020, Germany is predicted to spend around * percent less during Black Friday weekend.

The most popular retail event of the holiday season

Black Friday might be an American shopping phenomenon by origin, but European consumers have adopted it with similar degrees of zealotry. Across the EMEA region, Black Friday stood out among other special retail events during the holiday season in 2018. The number of daily retail transactions that took place on this day put Black Friday ahead of more traditional holiday season discount opportunities such as Christmas and Boxing Day sales. Perhaps not surprisingly, the most purchased products on Black Friday were in the tech category.

Millions of Black Friday transactions on Amazon

Black Friday is simultaneously a challenge and an opportunity for retailers, be it brick-and-mortar or online. One retailer that springs to mind immediately on this day is Amazon. In the UK, the total number of transactions made during 2018 Black Friday on Amazon’s UK platform saw a ** percent increase from about two weeks before. On Black Friday’s twin event, Cyber Monday, the total number of transactions was just shy of **** million.

Dataset

This dataset was created by Vernon

Released under MIT

Contents

View Il black friday deals import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

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