Coffee Sales Dataset

The Coffee Sales Dataset provides transaction-level data for analyzing coffee sales and customer purchasing patterns. It contains 3,547 records across 11 columns, making it useful for exploratory data analysis, business intelligence, and sales analytics.

The dataset includes fields such as coffee_name, cash_type, money, hour_of_day, Date, and Time. In addition, it provides derived time-related fields such as Time_of_Day, Weekday, Month_name, Weekdaysort, and Monthsort.

As a result, analysts can study both what customers buy and when they buy it. For example, the data can help identify popular coffee products, peak sales hours, payment preferences, and changes in sales over time.

Key Features

• 3,547 coffee sales transactions

• 11 data fields covering sales and time-related information.

• Coffee product information through the coffee_name field

• Transaction amount through the money field

• Payment method through cash_type

• Purchase hour through hour_of_day

• Date and exact transaction time

• Time-of-day categories

• Weekday and month information

• Numeric sorting fields for weekdays and months

• Suitable for sales analysis and data visualization

Data Included in the Coffee Sales Dataset

The dataset contains the following fields:

These fields support both categorical and numerical analysis. Moreover, the date and time fields make the dataset useful for studying purchasing patterns across different periods.

Applications of the Coffee Sales Dataset

Coffee Sales Analysis

Analyze sales by coffee type, transaction amount, day, month, and time of day. This can help identify products and periods that generate stronger sales.

Product Performance Analysis

Compare different coffee products to understand which beverages receive more transactions or generate higher revenue.

Peak Hour Analysis

Use hour_of_day and Time_of_Day to identify busy periods. Therefore, businesses can study when customer demand increases throughout the day.

Payment Method Analysis

The cash_type field allows analysts to compare different payment methods and understand transaction preferences.

Time-Series Analysis

The date and time fields make the dataset suitable for studying sales patterns over time. Researchers can analyze daily, weekly, or monthly trends and explore forecasting approaches.

Data Visualization

Create dashboards and charts showing coffee sales, revenue trends, popular products, peak hours, payment methods, and seasonal patterns. Existing projects using this dataset have explored sales by coffee type, hour, time of day, month, and revenue trends.

SQL and Business Analytics

The dataset can also support SQL-based analytics projects involving revenue calculations, product comparisons, aggregations, and time-based reporting.

Why Use the Coffee Sales Dataset?

The Coffee Sales Dataset combines transaction, product, payment, and time-based information in a single tabular dataset. Therefore, it works well for practical data analytics projects.

Moreover, the dataset provides enough time-related information to investigate customer purchasing behavior throughout the day, week, and month. Analysts can also combine these fields to create interactive dashboards and business reports.

For machine learning and forecasting projects, the date and time attributes can provide useful features for modeling sales patterns. However, users should first inspect the data, check distributions, and prepare the variables according to their specific modeling requirements.

Who Can Use This Dataset?

The Coffee Sales Dataset can be useful for:

• Data analysts

• Business analysts

• Data science students

• Machine learning practitioners

• SQL learners

• BI and dashboard developers

• Researchers

• Sales and retail analytics professionals

• Students working on time-series projects

Conclusion

The Coffee Sales Dataset provides 3,547 transaction records with detailed information about coffee products, payment methods, transaction amounts, and purchasing times. Therefore, it offers a practical resource for sales analytics, business intelligence, time-series analysis, and data visualization.

Researchers, students, and analysts can use the dataset to explore product performance, peak purchasing periods, payment preferences, and sales trends.

The dataset is available on Kaggle.