This project involves a comprehensive analysis of coffee order data from 2019 to 2022. The goal was to uncover trends and insights related to customer preferences, sales seasonality, and product performance across different coffee types. The analysis leverages data visualization and statistical techniques to deliver actionable insights for optimizing business decisions.
- Data Cleaning: Ensured data accuracy by handling missing values, removing inconsistencies, and structuring the dataset for analysis.
- Exploratory Data Analysis (EDA): Investigated trends across multiple dimensions, including year-over-year sales, monthly seasonality, and product-specific performance.
- Visualizations: Created visual representations of the data to easily communicate findings, including annual and monthly sales trends for each coffee type.
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Annual Sales Trends:
- "Lib" emerged as the top-selling coffee type, with a particularly strong performance in 2022.
- "Ara" and "Exc" showed stable sales, while "Rob" experienced significant variability, with a low in 2020 followed by recovery.
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Monthly Sales Trends:
- Seasonal patterns were observed, with "Lib" peaking during the latter part of the year.
- Other coffee types, such as "Ara" and "Exc," also exhibited seasonal variations, though with less intensity.
- Excel: Initial data cleanup and organization.
- Data: The
coffeeOrdersData.xlsxfile contains the raw data used for this analysis. - Visualizations: Graphs and charts illustrating key insights.
- Extend the analysis to include additional data points or more granular time periods.
- Incorporate advanced analytics techniques, such as predictive modeling, to forecast future sales trends.