Sales Trend and Time-Based Performance Analysis for Afficionado Coffee Roasters: An Exploratory Data Analysis Approach

This research paper presents a comprehensive analysis of sales trends and time-based performance for Afficionado Coffee Roasters using Exploratory Data Analysis (EDA) techniques. The objective of this study is to identify sales patterns, peak transaction hours, product demand, and store performance to support data-driven decision-making in the coffee retail industry.The dataset contains transactional records including product categories, transaction quantities, store locations, unit prices, and transaction times. Data preprocessing techniques such as data cleaning, duplicate removal, feature engineering, and outlier detection were applied to prepare the dataset for analysis.Exploratory Data Analysis was conducted using Python libraries including Pandas, NumPy, Matplotlib, and Seaborn. Time-based analysis was performed to evaluate hourly, daily, and monthly sales trends. The analysis also includes store-level comparisons and product category performance to identify revenue drivers.The results reveal key insights such as peak business hours, high-performing product categories, and store performance variations. These findings provide valuable recommendations for optimizing staffing schedules, improving marketing strategies, and enhancing inventory planning.The project also includes an interactive data analytics dashboard developed using Streamlit and Plotly to visualize sales trends and business performance metrics.