1. Overview This project involved analyzing sales data from a coffee shop to uncover key business insights and trends. The analysis aimed to provide actionable recommendations to improve sales performance and operational efficiency. create Date Table from column transaction_date Here are some of the transformations which I have performed as per requirement using DAX: Data Modeling: 2. Ask The objective of this project was to analyze the coffee shop’s sales data to answer critical business questions, including: Sales Trends Over Time: What are the sales trends over different time periods (daily, weekly, monthly)? Are there any seasonal patterns in the sales data? 2. Product Performance: Which products are the best sellers? What is the sales contribution of each product category? Are there any products with declining sales? 3. Customer Behavior: What are the peak hours for sales? How does customer purchasing behavior change throughout the day? 4. Revenue Insights: What are the revenue trends? How do discounts or promotions affect sales and revenue? 3. Approach Data Preparation Tools Used: Power BI, Power Query Steps Taken: Imported raw sales data into Power Query for initial cleaning and transformation. Created calculated fields, including date and time fields, to facilitate analysis. Generated a Date Table using DAX to support time series analysis. Data Exploration Tools Used: Power BI (instead of Excel PivotTables) Analysis Performed: Sliced and diced the data to uncover sales trends, product performance, and customer behavior patterns. Used time series analysis to identify trends across different periods. Conducted product-level analysis to assess the performance of individual items and categories.