Sales & Profit Dashboard

I started by sourcing a structured sales dataset from Kaggle, which included fields such as customer name, year, month, sales revenue, and cost.

Using Python with Pandas, I performed data preprocessing, including handling missing values, validating data types, removing inconsistencies, and standardizing customer names. I also implemented feature engineering by deriving profit as the difference between revenue and cost, and ensured the dataset was analysis-ready.

Once cleaned, I loaded the data into PostgreSQL, where I designed a structured table optimized for analytical queries. At the database level, I created key business KPIs using SQL, such as total sales, total profit, monthly and yearly trends, and top 5 customers based on revenue contribution.

I then connected the database to Microsoft Power BI to build an interactive dashboard. Within Power BI, I created DAX measures for dynamic calculations like total sales, total profit, and profit margin, enabling the dashboard to respond to filters such as year and month.

The final dashboard provided insights into sales trends over time, customer-level performance, and overall profitability, effectively transforming raw data into actionable business insights through a structured data pipeline.