I just engineered and deployed

I just engineered and deployed a live Online Retail Executive Dashboard analyzing over $10.6M+ in transaction data. Role: Data Analyst / BI Engineer

Tools Used: Python, Pandas, NumPy, MySQL, SQLAlchemy, PyMySQL, Streamlit, Plotly, Jupyter Notebook Project Overview:

Engineered and deployed an interactive executive analytics application designed to process and visualize over $10.6M+ in raw online retail transaction data across 19k+ customer orders. The system automates key performance metric tracking, sales seasonality analysis, peak shopping hour detection, and customer lifetime value (LTV) segmentation.

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Key Architecture & Execution Steps:

Database Architecture & Management: Structured relational database schemas and executed SQL queries via MySQL, SQLAlchemy, and PyMySQL to handle high-volume retail transactional records efficiently.

Data Pipeline & Transformation: Processed, cleaned, and aggregated raw order histories using Pandas and NumPy, handling missing values and calculating key financial KPIs.

Interactive BI Dashboard (Streamlit & Plotly): Built a responsive web dashboard featuring dynamic Plotly visualizations for monthly seasonality, hourly trend analyses, and top revenue-generating products.

Customer Segmentation & LTV Analysis: Isolated top VIP buyers driving significant revenue chunks to assist marketing and management in customer retention strategy.

Business Impact & Key Insights:

• Revenue Aggregation: Automated real-time tracking across $10.67M total revenue and 19k+ orders.

• Operational Efficiency: Identified peak shopping hours (12:00 PM – 2:00 PM) to inform targeted promotional schedules and server load planning.

• VIP Customer Targeting: Extracted lifetime value metrics to highlight high-value accounts responsible for major revenue share.