Analyzing global flight patterns, passenger volumes, and airline performance to identify travel trends and logistics bottlenecks

End-to-end logistics operations analytics project using SQL and Power BI. Built a relational database, created analytical views, and developed dashboards to analyze driver performance, revenue per trip, and on-time delivery trends.

Comprehensive analysis of Brazilian E-commerce data: orders, deliveries, reviews, seller performance, and revenue insights.

EDA and dashboard for e-commerce delivery data

Real-time logistics monitoring system using Kafka, Spark Streaming, PostgreSQL, Docker, and Power BI for processing and visualizing live inventory and GPS data.

An end-to-end Power BI dashboard analyzing sales performance, inventory efficiency, and logistics operations to identify revenue growth drivers and operational risks such as stockouts and late deliveries.

End-to-end Commerce & Logistics Analytics project using Python, SQL Server, and Power BI

"A comprehensive 5-page Power BI suite transforming logistics and fleet data into actionable insights for financial oversight, asset reliability, and supply chain optimization."

🏗️ Modern Analytics Engineering project using dbt and BigQuery to model fleet operations. Implementing a Medallion Architecture, it transforms raw GPS data into a reliable Star Schema. Focuses on resolving data quality issues like sensor noise and duplicates through automated testing and CI/CD to ensure production-grade reporting.

Power BI Transport & Delivery Analytics Dashboard optimizing logistics. Features real-time GPS fleet tracking, route optimization, and multi-timeframe analytics to achieve a 40% reduction in delivery time, a 35% decrease in fuel consumption, and $75,000 in annual cost savings for supply chain operations.

ML model to predict supply chain delivery delays using XGBoost

Interactive Excel logistics dashboard tracking OTIF, SLA compliance, delay trends and return rates across 200 shipments — revealing a 42.5% SLA breach rate and 235% increase in delays from January to July.

A Python-based decision support tool that forecasts short-horizon demand, evaluates capacity utilization risk, and produces prioritized mitigation actions with backtest accuracy (MAE/MAPE) and a professional PDF report.