This project aimed to develop a comprehensive forecasting model for Walmart’s weekly sales, leveraging three major analytical approaches: time series models (SARIMA, Winter’s Method), panel data models (Fixed Effects, Random Effects, Mundlak Models), and machine learning (Random Forest, Boosted Trees).

Discover how advanced analytics transformed customer retention and revenue strategies for a winery. This project highlights actionable insights from segmentation, CLV analysis, and predictive modeling to optimize engagement and profitability.

Understanding traffic stops can provide crucial insights into patterns of law enforcement and potential biases in policing. The project dives into the traffic stop data from Rhode Island, spanning from January 2005 to December 2015, sourced from the Stanford Open Policing Project. This detailed analysis reveals intriguing trends and disparities that call for further exploration and action.

Discover how our team, Altoid Aces, secured first place in the Littlefield Technologies simulation by mastering inventory management, capacity planning, and contract optimization. This article dives into the data-driven strategies and key takeaways that led to record-breaking profits and valuable lessons in supply chain excellence.

The primary goal of this analysis is to understand the factors influencing baggage delivery time and assess whether the process meets the industry standard of 21 minutes. The data set used in this analysis contains detailed information about the baggage unloading process from actual flights, including timestamps and various operational metrics.

Developed a real-time fugitive detection system using deep learning for facial and object recognition. This project aimed to improve public safety by identifying wanted individuals through traffic camera footage. It utilized cutting-edge techniques like Tensorflow and OpenCV, achieving an accuracy rate of 96%.

Developed a wearable safety device for elderly and pregnant individuals. This project integrated hardware (Arduino) with software (Python, Flutter) to create a watch capable of detecting falls, monitoring vital signs, and providing emergency alerts. This device aims to enhance safety and reduce stress for vulnerable populations.