Mid East Canadian Pharmaceutical Inc. seeks to enhance its inventory management and market trend analysis by leveraging data-driven insights. The project involves using Python and n8n to extract and integrate data from various sources, including sales, supply chain, and market data. The goal is to develop a predictive model that forecasts inventory needs and market trends, enabling the company to optimize stock levels and anticipate market demands. This project provides learners with the opportunity to apply their knowledge of data extraction, integration, and predictive modeling in a real-world context. The tasks are closely related and focus on data handling, model development, and validation, ensuring a cohesive learning experience. The project is designed to be completed by a team of learners within 80 hours, utilizing their skills in data science and analytics.

The project deliverables include a functional predictive model that forecasts inventory requirements and market trends. Learners will also provide a comprehensive report detailing the data extraction process, model development, and validation results. Additionally, a presentation summarizing the findings and recommendations for inventory management will be required. These deliverables will demonstrate the learners' ability to apply data science techniques to solve practical business challenges.