DansFarmaRoot is seeking to enhance its inventory management through the implementation of AI predictive analytics. The primary goal of this project is to develop machine learning models that can accurately forecast product demand by analyzing historical sales data, market trends, and external factors such as seasonality and market conditions. By predicting future demand more accurately, DansFarmaRoot aims to optimize inventory levels, thereby reducing instances of stockouts and overstock situations. This project will provide learners with the opportunity to apply their classroom knowledge of machine learning, data analysis, and predictive modeling to a real-world business problem. The tasks will include data collection and preprocessing, model selection and training, and performance evaluation.

- A comprehensive report detailing the data collection and preprocessing steps, including any challenges faced and how they were addressed.

- A set of trained machine learning models capable of predicting product demand based on historical sales data and external factors.

- A performance evaluation report comparing the accuracy and effectiveness of different models.

- A presentation summarizing the project findings, methodologies used, and recommendations for future improvements.

At DansFarmaRoot, we are committed to providing comprehensive support and mentorship to our co-op students. Our team will allocate dedicated staff time to guide and mentor students, ensuring they receive personalized assistance throughout their projects. Students will have access to advanced tools and technology, including our AI-driven infrastructure, to enhance their learning experience. Additionally, they will have access to real-time data and analytics, enabling them to make data-driven decisions and gain practical insights. Regular feedback sessions, workshops, and training programs will further support their development, ensuring they successfully complete their projects and gain valuable industry experience.