I Built a Demand Forecasting Model at a Real FMCG Company. It Failed First. (Part 1)

1. 1Build a demand forecasting model using historical sales data
2. 2Run experiments to evaluate the model's performance
3. 3Configure the model to handle seasonality and trends
4. 4Test the model with real-world data to identify potential issues
5. 5Apply corrective measures to address the issues that arose during testing

Data scientists and analysts working on demand forecasting projects can benefit from this article, as it provides a realistic example of the challenges and solutions involved

💡 Initial failures in demand forecasting models can be valuable learning opportunities for improving model performance

📊 Demand forecasting model fails at first, but lessons learned can help you succeed! 🚀