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Predictability Check: Demand Forecasting | Hillary Farmer | DSC DACH 24
Description
In her tech tutorial, Hillary provided a step-by-step guide to developing a robust demand forecasting model using Python, with a focus on XGBoost for high-accuracy predictions. She demonstrated feature engineering techniques to enhance predictive power and used Streamlit to create an interactive web application for real-time demand forecasting. The tutorial covered the entire process, from data preprocessing and model training to deploying a user-friendly application.
This tutorial by Hillary was held on September 11th as part of Tech Tutorials at the DSC DACH 24
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