In this guide, we will perform data engineering (data analysis and data preparation) and machine learning tasks to train a Linear Regression model to predict future ROI (Return On Investment) of variable ad spend budgets across multiple channels including search, video, social media, and email using Snowpark for Python, Streamlit and scikit-learn. By the end of the session, you will have an interactive web application deployed visualizing the ROI of different allocated advertising spend budgets.
For prerequisites, environment setup, step-by-step guide and instructions, please refer to the QuickStart Guide.
