Optimizing Model Performance: Baseline Selection and Tuning
10 Inputs
2 Hours 30 Minutes
Intermediate
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Industry
retail-and-cpg
Skills
approach
data-wrangling
ml-modelling
Tools
databricks
azure
python
Learning Objectives
Diagnose poor model performance (low R²) and identify underlying issues (e.g., multicollinearity, incorrect model assumptions)
Understand the limitations of linear regression in MMM
Address multicollinearity by using appropriate techniques
Improve model accuracy and performance using data transformation and feature engineering
Overview
You're working on a Market Mix Modeling (MMM) pipeline to understand how different advertising spends (TV, Radio, Online) influence sales. The team has provided you with a baseline Linear Regression model. However, the model performance is poor, with low R-squared (R²) and predictions that don’t seem to capture the real-world impact of ad spend on sales.
Your task is to identify the issues in the baseline model, propose a better approach, and improve the model performance.
Prerequisites
- Knowledge of Market Mix Modeling (MMM) concepts
- Familiarity with Linear Regression and log-transformation
- Understanding of multicollinearity and how it impacts regression models
- Experience with model evaluation metrics, specifically R²
- Knowledge of data preprocessing and correlation analysis