Implementing polynomial regression involves several steps:
Data Collection: Gather relevant data, such as patient records, treatment details, and genetic information. Data Preprocessing: Clean and normalize the data to ensure it is suitable for analysis. Feature Engineering: Create polynomial features from the original independent variables. Model Training: Use software tools like Python’s Scikit-learn to fit the polynomial regression model to the data. Model Evaluation: Assess the model’s performance using metrics like R-squared, Mean Squared Error, and Cross-Validation.
