This project demonstrates the application of polynomial regression to analyze relationships in ohmic heating. Specifically, it models how variables such as temperature, time, conductivity, and heating rate interact during the process. Polynomial regression helps capture nonlinear patterns in the data, providing insights for optimization and prediction. Features
Polynomial Regression Analysis: Fits data to quadratic or cubic models.
Visualization: Generates plots to compare actual data with fitted models.
Evaluation Metrics: Calculates metrics like Mean Squared Error (MSE) and R-squared (R²).
CSV Data Support: Reads and writes experimental data in CSV format.
Example Workflow
Load Data: Import experimental data (e.g., conductivity vs. heating rate).
Fit Polynomial Model: Apply quadratic or cubic regression.
Visualize Results: Plot actual data and fitted curves.
Evaluate Performance: Check MSE and R² values to assess model fit.
Results
The project shows how polynomial regression can:
Accurately model relationships between ohmic heating parameters.
Optimize operational conditions by analyzing trends.