investigates neural network prediction of critical heat flux in convective heat flow experiments.
Key Areas Examined:
- Data Cleaning & Preprocessing ✨: The project was focused on cleaning and preprocessing the data for effective modeling. This involved handling missing values and transforming features.
- Author & Geometry Analysis ️♀️: The analysis of author usage and geometry preferences within the dataset was continuously ongoing throughout the project.
- Neural Network Modeling ️: A Neural Network model was being built and trained to predict CHF based on the input features.
- Model Evaluation: The project continuously evaluated the model's performance using metrics like R-squared, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
This project provided a foundation for exploring Neural Network applications in predicting CHF. It also delved into understanding the underlying experimental data through continuous analysis.