Dataset for Bread Quality Prediction Based on Temperature and Humidity

This dataset contains real-world records of 1,000,000 bread baking cycles, including input parameters and output results. It also includes Python scripts for building a regression model based on a multilayer perceptron (MLP) neural network. The project aims to control temperature and humidity inside a bread oven using machine learning. These materials can be used for training AI models, performing analysis, and comparing with fuzzy logic control approaches.