Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Acoustic Extinguisher Fire dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.
INTRODUCTION: Fire is a disaster that can have many different causes, and traditional fire extinguishing methods can be harmful to people. In this study, the research team tested a sound wave flame-extinguishing system to extinguish the flames at an early fire stage. The researchers conducted 17,442 extinguishing experiments using different flame sizes, frequencies, and distance ranges in their study. The goal is to create an environmentally friendly system with innovative extinguishing methods.
ANALYSIS: The performance of the preliminary XGBoost model achieved an accuracy benchmark of 97.74%. After a series of tuning trials, the final model processed the training dataset with an accuracy score of 97.86%. When we processed the test dataset with the final model, the model achieved an accuracy score of 98.58%.
CONCLUSION: In this iteration, the XGBoost model appeared to be suitable for modeling this dataset.
Dataset Used: Acoustic Extinguisher Fire Dataset
Dataset ML Model: Binary classification with numerical and categorical features
Dataset Reference: https://www.muratkoklu.com/datasets/
The HTML formatted report can be found here on GitHub.