Enhancing Fire Alarm Systems with Machine Learning: A Data-Driven Approach to Early Fire Detection

Fire alarm systems play a crucial role in preventing deaths and reducing property loss during emergency scenarios. However, traditional smoke detectors have limitations such as response delay and high probability of failure. This study talks about applying machine learning algorithms to enhance the efficiency and accuracy of fire detection. With a data set that includes various environmental inputs such as temperature, humidity, atmospheric pressure, gaseous emissions, and particle concentrations, we create predictive models to detect fire risks in real-time. Preprocessing of data, feature engineering, and implementation of a set of models for classification i.e. Decision Trees, Logistic Regression, and Random Forest are used in the study. The findings suggest that the Random Forest classifier performs better than other models in terms of accuracy and reliability and hence it is the best method for detecting fire. Furthermore, correlation analysis and exploratory data analysis (EDA) are used to establish important feature correlations influencing fire incidence. The outcomes show the capabilities of machine learning-based fire detection systems in terms of response times and false alarm reduction, hence leading to improved safety.