Sonarr Ml Model : Stone V/s Mine

This study employs logistic regression for predicting underwater objects as rocks or mines using sonar technology. The dataset consists of labeled acoustic signals, with each signal categorized as either a rock or a mine. The research involves essential steps such as data preprocessing, feature extraction, and logistic regression model implementation. The research involves preparing and cleaning data, extracting relevant features, and implementing a logistic regression model to predict outcomes. Feature selection techniques are explored to identify critical acoustic features contributing to the predictive accuracy of the model. Performance is evaluated using metrics like accuracy, precision, recall, and F1 score, providing a comprehensive assessment of the logistic regression model. The objective is to enhance underwater security by improving the reliability of sonar-based systems in differentiating between harmless geological formations and potentially dangerous mines.