The Sonar Data-based Rock Presence Prediction Model is developed using machine learning methodologies to determine the presence of a rock or a mine by analyzing sonar data. This model utilizes a dataset containing various acoustic attributes or frequencies emitted by sonar equipment and their corresponding responses, indicating the presence a rock or a mine.

• Algorithm used: Logistic Regression
• Accuracy: 85%

The dataset is processed by handling missing values to enhance the model's predictive performance. Post-training on historical sonar data, the model effectively predicts the likelihood of a mine's presence based on unseen sonar readings