Rock vs mine prediction using machine learning is an important application in underwater object detection and sonar analysis.
This project examines sonar signal readings to classify whether an underwater object is a harmless rock or a dangerous mine.
Using Python and machine learning the model learns distinguishing patterns within sonar frequency data which supports safer marine navigation and defense applications.
Project Overview
- Machine learning based binary classification system
- Uses Logistic Regression as the main classification model
- Predicts whether sonar returns correspond to a rock or a mine
- Built using Python and commonly used data science libraries
Libraries Used
- Pandas for data loading and manipulation
- NumPy for numerical operations
- Scikit Learn for model building preprocessing and evaluation
- StandardScaler for feature scaling
- Train Test Split for dividing data into training and testing sets
Dataset Details
The dataset contains sonar signal intensity values measured at multiple frequencies.
Each record includes sixty continuous features representing reflected energy patterns.
The target label indicates
- R meaning rock
- M meaning mine
These labels are converted into numeric values for Logistic Regression training.
Preprocessing Steps
- Loaded and inspected the sonar dataset for consistency
- Converted categorical labels R and M into numerical form
- Standardized all feature columns using StandardScaler
- Split the dataset into input features and output labels for model training
Model Building
- Logistic Regression selected as the main classification algorithm
- Model trained to learn frequency based differences between rocks and mines
- Evaluated on test data to check prediction capability
- Logistic Regression provides clear decision boundaries suitable for binary classification
Performance and Accuracy
- Accuracy score calculated for both training and testing data
- Confusion matrix used to analyze correct and incorrect predictions
- Model delivers reliable classification performance on sonar signal datasets
Prediction Flow
1 User provides sixty sonar feature values
2 Input is converted into a numeric array for model processing
3 Logistic Regression model predicts the outcome
- R indicates rock
- M indicates mine
Deployment Possibilities
- Can be deployed using Flask or Streamlit for real time classification
- Useful for underwater robotics and naval detection systems
- Can be integrated into monitoring tools used in marine research
Key Takeaways
- Fully implemented end to end sonar classification pipeline
- Demonstrates the effectiveness of Logistic Regression for signal based analysis
- Shows strong potential for real world underwater safety applications
Future Enhancements
- Test advanced models such as Random Forest Support Vector Machines or Neural Networks
- Apply hyperparameter tuning and cross validation
- Add sonar waveform visualizations for better interpretation
- Develop a full operational dashboard for real time detection