SONAR Metal and Rock Detection Project
Project Overview
This project implements a binary classification system to distinguish between metal objects (mines) and rocks using sonar signal data. The system analyzes sonar return patterns to classify underwater or underground objects, which has practical applications in naval mine detection, underwater exploration, and geological surveys.
Technical Stack
- Python 3.x - Primary programming language
- Jupyter Notebook - Development environment
- NumPy - Numerical computations and array operations
- Pandas - Data manipulation and analysis
- Scikit-learn - Machine learning framework
Project Details
- Features: 60 sonar signal attributes (columns 0-59)
- Target Variable: Binary classification (Column 60): M = Mines (Metal objects), R = Rocks
- Data Format: CSV file without headers
Machine Learning Pipeline
- Data Preprocessing:
- Statistical analysis using
describe()
- Class distribution analysis with
value_counts()
- Feature-target separation
- Data Splitting:
- Training set: 90% of data
- Test set: 10% of data
- Stratified sampling to maintain class balance
- Random state = 1 for reproducibility
- Model Training:
- Algorithm: Logistic Regression
- Type: Binary classification
- Fits linear decision boundary between classes
- Model Evaluation:
- Training accuracy assessment
- Test accuracy assessment
- Performance comparison between training and test sets
- Prediction System:
- Real-time classification of new sonar readings
- Input: 60-dimensional feature vector
- Output: Classification as Rock or Mine
Key Features
- Binary Classification: Distinguishes between two object types
- Feature Engineering: Utilizes 60 sonar signal characteristics
- Model Validation: Separate training and testing phases
- Predictive System: Ready-to-use classification interface
Applications
- Naval mine detection systems
- Underwater object identification
- Geological surveying
- Marine archaeology
- Submarine navigation safety
This project demonstrates a practical implementation of machine learning for sonar-based object classification, combining signal processing concepts with supervised learning techniques.