This repository contains a machine learning project to classify SONAR signals as either rock or mine (metal cylinder) using various machine learning algorithms. The dataset used for this project is the SONAR dataset, which consists of 208 instances with 60 features each.
The goal of this project is to develop a machine learning model that can accurately classify SONAR signals as either rocks or mines. This has practical applications in underwater exploration and mining.
The dataset used in this project is the SONAR dataset from the UCI Machine Learning Repository. The dataset consists of 208 instances and 60 features. Each feature is a value representing the energy within a particular frequency band, integrated over a certain period of time.
- Classes: Rock, Mine
- Instances: 208
- Features: 60
To run the code in this repository, you need to have Python installed along with several libraries. You can install the necessary libraries using the following command:
pip install -r requirements.txtThe requirements.txt file should include the following libraries:
- numpy
- pandas
- sckit-learn
- matplotlib
- seaborn
- Clone the repository:
git clone https://github.com/yourusername/SONAR-Rock-and-Mine-Prediction-Using-Machine-Learning.git- Navigate to the project directory:
cd SONAR-Rock-and-Mine-Prediction-Using-Machine-Learning- Run the Jupyter Notebook or Python scripts to train and evaluate the models:
jupyter notebook - Open the test.ipynb notebook in the Jupyter interface and run the cells to train and evaluate the models.
If you have a Python script named test.py, you can run it directly:
python test.pyThe following machine learning algorithms have been implemented and evaluated in this project:
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Decision Tree
- Random Forest
- Naive Bayes
The performance of each model is evaluated using accuracy.
Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a pull request
UCI Machine Learning Repository for providing the SONAR dataset. Nitin Kumar Gupta for developing and maintaining this project.
Please note that the information contained in this repository is provided for informational purposes only. We make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability with respect to the information contained in this repository for any purpose. Any reliance on such information is therefore strictly at your own risk.
This project is licensed under the MIT License - see the LICENSE file for details.