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Sonar Rock or Mine Prediction

This project focuses on developing a machine learning model to predict whether a sonar signal represents a rock or a mine. I've trained a logistic regression model from scratch to analyze sonar signals and classify them into two categories: rock or mine.

Table of Contents

Introduction

Sonar technology is widely used for underwater detection and navigation. The ability to accurately differentiate between rocks and mines in sonar signals is crucial for various applications, including marine exploration, security, and environmental monitoring. This project aims to develop a machine learning model that can classify sonar signals as either rocks or mines.

Overview

This project provides a solution by implementing a logistic regression model from scratch to classify sonar signals. The model is trained on a labeled dataset containing features extracted from sonar readings. The features represent the energy distribution across different frequencies and angles. The labeled dataset consists of instances labeled as either "rock" or "mine" based on expert knowledge.

Key Steps

  1. Data Preparation: The dataset is preprocessed by handling missing values, normalizing or standardizing features, and splitting it into training and testing sets.
  2. Feature Extraction: Relevant features are extracted from the preprocessed data to represent the sonar signals effectively.
  3. Model Training: Logistic regression is implemented from scratch, utilizing gradient descent algorithm to learn the model's parameters.
  4. Model Evaluation: The trained model is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score.
  5. Prediction System: A separate section in the notebook allows users to input new sonar signal values and obtain real-time predictions on whether the signals represent rocks or mines.

Technologies Used

  • Python: The programming language used for implementing the logistic regression model, data preprocessing, and evaluation metrics calculation.
  • Jupyter Notebook: The interactive environment used for developing and running the project code.
  • NumPy and Pandas: Libraries utilized for data manipulation & feature extraction.

Future Enhancements

  1. Explore other classification algorithms, such as support vector machines or neural networks, to compare and improve the model's performance.
  2. Incorporate more advanced feature extraction techniques to capture additional information from the sonar signals.
  3. Develop a user-friendly web interface to facilitate easy interaction with the model and visualizations of the results.
  4. Collect more diverse and larger datasets to enhance the model's robustness and generalization capabilities.

Contributing

Contributions to this project are welcome! If you have any ideas, suggestions, or improvements, please open an issue or submit a pull request.

License

This project is licensed under the MIT License.