https://github.com/abshar-shihab/rock_vs_mine_prediction_using_machine-learning

This repository contains the implementation of a machine learning project for classifying sonar signals as either rocks or mines. The project uses a Logistic Regression model to achieve this classification, leveraging Python and popular machine learning libraries such as scikit-learn, pandas, and numpy.
https://github.com/abshar-shihab/rock_vs_mine_prediction_using_machine-learning

Last synced: about 1 month ago
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This repository contains the implementation of a machine learning project for classifying sonar signals as either rocks or mines. The project uses a Logistic Regression model to achieve this classification, leveraging Python and popular machine learning libraries such as scikit-learn, pandas, and numpy.

• Host: GitHub
• URL: https://github.com/abshar-shihab/rock_vs_mine_prediction_using_machine-learning
• Owner: Abshar-Shihab
• Created: 2024-11-19T12:34:26.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-11-19T12:43:51.000Z (about 1 month ago)
• Last Synced: 2024-11-19T13:42:54.436Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 60.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Rock_vs_Mine_Prediction_using_Machine-Learning

This repository contains the implementation of a machine learning project for classifying sonar signals as either rocks or mines. The project uses a **Logistic Regression** model to achieve this classification, leveraging Python and popular machine learning libraries such as `scikit-learn`, `pandas`, and `numpy`.

## Project Overview

Sonar signals can be used to detect and classify objects underwater, distinguishing between metal objects like mines and natural formations such as rocks. This project uses a dataset of sonar returns to build and train a logistic regression model that can accurately predict whether a signal corresponds to a rock or a mine.

### Dataset

The dataset used for this project is the **Sonar dataset**, which consists of 208 samples with 60 features each, representing energy measurements at various angles. The labels are categorical with:

- `R` for rocks

- `M` for mines

Each feature corresponds to the strength of a sonar return at different frequencies.

### Objective

- **Predict** whether the sonar return signals correspond to a rock or a mine.

- **Evaluate** the model's performance using metrics such as accuracy, precision, recall, and confusion matrix.

### Requirements

- Python 3.x

- Libraries:

  - `numpy`

  - `pandas`

  - `scikit-learn`

## Results

The logistic regression model should be evaluated based on various metrics. Typical performance indicators include:

- **Accuracy**: How often the model correctly classifies samples.

- **Confusion Matrix**: A table that displays the number of correct and incorrect predictions.

- **Classification Report**: Provides precision, recall, F1-score, and support for each class.

## Conclusion

This project demonstrates the use of logistic regression for binary classification problems. The sonar signal dataset provides a real-world application of machine learning techniques to distinguish between natural and man-made objects.

## Future Work

Potential future improvements:

- Experimenting with different models (e.g., Support Vector Machine, Random Forest).

- Hyperparameter tuning for optimized performance.

- Cross-validation for more robust evaluation.