https://github.com/massimilianovisintainer/rock-vs-mine-prediction

Rock vs. Mine classification using Logistic Regression
https://github.com/massimilianovisintainer/rock-vs-mine-prediction

machine-learning model numpy pandas sklearn

Last synced: about 23 hours ago
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Rock vs. Mine classification using Logistic Regression

• Host: GitHub
• URL: https://github.com/massimilianovisintainer/rock-vs-mine-prediction
• Owner: MassimilianoVisintainer
• Created: 2024-07-24T14:37:00.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-07-24T14:44:54.000Z (2 months ago)
• Last Synced: 2024-09-23T06:32:44.259Z (5 days ago)
• Topics: machine-learning, model, numpy, pandas, sklearn
• Language: Python
• Homepage:
• Size: 33.2 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Rock vs. Mine Prediction

This project implements a machine learning model using Logistic Regression to classify objects as either "Rock" or "Mine" based on sonar data.

## Functionality

Leverages the scikit-learn library for data manipulation and model training.

Loads sonar data from a CSV file.

Preprocesses the data by handling missing values and converting non-numeric data.

Splits the data into training and testing sets for model evaluation.

Trains a Logistic Regression model on the training data.

Evaluates the model's accuracy on both training and testing data.

Provides a prediction system for classifying a new data point as "Rock" or "Mine."

## Dependencies

- numpy

- pandas

- scikit-learn

## Usage

Install dependencies:

   ```Bash

      pip install numpy pandas scikit-learn

   ```

Run the script:

   ```Bash

       python Rock_vs_Mine_Prediction.ipynb

   ```

This will execute the code in the Jupyter Notebook, performing data loading, training, evaluation, and prediction.

## Input Data Format

The prediction system expects a list of 60 floating-point numbers representing sonar readings for a single object. You can replace the provided input_data example with your own data.

## Output

The script will print the model's accuracy on the training and testing data, followed by the predicted class ("Rock" or "Mine") for the input data.

## Additional Notes

Ensure the data in your CSV file matches the format expected by the script (60 columns of numeric values, with the last column representing the class label).

## Contributing

Feel free to submit pull requests with improvements or additional functionalities.