Sonar-Rock-vs-Mine

Overview

This project is a machine-learning classification task that aims to make a classification between rock and mine on certain rock/mine-related features. It utilizes a pre-trained logistic regression model to make predictions.

Dataset

• Dataset Name:Sonar-Rock-vs-Mine
• Data Source: upload on git.
• The dataset contains the following attributes:
  • Feature columns (61): Numerical values representing various rock/mine-related features.
  • Target column: Binary variable (Rock, Mine).

Project Structure

• README.md: Documentation of the project.
• main.py: Python script for making diabetes predictions.
• logistic_regression_model.pkl: Pre-trained logistic regression model for Rock vs mine classification.

Setup

1. Clone the repository:

   git clone <repository-url>
   cd Sonar-Rock-vs-Mine

Create a virtual environment (recommended) and install the required dependencies: python -m venv venv source venv/bin/activate # On Windows, use: venv\Scripts\activate pip install -r requirements.txt

Usage

Clone this repository to your local machine. Ensure you have the pre-trained logistic regression model ('logistic_regression_model.pkl') in the same directory as the script ('diabetes_prediction.py'). Open a command prompt or terminal and navigate to the directory where the script is located. Run the script with the --value argument followed by a comma-separated list of feature values that you want to classify.

For example:

python diabetes_prediction.py --value "6,148,72,35,0,33.6,0.627,50"

Follow the instructions in the script to make predictions.

Model Training

The project uses a logistic regression model to classify individuals into two classes: diabetes and no diabetes. The pre-trained model is saved as 'logistic_regression_model.pkl'.

Evaluation

The script provides binary predictions. You can evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score.

Results

The project provides predictions for Rock vs mine based on the input features. The performance of the model may vary depending on the dataset used.

Future Improvements

There are several ways to improve the model and the project:

Explore more advanced machine learning techniques. Fine-tune hyperparameters for better model performance. Gather more labeled data for improved accuracy.

References

Author: Muhammad Mubashir Ali Contact: muhammadmubashirali63@gmail.com. Feel free to customize this README to include any additional information you want to provide about the project.