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Mine vs Rock Classification using Logistic Regression

This repository contains an implementation of a machine learning model using the Logistic Regression algorithm to classify mines vs rocks dataset. The dataset is a classic example in the field of machine learning used for binary classification tasks.

Dataset

The dataset used in this project consists of features obtained from sonar signals bounced off a metal cylinder and a roughly cylindrical rock. It includes 208 samples, with each sample containing 60 features.

Implementation

The Rock vs Mine prediction jupyter notebook.ipynb notebook contains the Python code for data preprocessing, model training, and evaluation. The model is trained on the dataset to distinguish between mines and rocks based on the input features.

Dependencies

  • Python 3
  • NumPy
  • Pandas
  • Matplotlib
  • Scikit-learn

Usage

  1. Clone the repository:

    git clone <[https://github.com/Bramitha-gowda-M/Rock-vs-Mine-prediction-using-sonar-Dataset-using-Logistic-Regression)https://github.com/Bramitha-gowda-M/Rock-vs-Mine-prediction-using-sonar-Dataset-using-Logistic-Regression]>

  2. Open and run the Rock vs Mine prediction jupyter notebook.ipynb notebook in Jupyter Notebook or JupyterLab.

  3. Follow the instructions and execute the cells to preprocess the data, train the logistic regression model, and evaluate its performance.

  4. Feel free to explore, modify, and experiment with the code to learn more about machine learning and logistic regression classification!

References

Dataset Source: UCI Machine Learning Repository - Mines vs Rocks

License

This project is licensed under the MIT License.