🪨 Rock vs Mine Classifier – Sonar Signal Classification This project is a machine learning system that classifies sonar signals as either a rock or a mine based on a set of 60 numerical features derived from sonar signal readings.

It was built using Logistic Regression and enhanced with additional steps including evaluation, model deployment, and explainability.

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📊 Dataset Source: UCI Machine Learning Repository – Sonar Dataset

Description:

208 samples

60 numerical features per sample (sonar signal strength)

Target variable:

M: Mine

R: Rock

🛠️ Tools & Technologies Python

Pandas, NumPy, Scikit-learn

Matplotlib, Seaborn

Gradio (Web App Interface)

Git, GitHub (Version Control)

✅ Features 📈 Trained Logistic Regression model

📊 Advanced evaluation metrics (confusion matrix, ROC-AUC, classification report)

💻 Interactive web app (Gradio/Streamlit)

🚀 Deployment-ready structure

📦 Model saved in .pkl format for reuse

Interface