SONAR Rock vs. Mine Prediction

A machine learning model to classify objects as rocks or mines based on SONAR signals.

🔹 Overview

This project leverages Machine Learning to classify SONAR signals as either rock or mine using supervised learning techniques.

✅ Key Features:

• Binary classification of SONAR signals 🛠️
• Exploratory Data Analysis (EDA) 📊
• Feature selection & model training 🎯
• Performance evaluation using accuracy metrics ✅

🔹 Tech Stack

• 🟢 Python (Pandas, NumPy, Matplotlib, Seaborn)
• 🟢 Scikit-learn (Logistic Regression, SVM, Random Forest)
• 🟢 Jupyter Notebook / Google Colab

🔹 Dataset

• SONAR signal dataset from UCI Machine Learning Repository
• Features: 60 frequency-based attributes
• Labels: Rock (R) / Mine (M)

🔹 Installation & Setup

# Clone the repository
git clone https://github.com/Rishita-rm/SONAR-Rock-vs-Mine-Prediction-with-Python.git

# Navigate to the project folder
cd SONAR-Rock-vs-Mine-Prediction-with-Python

# Install dependencies
pip install -r requirements.txt

# Run the Jupyter Notebook
jupyter notebook

🔹 Implementation Steps

1️⃣ Load & preprocess dataset
2️⃣ Perform exploratory data analysis
3️⃣ Train models (Logistic Regression, SVM, Random Forest)
4️⃣ Evaluate performance using accuracy, precision, recall
5️⃣ Predict new SONAR readings

🔹 Results & Accuracy

📊 Model Accuracy:

• Logistic Regression: 85%
• SVM: 88%
• Random Forest: 92% ✅ (Best Model)

📌 Confusion Matrix & Performance Metrics:

• Precision, Recall, F1-Score calculated for each model

🔹 How to Use?

1. Load dataset in the Jupyter Notebook
2. Train models using provided scripts
3. Test the classifier on new SONAR readings
4. Deploy the model for real-world classification

🔹 Future Improvements

✅ Implement deep learning models for better accuracy 🧠
✅ Deploy as a web app using Flask / Streamlit 🌐
✅ Optimize feature selection for improved model performance

🔹 Contributing

Want to contribute? Follow these steps:

1. Fork the repository
2. Create a new branch (feature-xyz)
3. Commit changes
4. Push to the branch
5. Open a Pull Request