🪨 Rock vs Mine Prediction using Machine Learning

This project applies machine learning to classify sonar signal data as either rocks or mines. It uses supervised learning techniques to train a model on labeled data and predict outcomes based on sonar features.

📌 Features

• Data loading and preprocessing
• Exploratory data analysis (EDA)
• Supervised model training (e.g., Logistic Regression)
• Model evaluation using accuracy and confusion matrix
• Real-time prediction using custom input

🛠️ Tech Stack

• Language: Python
• Environment: Jupyter Notebook
• Libraries:
  • pandas
  • numpy
  • scikit-learn

📁 Files

• Rock_vs_Mine_Prediction.ipynb: The main notebook containing code, analysis, and model
• requirements.txt: List of dependencies to install

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/rock-vs-mine-prediction.git
   cd rock-vs-mine-prediction

2. Install the dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook and open:

   jupyter notebook

4. Open and run Rock_vs_Mine_Prediction.ipynb.

🧪 Model

• Trained using supervised learning algorithms
• Evaluated on test data with high accuracy
• Accepts user input for prediction (via Python input)

📊 Output Highlights

• Accuracy metrics
• Confusion matrix visualization
• Prediction for custom sonar readings

📄 License

This project is licensed under the MIT License.