Rock vs Mine Detection System

This project implements a machine learning model to classify objects as either rocks or mines based on sonar data. It uses logistic regression to build and evaluate the model.

Dataset

The dataset used in this project is the Sonar Data Set, which contains 60 frequency-based attributes representing sonar signals. Each data point is labeled as:

• R: Rock
• M: Mine

Dataset Features

• 60 columns represent frequency responses.
• The 61st column is the label (R or M).

Installation and Setup

1. Clone the repository:

   git clone https://github.com/<your-username>/<repo-name>.git

2. Navigate to the project directory:

   cd <repo-name>

3. Install the required libraries:

   pip install pandas numpy scikit-learn

4. Open the Jupyter Notebook or run the script in your preferred environment (e.g., Google Colab).

Project Workflow

1. Data Loading

The dataset is loaded into a Pandas DataFrame. The labels and features are separated for processing.

2. Data Splitting

The data is split into training and testing sets (90% training, 10% testing) while maintaining the class distribution using train_test_split.

3. Model Training

A Logistic Regression model is trained on the training data.

4. Model Evaluation

The model's accuracy is calculated on both the training and testing datasets.

5. Prediction System

A custom prediction system allows you to input new sonar data and classify it as a rock or a mine.

Results

• Training Accuracy: ~83%
• Testing Accuracy: ~83%

Example Prediction

Input data:

input_data = (
    0.0130, 0.0120, 0.0436, 0.0624, 0.0428, 0.0349, 0.0384, 0.0446, 0.1318, 0.1375,
    0.2026, 0.2389, 0.2112, 0.1444, 0.0742, 0.1533, 0.3052, 0.4116, 0.5466, 0.5933,
    0.6663, 0.7333, 0.7136, 0.7014, 0.7758, 0.9137, 0.9964, 1.0000, 0.8881, 0.6585,
    0.2707, 0.1746, 0.2709, 0.4853, 0.7184, 0.8209, 0.7536, 0.6496, 0.4708, 0.3482,
    0.3508, 0.3181, 0.3524, 0.3659, 0.2846, 0.1714, 0.0694, 0.0303, 0.0292, 0.0116,
    0.0024, 0.0084, 0.0100, 0.0018, 0.0035, 0.0058, 0.0011, 0.0009, 0.0033, 0.0026
)

Output:

The object is a mine.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn

How to Use

1. Load the project in your environment.
2. Modify the input data in the input_data variable for new predictions.
3. Run the script or notebook to view predictions and model performance.

Repository

Find the project repository here.

License

This project is licensed under the MIT License.