Sonar Detector Model

This project implements a Sonar Detector that identifies whether an object detected underwater is a rock or a mine. The model takes in 60 features per sample, provided by sonar sensors, and uses a Logistic Regression algorithm for classification.

Table of Contents

• Overview
• Model Performance
• Usage

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Overview

Sonar sensors emit sound waves to detect objects underwater. The collected data contains 60 features for each sample. These features are used to classify the object as either:

• R: Rock
• M: Mine

This model provides fast and accurate predictions based on real-time sonar data.

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Model Performance

Here are the accuracy results for the logistic regression model:

• Training Accuracy: 83.42%
• Testing Accuracy: 76.19%

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Usage

To use the sonar detector model, input your own sonar data as a tuple of 60 features. Below is the sample usage code:

# Example input data: 60 features from sonar sensors
input_data = (0.0707, 0.1252, 0.1447, 0.1644, 0.1693, 0.0844, 0.0715, 0.0947, 
              0.1583, 0.1247, 0.2340, 0.1764, 0.2284, 0.3115, 0.4725, 0.5543, 
              0.5386, 0.3746, 0.4583, 0.5961, 0.7464, 0.7644, 0.5711, 0.6257, 
              0.6695, 0.7131, 0.7567, 0.8077, 0.8477, 0.9289, 0.9513, 0.7995, 
              0.4362, 0.4048, 0.4952, 0.1712, 0.3652, 0.3763, 0.2841, 0.0427, 
              0.5331, 0.6952, 0.4288, 0.3063, 0.5835, 0.5692, 0.2630, 0.1196, 
              0.0983, 0.0374, 0.0291, 0.0156, 0.0197, 0.0135, 0.0127, 0.0138, 
              0.0133, 0.0131, 0.0154, 0.0218)

# Convert input data to a NumPy array
input_data_as_numpy_array = np.asarray(input_data)

# Reshape the input data for a single prediction (1 sample, 60 features)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)

# Make a prediction using the trained model
prediction = model.predict(input_data_reshaped)

# Interpret the prediction
if prediction[0] == 'R':
    print("Sonar detected a Rock")
else:
    print("Sonar detected a Mine")

Feel free to reach out if you have any questions or suggestions!