SONAR Data Analysis with Logistic Regression

Overview

This project focuses on building a system in Python to predict whether an object is either a rock or a mine using SONAR data. SONAR data is commonly used in various applications, including underwater object detection. In this case, we utilize a Logistic Regression Model for our prediction task.

Technologies Used

• Python: The primary programming language used for implementing the system.
• Scikit-learn: A powerful machine learning library in Python, used for building and training the Logistic Regression Model.
• Pandas: Utilized for data manipulation and preprocessing tasks.
• NumPy: Essential for numerical computations and array operations.

Project Structure

• Mine vs Rock Prediction.ipynb: The main Python script containing the implementation of the Logistic Regression Model and data analysis.
• sonar-data.csv: The dataset used for training and testing the model.
• README.md: This file, providing an overview of the project, technologies used, and other relevant information.

Usage

To run the project:

1. Clone this repository to your local machine.
2. Ensure you have Python installed.
3. Install the required libraries using pip: pip install scikit-learn pandas numpy

Results

After training the Logistic Regression Model on the provided SONAR data, we achieved an accuracy of 0.7619047619047619 on the test set. Further analysis of the model's performance can be found in the Mine vs Rock Prediction.ipynb script.

Learnings

Throughout this project, several key learnings were obtained:

• Understanding and preprocessing SONAR data for machine learning tasks.
• Implementing a Logistic Regression Model for binary classification.
• Evaluating model performance using accuracy metrics and visualizations.
• Working with Python libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib for data analysis and machine learning tasks.

Future Improvements

• Experiment with different machine learning algorithms to potentially improve prediction accuracy.
• Explore feature engineering techniques to enhance model performance.
• Consider collecting additional data or augmenting existing datasets to train more robust models.