This project focused on classifying objects as either rocks or mines based on sonar signal frequency patterns. By applying logistic regression, the project aimed to develop a reliable solution for underwater object classification, with potential applications in marine exploration and defense systems.

• Methodology:

  • Data Preprocessing:

    • Utilized a sonar dataset with attributes representing sound wave reflection intensities.

    • Standardized the features to ensure uniform scaling and improved convergence of the logistic regression model.

  • Feature Analysis:

    • Conducted Exploratory Data Analysis (EDA) to understand the distribution of features and their correlation with the target variable.

  • Model Development:

    • Implemented a Logistic Regression model for binary classification, predicting whether an object is a rock or a mine.

    • Evaluated model performance using metrics such as accuracy, precision, recall, and ROC-AUC score.

  • Hyperparameter Optimization:

    • Tuned the regularization parameter to balance model complexity and performance.

• Key Results and Insights:

  • Achieved an accuracy of XX% with a high ROC-AUC score, demonstrating the model's ability to distinguish between classes effectively.

  • Identified key frequency patterns contributing to the classification, showcasing the relevance of signal analysis in prediction.

  • Highlighted the interpretability and simplicity of logistic regression for real-world classification tasks.

• Skills and Tools:

  • Machine Learning (Logistic Regression), Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn), Feature Standardization, and Model Evaluation.

This project demonstrates the practical application of machine learning in signal processing, providing insights into object detection and classification through sonar data.

Github Link: https://github.com/sm98code/Sonar-Rock-vs-Mine-Prediction-using-Logistic-Regression.git