This machine learning project implements a binary classification model using Logistic Regression to analyze sonar data. The model processes sonar frequencies and acoustic signals to accurately distinguish between metal cylinders (mines) and rocks on the ocean floor.
- Language: Python
- Algorithm: Logistic Regression
- Libraries: Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn
- Signal Analysis: Processed 60 different sonar frequency attributes to identify patterns in sound wave reflections.
- Model Training: Implemented data splitting, training, and testing pipelines, achieving a high accuracy score on unseen data.
- Evaluation: Utilized confusion matrices and accuracy metrics to evaluate model robustness and limit false negatives.
💡 Domain Transferability (Automotive Application): The core principles of acoustic signal classification and frequency analysis applied here are highly transferable to Predictive Maintenance & Quality Control in the Automotive Sector. This framework directly mirrors systems used to analyze vehicle engine noises, wheel bearing vibrations, and component wear-and-tear to predict parts failure before it occurs.