Binary classification model using sonar dataset to classify rocks vs mines. This project uses a binary classification model to distinguish between sonar signals bounced off metal cylinders (mines) and rock surfaces, based on the Sonar dataset from the UCI Machine Learning Repository. Built and trained in Google Colab using Python and scikit-learn.
Problem Statement
The goal is to classify whether a sonar return is from a rock or a mine using 60 numeric attributes representing energy values at various frequencies.
Algorithms Used
- Logistic Regression (Baseline)
- Train-Test Split
- Model Evaluation using Accuracy Score