This project aims to predict whether a sonar signal reflects from a mine or rock using a machine learning model. The dataset used for this project is the Sonar Dataset, which contains 60 features derived from sonar signals. The model developed in this project utilizes Logistic Regression for classification.
The Sonar Dataset is publicly available and can be accessed from the UCI Machine Learning Repository. The dataset contains 208 samples with 60 features. Each sample indicates whether the sonar signal corresponds to a mine (M) or rock (R).
- Features: 60 sonar signal features
- Labels: M (mine) and R (rock)
This project employs Logistic Regression, a statistical model used for binary classification. The model was trained using the Sonar dataset, and its performance was evaluated using accuracy, precision, recall, and F1 score.
- Data Preprocessing: Normalization and splitting into training/testing sets.
- Model Training: Logistic Regression.