This repository contains the Python code and datasets used in the Rock vs. Mine Prediction project, which utilizes machine learning to distinguish between rocks and mines based on sonar signal data. The project employs Logistic Regression to create a binary classification model that is both accurate and reliable.
The goal of this project is to apply statistical modeling techniques to sonar data to predict whether the detected object is a rock or a mine. This has practical applications in navigation, underwater exploration, and safety protocols.
Python 3 Pandas NumPy Matplotlib Seaborn Scikit-Learn Features Data preprocessing including normalization and outlier removal. Exploratory data analysis with visualizations of data distributions and correlations. Implementation of Logistic Regression with hyperparameter tuning. Evaluation of model performance using accuracy score and confusion matrix.