Title: Rock vs. Mine Classification using Logistic Regression"

Description: It is a machine-learning project designed to distinguish between rocks and mines in underwater environments using logistic regression. With logistic regression as our primary model, we aim to develop a reliable and efficient solution for accurately identifying submerged objects, contributing to improved navigation safety and environmental monitoring in marine settings. Leveraging sonar data and logistic regression techniques, this project offers a streamlined approach to underwater object classification.

Objectives:

1. Dataset Acquisition and Exploration: Gather a comprehensive dataset containing sonar readings from both rocks and mines in underwater environments. Explore the dataset to understand its characteristics and ensure data quality.
2. Data Preprocessing: Clean the dataset by handling missing values, removing noise, and standardizing features to prepare it for model training.
3. Feature Selection: Identify relevant features from the sonar data that are most informative for distinguishing between rocks and mines. Consider frequency patterns, amplitude variations, and other relevant characteristics.
4. Model Training: Implement logistic regression to train a classification model on the preprocessed dataset. Tune hyperparameters such as regularization strength to optimize model performance.
5. Model Evaluation: Assess the performance of the logistic regression model using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score. Utilize techniques like k-fold cross-validation to ensure robustness and generalization.
6. Prediction Pipeline Development: Develop a user-friendly prediction pipeline that accepts sonar data inputs and outputs predictions of whether the detected object is a rock or a mine.
7. Testing and Validation: Test the logistic regression model in simulated and real-world underwater scenarios to validate its accuracy and reliability. Evaluate its performance under various conditions to ensure practical utility.
8. Documentation and Reporting: Document the entire project, including data preprocessing steps, feature selection process, model training details, evaluation results, and insights gained. Provide comprehensive reports to stakeholders, highlighting the effectiveness and potential applications of the logistic regression-based classification solution in underwater surveillance and navigation.

The project aims to deliver a dependable and accessible solution for rock vs. mine classification in underwater environments, contributing to enhanced safety and efficiency in marine operations and environmental conservation efforts.