A machine learning model that helps distinguish between rocks and mines using sonar signal classification.
This project aims to develop a machine learning model to classify sonar signals as either "Rock" or "Mine." The dataset used contains 208 samples, each with 60 features, representing sonar signals. The features are numerical values derived from the reflection of sonar waves. The labels "R" and "M" correspond to rocks and mines, respectively.
- Data Exploration: Analyzing the dataset to understand its structure and distribution
- Preprocessing: Handling missing values, feature scaling, and preparing the dataset for model training
- Model Development: Implementing and training machine learning models (Logistic Regression, Decision Trees, etc.)
- Model Evaluation: Evaluating model performance using metrics like accuracy, precision, recall, and confusion matrix
The dataset contains:
- 208 samples with 60 features each
- Binary classification: Rock (R) vs Mine (M)
- Sonar signals: Numerical values from sonar wave reflections
- Clone the repository:
git clone https://github.com/HeerakKashyap/Rock-vs-Mines-classification-Model.git
cd Rock-vs-Mines-classification-Model- Install required dependencies:
pip install pandas numpy scikit-learn matplotlib seaborn jupyter- Open the Jupyter notebook:
jupyter notebook Rock_vs_Mines.ipynb- Run all cells to execute the complete analysis pipeline
The model uses Logistic Regression to achieve classification between rocks and mines based on sonar signal patterns.
Feel free to submit issues and enhancement requests!
This project is open source and available under the MIT License.