This project builds a machine learning model to classify sonar signals as either "Rock" or "Mine" using Logistic Regression. 🧠✨
The goal of this project is to analyze sonar data and accurately predict whether an object is a rock or a mine based on the sonar signal features. The workflow includes data loading, preprocessing, model training, evaluation, and making predictions on new data.
- Data loading and exploration 📊
- Data preprocessing & splitting 🗂️
- Logistic regression model training 🏋️♂️
- Model evaluation with accuracy scores 🎯
- Predictive system for new sonar inputs 🔮
- Python 3 🐍
- NumPy 🌟
- Pandas 📝
- scikit-learn 🧪
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Data exploration and insights
Model accuracy and predictions
Make sure you have the following installed:
- Python 3.x
- pandas, numpy, scikit-learn
You can install required libraries with:
pip install pandas numpy scikit-learn- Download the
.ipynbnotebook from this repository. - Upload it to Google Colab or run locally in your preferred Python environment.
- Follow the notebook to train and evaluate the model.
- Use the prediction section to test with new sonar data.
Input your sonar features in the prediction section, and the model will classify the object as either "Rock" or "Mine".
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Contributions are welcome! Please fork the repository, create a feature branch, and submit a pull request.
This project is for educational purposes only. Feel free to adapt and extend it!
Happy Coding! 😊👩💻👨💻
You can replace the placeholder image paths with actual images/screenshots related to your project. Would you like me to generate or suggest specific images or diagrams for this README?
Sources