Rock vs Mine Prediction using Machine Learning

Welcome to the Rock vs Mine Predictor project!
This machine learning model uses sonar signal data to predict whether an object is a rock or a mine — a classic binary classification task.

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About the Project

• Objective: Classify objects detected by sonar as either Rock or Mine
• Dataset: UCI Sonar Dataset (60 features per instance)
• ML Model: Logistic Regression / SVM / Random Forest (can be customized)
• Tech Used: Python, Pandas, NumPy, Scikit-learn, Jupyter Notebook

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Dataset Details

• Total Samples: 208
• Features: 60 numeric values (representing sonar signal energy)
• Target Labels:
• R → Rock
• M → Mine

Dataset Source: https://1drv.ms/x/c/2b0ffd83beaaf2d2/Eex_Ujmf3nhDmoB_hv6OuXsBjU-poWhxvOeJtyO4_SaEPg?e=wLhKcK

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Folder Structure

Rock-vs-Mine-Prediction-with-Python/ │

├── rock_vs_mine.ipynb # Main notebook

├── sonar.all-data.csv # Dataset file

├── README.md # This file

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How to Run

1. Clone the repository:

git clone https://github.com/Bhupendra-glitch/Rock-vs-Mine-Prediction-with-Python.git

2. Install required packages:

pip install pandas pip install numpy pip install scikit-learn

3. Run the notebook:

jupyter notebook rock_vs_mine.ipynb