Rock v/s Mine prediction system

Project Overview

This project aims to develop a predictive system tailored for submarines, with the primary goal of accurately distinguishing rocks from mines detected underwater. By utilizing libraries such as NumPy, Pandas, scikit-learn, and the Logistic Regression model, the Rock vs Mine Prediction System aims to enhance submarine capabilities in accurately detecting and classifying underwater objects. Its applications span from marine exploration to defense, improving safety and efficiency in underwater operations.

Functionalities of the predictive system:

• • Data manipulation and preprocessing tasks performed using numpy and pandas libraries.
• • train_test_split function from sklearn.model_selection library used to split the dataset into training and testing subsets.
• • LogisticRegression used as the predictive model.
• • Accuracy_score metric function from sklearn.metrics library utilized to assess the model's performance and measure prediction accuracy.