Accessory code and meta information to the HODOR dataset

Rock vs Mine Prediction using Logistic Regression and Sonar Dataset

Used machine learning to analyse sonar signal data and interpret the metrics

A simple project to train and evaluate two multilayer perceptron models on the Sonar data using TensorFlow, SciKeras, and Scikit-Learn — one without data standardization and another with standardized input data.

Rock vs Mine Predictor is a machine learning project that classifies objects as either rock or mine based on sonar signal data. Using supervised learning algorithms like Logistic Regression and SVM, the model is trained on the Sonar dataset to accurately identify underwater objects.

Machine Learning project that classifies sonar signals as Rock or Mine using Logistic Regression. The model is trained on the Sonar dataset and predicts underwater object type based on signal patterns.

Machine Learning project for classifying objects as Rock or Mine using Sonar Datamachine-learning, logistic-regression, sonar-dataset, python, classification