I explore rock vs. mine prediction with Python using a SONAR dataset. Using a Logistic Regression Model for my prediction algorithm, I intend on predicting what an object is based on supervised learning.
This table is taken from kaggle and contains data specifying whether a given subject is a rock or a mine.
In a hypothetical scenario, two countries are at war over a large body of water, using submarines as the main source of battle combat.
Using SONAR radar, the submarines screen for rocks or mines in their vicinity and must then be able to accurately determine whether the object in question is a rock or a mine based on data provided by the SONAR.
For the purposes of this notebook, I will use a subset of the SONAR dataset provided by the kaggle database.
- Begin by collecting SONAR data and understanding the content of the dataset
- Preprocess data
- Train - Test data
- Feed a logistic regression model after determining data subset to be satisfactory