End-to-End Machine Learning: Sonar Prediction in R

End-to-End Machine Learning: Sonar Prediction in R

Sonar prediction is a machine learning task that involves identifying whether an underwater object detected by sonar is a rock or a metal cylinder based on certain characteristics such as the signal’s frequency and signal strength. Sonar is used by ships and submarines to navigate and detect objects underwater and being able to classify the objects is important for safe navigation.

In R, there are several libraries such as caret, randomForest, glmnet and xgboost that provide functions to train machine learning models for sonar prediction. The process of building a sonar prediction model typically involves the following steps:

 

  1. Collecting and cleaning the data. This includes acquiring a dataset of sonar signals and relevant features such as frequency and signal strength.
  2. Exploratory data analysis. This includes visualizing and understanding the relationship between different features and the outcome of interest, classifying the object as a rock or metal cylinder.
  3. Preprocessing the data. This includes normalizing, scaling, or transforming the data to prepare it for the model.
  4. Choosing and training a model. This includes selecting an appropriate model, such as a logistic regression or a random forest, and training it on the preprocessed data.
  5. Evaluation. This includes evaluating the model’s performance on a separate test dataset and comparing it to other models or to a baseline.
  6. Fine-tuning. This includes finding the optimal parameters for the chosen model using techniques like grid search or random search.

 

It’s important to note that sonar prediction is a well-established area of machine learning and there are many models and techniques that can be used to improve the performance of the model. Additionally, it’s important to use cross-validation to ensure that the model generalizes well to new data. The performance metric used to evaluate the model will depend on the specific use case, for example in a navigation setting, it may be more important to have a high accuracy rather than high precision.

Overall, using machine learning techniques to predict the objects in sonar data in R can help to improve the understanding of the objects in the underwater and aid safe navigation. It’s important to use appropriate techniques like cross-validation to ensure that the model generalizes well to new data, and to use appropriate performance metrics that align with the specific use case of the model.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: Sonar Prediction in R.

End-to-End Machine Learning: Sonar Prediction in R

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!