{"id":3512,"date":"2023-08-31T11:12:58","date_gmt":"2023-08-31T11:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/palmer-penguins\/"},"modified":"2025-09-15T17:24:08","modified_gmt":"2025-09-15T15:24:08","slug":"palmer-penguins","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/palmer-penguins\/","title":{"rendered":"Classify Palmer penguins using machine learning"},"content":{"rendered":"
<\/section>\n

This example builds a machine learning model to classify size measurements for adult foraging penguins near Palmer Station, Antarctica.<\/p>\n

We used data on 11 variables obtained during penguin sampling in Antarctica.<\/p>\n

This data is obtained from the palmerpenguins package<\/a>.<\/p>\n

This example is solved with Neural Designer<\/a>. We recommend you follow it step by step using the free trial<\/a>.<\/p>\n

Contents<\/h3>\n
    \n
  1. Application type<\/a>.<\/li>\n
  2. Data set<\/a>.<\/li>\n
  3. Neural network<\/a>.<\/li>\n
  4. Training strategy<\/a>.<\/li>\n
  5. Testing analysis<\/a>.<\/li>\n
  6. Model deployment<\/a>.<\/li>\n<\/ol>\n<\/section>\n
    \n

    1. Application type<\/h2>\n

    The predicted variable can have three values corresponding to a penguin species: Adelie, Gentoo, and Chinstrap. Therefore, this is a multiple classification<\/a> project.<\/p>\n

    <\/p>\n

    The goal of this example is to model the probability of each sample belonging to a penguin species.<\/p>\n<\/section>\n

    \n

    2. Data set<\/h2>\n

    Data source<\/h3>\n

    The penguin_dataset.csv<\/a> file contains the data for this example.<\/p>\n

    Target variables have three values in our classification model: Adelie (0), Gentoo (1), and Chinstrap (2).<\/p>\n

    The number of rows (instances) in the data set is 334, and the number of variables (columns) is 11.<\/p>\n

    Variables<\/h3>\n

    The following list summarizes the variables’<\/a> information:<\/p>\n