Penguin Species Prediction
Learn about data visualisation and classification problems through this penguin-related challenge 🐧
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Penguin Species Prediction 🐧
This educational challenge is to develop a machine learning model for classifying the species of penguins living on islands in the Palmer Archipelago, Antarctica using features such as their flipper lengths and body masses.
This is a great opportunity to experiment with and learn about a number of core concepts in machine learning using pandas, seaborn and scikit-learn.
If you have any questions, free free to ask them in the DOXA AI Community Discord server.
Getting started
To get started, take a look at our tutorial notebook:
https://github.com/DoxaAI/educational-challenges/blob/main/palmer-penguins/getting-started.ipynb
If you have any questions, feel free to ask them in the DOXA AI community Discord server.
The dataset
The data for this challenge was originally made available by AM Horst, AP Hill and KB Gorman as an R package under a CC0 license.
It contains the following data variables:
island: the island the pengiun is from (Biscoe,DreamorTorgersen)bill_length_mm: the length of the penguin's culmen (i.e. the upper ridge of its bill) in millimetresbill_depth_mm: the depth of the penguin's culmen in millimetresflipper_length_mm: the penguin's flipper length in millimetresbody_mass_g: the penguin's body mass in gramssex: the sex of the penguin (maleorfemale)year: the year
Artwork by @allison_horst