To summarize the previous three chapters, here is a table showing what **encoder** you should use:

In this challenge, you have the **penguins dataset** file (with no missing values). You need to deal with all the categorical values, including the target (`'species'` column).  

Here is the reminder of the data you will work with:

import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_imputed.csv')

print(df.head())

Keep in mind that `'island'` and `'sex'` are categorical **features** and `'species'` is a categorical **target**.

Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.

Learn the Machine Learning concepts and the ML project workflow.

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

Challenge: Encoding Categorical Variables

Lösung

Challenge: Encoding Categorical Variables

Lösung