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ML Introduction with scikit-learn

Encode Categorical VariablesEncode Categorical Variables

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

ColumnEncoder
X, ordinal valuesOrdinalEncoder
X, nominal valuesOneHotEncoder
yLabelEncoder

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:

Here 'island' and 'sex' are categorical features and 'species' is a categorical target

Task

Encode all the categorical values. For this, you need to choose the correct encoder for the 'island', and 'sex' columns and follow the steps.

  1. Import the correct encoder for features.
  2. Initialize the features encoder object.
  3. Fit and transform the categorical feature columns using the feature_enc object.
  4. Fit and transform the target using LabelEncoder.

Everything was clear?

Section 2. Chapter 8
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course content

Course Content

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary

3. Pipelines

What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate a Complete ML Pipeline

4. Modeling

ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

ML Introduction with scikit-learn

Encode Categorical VariablesEncode Categorical Variables

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

ColumnEncoder
X, ordinal valuesOrdinalEncoder
X, nominal valuesOneHotEncoder
yLabelEncoder

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:

Here 'island' and 'sex' are categorical features and 'species' is a categorical target

Task

Encode all the categorical values. For this, you need to choose the correct encoder for the 'island', and 'sex' columns and follow the steps.

  1. Import the correct encoder for features.
  2. Initialize the features encoder object.
  3. Fit and transform the categorical feature columns using the feature_enc object.
  4. Fit and transform the target using LabelEncoder.

Everything was clear?

Section 2. Chapter 8
toggle bottom row
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