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ML Introduction with scikit-learn
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ML Introduction with scikit-learn

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

bookEncode 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:


              
12345

            
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())
copy

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

Завдання
test

Swipe to show code editor

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.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 2. Розділ 8
toggle bottom row

bookEncode 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:


              
12345

            
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())
copy

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

Завдання
test

Swipe to show code editor

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.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 2. Розділ 8
toggle bottom row

bookEncode 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:


              
12345

            
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())
copy

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

Завдання
test

Swipe to show code editor

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.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

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:


              
12345

            
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())
copy

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

Завдання
test

Swipe to show code editor

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.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 2. Розділ 8
Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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