Conteúdo do Curso

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is ML Types of Machine Learning Training Set Types of Data Machine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn Concepts Getting Familiar with Dataset Dealing with Missing Values Challenge: Imputing Missing Values OrdinalEncoder One-Hot Encoder LabelEncoder Challenge: Encoding Categorical Variables Why Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScaler Challenge: Scaling the Features Preprocessing Summary

3. Pipelines

What is Pipeline ColumnTransformer Efficient Data Preprocessing with Pipelines Challenge: Creating a Pipeline Final Estimator Challenge: Creating a Complete ML Pipeline

4. Modeling

Models KNeighborsClassifier Evaluating a Model. Train-Test split.Cross-Validation Evaluate the Model with Cross-Validation GridSearchCV The Flaw of GridSearchCV Tune Hyperparameters with RandomizedSearchCV Modeling Summary Putting It All Together

Challenge: Encoding Categorical Variables

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:


              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())

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

Tarefa

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.

Import the correct encoders for features and target.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 8

Challenge: Encoding Categorical Variables

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:


              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())

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

Tarefa

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.

Import the correct encoders for features and target.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 8

Challenge: Encoding Categorical Variables

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:


              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())

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

Tarefa

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.

Import the correct encoders for features and target.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

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:


              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())

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

Tarefa

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.

Import the correct encoders for features and target.
Initialize the features encoder object.
Encode the categorical feature columns using the feature_enc object.
Initialize the target encoder object.
Encode the target using the label_enc object.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Seção 2. Capítulo 8

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo