Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Learn Challenge: Scaling the Features | Preprocessing Data with Scikit-learn
ML Introduction with scikit-learn
course content

Course Content

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 ValuesChallenge: Imputing Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderChallenge: Encoding Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerChallenge: Scaling the Features
3. Pipelines
What is PipelineColumnTransformerEfficient Data Preprocessing with PipelinesChallenge: Creating a PipelineFinal EstimatorChallenge: Creating a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating the ModelCross-ValidationChallenge: Evaluating the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVChallenge: Tuning Hyperparameters with RandomizedSearchCVModeling SummaryChallenge: Putting It All Together

book
Challenge: Scaling the Features

In this challenge, you need to scale the features using StandardScaler. The data is the penguins dataset (encoded and with no missing values).


              
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_encoded.csv')

print(df)
copy

Here is a little reminder of the StandardScaler class.

Task

Swipe to start coding

  1. Import the class that standardizes features by making the mean equal to 0 and the variance equal to 1.
  2. Initialize the scaler.
  3. Scale the X matrix of features.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 2. ChapterΒ 11
toggle bottom row

book
Challenge: Scaling the Features

In this challenge, you need to scale the features using StandardScaler. The data is the penguins dataset (encoded and with no missing values).


              
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_encoded.csv')

print(df)
copy

Here is a little reminder of the StandardScaler class.

Task

Swipe to start coding

  1. Import the class that standardizes features by making the mean equal to 0 and the variance equal to 1.
  2. Initialize the scaler.
  3. Scale the X matrix of features.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 2. ChapterΒ 11
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt