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Challenge 3: Pipelines | Scikit-learn
Data Science Interview Challenge
course content

Зміст курсу

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

bookChallenge 3: Pipelines

Pipelines play a crucial role in streamlining machine learning workflows, ensuring the coherent and efficient transition of data from one processing stage to another. Essentially, a pipeline bundles together a sequence of data processing steps and modeling into a single, unified structure. The primary advantage of using pipelines is the minimization of common workflow errors, such as data leakage when standardizing or normalizing data.

Завдання
test

Swipe to show code editor

Apply data scaling to the wine dataset, and then use the KMeans algorithm for clustering wines based on their chemical properties.

  1. Apply data standard scaling to the features of the wine dataset.
  2. Use the KMeans algorithm to cluster wines based on their chemical properties. You need 3 clusters.
  3. Apply the pipeline to the data

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 7. Розділ 3
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bookChallenge 3: Pipelines

Pipelines play a crucial role in streamlining machine learning workflows, ensuring the coherent and efficient transition of data from one processing stage to another. Essentially, a pipeline bundles together a sequence of data processing steps and modeling into a single, unified structure. The primary advantage of using pipelines is the minimization of common workflow errors, such as data leakage when standardizing or normalizing data.

Завдання
test

Swipe to show code editor

Apply data scaling to the wine dataset, and then use the KMeans algorithm for clustering wines based on their chemical properties.

  1. Apply data standard scaling to the features of the wine dataset.
  2. Use the KMeans algorithm to cluster wines based on their chemical properties. You need 3 clusters.
  3. Apply the pipeline to the data

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 7. Розділ 3
toggle bottom row

bookChallenge 3: Pipelines

Pipelines play a crucial role in streamlining machine learning workflows, ensuring the coherent and efficient transition of data from one processing stage to another. Essentially, a pipeline bundles together a sequence of data processing steps and modeling into a single, unified structure. The primary advantage of using pipelines is the minimization of common workflow errors, such as data leakage when standardizing or normalizing data.

Завдання
test

Swipe to show code editor

Apply data scaling to the wine dataset, and then use the KMeans algorithm for clustering wines based on their chemical properties.

  1. Apply data standard scaling to the features of the wine dataset.
  2. Use the KMeans algorithm to cluster wines based on their chemical properties. You need 3 clusters.
  3. Apply the pipeline to the data

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!

Pipelines play a crucial role in streamlining machine learning workflows, ensuring the coherent and efficient transition of data from one processing stage to another. Essentially, a pipeline bundles together a sequence of data processing steps and modeling into a single, unified structure. The primary advantage of using pipelines is the minimization of common workflow errors, such as data leakage when standardizing or normalizing data.

Завдання
test

Swipe to show code editor

Apply data scaling to the wine dataset, and then use the KMeans algorithm for clustering wines based on their chemical properties.

  1. Apply data standard scaling to the features of the wine dataset.
  2. Use the KMeans algorithm to cluster wines based on their chemical properties. You need 3 clusters.
  3. Apply the pipeline to the data

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 7. Розділ 3
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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