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Leer Loading Data Sets | The Very First Steps
Introduction to Scikit Learn

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Scikit-learn is one of the most widely used Python packages for Data Science and Machine Learning. Scikit-learn is a library implementing the algorithms for Supervised Learning and Unsupervised Learning. First of all, to work in Data Science you have to work with data. So, you have to get the data from somewhere.

One of the advantages of this library is that it already contains some basic datasets that can be used immediately. For this there are special functions in Scikit-learn. Let's look at them.

  1. Let's get data about people with diabets. For this dataset there is load_diabetes() function in the Scikit-learn library.

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# Importing needed function from library from sklearn.datasets import load_boston # Using function fo get data about diabets from library X, y = load_diabetes(return_X_y = True, as_frame = True) # Displaying independent variables print(X) # Dislpaying dependent variable print(y)
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Here, we have very important bool parameters: return_X_y and as_frame. Exaplain.

  1. Let's take a look at the another popular dataset about iris flowers and their features. See the example below.

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# Importing needed function from library from sklearn.datasets import load_iris # Using function fo get data about flowers from library X, y = load_iris(return_X_y = True, as_frame = True) # Displaying independent variables print(X) # Dislpaying dependent variable print(y)
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Taak

Swipe to start coding

  1. You have to load_wine function from sklearn library.
  2. You have to use imported function to get the data about properties and qualities of wines.
  3. Please, use very important parameters in the right way:
  • you have to get the data, whica are divided by dependent and independetn variables;
  • you have to get this data in the data frame format.
  1. Display independent variables(X) and dependent variable(y) on the screen.

Oplossing

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book
Loading Data Sets

Scikit-learn is one of the most widely used Python packages for Data Science and Machine Learning. Scikit-learn is a library implementing the algorithms for Supervised Learning and Unsupervised Learning. First of all, to work in Data Science you have to work with data. So, you have to get the data from somewhere.

One of the advantages of this library is that it already contains some basic datasets that can be used immediately. For this there are special functions in Scikit-learn. Let's look at them.

  1. Let's get data about people with diabets. For this dataset there is load_diabetes() function in the Scikit-learn library.

12345678
# Importing needed function from library from sklearn.datasets import load_boston # Using function fo get data about diabets from library X, y = load_diabetes(return_X_y = True, as_frame = True) # Displaying independent variables print(X) # Dislpaying dependent variable print(y)
copy

Here, we have very important bool parameters: return_X_y and as_frame. Exaplain.

  1. Let's take a look at the another popular dataset about iris flowers and their features. See the example below.

12345678
# Importing needed function from library from sklearn.datasets import load_iris # Using function fo get data about flowers from library X, y = load_iris(return_X_y = True, as_frame = True) # Displaying independent variables print(X) # Dislpaying dependent variable print(y)
copy
Taak

Swipe to start coding

  1. You have to load_wine function from sklearn library.
  2. You have to use imported function to get the data about properties and qualities of wines.
  3. Please, use very important parameters in the right way:
  • you have to get the data, whica are divided by dependent and independetn variables;
  • you have to get this data in the data frame format.
  1. Display independent variables(X) and dependent variable(y) on the screen.

Oplossing

Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 1. Hoofdstuk 1
Switch to desktopSchakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties
Onze excuses dat er iets mis is gegaan. Wat is er gebeurd?
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