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This article mainly shows you "how to use the Scikit-learn Python library for data science projects", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn "how to use the Scikit-learn Python library for data science projects" this article.

What is Scikit-learn?

Scikit-learn is an open source Python library with powerful data analysis and data mining tools. Available under BSD license and built on the following machine learning libraries:

NumPy, a library for manipulating multidimensional arrays and matrices. It also has an extensive collection of mathematical functions that can be used to perform various calculations.

SciPy, an ecosystem of libraries, is used to accomplish technical computing tasks.

Matplotlib, a library for drawing all kinds of charts and graphs.

Scikit-learn provides a wide range of built-in algorithms that can be fully used in data science projects.

Here are the main ways to use the Scikit-learn library.

1. Classification

The classification tool identifies the categories associated with the provided data. For example, they can be used to classify e-mail messages as spam or non-spam.

The classification algorithms in Scikit-learn include:

Support vector machine Support vector machines

(SVM)

Random forest2, regression of the nearest Nearest neighbors random forest

Regression involves creating a model to try to understand the relationship between input and output data. For example, regression tools can be used to understand the behavior of stock prices.

Regression algorithms include:

Support vector machine Support vector machines

(SVM)

Ridge regression Ridge regression

Lasso (LCTT translation note: Lasso is least absolute shrinkage and selection operator, also translated as minimum absolute convergence and selection operator, lasso algorithm)

3. Clustering

The Scikit-learn clustering tool is used to automatically group data with the same characteristics. For example, customer data can be subdivided according to where it is located.

Clustering algorithms include:

K-means

Spectral clustering Spectral clustering

Mean-shift

4. Dimension reduction

Dimensionality reduction reduces the number of random variables used for analysis. For example, to improve visualization efficiency, peripheral data may not be considered.

Dimensionality reduction algorithms include:

Principal component analysis Principal component analysis

(PCA)

Function selection Feature selection non-negative matrix factorization Non-negative matrix factorization5, model selection

Model selection algorithms provide tools for comparing, validating, and selecting parameters and models to be used in data science projects.

Model selection modules that can enhance accuracy through parameter adjustment include:

Grid search Grid search cross-validation Cross-validation indicators Metrics6, preprocessing

Scikit-learn preprocessing tools are very important in feature extraction and normalization during data analysis. For example, you can use these tools to transform input data, such as text, and apply its characteristics in the analysis.

The preprocessing module includes:

Pretreatment

Feature extraction

Scikit-learn library exampl

Let's use a simple example to illustrate how to use the Scikit-learn library in a data science project.

We will use the Iris Flower dataset, which is contained in the Scikit-learn library. The Iris data set contains 150 details about three flower species, which are:

Setosa: marked as 0

Versicolor: marked as 1

Virginica: marked as 2

The dataset includes the following characteristics (in centimeters) of each flower species:

Sepal length

Sepals width

Petal length

Petal width

Step 1: import the library

Because the iris flower dataset is included in the Scikit-learn data science library, we can load it into our workspace, as follows:

From sklearn import datasetsiris = datasets.load_iris ()

These commands import the dataset datasets module from sklearn, and then use the load_iris () method in datasets to include the data in the workspace.

Step 2: get dataset characteristics

The dataset datasets module contains several methods to make it easier for you to become familiar with working with data.

In Scikit-learn, a dataset is a dictionary-like object that contains all the details about the data. Use the .data key to store data, which is an array list.

For example, we can use iris.data to output information about the data set of iris flowers.

Print (iris.data)

This is the output (the result has been truncated):

[[5.1 3.5 1.4 0.2] [4.9 3. 1.4 0.2] [4.7 3.2 1.3 0.2] [4.6 3.1 1.5 0.2] [5. 3.6 1.4 0.2] [5.4 3.9 1.7 0.4] [4.6 3.4 1.4 0.3] [5. 3.4 1.5 0.2] [4.4 2.9 1.4 0.2] [4.9 3.1 1.5 0.1] [5.4 3.7 1.5 0.2] [4.8 3.4 1.6 0.2] [4.8 3. 1.4 0.1] [4.3 3. 1.1 0.1] [5.8 4. 1.2 0.2] [5.7 4.4 1.5 0.4] [5.4 3.9 1.3 0.4] [5.1 3.5 1.4 0.3]

We also use iris.target to provide us with information about the different labels of flowers.

Print (iris.target)

This is the output:

[0 0 0 1 1 1 2 2 2 2 2 2]

If we use iris.target_names, we will output an array of tag names found in the dataset.

Print (iris.target_names)

The following is the result of running the Python code:

['setosa'' versicolor' 'virginica'] step 3: visualize the dataset

We can use a box diagram to generate a visual representation of the iris data set. The box chart shows how the data is distributed on the plane through the quartile.

Here is how to achieve this goal:

Import seaborn as snsbox_data = iris.data # represents the data array variable box_target = iris.target # represents the label array variable sns.boxplot (data = box_data,width=0.5,fliersize=5) sns.set (rc= {'figure.figsize': (2mem15)})

Let's look at the results:

On the horizontal axis:

0 is the length of the sepals

1 is the width of the sepals

2 is the length of petals

3 is the width of petals

The dimensions of the vertical axis are in centimeters.

Summary

The following is the complete code for this simple Scikit-learn data science tutorial.

From sklearn import datasetsiris = datasets.load_iris () print (iris.data) print (iris.target) print (iris.target_names) import seaborn as snsbox_data = iris.data # variable for data array box_target = iris.target # variable for label array sns.boxplot (data = box_data,width=0.5 Fliersize=5) sns.set (rc= {'figure.figsize': (2Power15)}) are all the contents of the article "how to use the Scikit-learn Python Library for data Science projects" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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