Scikit-learn Tutorial for Machine Learning

Scikit-learn is one of the most useful Python libraries for machine learning. All the concepts that we study about machine learning theoretically can be implemented by using the Scikit-learn library in Python. In this article, I will take you through a tutorial on Scikit-learn for machine learning using Python.

What is Scikit-learn?

Scikit-learn is a Python library that is one of the most useful Python libraries for machine learning. It includes all the algorithms and tools that we need for the task of classification, regression and clustering. It also includes all the methods for evaluating the performance of a machine learning model.

Below are some of the advantages of using Scikit-learn for machine learning:

  1. It is very simple to use.
  2. It provides very efficient tools for predictive analytics.
  3. Easily accessible to everyone.
  4. Built on Numpy, sciPy, and matplotlib libraries in Python.
  5. Just like the Python programming language, it is also open-source and commercially usable.

Many companies are using Scikit-learn in their machine learning models. some of the big names among those companies are J.P. Morgan and Spotify. In J.P. Morgan, the Scikit-learn toolkit is widely used across all application of the bank for the tasks of classification and predictive analytics. In Spotify, Scikit-learn is used for generating music recommendations to provide a better user experience.

Scikit-learn Tutorial using Python

The Scikit-learn library in Python is very easy to use for all the tasks of machine learning. if you are working on applications that deal with classification, regression or clustering then most of the work will be implemented using this library only. Now, I will take you through a tutorial on the Scikit-learn library in Python for machine learning.

The use of this library generally starts with splitting the dataset into training and test sets, here is how you can split your data:

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)

Then we need to process the data to fit it into a machine learning model. Here we generally need to scale the data which can be done by using Standardization and normalization. Below is the scikit-learn’s way of processing the data:

As the next step, we need to fit the data into the model. Below is an implementation of training some of the most common machine learning algorithms:

The next step is to make predictions on the test set:

y_pred = lr.predict(x_test)
ypred = k_means.predict(x_test)
y_pred = knn.predict_proba(x_test)

The last step is to determine how the machine learning model performed on the test set. Below are the method provided by the Scikit-learn library to evaluate the performance of machine learning models for the tasks of classification, regression, and clustering:


This was just a simple overview of the methods provided by the Scikit-learn library in Python for machine learning. This library has so many functions that cannot be covered in one article. Thus, all of the methods provided by scikit-learn can be learned from here among several other libraries and models for machine learning. Hope you liked this article on the tutorial on the Scikit-library for machine learning using Python. Please feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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