Step By Step Tutorial: Introduction to Scikit-Learn for Machine Learning in 5 Minutes

In this tutorial, we will give you a brief introduction to Scikit-Learn, a popular machine learning library in Python. Scikit-Learn provides simple and efficient tools for data mining and data analysis. It is built on NumPy, SciPy, and matplotlib and it is open source and free to use.

Step 1: Installation

The first step is to install Scikit-Learn. You can install it using pip by running the following command:

pip install scikit-learn

Step 2: Importing Scikit-Learn

Once you have installed Scikit-Learn, you can import it in your Python code by using the following statement:

import sklearn

Step 3: Loading a Dataset

Scikit-Learn comes with some built-in datasets that you can use to practice. You can load a dataset by using the following code:

from sklearn import datasets
iris = datasets.load_iris()

Step 4: Training a Model

Now that we have loaded the dataset, we can train a machine learning model on it. Let’s use a simple classification algorithm called Decision Tree:

from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier()
classifier.fit(iris.data, iris.target)

Step 5: Making Predictions

Once we have trained the model, we can make predictions on new data. Let’s predict the class of a new iris sample:

new_data = [[5.1, 3.5, 1.4, 0.2]]
prediction = classifier.predict(new_data)
print(prediction)

Conclusion

Congratulations! You have completed a quick introduction to Scikit-Learn for machine learning. This tutorial covered the basic steps of installing Scikit-Learn, loading a dataset, training a model, and making predictions. Feel free to explore more advanced features of Scikit-Learn to further enhance your machine learning skills.