Scikit-Learn is a powerful Python library for machine learning. It provides simple and efficient tools for data analysis and modeling. In this tutorial, we will explore the basics of Scikit-Learn and how to use it for various machine learning tasks.
Installation
To start using Scikit-Learn, you first need to install it. You can do this using pip:
pip install scikit-learn
Getting Started
Scikit-Learn provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction. Let's start by importing the necessary modules:
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
Data Preparation
Before you can train a model, you need to prepare your data. Scikit-Learn provides several datasets that you can use for practice:
iris = datasets.load_iris()
X = iris.data
y = iris.target
It's also important to split your data into training and testing sets:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
You may also want to standardize your data:
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
Model Training
Now that your data is prepared, you can train a model. Let's use Logistic Regression as an example:
model = LogisticRegression()
model.fit(X_train, y_train)
Model Evaluation
After training your model, it's important to evaluate its performance. Scikit-Learn provides various metrics for this purpose:
from sklearn.metrics import accuracy_score, classification_report
predictions = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions)}")
print(classification_report(y_test, predictions))
Further Reading
For more information on Scikit-Learn, you can visit the official documentation: Scikit-Learn Documentation
Images
Here are some examples of machine learning models: