- [Introduction to scikit-learn](sklearn-introduction.md)

scikit-learn, also known as sklearn, is a popular open-source Python library that provides simple and efficient tools for data mining and data analysis. It is built on NumPy, SciPy, and matplotlib. The library is designed to interoperate with the Python numerical and scientific libraries.

- **Classification**: Identifying which category an object belongs to. Example algorithms include SVM, nearest neighbors, random forest.

- **Regression**: Predicting a continuous-valued attribute associated with an object. Example algorithms include support vector regression (SVR), ridge regression, Lasso.

- **Clustering**: Automatic grouping of similar objects into sets. Example algorithms include k-means, spectral clustering, mean-shift.

- **Dimensionality Reduction**: Reducing the number of random variables to consider. Example algorithms include PCA, feature selection, non-negative matrix factorization.

- **Model Selection**: Comparing, validating, and choosing parameters and models. Example methods include grid search, cross-validation, metrics.

- **Preprocessing**: Feature extraction and normalization.

- **Use scikit-learn if**:

- You are working on machine learning tasks such as classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

- You need an easy-to-use, well-documented library.

- You require tools that are compatible with NumPy and SciPy.

- **Do not use scikit-learn if**:

- You need to perform deep learning tasks. In such cases, consider using TensorFlow or PyTorch.

- You need out-of-the-box support for large-scale data. scikit-learn is designed to work with in-memory data, so for very large datasets, you might want to consider libraries like Dask-ML.

You can install scikit-learn using pip:

pip install scikit-learn

Or via conda:

conda install scikit-learn

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

For illustration, let's create a simple synthetic dataset:

from sklearn.datasets import make_classification

X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)

Split the dataset into training and testing sets:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Standardizing the features:

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

Train a Logistic Regression model:

model = LogisticRegression()

model.fit(X_train, y_train)

Make predictions on the test set:

y_pred = model.predict(X_test)

Evaluate the accuracy of the model:

accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy * 100:.2f}%")

Here is a complete example from data loading to model evaluation:

import numpy as np

from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

model = LogisticRegression()

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy * 100:.2f}%")

scikit-learn is a powerful and versatile library that can be used for a wide range of machine learning tasks. It is particularly well-suited for beginners due to its easy-to-use interface and extensive documentation. Whether you are working on a simple classification task or a more complex clustering problem, scikit-learn provides the tools you need to build and evaluate your models effectively.