Python Scikit-Learn Module
Scikit-learn is a simple and efficient tool for predictive data analysis. It is built on NumPy, SciPy, and matplotlib.
Installation
pip install scikit-learnImporting the Module
import sklearnBasic Workflow
The typical workflow in scikit-learn involves:
- Loading data
- Splitting data into training and testing sets
- Initializing a model
- Fitting the model to the training data
- Predicting on new data
- Evaluating the model
Loading Data
Scikit-learn comes with a few standard datasets, for example the iris and digits datasets for classification and the diabetes dataset for regression.
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data # Features
y = iris.target # LabelsSplitting Data
To evaluate the performance of a model, it is important to split the data into a training set and a testing set.
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)Training a Model
Here is an example using the K-Nearest Neighbors (KNN) classifier.
from sklearn.neighbors import KNeighborsClassifier
# Initialize the model
knn = KNeighborsClassifier(n_neighbors=3)
# Train the model
knn.fit(X_train, y_train)Making Predictions
predictions = knn.predict(X_test)
print(predictions)Evaluating the Model
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, predictions)
print(f"Accuracy: {accuracy}")Regression Example
Here is a quick example using Linear Regression.
from sklearn.linear_model import LinearRegression
import numpy as np
# Dummy data
X = np.array(<a href='/1%5D%2C%20%5B2%5D%2C%20%5B3%5D%2C%20%5B4%5D%2C%20%5B5'>1], [2], [3], [4], [5</a>)
y = np.array([2, 4, 6, 8, 10])
model = LinearRegression()
model.fit(X, y)
print(f"Prediction for 6: {model.predict(<a href='/6'>6</a>)}")
# Output: Prediction for 6: [12.]