Learn SCIKIT-LEARN with Real Code Examples
Updated Nov 24, 2025
Code Sample Descriptions
1
Scikit-learn Simple Linear Regression
from sklearn.linear_model import LinearRegression
import numpy as np
x_train = np.array([[1],[2],[3],[4]])
y_train = np.array([2,4,6,8])
model = LinearRegression()
model.fit(x_train,y_train)
y_pred = model.predict([[10]])
print('Prediction for 10:', y_pred[0])
A minimal Scikit-learn example performing linear regression on sample data.
2
Scikit-learn Logistic Regression
from sklearn.linear_model import LogisticRegression
import numpy as np
x_train = np.array([[0],[1],[2],[3]])
y_train = np.array([0,0,1,1])
model = LogisticRegression()
model.fit(x_train,y_train)
y_pred = model.predict([[1.5]])
print('Predicted class:', y_pred[0])
Performs binary classification using logistic regression.
3
Scikit-learn Decision Tree Classifier
from sklearn.tree import DecisionTreeClassifier
import numpy as np
x_train = np.array([[0,0],[1,1],[0,1],[1,0]])
y_train = np.array([0,1,1,0])
model = DecisionTreeClassifier()
model.fit(x_train,y_train)
y_pred = model.predict([[0,1]])
print('Predicted class:', y_pred[0])
A simple decision tree classifier example.
4
Scikit-learn K-Nearest Neighbors
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
x_train = np.array([[0,0],[1,1],[0,1],[1,0]])
y_train = np.array([0,1,1,0])
model = KNeighborsClassifier(n_neighbors=3)
model.fit(x_train,y_train)
y_pred = model.predict([[0,0]])
print('Predicted class:', y_pred[0])
Performs classification using KNN.
5
Scikit-learn Support Vector Machine
from sklearn.svm import SVC
import numpy as np
x_train = np.array([[0,0],[1,1],[0,1],[1,0]])
y_train = np.array([0,1,1,0])
model = SVC()
model.fit(x_train,y_train)
y_pred = model.predict([[1,0]])
print('Predicted class:', y_pred[0])
Simple SVM classifier example.
6
Scikit-learn Random Forest Classifier
from sklearn.ensemble import RandomForestClassifier
import numpy as np
x_train = np.array([[0,0],[1,1],[0,1],[1,0]])
y_train = np.array([0,1,1,0])
model = RandomForestClassifier(n_estimators=10)
model.fit(x_train,y_train)
y_pred = model.predict([[1,1]])
print('Predicted class:', y_pred[0])
Classifies data using random forest.
7
Scikit-learn Naive Bayes Classifier
from sklearn.naive_bayes import GaussianNB
import numpy as np
x_train = np.array([[0,0],[1,1],[0,1],[1,0]])
y_train = np.array([0,1,1,0])
model = GaussianNB()
model.fit(x_train,y_train)
y_pred = model.predict([[0,1]])
print('Predicted class:', y_pred[0])
Performs classification using Gaussian Naive Bayes.
8
Scikit-learn StandardScaler Example
from sklearn.preprocessing import StandardScaler
import numpy as np
x = np.array([[1,2],[3,4],[5,6]])
scaler = StandardScaler()
x_scaled = scaler.fit_transform(x)
print('Scaled features:\n', x_scaled)
Scales features using StandardScaler.
9
Scikit-learn PCA Example
from sklearn.decomposition import PCA
import numpy as np
x = np.array([[1,2,3],[4,5,6],[7,8,9]])
pca = PCA(n_components=2)
x_reduced = pca.fit_transform(x)
print('Reduced data:\n', x_reduced)
Performs dimensionality reduction using PCA.
10
Scikit-learn Train-Test Split Example
from sklearn.model_selection import train_test_split
import numpy as np
x = np.arange(10).reshape((5,2))
y = np.array([0,1,0,1,0])
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.4,random_state=42)
print('X_train:', x_train)
print('X_test:', x_test)
Splits dataset into training and testing sets.