Last Updated: November 21, 2025
Classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train model
clf = RandomForestClassifier()
clf.fit(X_train, y_train)
# Predict
predictions = clf.predict(X_test)
score = clf.score(X_test, y_test)Common Algorithms
| Item | Description |
|---|---|
LogisticRegression
|
Binary classification |
RandomForest
|
Ensemble learning |
SVM
|
Support vector machine |
KMeans
|
Clustering |
LinearRegression
|
Regression |
Preprocessing
StandardScaler
Standardize features
MinMaxScaler
Scale to range
LabelEncoder
Encode labels
train_test_split
Split dataset
💡 Pro Tip:
Always scale your features before training!