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Scikit-Learn for ML

import sklearn
import sklearn.model_selection # Splitting, CV, Hyperparameter tuning
import sklearn.preprocessing   # Scaling, Encoding
import sklearn.impute          # Handle missing values
import sklearn.metrics         # Evaluation metrics
import sklearn.linear_model    # Linear models
import sklearn.tree            # Decision trees
import sklearn.ensemble        # Ensemble methods
import sklearn.svm             # SVM
import sklearn.neighbors       # K-NN
import sklearn.cluster         # KMeans
import sklearn.pipeline        # Chaining models and processing
import sklearn.compose         # Column Transformers

Utility

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
)

Scaling

# Standard scaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled  = scaler.transform(X_test)

# Minmax Scaler
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled  = scaler.transform(X_test)