Scikit-learn Basics

Install scikit-learn
pip install scikit-learn

Common imports
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
import numpy as np

Split train/test data
X_train, X_test, y_train, y_test = train_test_split(
  X, y,
  test_size=0.2,
  random_state=42
)

Stratified split
X_train, X_test, y_train, y_test = train_test_split(
  X, y,
  test_size=0.2,
  stratify=y
)

Standardize features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Normalize features
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

Encode categorical data
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
y = encoder.fit_transform(y)

Train model
from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)

Make predictions
y_pred = model.predict(X_test)

Evaluate
from sklearn.metrics import mean_squared_error, r2_score
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)