Splitting The Data Into A Training And A Testing Set In Sklearn

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Your data
X = np.array([150, 160, 170]).reshape(-1, 1)
y = np.array([50, 56, 63])

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Predict on test data
y_pred = model.predict(X_test)

# Evaluate
print("Predicted:", y_pred)
print("Actual:   ", y_test)

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