How to Use Linear Regression with sklearn in Python

To use LinearRegression from sklearn.linear_model in Python, first import it, create a model instance, then fit it with training data using fit(X, y). After training, use predict(X_new) to get predictions.

Syntax

The basic steps to use linear regression in sklearn are:

• Import: Bring in LinearRegression from sklearn.linear_model.
• Create model: Make an instance with model = LinearRegression().
• Fit model: Train with data using model.fit(X, y), where X is input features and y is target values.
• Predict: Use model.predict(X_new) to get output for new inputs.

python

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X, y)
predictions = model.predict(X_new)

Example

This example shows how to train a linear regression model on simple data and predict new values.

python

from sklearn.linear_model import LinearRegression
import numpy as np

# Sample data: X is feature, y is target
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([3, 5, 7, 9, 11])

# Create and train model
model = LinearRegression()
model.fit(X, y)

# Predict new values
X_new = np.array([[6], [7]])
predictions = model.predict(X_new)

print("Predictions:", predictions)
print("Model coefficient:", model.coef_)
print("Model intercept:", model.intercept_)

Output

Predictions: [13. 15.]
Model coefficient: [2.]
Model intercept: 1.0

Common Pitfalls

Common mistakes when using sklearn linear regression include:

• Not reshaping input X to 2D array (e.g., shape (n_samples, n_features)).
• Mixing up X and y in fit.
• Using non-numeric data without preprocessing.
• Forgetting to import necessary modules.

Always check your data shape and types before fitting.

python

import numpy as np
from sklearn.linear_model import LinearRegression

# Wrong: X is 1D array
X_wrong = np.array([1, 2, 3, 4, 5])
y = np.array([3, 5, 7, 9, 11])

model = LinearRegression()

# This will raise an error
# model.fit(X_wrong, y)

# Correct: reshape X to 2D
X_correct = X_wrong.reshape(-1, 1)
model.fit(X_correct, y)
print("Model trained successfully with correct input shape.")

Output

Model trained successfully with correct input shape.

Quick Reference

Summary tips for using sklearn LinearRegression:

• Input X must be 2D array: shape (samples, features).
• Target y can be 1D array.
• Use fit() to train, predict() to get predictions.
• Access coef_ for slope(s) and intercept_ for bias.
• Works best with numeric, clean data.

Key Takeaways

Always reshape input features X to 2D array before fitting the model.

Use model.fit(X, y) to train and model.predict(X_new) to get predictions.

Check model.coef_ and model.intercept_ to understand the learned line.

Ensure your data is numeric and clean for best results.

Import LinearRegression from sklearn.linear_model before use.