Regression with Linear Models

Regression is a type of *upervised learning used to predict continuous numeric values.

Unlike classification, which predicts categories, regression focuses on estimating a quantitative outcome based on input features.

Common applications of regression include:

• Predicting house prices based on square footage
• Estimating temperatures from weather measurements
• Forecasting sales using historical data

• Simple Linear Regression: Uses a single feature to predict a target variable.
• Multiple Linear Regression: Uses two or more features to predict a target.
• Regularized Linear Regression: Adds a penalty to reduce overfitting (e.g., Ridge, Lasso).

Let’s see how to apply linear regression in Scikit-learn to predict house prices based on square footage.

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

# Larger sample dataset
X = np.array([[1000], [1500], [2000], [2500], [3000], [3500], [4000], [4500]])  # square footage
y = np.array([200000, 250000, 300000, 350000, 400000, 450000, 500000, 550000])  # prices

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# Create and train model
model = LinearRegression()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Mean Squared Error:", mse)
print("R² Score:", r2)

This model learns a linear relationship between square footage (X) and price (y), then evaluates performance using Mean Squared Error (MSE) and R² score.