Regression Algorithms in Machine Learning

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Introduction

Regression is used to predict continuous values like price, salary, or temperature.

1. Linear Regression

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)
  

2. Polynomial Regression

from sklearn.preprocessing import PolynomialFeatures

poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)
  

3. Ridge Regression

from sklearn.linear_model import Ridge
model = Ridge(alpha=1.0)
  

4. Lasso Regression

from sklearn.linear_model import Lasso
model = Lasso(alpha=0.1)
  

5. Random Forest Regression

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor()
  

6. Evaluation Metrics

• MAE – Mean Absolute Error
• MSE – Mean Squared Error
• RMSE – Root Mean Squared Error
• R² Score

Conclusion

Regression algorithms help solve prediction tasks involving continuous values. Choosing the right model depends on dataset size, complexity, and feature relationships.