Multiple Regression in Python

Multiple Regression is an extension of Simple Linear Regression that uses multiple independent variables to predict a dependent variable.

1. Import Required Libraries

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import r2_score

2. Load Data

Let's assume we have a dataset with multiple independent variables (e.g., Experience, Education, Age) and one dependent variable (Salary).

# Sample datasetdata = {    "Experience": [1, 3, 5, 7, 9, 11, 13, 15, 17, 19],    "Education": [10, 12, 12, 14, 16, 16, 18, 18, 20, 22],    "Age": [22, 25, 28, 31, 34, 37, 40, 43, 46, 49],    "Salary": [30000, 35000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000]}# Convert to DataFramedf = pd.DataFrame(data)# Display first few rowsprint(df.head())

3. Define Independent and Dependent Variables

X = df[["Experience", "Education", "Age"]]  # Independent variablesy = df["Salary"]  # Dependent variable

4. Split Data into Training and Testing Sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. Train the Multiple Regression Model

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

6. Make Predictions

y_pred = model.predict(X_test)print("Predicted Salaries:", y_pred)

7. Evaluate the Model

r2 = r2_score(y_test, y_pred)print(f"R² Score: {r2:.2f}")

The R² score tells us how well the model fits the data (closer to 1 is better).

8. Model Coefficients & Intercept

print("Intercept:", model.intercept_)print("Coefficients:", model.coef_)

Each coefficient represents the impact of a corresponding feature on the dependent variable.

9. Predict Salary for a New Candidate

new_candidate = np.array([[10, 16, 30]])  # Example: 10 years of experience, 16 years of education, 30 years oldpredicted_salary = model.predict(new_candidate)print(f"Predicted Salary: {predicted_salary[0]:.2f}")

10. Visualizing the Results

Although we can't visualize multi-dimensional data easily, we can plot actual vs predicted salaries.

plt.scatter(y_test, y_pred, color="blue")plt.xlabel("Actual Salary")plt.ylabel("Predicted Salary")plt.title("Actual vs Predicted Salary")plt.show()

Summary

This is how you can implement Multiple Regression in Python using scikit-learn! ?