7.
Develop a program to demonstrate the working of Linear Regression and
Polynomial Regression. Use Boston Housing Dataset for Linear Regression
and Auto MPG Dataset (for vehicle fuel efficiency prediction) for Polynomial
Regression.
mport numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error, r2_score
def linear_regression_california():
housing = fetch_california_housing(as_frame=True)
X = housing.data[["AveRooms"]]
y = housing.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
plt.scatter(X_test, y_test, color="blue", label="Actual")
plt.plot(X_test, y_pred, color="red", label="Predicted")
plt.xlabel("Average number of rooms (AveRooms)")
plt.ylabel("Median value of homes ($100,000)")
plt.title("Linear Regression - California Housing Dataset")
plt.legend()
plt.show()
print("Linear Regression - California Housing Dataset")
print("Mean Squared Error:", mean_squared_error(y_test, y_pred))
print("R^2 Score:", r2_score(y_test, y_pred))
def polynomial_regression_auto_mpg():
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/auto-
mpg/auto-mpg.data"
column_names = ["mpg", "cylinders", "displacement", "horsepower",
"weight", "acceleration", "model_year", "origin"]
data = pd.read_csv(url, sep='\s+', names=column_names, na_values="?")
data = data.dropna()
X = data["displacement"].values.reshape(-1, 1)
y = data["mpg"].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
poly_model = make_pipeline(PolynomialFeatures(degree=2),
StandardScaler(), LinearRegression())
poly_model.fit(X_train, y_train)
y_pred = poly_model.predict(X_test)
plt.scatter(X_test, y_test, color="blue", label="Actual")
plt.scatter(X_test, y_pred, color="red", label="Predicted")
plt.xlabel("Displacement")
plt.ylabel("Miles per gallon (mpg)")
plt.title("Polynomial Regression - Auto MPG Dataset")
plt.legend()
plt.show()
print("Polynomial Regression - Auto MPG Dataset")
print("Mean Squared Error:", mean_squared_error(y_test, y_pred))
print("R^2 Score:", r2_score(y_test, y_pred))
if __name__ == "__main__":
print("Demonstrating Linear Regression and Polynomial Regression\n")
linear_regression_california()
polynomial_regression_auto_mpg()
