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import numpy as np # For numerical operations
import matplotlib.pyplot as plt # For plotting
from sklearn.datasets import make_regression # For generating synthetic data
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error, r2_score
# Generate synthetic dataset
X, y = make_regression(
n_samples=200,
n_features=1,
noise=0.1,
random_state=42
)
# Split the dataset 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
)
# Create and train the KNN regressor
knn_regressor = KNeighborsRegressor(n_neighbors=5)
knn_regressor.fit(X_train, y_train)
# Make predictions on the test data
y_pred = knn_regressor.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f'Mean Squared Error: {mse}')
print(f'R-squared: {r2}')
# Visualize the results
plt.scatter(X_test, y_test, color='blue', label='Actual')
plt.scatter(X_test, y_pred, color='red', label='Predicted')
plt.title('KNN Regression')
plt.xlabel('Feature')
plt.ylabel('Target')
plt.legend()
plt.show()
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