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# KNN REGRESSION from sklearn.neighbors import KNeighborsRegressor from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error # GENERATE SOME EXAMPLES DATA X, y = make_regression(n_samples=100, n_features=10, noise=0.5 ,random_state=42) # SPLIT THE 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) # CREATE AND FIT THE KNN REGRESSION MODEL k = 5 # NUMBER OF NEIGHBORS knn = KNeighborsRegressor (n_neighbors=k) knn.fit (X_train, y_train) # PREDICT ON THE TESTING SET y_pred = knn.predict (X_test) # CALCULATE MEAN SQUARED ERROR mse = mean_squared_error(y_test, y_pred) print("Mean Squared Error:", mse)
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