Practice Python Ai Procees Example

here example work Python Ai Procees Code simple

"from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

# Load the iris dataset

iris = load_iris()

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# Create a KNN classifier

knn = KNeighborsClassifier(n_neighbors=3)

# Fit the model on the training data

knn.fit(X_train, y_train)

# Use the model to make predictions on the test data

y_pred = knn.predict(X_test)

# Print the accuracy of the model

print("Accuracy: ", knn.score(X_test, y_test))

 In this code, we first load the iris dataset using the load_iris() function from scikit-learn. We then split the data into training and testing sets using the train_test_split() function. Next, we create a K-nearest neighbors (KNN) classifier with n_neighbors=3. We fit the model on the training data using the fit() method and then use the predict() method to make predictions on the test data. Finally, we print the accuracy of the model using the score() method. This code demonstrates how scikit-learn can be used to create a simpl