!pip install anvil-uplink

import anvil.server anvil.server.connect("7DXSIWHYUGHOII53HYY3YPCL-OHXSSWLWIY6MAIQD")

import matplotlib.pyplot as plt from sklearn import metrics from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier iris = load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2 , random_state=4) # The following code is used only when needing to find the optimal n_neighbors """ scores = {} scores_list = [] k_range = range(1, 26) for k in k_range: knn = KNeighborsClassifier(n_neighbors=k) knn.fit(X_train, y_train) y_pred = knn.predict(X_test) scores[k] = metrics.accuracy_score(y_test, y_pred) scores_list.append(metrics.accuracy_score(y_test, y_pred)) plt.plot(k_range,scores_list) plt.xlabel('Value of K for KNN') plt.ylabel('Testing Accuracy') """ knn = KNeighborsClassifier(n_neighbors=10) knn.fit(X,y)

@anvil.server.callable def predict_iris(sepal_length, sepal_width, petal_length, petal_width): classification = knn.predict([[sepal_length, sepal_width, petal_length, petal_width]]) return iris.target_names[classification][0]