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# # Iris Flowers Classification Model
# ## Task Complelted By = <NAME>
# ## Task Provided By  = Lets Grow More 

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

import numpy as np

iris_dataset = load_iris()

print("Target names : {}".format(iris_dataset['target_names']))

print("Feature names : {}".format(iris_dataset['feature_names']))

print("Type of data : {}".format(type(iris_dataset['data'])))

print("Shape of data: {}".format(iris_dataset['data'].shape))

print("Type of target : {}".format(type(iris_dataset['target'])))

print("Shape of target : {}".format(type(iris_dataset['target'].shape)))

print("Target:\n{}".format(iris_dataset['target']))

X_train, X_test, y_train, y_test = train_test_split(iris_dataset['data'], iris_dataset['target'], random_state=0)

print("X_train shape: {}".format(X_train.shape))

print("y_train shape: {}".format(y_train.shape))

# +
# Same for the test samples
# -

print("X_test shape: {}".format(X_test.shape))

print("y_test shape: {}".format(y_test.shape))

knn = KNeighborsClassifier(n_neighbors=1)

knn.fit(X_train,y_train )

X_new = np.array([[5, 2.9, 1, 0.2]])

print("X_new.shape : {}".format(X_new.shape))

prediction = knn.predict(X_new)

print("Prediction : {}".format(prediction))

print("Predicted target name : {}".format(iris_dataset['target_names'][prediction]))

y_pred = knn.predict(X_test)

print("Test set predictions:\n {}".format(y_pred))

print("Test set score (np.mean) : {:.2f}".format(np.mean(y_pred == y_test)))

print("Test set score (knn.score : {:.2f})".format(knn.score(X_test, y_test)))



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