Python Iris Classification Implementation

#coding: Utf-8

From sklearn.datasets import Load_iris
From sklearn.model_selection import train_test_split
From sklearn.neighbors import kneighborsclassifier
import NumPy as NP
import Matplotlib.pyplot as Plt

Iris_dataset = Load_iris () # Get Data
# print ("Keys of iris_dataset:\n{}". Format (Iris_dataset.keys ()))
# Print (iris_dataset["DESCR"][:193]+ "\ n ...")
# print ("Target names:{}". Format (iris_dataset["Target_names"))
# Print ("Feature names:{}". Format (iris_dataset["Feature_names"] )
# Print (iris_dataset["data"][:5])
# Print (iris_dataset["Data"], iris_dataset["target"])
# split the data into training data and test data
X_train, X_test, y_train, y_test = Train_test_split (iris_dataset["Data"], iris_dataset["target"], random_state=0) 
# Print (X_train, x_test, Y_train, y_test)

KNN = Kneighborsclassifier (n_neighbors=1) # get KNN object
Knn.fit (X_train, Y_train) # Training Model

# Evaluation Model
Y_pre = knn.predict (x_test)
score = Knn.score (X_test, y_test) # Call the scoring function
print ("Test set predictions:\n{}". Format (y_test))
print ("Test set score:{:.2f}". Format (score))
if score > 0.9:
x_new = Np.array ([[5, 2.9, 1, 0.3]])
print ("x_new.shape:{}". Format (x_new.shape))
prediction = Knn.predict (x_new) # forecast
print ("prediction:{}". Format (prediction))
print ("predicted target name:{}". Format (iris_dataset["Target_names"][prediction] )

# Visual Display
plt.title ("KNN classification")
Plt.plot (X_train, Y_train, "B.") # Training Data Dot
Plt.plot (x_test, Y_test, "Y.") # test Data dot
Plt.plot (x_new, prediction, "RO") # Predictive Data dot
plt.show ()
Else:
print ("Used train or test data is not available!")

Python Iris Classification Implementation