Write a Python program using Scikit-learn to split the iris dataset into 80% train data and 20% test data

# Import necessary modules 
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.model_selection import train_test_split  

iris = pd.read_csv("iris.csv")
#Drop id column
iris = iris.drop('Id',axis=1)
X = iris.iloc[:, :-1].values
y = iris.iloc[:, 4].values
#Split arrays or matrices into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) 
knn = KNeighborsClassifier(n_neighbors=7)  
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X_train,y_train)

print("Preliminary model score:")
print(knn.score(X_test,y_test))

no_neighbors = np.arange(1, 9)
train_accuracy = np.empty(len(no_neighbors))
test_accuracy = np.empty(len(no_neighbors))

for i, k in enumerate(no_neighbors):
    # We instantiate the classifier
    knn = KNeighborsClassifier(n_neighbors=k)
    # Fit the classifier to the training data
    knn.fit(X_train,y_train)
    
    # Compute accuracy on the training set
    train_accuracy[i] = knn.score(X_train, y_train)

    # Compute accuracy on the testing set
    test_accuracy[i] = knn.score(X_test, y_test)

# Visualization of k values vs accuracy

plt.title('k-NN: Varying Number of Neighbors')
plt.plot(no_neighbors, test_accuracy, label = 'Testing Accuracy')
plt.plot(no_neighbors, train_accuracy, label = 'Training Accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show()

Sample Output: