Import the library.

  Load the sample data set..

  Split the data train and test.

  Define the number of n_neighbours.

  Fit the train data into Gridsearch model.

  Find the optimal number of n_neighbours.

  Improve the model performance.

  Calculate precision, recall and accuracy.

#import libraries
import warnings
warnings.filterwarnings(“ignore”)
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

#load the sample data from iris.csv file
data = pd.read_csv(‘/home/soft50/soft50/Sathish/practice/iris.csv’)

#Make it as a data frame
df = pd.DataFrame(data)

#feature variables
X = df.iloc[:,0:4]
y = df.iloc[:,4]

#Split the data into train and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

#Knn
knn = KNeighborsClassifier()
#create a dictionary of all values we want to test for n_neighbors
params_knn = {‘n_neighbors’: np.arange(1, 25)}
#use gridsearch to test all values for n_neighbors
knn_gs = GridSearchCV(knn, params_knn, cv=5)
#fit model to training data
knn_gs.fit(X_train, y_train)
knn_best = knn_gs.best_estimator_
print(“Optimal number of n_neighbours\n\n”,knn_best)
y_pred = knn_best.predict(X_test)

#Evaluate the model
print(“\n”)
print(“Classification report for KNN\n”)
print(classification_report(y_test, y_pred))
print(“Confusion matrix\n”)
print(confusion_matrix(y_test, y_pred))
print(“\n”)
print(“Accuracy score”)
print(accuracy_score(y_test, y_pred))