Python Scikit-learn: K Nearest Neighbors - Split the iris dataset into 70% train data and 30% test data

Python Machine learning K Nearest Neighbors: Exercise-4 with Solution

Write a Python program using Scikit-learn to split the iris dataset into 70% train data and 30% test data. Out of total 150 records, the training set will contain 105 records and the test set contains 45 of those records. Predict the response for test dataset (SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm) using the K Nearest Neighbor Algorithm. Use 5 as number of neighbors.

Sample Solution:

Python Code:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
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 random train and test subsets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)
'''
print("\n70% train data:")
print(X_train)
print(y_train)
print("\n30% test data:")
print(X_test)
print(y_test)
'''
#Create KNN Classifier
#Number of neighbors to use by default for kneighbors queries.
knn = KNeighborsClassifier(n_neighbors=5)
#Train the model using the training sets
knn.fit(X_train, y_train)
#Predict the response for test dataset
print("Response for test dataset:")
y_pred = knn.predict(X_test)
print(y_pred)

Sample Output:

Response for test dataset:
['Iris-versicolor' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'
 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor' 'Iris-setosa'
 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'
 'Iris-versicolor' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'
 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-virginica'
 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'
 'Iris-virginica' 'Iris-setosa' 'Iris-versicolor' 'Iris-versicolor'
 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor'
 'Iris-versicolor' 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica'
 'Iris-setosa' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa' 'Iris-setosa'
 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica']
 

Python Code Editor:

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Previous: Write a Python program using Scikit-learn to convert Species columns in a numerical column of the iris dataframe. To encode this data map convert each value to a number. e.g. Iris-setosa:0, Iris-versicolor:1, and Iris-virginica:2. Now print the iris dataset into 80% train data and 20% test data. Out of total 150 records, the training set will contain 120 records and the test set contains 30 of those records. Print both datasets.
Next: Write a Python program using Scikit-learn to split the iris dataset into 80% train data and 20% test data. Out of total 150 records, the training set will contain 120 records and the test set contains 30 of those records. Train or fit the data into the model and calculate the accuracy of the model using the K Nearest Neighbor Algorithm.