K-Nearest Neighbors

Introduction

This guide provides steps to perform KNN analysis in Cycon ML/AI platform, and compares it with the code available within Kaggle platform.

Note

• Name: Iris CSV

• Path: Tests/sampleCSV_MLA_Classification/iris.csv

• Kaggle: https://www.kaggle.com/code/skalskip/iris-data-visualization-and-knn-classification

• Shape: (150, 5)

• Classes: Iris-setosa, Iris-versicolor, Iris-virginica

• Purpose: Identify class of iris flowers given petal information.

Data

Preprocessing

CyCon

Kaggle

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)

Method

CyCon

kaggle

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
KNN = KNeighborsClassifier(n_neighbors=3)
KNN.fit(X_train,y_train)

Result

CyCon

Kaggle