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A Practical Guide to Machine Learning with Python

k-NN Implementation Steps: 1 to 5

Explore key steps to implement a k-Nearest Neighbors classification model, including importing libraries, preparing data, removing irrelevant variables, scaling features, and defining inputs and outputs. Understand how to preprocess data to optimize k-NN performance using Python.

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1) Import libraries

This model is built using the KNeighborsClassifier from Scikit-learn. You’ll also be relying on StandardScaler to standardize the data, as you did earlier with principal component analysis.

Python 3.5
#1- Import libraries
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report, confusion_matrix

Note: Codes of further steps won’t ...