# Python Program to Create K Nearest Neighbour Classifier Assignment Solution

July 11, 2024
Dr. Henry
Machine Learning
Dr. Henry Nguyen, a distinguished alumnus of the University of Melbourne, possesses a Ph.D. in Computer Science. With 6 years of experience, he has successfully completed over 400 Supervised Learning Assignments. Dr. Nguyen's research focuses on machine learning applications in healthcare, contributing significantly to advancements in medical diagnostics. His meticulous attention to detail and passion for innovation drive him to excel in academia and industry alike.
Key Topics
• Instructions
• Requirements and Specifications
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## Instructions

Objective

Write a program to create K nearest neighbour classifier in python.

## Requirements and Specifications

Source Code

```# -*- coding: utf-8 -*- import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt data_url = "http://lib.stat.cmu.edu/datasets/boston" raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None) raw_df.head() data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]]) target = raw_df.values[1::2, 2] """## Split into Train and Test""" X_train, X_test, y_train, y_test = train_test_split(data, target, test_size = 0.33, random_state = 42) X_train = np.array([ [5, 45], [5.11, 26], [5.6, 30], [5.9, 34], [4.8, 40], [5.8, 36], [5.3, 19], [5.8, 28], [5.5, 23], [5.6, 32], ]) y_train = np.array([77, 47, 55, 59, 72, 60, 40, 60, 45, 58]) X_test = np.array([ [5.5, 38] ]) y_test = np.array([65.2]) """## Define function to calculate the euclidean distance""" def euclidean_distance(X, x): return np.sqrt(np.sum(np.power(X-x,2), axis=1)) """## Define K """ K = 3 """## Function to predict""" def predict(x, X_train, y_train, K): # Compute euclidean distances D = euclidean_distance(X_train, x) # Sort in ascending order D = np.argsort(D) # Pick index of last K elements D_ = D[:K] # Get target values of the select K elements y_ = y_train[D_] # Calculate mean yi = np.mean(y_) return yi """## Estimate""" y_pred = np.zeros(y_test.shape) for i, x in enumerate(X_test): yi = predict(x, X_train, y_train, K) # Store predicted value y_pred[i] = yi """## Compute error""" err = np.sqrt(1/len(y_test) *np.sum(np.power(y_test-y_pred, 2))) print("The RMSE error is: {:.4f}".format(err)) """## Compute error for different values of K""" errors = list() for k in range(1, 10): y_pred = np.zeros(y_test.shape) for i, x in enumerate(X_test): yi = predict(x, X_train, y_train, k) y_pred[i] = yi # Compute error err = np.sqrt(1/len(y_test) *np.sum(np.power(y_test-y_pred, 2))) errors.append(err) # Plot plt.figure() plt.plot(range(1, 10), errors) plt.grid(True) plt.show() """### We see that the optimal value of K is K = 5""" ```

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