## 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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