03-k-nearest-neighbors.py

#Common imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

#Import the data set
raw_data = pd.read_csv('classified_data.csv', index_col = 0)

#Import standardization functions from scikit-learn
from sklearn.preprocessing import StandardScaler

#Standardize the data set
scaler = StandardScaler()
scaler.fit(raw_data.drop('TARGET CLASS', axis=1))
scaled_features = scaler.transform(raw_data.drop('TARGET CLASS', axis=1))
scaled_data = pd.DataFrame(scaled_features, columns = raw_data.drop('TARGET CLASS', axis=1).columns)

#Split the data set into training data and test data
from sklearn.model_selection import train_test_split
x = scaled_data
y = raw_data['TARGET CLASS']
x_training_data, x_test_data, y_training_data, y_test_data = train_test_split(x, y, test_size = 0.3)

#Train the model and make predictions
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors = 1)
model.fit(x_training_data, y_training_data)
predictions = model.predict(x_test_data)

#Performance measurement
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
print(classification_report(y_test_data, predictions))
print(confusion_matrix(y_test_data, predictions))

#Selecting an optimal K value
error_rates = []
for i in np.arange(1, 101):
    new_model = KNeighborsClassifier(n_neighbors = i)
    new_model.fit(x_training_data, y_training_data)
    new_predictions = new_model.predict(x_test_data)
    error_rates.append(np.mean(new_predictions != y_test_data))

plt.figure(figsize=(16,12))
plt.plot(error_rates)