|
import pandas as pd
|
|
import numpy as np
|
|
from sklearn.neighbors import KNeighborsClassifier
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.metrics import classification_report
|
|
|
|
# Load the data from the csv file
|
|
df = pd.read_csv('data.csv')
|
|
|
|
# Split the data into training and testing sets
|
|
X_train, X_test, y_train, y_test = train_test_split(
|
|
df[['avg_r', 'avg_g', 'avg_b', 'median_r', 'median_g', 'median_b', 'mode_r', 'mode_g', 'mode_b', 'max_r']], df['result'], test_size=0.2, random_state=42)
|
|
|
|
# Create the KNN classifier
|
|
knn = KNeighborsClassifier(n_neighbors=5)
|
|
|
|
# Fit the classifier to the training data
|
|
knn.fit(X_train, y_train)
|
|
|
|
# Make predictions on the testing data
|
|
y_pred = knn.predict(X_test)
|
|
|
|
# Evaluate the performance of the classifier
|
|
print(classification_report(y_test, y_pred))
|