a5

#program 5
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
# Load the dataset without specifying column names
dataset = pd.read_csv("P5Data.csv")
# Print the first few rows to check the data
print(dataset.head())
# Ensure that we only use the numeric columns for X (features)
X = dataset.iloc[:, :-1].values # Features (all rows, all columns
except the last)
y = dataset.iloc[:, -1].values # Target (the last column)
# Split the dataset into train and test sets
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.10)
# Initialize and train the classifier
classifier = KNeighborsClassifier(n_neighbors=5).fit(Xtrain, ytrain)
# Predict the labels for the test set
ypred = classifier.predict(Xtest)
# Print the results
print("\nOriginal vs Predicted Labels:")
for true, pred in zip(ytest, ypred):
 print(f"{true: <25} {pred: <25} {'Correct' if true == pred else
'Wrong'}")
# Confusion matrix and classification report
print("\nConfusion Matrix:")
print(metrics.confusion_matrix(ytest, ypred))
print("\nClassification Report:")
print(metrics.classification_report(ytest, ypred))
# Accuracy score
print(f"Accuracy: {metrics.accuracy_score(ytest, ypred):.2f}")