# Step 1: Import Libraries
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# Step 2: Load Dataset
df = sns.load_dataset('iris')

# Step 3: Divide Features & Labels
X = df.iloc[:, 0:4]  # Features
y = df.iloc[:, -1]   # Target (Species)

# Step 4: Encode Labels (convert categories to numbers)
le = LabelEncoder()
y = le.fit_transform(y)

# Step 5: Split Data into Train & Test Sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=45)

# Step 6: Train Model
clf = KNeighborsClassifier(n_neighbors=5)
clf.fit(X_train, y_train)

# Step 7: Prediction & Accuracy
y_pred = clf.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))

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