4. ML

 import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

from sklearn.preprocessing import StandardScaler

from sklearn.decomposition import PCA

data= pd.read_csv("Iris.csv")

data.head()

X = data.drop('Species' , axis = 1)

scaler = StandardScaler()

X_scaled = scaler.fit_transform(X)

sse= []

for k in range(1, 11):

    kmeans = KMeans(n_clusters=k, random_state=42)

    kmeans.fit(X_scaled)

    sse.append(kmeans.inertia_)

    

plt.figure(figsize=(8, 6))

plt.plot(range(1,11), sse , marker='o')

plt.plot('Elbow Method')

plt.xlabel('Number of Clusters')

plt.xlabel('SSE (Sum of Squared Distance)')

plt.show()