Lab_ML(17/03/25)

# %%
from sklearn.cluster import KMeans
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from  matplotlib import pyplot as plt
from  sklearn.datasets import load_iris
import matplotlib

# %%
iris = load_iris()

# %%
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df.head()

# %%
df['flower'] = iris.target
df.head()

# %%
df.drop(['sepal length (cm)','sepal width (cm)','flower'],axis='columns',inplace=True)

# %%
df.head(3)

# %%
km = KMeans(n_clusters=3)
yp = km.fit_predict(df)
yp

# %%
df['cluster'] = yp
df.head(2)

# %%
df.cluster.unique()

# %%
df1 = df[df.cluster==0]
df2 = df[df.cluster==1]
df3 = df[df.cluster==2]

# %%
plt.scatter(df1['petal length (cm)'],df1['petal width (cm)'],color='blue')
plt.scatter(df2['petal length (cm)'],df2['petal width (cm)'],color='green')
plt.scatter(df3['petal length (cm)'],df3['petal width (cm)'],color='yellow')

# %%
sse = []
k_rng = range(1,10)
for k in k_rng:
    km = KMeans(n_clusters=k)
    km.fit(df)
    sse.append(km.inertia_)

plt.xlabel('K')
plt.ylabel('Sum of squared error')
plt.plot(k_rng,sse)
plt.title('The Elbow Method showing the optimal value of k')