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Kümeleme

import pandas as pd
from matplotlib import pyplot as plt
import numpy as np

Veri dosyasını indirmek için tıklayınız.

df = pd.read_csv("03c_income.csv")
df.head()
Name Age Income($)
0 Rob 27 70000
1 Michael 29 90000
2 Mohan 29 61000
3 Ismail 28 60000
4 Kory 42 150000 
data=df.to_numpy()

age=data[:,1].astype(np.int64)
income=data[:,2].astype(np.int64)

data=data[:,1:].astype(np.int64)

plt.scatter(age,income)
plt.xlabel('Age')
plt.ylabel('Income($)')
plt.show()

png

from sklearn.cluster import KMeans

km = KMeans(n_clusters=3)
y_predicted = km.fit_predict(data)
y_predicted

C:\anaconda3\envs\lsp\lib\site-packages\sklearn\cluster\_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\anaconda3\envs\lsp\lib\site-packages\sklearn\cluster\_kmeans.py:1440: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(





array([0, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2])

age

array([27, 29, 29, 28, 42, 39, 41, 38, 36, 35, 37, 26, 27, 28, 29, 32, 40,
       41, 43, 39, 41, 39], dtype=int64)

income

array([ 70000,  90000,  61000,  60000, 150000, 155000, 160000, 162000,
       156000, 130000, 137000,  45000,  48000,  51000,  49500,  53000,
        65000,  63000,  64000,  80000,  82000,  58000], dtype=int64)

centers=km.cluster_centers_
centers

array([[3.40000000e+01, 8.05000000e+04],
       [3.82857143e+01, 1.50000000e+05],
       [3.29090909e+01, 5.61363636e+04]])

labels = km.labels_

labels

array([0, 0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2])

plt.figure(figsize=(8, 6))
colors = ['red', 'blue', 'green']
for i in range(3):
    plt.scatter(data[labels == i, 0], data[labels == i, 1], c=colors[i], label=f'Cluster {i}')

# Küme Merkezlerini Çizdirme
plt.scatter(centers[:, 0], centers[:, 1], c='black', marker='X', s=50, label='Centroids')

plt.xlabel('Age')
plt.ylabel('Income')
plt.title('KMeans Clustering of Age vs Income')
plt.legend()
plt.grid()
plt.show()

png

Normalizasyon, verilerin ölçeklendirilmesi

from sklearn.preprocessing import MinMaxScaler

data.shape

(22, 2)

scaler = MinMaxScaler()
# Veriyi ölçekle
data_scaled = scaler.fit_transform(data)
# scaler.inverse_transform(data_scaled)

plt.scatter(data_scaled[:,0],data_scaled[:,1])
plt.show()

png

kmeans = KMeans(n_clusters=3)
kmeans.fit(data_scaled)

labels = kmeans.labels_

centers = scaler.inverse_transform(kmeans.cluster_centers_)

plt.figure(figsize=(8, 6))
colors = ['red', 'blue', 'green']
for i in range(3):
    plt.scatter(data[labels == i, 0], data[labels == i, 1], c=colors[i], label=f'Cluster {i}')
    
    
plt.scatter(centers[:, 0], centers[:, 1], c='black', marker='X', s=50, label='Centroids')

plt.xlabel('Age')
plt.ylabel('Income')
plt.title('KMeans Clustering (MinMax Scaled Data)')
plt.legend()
plt.grid()
plt.show()

png

Kaynak: