Texnologiyalari universiteti fan: Ma’lumotlarning intelektual tahlili amaliy ish №3

O‘ZBEKISTON RESPUBLIKASI AXBOROT TEXNOLOGIYALARI VA

KOMMUNIKATSIYALARINI RIVOJLANTIRISH VAZIRLIGI

MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT

TEXNOLOGIYALARI UNIVERSITETI

Fan: Ma’lumotlarning intelektual tahlili

AMALIY ISH № 3

Guruh: 211-18 KI

Bajardi:Jumanazarov Maxmud

Tekshirdi: Xusanov O'rol

import numpy as np

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

df=pd.read_csv("Mall_Customers.csv")

df.head()

import matplotlib.pyplot as plot

from sklearn.model_selection import train_test_split

from sklearn.metrics import confusion_matrix

plt.scatter(df['Annual Income (k$)'],df["Spending Score (1-100)"])

plt.show()



x=df.iloc[:,3:]

wcss=[]

for i in range(1,11):

kmeans=KMeans(n_clusters=i,init="k-means++",random_state=0)

kmeans.fit(x)

wcss.append(kmeans.inertia_)

plt.plot(range(1,11),wcss)

plt.show()



kmeans = KMeans(5)

kmeans.fit(x)

identified_clusters = kmeans.fit_predict(x)

identified_clusters



data_with_clusters = x.copy()

data_with_clusters['Cluster'] = identified_clusters

data_with_clusters



plt.scatter(data_with_clusters["Annual Income (k$)"],data_with_clusters["Spending Score (1-100)"],c=data_with_clusters['Cluster'],cmap='rainbow')

plt.show



data_with_clusters.to_csv('k-means.csv', index=False)

x = data_with_clusters[['Annual Income (k$)', 'Spending Score (1-100)']]

y = data_with_clusters['Cluster']

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier(n_neighbors=3)

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)

# Train the model using the training sets

model.fit(X_train,y_train)

#Predict Output

predicted= model.predict([[0,2]]) # 0:Overcast, 2:Mild

print(predicted)

import seaborn as sns

sns.scatterplot(data=data_with_clusters, x="Annual Income (k$)", y="Spending Score (1-100)", hue="Cluster", palette='rainbow')



predicted= model.predict([[0,2]])

#manashu joyida o'zizni ma'lumotlarizdan kelib chiqib qiymatlar yozing. [0,2] ni o'rniga [80,100] va hz.

preds = model.predict(X_test)

confuse = confusion_matrix(y_test, preds)

import seaborn as sns

import matplotlib.pyplot as plt

fig = sns.heatmap(confuse, annot=True)

plt.xlabel('Actual')

plt.ylabel('Predcited')

plt.show()