2-laboratoriya ishi guruh

O’ZBEKISTON RESPUBLIKASI AXBOROT TEXNALOGIYALARI VA KOMUNIKATSIYALARINI RIVOJLANTIRISH VAZIRLIGI

MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT TEXNALOGIYALARI UNIVERSITETI

Kompyuter tizimlari kafedrasi

Ma’lumotlar intelektual tahlili fani bo’yicha
2-LABORATORIYA ISHI

Guruh: KIF 214-18

Bajardi: Durnazarov Yorqin

Tekshirdi: Ochilov Temur.

TOSHKENT – 2021

Laboratoriya-2

A) Har bir guruhda talabalar jurnal boshidan 3 ta kichik guruhlarga bo’linadi. Agar guruhdagi talabalar sonini 3 ga bo`lib 3 ta guruh hosil qilamiz. Birinchi guruh Salary_data.csv faylini oladi. Ikkinchi guruh housing_data.csv faylini oladi. Uchinchi guruh brain_body.txt faylini oladi.

B) Ma`lumotlarni o`qib olish

C) O`qib olingan ma`lumotlarni ekranga chiqarish

D) Ularni scatter plot orqali visuallashtirish

E) StatsModels kutubxonasidan foydalanib ushbu ma`lumotlar uchun statistikalarni ekranga chiqarish

F) StatsModels kutubxonasidan foydalangan holda berilgan dataset orqali chiziqli regressiya(linear regression) quring.

G) Berilgan ma’lumotlarni umumlashtiruvchi chiziqni(Best fit line ) toping

H) Ushbu chiziqning matematik ko’rinishini yozing(Misol uchun y=x*0.0017+0.2750)
Ishni bajarish:

1. 
   2 – guruh: Salary_data.csv
   
2. 
   Ma’lumotlarni o’qib olish:
   

Dastlab kerakli kutubxona va modullarni import qilamiz:

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import statsmodels.api as sm

import seaborn as sns

sns.set()

'Mall_Customers.csv' fayldan ma’lumotlarni o’qib olamiz:
data = pd.read_csv('Mall_Customers.csv')

3. 
   o’qilgan malumotlarni ekranga chiqarish:
   

data

4. 
   Ularni scatter plot orqali visuallashtirish
   

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

plt.xlim(0, 150)

plt.ylim(-10, 110)

plt.show

5. 
   Xususiyatlarni belgilash
   

x = data.iloc[:,3:5]

x

D) Klassterlash

kmeans = KMeans(5)

kmeans.fit(x)

6. 
   Klassterlash natijalari:
   

identified_clusters = kmeans.fit_predict(x)

identified_clusters

data_with_clusters = data.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.xlim(0, 150)

plt.ylim(-10, 110)

plt.show()

Klasterlar nomerini tanlash

WCSS

kmeans.inertia_

wcss = []

for i in range(1, 8):

kmeans = KMeans(i)

kmeans.fit(x)

wcss_iter = kmeans.inertia_

wcss.append(wcss_iter)

wcss
The Elbow Method

number_clusters = range(1, 8)

plt.plot(number_clusters, wcss)

plt.title("The Elbow Method")

plt.xlabel('Number of cluster')

plt.ylabel('Within-cluster Sun of Squares')