O‘zbekiston respublikasi oliy va o‘rta ta’lim

O‘ZBEKISTON RESPUBLIKASI OLIY VA O‘RTA TA’LIM

VAZIRLIGI

Sharof Rashidov nomidagi Samarqand Davlat Universiteti
Intelektual tizimlar va Kompyuter texnologiyalar fakulteti
Kompyuter ilmlari va dasturlash texnologiyalari yo’nalishi
402-guruh talabasi
Aralova Sabinaning
Suniy intelekt fanidan
Labaratoriya ishi
Tekshirdi: Yormatov Sh
Samarqand 2023
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import linear_model

data={
'Rusumi':['Nexia','Damas','Spark','Lasetti','Cobalt','Malibu','Tahoe','Kia','Matiz','Lamborjini','Toyota','BMW','Tracker'],
'Rangi':['oq','qora','qizil','sariq','yashil','pushti','malla','kok','jigarrang','toqsariq','moviy','seriy','siyohrang'],
'Sanasi':[2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012],
'Narxi':[90000000,75928000,85000000,150000000,120000000,278322334,300000000,400000000,60000000,500000000,550000000,1000000000,222150992]
}

df=pd.DataFrame(data)
df




Rusumi1={'Nexia':1,'Damas':2,'Spark':3,'Lasetti':4,'Cobalt':5,'Malibu':6,'Tahoe':7,'Kia':8,'Matiz':9,'Lamborjini':10,'Toyota':11,'BMW':12,'Tracker':13}
df['Rusumi']=df['Rusumi'].map(Rusumi1)
df.head()



df=df.drop('Rangi',axis=1)
df

from sklearn.preprocessing import StandardScaler
df1=df.columns.difference(['Narxi'])
scaler=StandardScaler()
df[df1]=scaler.fit_transform(df[df1])
df.head()

train_set,test_set=train_test_split(df,test_size=0.10,random_state=42)
x_train=train_set.drop('Narxi',axis=1).values
y_train=train_set['Narxi'].values
x_test=test_set.drop('Narxi',axis=1).values
y_test=test_set['Narxi'].values

from sklearn.ensemble import GradientBoostingRegressor
#Initialize the Gradient Boosting Regressor
gb_regressor=GradientBoostingRegressor()

#Fit the model on the test data
gb_regressor.fit(x_train, y_train)

#Make predictions on the test data
y_pred=gb_regressor.predict(x_test)

from sklearn.metrics import *
MAE=mean_absolute_error(y_test, y_pred)
RMSE=np.sqrt(mean_squared_error(y_test, y_pred))
r2=r2_score(y_test, y_pred)
print(f'MAE: {MAE}, MSE: {RMSE}, R^2: {r2}')

import xgboost as xgb
from sklearn.metrics import *
xgb_reg=xgb.XGBRegressor()
xgb_reg.fit(x_train, y_train)
y_pred=xgb_reg.predict(x_test)

MAE=mean_absolute_error(y_test, y_pred)
RMSE=np.sqrt(mean_squared_error(y_test, y_pred))
r2=r2_score(y_test, y_pred)
print(f'MAE: {MAE}, MSE: {RMSE}, R^2: {r2}')


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import *
from sklearn import linear_model

from xgboost.sklearn import XGBRegressor
MLR_model.fit(x_train, y_train)
rf_reg=RandomForestRegressor()
rf_reg.fit(x_train, y_train)
gb_regressor=GradientBoostingRegressor()
gb_regressor.fit(x_train, y_train)
MLR_model=linear_model.LinearRegression()


regressor=[('Liner Regrassion',MLR_model),
('XGBRegressor',xgb_reg),
('GradientB',gb_regressor),
('Random Forest' ,rf_reg)]
ensamble=VotingRegressor(estimators=regressor)
ensamble.fit(x_train, y_train)
y_pred=ensamble.predict(x_test)
y_pred=ensamble.predict(x_test)
MAE=mean_absolute_error(y_test, y_pred)
RMSE=np.sqrt(mean_squared_error(y_test, y_pred))
r2=r2_score(y_test, y_pred)
print(f'MAE: {MAE}, MSE: {RMSE}, R^2: {r2}')