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import numpy as np | |
import pandas as pd | |
import gradio as gr | |
def greet(year,co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission): | |
#1996 | |
#data collection | |
data1=pd.read_excel("FINAL_DATASET.xlsx") | |
df1 = data1.drop(['YEAR'], axis=1) | |
#data indexing | |
x=df1.iloc[:,1:].values | |
y=df1.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred1=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total1="2.29209688*(x1)+(-17.24834114)*(x2)+(-34.46449984)*(x3)+441.88734541(x4)+(-10.5704468)*(x5)+3032.3276611889232" | |
#1997 | |
#data collection | |
data2=pd.read_excel("ans1.xlsx") | |
df2 = data2.drop(['YEAR '], axis=1) | |
#data indexing | |
x=df2.iloc[:,1:].values | |
y=df2.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred2=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total2="1.87191609*(x1)+19.64115875*(x2)+91.64048224*(x3)+188.38350818*(x4)+23.55498894*(x5)-10954.252919457198" | |
#1998 | |
#data collection | |
data3=pd.read_excel("ans2.xlsx") | |
df3 = data3.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df3.iloc[:,1:].values | |
y=df3.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred3=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total3="-16.16933055*(x1)+222.22199705*(x2)+137.12332335*(x3)+325.31073157*(x4)+(-123.63496668)*(x5)+56972.685015326366" | |
#1999 | |
#data collection | |
data4=pd.read_excel("ans3.xlsx") | |
df4 = data4.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df4.iloc[:,1:].values | |
y=df4.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred4=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total4="24.45036879*(x1)+(-30.15985323)*(x2)+40.89603753*(x3)+102.95011027*(x4)+(-26.35323684)*(x5)+7934.309705068432" | |
#2000 | |
#data collection | |
data5=pd.read_excel("ans4.xlsx") | |
df5 = data5.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df5.iloc[:,1:].values | |
y=df5.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred5=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total5="9.64446417*(x1)+1536.50170104*(x2)+(-43.51829088)*(x3)+(-209.86341104)*(x4)+(-56.82344007)*(x5)+21672.006533155447" | |
#2001 | |
#data collection | |
data6=pd.read_excel("ans5.xlsx") | |
df6 = data6.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df6.iloc[:,1:].values | |
y=df6.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred6=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total6="(-17.95980305)*(x1)+397.29027184*(x2)+332.85116421*(x3)+176.63505073*(x4)+(-100.69005777)*(x5)+47882.75497380103" | |
#2002 | |
#data collection | |
data7=pd.read_excel("ans6.xlsx") | |
df7 = data7.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df7.iloc[:,1:].values | |
y=df7.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred7=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total7="4.08573322*(x1)+531.87792204*(x2)+(-17.3614085 )*(x3)+(-11.17919737)*(x4)+(-53.48796076)*(x5)+22953.88111229325" | |
#2003 | |
#data collection | |
data8=pd.read_excel("ans7.xlsx") | |
df8 = data8.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df8.iloc[:,1:].values | |
y=df8.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred8=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total8="31.82512443*(x1)+(-521.96868383 )*(x2)+(-43.51829088)*(x3)+ 205.27514768 *(x4)+(-97.91577198)*(x5)+37973.451433772294" | |
#2004 | |
#data collection | |
data9=pd.read_excel("ans8.xlsx") | |
df9 = data9.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df9.iloc[:,1:].values | |
y=df9.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred9=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total9="9.64446417*(x1)+1536.50170104*(x2)+(-43.51829088)*(x3)+(-209.86341104)*(x4)+(-56.82344007)*(x5)+21672.006533155447" | |
#2005 | |
#data collection | |
data10=pd.read_excel("ans9.xlsx") | |
df10 = data10.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df10.iloc[:,1:].values | |
y=df10.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred10=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total10="(-46.41395388)*(x1)+27.19076539*(x2)+(442.44336049)*(x3)+(-205.61881527)*(x4)+120.39426307*(x5)-46289.48823133327" | |
#2006 | |
#data collection | |
data11=pd.read_excel("ans10.xlsx") | |
df11 = data11.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df11.iloc[:,1:].values | |
y=df11.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred11=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total11="(-15.45736104)*(x1)+23.92398419*(x2)+334.30252317*(x3)+151.55678804*(x4)+(-66.42769537)*(x5)+29294.014037250927" | |
#2007 | |
#data collection | |
data12=pd.read_excel("ans11.xlsx") | |
df12 = data12.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df12.iloc[:,1:].values | |
y=df12.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred12=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total12="33.41323832*(x1)+(-36.18735569)*(x2)+768.11444325*(x3)+(-182.42626044 )*(x4)+(14.70116631)*(x5)-6967.764713347897" | |
#2008 | |
#data collection | |
data13=pd.read_excel("ans12.xlsx") | |
df13 = data13.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df13.iloc[:,1:].values | |
y=df13.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred13=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total13="180.34683409 *(x1)+49.48628012*(x2)+152.71729516*(x3)+( -174.89679207)*(x4)+(-144.40854904)*(x5)+30420.505686819404" | |
#2009 | |
#data collection | |
data14=pd.read_excel("ans13.xlsx") | |
df14 = data14.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df14.iloc[:,1:].values | |
y=df14.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred14=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total14="17.11355138 *(x1)+37.59837451*(x2)+156.43469383*(x3)+(-104.8362236)*(x4)+81.10973597*(x5)-38919.678559060834" | |
#2010 | |
#data collection | |
data15=pd.read_excel("ans14.xlsx") | |
df15 = data15.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df15.iloc[:,1:].values | |
y=df15.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred15=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total15="39.06418699 *(x1)+148.53455807*(x2)+14.69213499 *(x3)+107.43795246*(x4)+(-207.77185028)*(x5)+82358.63651384937" | |
#2011 | |
#data collection | |
data16=pd.read_excel("ans15.xlsx") | |
df16 = data16.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df16.iloc[:,1:].values | |
y=df16.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred16=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total16="36.2551509 *(x1)+-21.16118114*(x2)+372.06856269*(x3)+(-59.04384028)*(x4)+(-49.61395171)*(x5)+18259.681897588325" | |
#2012 | |
#data collection | |
data17=pd.read_excel("ans16.xlsx") | |
df17 = data17.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df17.iloc[:,1:].values | |
y=df17.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred17=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total17="76.15862868 *(x1)+24.66304806*(x2)+(-31.1753211)*(x3)+(-281.13550722 )*(x4)+48.76763872*(x5)-27641.15357666507" | |
#2013 | |
#data collection | |
data18=pd.read_excel("ans17.xlsx") | |
df18 = data18.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df18.iloc[:,1:].values | |
y=df18.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred18=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total18="138.94519275 *(x1)+19.41784298*(x2)+160.13405515*(x3)+1190.40134987*(x4)+(-787.72926112)*(x5)+340350.32984524494" | |
#2014 | |
#data collection | |
data19=pd.read_excel("ans18.xlsx") | |
df19 = data19.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df19.iloc[:,1:].values | |
y=df19.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred19=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total19="83.98184027*(x1)+61.59628945*(x2)+740.33672736*(x3)+(-347.39343539)*(x4)+(-293.6388187)*(x5)+121547.59923111903" | |
#2015 | |
#data collection | |
data20=pd.read_excel("ans19.xlsx") | |
df20 = data20.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df20.iloc[:,1:].values | |
y=df20.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred20=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total20="25.74397202*(x1)+(-109.5936775)*(x2)+293.36826631*(x3)+(-52.97554351)*(x4)+178.24908664*(x5)-80332.13002824014" | |
#2016 | |
#data collection | |
data21=pd.read_excel("ans20.xlsx") | |
df21 = data21.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df21.iloc[:,1:].values | |
y=df21.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred21=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total21="-9.33709575 *(x1)+(-60.54283141)*(x2)+1291.89291784*(x3)+112.70137053*(x4)+167.06117048*(x5)-76365.90014799789" | |
#2017 | |
#data collection | |
data22=pd.read_excel("ans21.xlsx") | |
df22 = data22.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df22.iloc[:,1:].values | |
y=df22.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred22=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total22="-12.58553956 *(x1)+54.81099258*(x2)+224.41124874*(x3)+437.35226861*(x4)+(-160.78658794)*(x5)+68323.07737183299" | |
#2018 | |
#data collection | |
data23=pd.read_excel("ans22.xlsx") | |
df23 = data23.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df23.iloc[:,1:].values | |
y=df23.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred23=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total23="73.20314723*(x1)+158.24671048*(x2)+(-3876.80695302)*(x3)+356.25236863*(x4)+(-195.73184137)*(x5)+85757.9509512224" | |
#2019 | |
#data collection | |
data24=pd.read_excel("ans23.xlsx") | |
df24 = data24.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df24.iloc[:,1:].values | |
y=df24.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred24=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total24="104.06131346*(x1)+110.40576115*(x2)+(-3143.30201973)*(x3)+(-466.5687285)*(x4)+(-40.30732688)*(x5)+6946.199087391373" | |
#2020 | |
#data collection | |
data25=pd.read_excel("ans24.xlsx") | |
df25 = data25.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df25.iloc[:,1:].values | |
y=df25.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred25=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total25="22.78813682*(x1)+46.1536507*(x2)+78.00814512*(x3)+(-71.38031119)*(x4)+(-37.57839411)*(x5)+12559.184605195129" | |
#2021 | |
#data collection | |
data26=pd.read_excel("ans25.xlsx") | |
df26 = data26.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df26.iloc[:,1:].values | |
y=df26.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred26=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total26="63.70545758*(x1)+9.57432502*(x2)+1734.12898357*(x3)+(-230.53815238)*(x4)+93.1299683*(x5)-51860.81441391745" | |
#2022 | |
#data collection | |
data27=pd.read_excel("ans26.xlsx") | |
df27 = data27.drop([' YEAR '], axis=1) | |
#data indexing | |
x=df27.iloc[:,1:].values | |
y=df27.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#traing the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred27=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total27="15.98972327*(x1)+5568.67299429*(x2)+79.28661735*(x3)+16.79333316*(x4)+(-87.10169494)*(x5)+40155.32700035415" | |
#above 2023 | |
data28=pd.read_excel("bara shigiri - Copy.xlsx") | |
#data indexing | |
x=data28.iloc[:,1:].values | |
y=data28.iloc[:,0].values | |
np.reshape(y,(-1,1)) | |
#split the dataset | |
from sklearn.model_selection import train_test_split | |
X_train, X_test, y_train, y_test = train_test_split( | |
x, y, test_size=0.33, random_state=42) | |
#training the dataset | |
from sklearn.linear_model import LinearRegression | |
reg = LinearRegression().fit(X_train, y_train) | |
y_pred28=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]]) | |
#Equation | |
total28="-1.50608097*(x1)+10.05272793*(x2)+(-10.52662062)*(x3)+336.60218769*(x4)+(-23.82408478)*(x5)+12129.976853563849" | |
#app section | |
if(year==1996): | |
return y_pred1 | |
elif(year==1997): | |
return y_pred2 | |
elif(year==1998): | |
return y_pred3 | |
elif(year==1999): | |
return y_pred4 | |
elif(year==2000): | |
return y_pred5 | |
elif(year==2001): | |
return y_pred6 | |
elif(year==2002): | |
return y_pred7 | |
elif(year==2003): | |
return y_pred8 | |
elif(year==2004): | |
return y_pred9 | |
elif(year==2005): | |
return y_pred10 | |
elif(year==2006): | |
return y_pred11 | |
elif(year==2007): | |
return y_pred12 | |
elif(year==2008): | |
return y_pred13 | |
elif(year==2009): | |
return y_pred14 | |
elif(year==2010): | |
return y_pred15 | |
elif(year==2011): | |
return y_pred16 | |
elif(year==2012): | |
return y_pred17 | |
elif(year==2013): | |
return y_pred18 | |
elif(year==2014): | |
return y_pred19 | |
elif(year==2015): | |
return y_pred20 | |
elif(year==2016): | |
return y_pred21 | |
elif(year==2017): | |
return y_pred22 | |
elif(year==2018): | |
return y_pred23 | |
elif(year==2019): | |
return y_pred24 | |
elif(year==2020): | |
return y_pred25 | |
elif(year==2021): | |
return y_pred26 | |
elif(year==2022): | |
return y_pred27 | |
elif(year>=2023): | |
return y_pred28 | |
else: | |
return 0 | |
demo = gr.Interface( | |
fn=greet, | |
inputs=['number','number','number','number','number','number'], | |
outputs=['number'], | |
title="BARA SHIGRI", | |
description="Bara Shigri feeds the Chandra River which after its confluence at Tandi with the Bhaga River is known as Chandrabhaga or Chenab." | |
"According to Hugh Whistler’s 1924 writing, Shigri is applied par-excellence to one particular glacier that emerges from the mountains on the left bank of the Chenab. It is said to be several miles long, and the snout reaches right down to the river, lying athwart the customary road from Kulu to Spiti... In 1836 this glacier dammed the Chenab River, causing the formation of a large lake, which eventually broke loose and carried devastation down the valley." | |
"Across the Bara Shigri is another glacier known as Chhota Shigri. It is, as the name suggests, a comparatively smaller glacier.", | |
css='div {background-image: url("https://drive.google.com/uc?export=view&id=1gQA86IDVYGmnXAccJHnULm3V99jn7sUn");background-size: 2000px 2000px;}' | |
) | |
demo.launch(inline=False) |