Update app.py

app.py

@@ -15,962 +15,7 @@ def greet(year,co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emi
   eq1=2.29209688*(co2_emission)+(-17.24834114)*(No2_emission)+(-34.46449984)*(so2_emission)+441.88734541*(Global_Warming)+(-10.5704468)*(Methane_emission)+3032.3276611889232
   
 
-  #1997
-
-  #data collection
-  data2=pd.read_excel("/content/ans1 (1).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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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("/content/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"
   
 
 
@@ -982,83 +27,7 @@ def greet(year,co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emi
   if(year==1996):
     return total1,eq1
 
-  elif(year==1997):
-    return total2,y_pred2
-
-  elif(year==1998):
-    return total3,y_pred3
-
-  elif(year==1999):
-    return total4,y_pred4
-
-  elif(year==2000):
-    return total5,y_pred5
-
-  elif(year==2001):
-    return total6,y_pred6
-
-  elif(year==2002):
-    return total7,y_pred7
-  
-  elif(year==2003):
-    return total8,y_pred8
-
-  elif(year==2004):
-    return total9,y_pred9
-
-  elif(year==2005):
-    return total10,y_pred10
-
-  elif(year==2006):
-    return total11,y_pred11
-
-  elif(year==2007):
-    return total12,y_pred12
-
-  elif(year==2008):
-    return total13,y_pred13
-
-  elif(year==2009):
-    return total14,y_pred14
-
-  elif(year==2010):
-    return total15,y_pred15
-
-  elif(year==2011):
-    return total16,y_pred16
-
-  elif(year==2012):
-    return total17,y_pred17
-
-  elif(year==2013):
-    return total18,y_pred18
-
-  elif(year==2014):
-    return total19,y_pred19
-
-  elif(year==2015):
-    return total20,y_pred20
-
-  elif(year==2016):
-    return total21,y_pred21
-
-  elif(year==2017):
-    return total22,y_pred22
-
-  elif(year==2018):
-    return total23,y_pred23
-
-  elif(year==2019):
-    return total24,y_pred24
-
-  elif(year==2020):
-    return total25,y_pred25
-
-  elif(year==2021):
-    return total26,y_pred26
-
-  elif(year==2022):
-    return total27,y_pred27
+ 
       
   else:
       return "no",0