Update app.py

app.py

@@ -75,533 +75,393 @@ def greet(year,co2_emission,no2_emission,so2_emission,global_warming,methane_emi
     
     #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(
+    
+    #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(
+    
+    #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"
-  
-
-
+    
+    #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(
+    
+    #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"
+    
+    #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 collection
+    
+    data7=pd.read_excel("/content/ans6.xlsx")
+    df7 = data7.drop([' YEAR '], axis=1)
 
-  #data indexing
-  x=df12.iloc[:,1:].values
-  y=df12.iloc[:,0].values
-  np.reshape(y,(-1,1))
+    #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(
+    #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]])
 
 
-  #traing the dataset
-  from sklearn.linear_model import LinearRegression
-
-  reg = LinearRegression().fit(X_train, y_train)
+    #Equation
+    total7="4.08573322*(x1)+531.87792204*(x2)+(-17.3614085 )*(x3)+(-11.17919737)*(x4)+(-53.48796076)*(x5)+22953.88111229325"
   
 
-  y_pred12=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])
+    #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
-  total12="33.41323832*(x1)+(-36.18735569)*(x2)+768.11444325*(x3)+(-182.42626044 )*(x4)+(14.70116631)*(x5)-6967.764713347897"
+    #Equation
+    total8="31.82512443*(x1)+(-521.96868383 )*(x2)+(-43.51829088)*(x3)+ 205.27514768 *(x4)+(-97.91577198)*(x5)+37973.451433772294"
   
 
+    #2004
 
-    #2008
-
-  #data collection
-  data13=pd.read_excel("/content/ans12.xlsx")
-  df13 = data13.drop([' YEAR '], axis=1)
-
-
+    #data collection
+    data9=pd.read_excel("/content/ans8.xlsx")
+    df9 = data9.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(
+    #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]])
 
-  #traing the dataset
-  from sklearn.linear_model import LinearRegression
 
-  reg = LinearRegression().fit(X_train, y_train)
+    #Equation
+    total9="9.64446417*(x1)+1536.50170104*(x2)+(-43.51829088)*(x3)+(-209.86341104)*(x4)+(-56.82344007)*(x5)+21672.006533155447"
   
 
-  y_pred13=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])
-
+    #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"
   
 
-  #Equation
-  total13="180.34683409 *(x1)+49.48628012*(x2)+152.71729516*(x3)+( -174.89679207)*(x4)+(-144.40854904)*(x5)+30420.505686819404"
-  
+    #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]])
 
 
-    #2009
-
-  #data collection
-  data14=pd.read_excel("/content/ans13.xlsx")
-  df14 = data14.drop([' YEAR '], axis=1)
-
-
+    #Equation
+    total11="(-15.45736104)*(x1)+23.92398419*(x2)+334.30252317*(x3)+151.55678804*(x4)+(-66.42769537)*(x5)+29294.014037250927"
+    
+    #2007
 
-  #data indexing
-  x=df14.iloc[:,1:].values
-  y=df14.iloc[:,0].values
-  np.reshape(y,(-1,1))
+    #data collection
+    data12=pd.read_excel("/content/ans11.xlsx")
+    df12 = data12.drop([' YEAR '], axis=1)
 
-  #split the dataset
-  from sklearn.model_selection import train_test_split
-  X_train, X_test, y_train, y_test = train_test_split(
+    #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]])
 
 
-  #traing the dataset
-  from sklearn.linear_model import LinearRegression
-
-  reg = LinearRegression().fit(X_train, y_train)
+    #Equation
+    total12="33.41323832*(x1)+(-36.18735569)*(x2)+768.11444325*(x3)+(-182.42626044 )*(x4)+(14.70116631)*(x5)-6967.764713347897"
   
+    #2008
 
-  y_pred14=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])
-
+    #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
-  total14="17.11355138 *(x1)+37.59837451*(x2)+156.43469383*(x3)+(-104.8362236)*(x4)+81.10973597*(x5)-38919.678559060834"
+    #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)
 
-    #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(
+    #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]])
 
 
-  #traing the dataset
-  from sklearn.linear_model import LinearRegression
-
-  reg = LinearRegression().fit(X_train, y_train)
+    #Equation
+    total14="17.11355138 *(x1)+37.59837451*(x2)+156.43469383*(x3)+(-104.8362236)*(x4)+81.10973597*(x5)-38919.678559060834"
   
 
-  y_pred15=reg.predict([[co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission]])
-
-
+    #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"
+    #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 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))
+    #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(
+    #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]])
-
-
-
-  
+    #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"
+    #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")
+    
+    #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))
 
-
-
-  #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(
+    #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]])
-
-
+    #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"
+    #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 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))
+    #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(
+    #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)