Update app.py

app.py

@@ -4,32 +4,7 @@ import numpy as np
 import pandas as pd
 def greet(year,co2_emission,No2_emission,so2_emission,Global_Warming,Methane_emission):
   
-  #1996
   
-  #data collection
-  data1=pd.read_excel("/content/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]])