ml1.py

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

def model_1(data):
          
         # data = pd.read_csv("finaldata.csv")


          # Feature Scaling 
          X_train = data.iloc[:,2:8] 
          for i in range(len(X_train.columns)):
             X_train.iloc[:,i]= X_train.iloc[:,i]/(np.max(X_train.iloc[:,i])-np.min(X_train.iloc[:,i])) - (np.mean(X_train.iloc[:,i])/(np.max(X_train.iloc[:,i])-np.min(X_train.iloc[:,i]))) 
          X_train = X_train.fillna(0)

          Y_train = data.iloc[:,-1]
          
          

          model = LinearRegression()


          lr=model.fit(X_train,Y_train)

          predict = lr.predict(X_train)
          pred = abs(predict)
          predict1 = np.zeros(len(predict))

          for i in range(len(pred)):
             predict1[i] = pred[i]/(1+pred[i])
             predict1[i]=predict1[i] *100
             predict1[i] = round(predict1[i])
          cities = data.iloc[:,0]
    
          return predict1[5]