new_contents: # import libraries
  import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  from sklearn import linear_model
  from sklearn.model_selection import train_test_split
  
  # define the function to predict solar data in the future
  def pred_nuclear(samplefile, filelist):
    # read data
    data = pd.read_csv(samplefile)
    year1 = data[['Year']][:44]
    #print(year1.shape)
    year2 = data[['Year']][-11:]
  
    # predict nuclear for future
    year3 = year2 = data[['Year']][-6:]
    year3 = year3.set_index([[0, 1, 2, 3, 4, 5]])
  
    #statelist=["AK","AL","AR","AZ","CA","CO","CT","DE","FL","GA","IA","ID","IL","IN","KS","KY","LA","MA","MD","ME","MI","MN","MO","MS","MT","NC","ND","NE","NH","NJ","NM","NV","NY","OH","OK","OR","PA","RI","SC","SD","TN","TX","UT","VA","VT","WA","WI","WV","WY"]
    #print(len(statelist))
  
    future = year3
    # do ridge regression on train data
  
    for i in range(49):
      data = pd.read_csv('%s.csv' % (statelist[i]))
  
      year1 = data[['Year']][:44]
      #print(year1.shape)
      year2 = data[['Year']][-11:]
      # Split data for train and test
      #print(i)
      all_x = data[['GDP','CLPRB','EMFDB','ENPRP','NGMPB','PAPRB','PCP','ZNDX','Nominal Price', 'Inflation Adjusted Price']][0:55]
      all_y = data[['SOEGP']][0:55]
      train_x, test_x, train_y, test_y = train_test_split(all_x, all_y, test_size=0.2)
      regr2 = linear_model.Ridge(alpha = 0.75)
      regr2.fit(train_x, train_y)
      # predict SOEGP for future
      #year3 = data[['Year']][-6:]
      #year3 = year3.set_index([[0, 1, 2, 3, 4, 5]])
      #print(year3)
      future_x = data[['GDP','CLPRB','EMFDB','ENPRP','NGMPB','PAPRB','PCP','ZNDX','Nominal Price', 'Inflation Adjusted Price']][-6:]
      pred = pd.DataFrame(regr2.predict(future_x))
      pred.columns = [statelist[i]]
      #print(pred)
      future = pd.concat([future, pred], axis=1)
  
subject: Add code for ridge prediction for solar energy