new_contents: import numpy as np 
  import pandas as pd 
  from pandas import Series, DataFrame 
  import lightgbm as lgb 
  import xgboost as xgb 
  from sklearn.preprocessing import LabelEncoder,OneHotEncoder
  
  data=pd.read_csv('adult_train.csv',header=None) 
  # assign column names to the data
  data.columns=['age','workclass','fnlwgt','education','education-num','marital_Status','occupation','relationship','race','sex','capital_gain','capital_loss','hours_per_week','native_country','Income'] 
  data.head()
  
  # encode label
  l=LabelEncoder() 
  l.fit(data.Income) 
  l.classes_ 
  data.Income=Series(l.transform(data.Income))
  data.Income.value_counts() # label has been encoded as 0, 1
  
  # convert categorical data into one-hot, and drop original categorical data
  one_hot_workclass=pd.get_dummies(data.workclass) 
  one_hot_education=pd.get_dummies(data.education) 
  one_hot_marital_Status=pd.get_dummies(data.marital_Status) 
  one_hot_occupation=pd.get_dummies(data.occupation)
  one_hot_relationship=pd.get_dummies(data.relationship) 
  one_hot_race=pd.get_dummies(data.race) 
  one_hot_sex=pd.get_dummies(data.sex) 
  one_hot_native_country=pd.get_dummies(data.native_country) 
  
  data.drop(['workclass','education','marital_Status','occupation','relationship','race','sex','native_country'],axis=1,inplace=True) 
  data=pd.concat([data,one_hot_workclass,one_hot_education,one_hot_marital_Status,one_hot_occupation,one_hot_relationship,one_hot_race,one_hot_sex,one_hot_native_country],axis=1) 
  
subject: create lightGBM