text: 
  import pandas as pd
  import numpy as np
  from sklearn import preprocessing 
  from scipy import sparse
  from sklearn import metrics, ensemble
  
  
  d_train = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/train-1m.csv")
  d_test = pd.read_csv("https://s3.amazonaws.com/benchm-ml--main/test.csv")
  
  
  d_all = pd.concat([d_train,d_test])
  
  vars_cat = ["Month","DayofMonth","DayOfWeek","UniqueCarrier", "Origin", "Dest"]
  vars_num = ["DepTime","Distance"]
  for col in vars_cat:
    d_all[col] = preprocessing.LabelEncoder().fit_transform(d_all[col])
    
  X_all_cat = preprocessing.OneHotEncoder(categories="auto").fit_transform(d_all[vars_cat])   
  X_all = sparse.hstack((X_all_cat, d_all[vars_num])).tocsr()                               
  y_all = np.where(d_all["dep_delayed_15min"]=="Y",1,0)         
  
  X_train = X_all[0:d_train.shape[0],]
  y_train = y_all[0:d_train.shape[0]]
  X_test = X_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0]),]
  y_test = y_all[d_train.shape[0]:(d_train.shape[0]+d_test.shape[0])]
  
  
  md = ensemble.RandomForestClassifier(max_depth = 10, n_estimators = 100, n_jobs = -1)
  %time md.fit(X_train, y_train)
  
  y_pred = md.predict_proba(X_test)[:,1]
  
  print(metrics.roc_auc_score(y_test, y_pred))
  
  