code: import seaborn as sb
  import matplotlib.pyplot as plt
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LogisticRegression
  from sklearn import metrics
  
  person = {'finances':[1,2,3,4,5,6,7,8,7,6,5,4,3,2,1,7,4,1,8,5,2,9,6,3,9,8,7,6,5,4,3,2,1,1,9,7,3,8,2,7],
             'management':[1,2,3,4,5,6,7,8,7,6,5,4,3,2,1,7,4,1,8,5,2,9,6,3,9,8,7,6,5,4,3,2,1,1,9,7,3,8,2,7],
             'logistic':[1,2,3,4,5,6,7,8,7,6,5,4,3,2,1,7,4,1,8,5,2,9,6,3,9,8,7,6,5,4,3,2,1,1,9,7,3,8,2,7],
             'get_work':[0,0,1,0,0,1,0,0,1,1,1,1,1,1,1,0,0,0,1,0,0,1,0,0,1,1,0,1,0,0,0,0,1,1,1,0,1,0,0,1]
             }
  
  database = pd.DataFrame(person, columns=['finances','management','logistic','get_work'])
  
  print(database[['finances','management','logistic']])
  
  x = database[['finances','management','logistic']]
  y = database['get_work']
  
  #30% buat tes, 70 buat training
  x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=0)
  
  lr=LogisticRegression()
  lr.fit(x_train,y_train)
  
  y_predict=lr.predict(x_test)
  
  confusion_mat = pd.crosstab(y_test,y_predict,rownames=["true"],colnames=["prediction"])
  
  sb.heatmap(confusion_mat,annot=True)
  
  print("Accuracy = ", metrics.accuracy_score(y_test,y_predict))
  print(confusion_mat)
  plt.show()
  
  
apis: pandas.crosstab pandas.DataFrame sklearn.metrics.accuracy_score matplotlib.pyplot.show sklearn.linear_model.LogisticRegression sklearn.model_selection.train_test_split