text: # --------------
  # import the libraries
  import numpy as np
  import pandas as pd
  import seaborn as sns
  from sklearn.model_selection import train_test_split
  import warnings
  warnings.filterwarnings('ignore')
  
  # Code starts here
  df = pd.read_csv(path)
  print(df.head())
  X = df.drop(columns=['insuranceclaim'], axis=1)
  y = df['insuranceclaim']
  X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=6)
  
  # Code ends here
  
  
  # --------------
  import matplotlib.pyplot as plt
  
  
  # Code starts here
  plt.boxplot(X_train['bmi'])
  q_value = X_train['bmi'].quantile(.95)
  y_train.value_counts()
  # Code ends here
  
  
  # --------------
  # Code starts here
  relation = X_train.corr()
  print(relation)
  sns.pairplot(X_train)
  # Code ends here
  
  
  # --------------
  import seaborn as sns
  import matplotlib.pyplot as plt
  
  # Code starts here
  cols = ['children','sex','region','smoker']
  # create subplot 
  
  fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20,20))
  
  # create loop for plotting countplot
  
  for i in range(0,2):
      for j in range(0,2):
               col=cols[i*2 + j]
               sns.countplot(x=X_train[col], hue=y_train, ax=axes[i,j])
  
  
  # Code ends here
  
  
  # --------------
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score
  
  
  # parameters for grid search
  parameters = {'C':[0.1,0.5,1,5]}
  
  # Code starts here
  lr = LogisticRegression()
  grid = GridSearchCV(estimator=lr, param_grid=parameters)
  grid.fit(X_train, y_train)
  y_pred = grid.predict(X_test)
  accuracy = accuracy_score(y_test, y_pred)
  print('accuracy: ',accuracy)
  # Code ends here
  
  
  # --------------
  from sklearn.metrics import roc_auc_score
  from sklearn import metrics
  
  # Code starts here
  score = roc_auc_score(y_test, y_pred)
  y_pred_proba = grid.predict_proba(X_test)[:,1]
  fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_proba)
  roc_auc = roc_auc_score(y_test, y_pred_proba)
  plt.plot(fpr,tpr,label="Logistic model, auc="+str(roc_auc))
  
  
  # Code ends here
  
  
  