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)
  df.iloc[:,:5]
  X = df.iloc[:,:-1]
  y = df.iloc[:,-1]
  X_train,X_test,y_train,y_test= train_test_split(X, y,random_state=6,test_size = 0.2)
  
  # Code ends here
  
  
  # --------------
  import matplotlib.pyplot as plt
  
  
  # Code starts here
  
  plt.boxplot(X_train['bmi'])
  q_value = np.quantile(X_train['bmi'],q=0.95)
  y_train.value_counts()
  # Code ends here
  
  
  # --------------
  # Code starts here
  
  relation = X_train.corr()
  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']
  fig ,axes = plt.subplots(nrows = 2 , ncols = 2,figsize=(8,8))
  for i in range(2):
      for j in range(2):
         col =  cols[ i * 2 + j]
         sns.countplot(x=X_train[col], hue=y_train, ax=axes[i,j])
  plt.show()
  
  # 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(lr,param_grid=parameters)
  grid.fit(X_train,y_train)
  y_pred = grid.predict(X_test)
  accuracy = accuracy_score(y_test,y_pred)
  accuracy
  
  
  # Code ends here
  
  
  # --------------
  from sklearn.metrics import roc_auc_score
  from sklearn import metrics
  
  # Code starts here
  
  score = roc_auc_score(y_pred,y_test)
  print(score)
  y_pred_proba = grid.predict_proba(X_test)[:,1]
  print(y_pred_proba)
  
  fpr,tpr,th= metrics.roc_curve(y_test,y_pred_proba)
  roc_auc = roc_auc_score(y_test,y_pred_proba)
  print(roc_auc)
  plt.plot(fpr,tpr,label="Logistic model, auc="+str(roc_auc))
  
  
  # Code ends here
  
  
  