# --------------
# 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('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(0.95)
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']
fig, axes = plt.subplots(2, 2, figsize=(10,10))

for i in range(2):
    for j in range(2):
        ax = axes[i,j]
        col = cols[i*2+j]
        sns.countplot(x=X_train[col],hue=y_train,ax=ax)
# 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,parameters)
grid.fit(X_train,y_train)
y_pred = grid.predict(X_test)

accuracy = accuracy_score(y_test,y_pred)
print(accuracy)
# Code ends here


# --------------
from sklearn.metrics import roc_auc_score, auc
from sklearn import metrics

# Code starts here
#y_scores = grid.decision_function(X_test)
score = roc_auc_score(y_pred,y_test)
y_pred_proba = grid.predict_proba(X_test)[:,1]
fpr, tpr,_ = metrics.roc_curve(y_test,y_pred)

roc_auc = roc_auc_score(y_test, y_pred_proba)
auc = auc(fpr,tpr)
plt.plot(fpr,tpr,label='Logistic model, auc='+str(auc))
# Code ends here


