<reponame>Indrajeetja/ga-learner-dsmp-repo
# --------------
# 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.head()

X = df.iloc[:,:-1]
y = df.iloc[:,-1]
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)

# Check the value counts
y_train.value_counts(normalize=True)
# Code ends here


# --------------
# Code starts here

relation=X_train.corr()

# 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)
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)
# 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, _ = 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



# --------------
# 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.head()

X = df.iloc[:,:-1]
y = df.iloc[:,-1]
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)

# Check the value counts
y_train.value_counts(normalize=True)
# Code ends here


# --------------
# Code starts here

relation=X_train.corr()

# 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)
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)
# 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, _ = 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
# -------------- # import the libraries # Code starts here # Code ends here # -------------- # Code starts here # Check the value counts # Code ends here # -------------- # Code starts here # Code ends here # -------------- # Code starts here # Code ends here # -------------- # parameters for grid search # Code starts here # Code ends here # -------------- # Code starts here # Code ends here