ipynb.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd


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pima=pd.read_csv("/Users/sudipmajumder/Downloads/diabetes.csv")


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pima.head()


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X=pima[['BloodPressure','Insulin','BMI','Age']]


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y=pima.Outcome


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X


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# split X and y into training and testing sets
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)


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from sklearn.linear_model import LogisticRegression


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logreg = LogisticRegression()
# fit the model with data
logreg.fit(X_train,y_train)
#
y_pred=logreg.predict(X_test)


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# import the metrics class
from sklearn import metrics
cnf_matrix = metrics.confusion_matrix(y_test, y_pred)
cnf_matrix


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print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
print("Precision:",metrics.precision_score(y_test, y_pred))
print("Recall:",metrics.recall_score(y_test, y_pred))


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import matplotlib
import numpy as np
import matplotlib.pyplot as plt
y_pred_proba = logreg.predict_proba(X_test)[::,1]
fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)
auc = metrics.roc_auc_score(y_test, y_pred_proba)
plt.plot(fpr,tpr,label="data 1, auc="+str(auc))
plt.legend(loc=4)
plt.show()


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