2. ML

 import pandas as pd

import matplotlib.pyplot as plt

data = pd.read_csv('diabetes.csv')

import numpy as np

x=data.iloc[:,:-1].values

y=data.iloc[:,-1].values

from sklearn.model_selection import train_test_split

xtrain, xtest , ytrain, ytest = train_test_split(x, y, test_size=0.25, random_state=0)

from sklearn.preprocessing import StandardScaler

sc_x = StandardScaler()

xtrain = sc_x.fit_transform(xtrain)

xtest= sc_x.fit_transform(xtest)

from sklearn.linear_model import LogisticRegression

classifier = LogisticRegression(random_state=0)

classifier.fit(xtrain, ytrain)

y_pred = classifier.predict(xtest)

from sklearn.metrics import accuracy_score

print ("Accuracy : ", accuracy_score(ytest, y_pred))