<jupyter_start><jupyter_text><jupyter_code>import numpy as np
import pandas as pd
import seaborn as sns
sns.set_palette('husl')
import matplotlib.pyplot as plt
%matplotlib inline

from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

data = pd.read_csv('diabetes.csv')
data.head()
data.info()
data.describe()
data['Glucose'].value_counts()
X = data.drop(['Pregnancies', 'SkinThickness'], axis=1)
y = data['Outcome']
# print(X.head())
print(X.shape)
# print(y.head())
print(y.shape)<jupyter_output>(768, 7)
(768,)
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