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from sklearn.preprocessing import StandardScaler

import csv

import matplotlib.pyplot as plt

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

path = '/kaggle/input/pima-indians-diabetes-database/diabetes.csv'
raw = open(path, 'rt', encoding='utf8')
reader = csv.reader(raw, delimiter=',')
header = next(reader)
data = list(reader)
data = np.array(data).astype('float')

dataset = pd.read_csv(path)
dataset.shape

dataset.dtypes

dataset.corr()

dataset.skew()

import matplotlib.pyplot as plt

import seaborn as sns
corr = dataset.corr()

X = dataset.drop('Outcome', axis=1).values
y = dataset['Outcome'].values

from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
ss.fit(X)
X_ss = ss.transform(X)
print(X[0])
print(X_ss[0])