import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# sns.set(style="whitegrid")
import warnings
from sklearn.svm import SVC, NuSVC
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.neighbors  import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
import xgboost as xgb
from scipy import stats
from scipy.stats import uniform, randint
from sklearn.metrics import f1_score
from sklearn.model_selection import KFold, StratifiedKFold, RepeatedStratifiedKFold
from sklearn.metrics import roc_curve, auc, accuracy_score
# from tflearn.data_utils import to_categorical
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.metrics import classification_report
from scipy import interp
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.decomposition import FastICA
from keras.utils import to_categorical
from sklearn.ensemble import VotingClassifier
from sklearn.metrics import roc_auc_score, accuracy_score


data = pd.read_csv("diabetes.csv")
data.shape
for i in range(8):
    temp_data=data.iloc[:,[i,8]]
    sns.set_style('whitegrid')
    pair_plot =sns.pairplot(data=temp_data,
                          height=2.1,
                          hue='Outcome',
                          diag_kind='kde', aspect=9.5/8.27)
    plt.show()