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import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split,cross_validate,KFold,cross_val_score,cross_val_predict,GridSearchCV,RandomizedSearchCV
from sklearn.metrics import confusion_matrix,accuracy_score,classification_report,precision_score,roc_auc_score
from sklearn.datasets import load_iris,load_diabetes
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cluster import KMeans
from sklearn.naive_bayes import MultinomialNB

import warnings
warnings.simplefilter("ignore")
Get data
data = load_iris()
d = data.data
t = data.target
print(data.target_names)
['setosa' 'versicolor' 'virginica']
get shape