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Math for developers

Leandro Cruvinel Lemes leandrocl2005

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Math for developers
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leandrocl2005 / testePythonGist.py
Created March 9, 2019 21:12
Teste Python gists
import matplotlib.pyplot as plt
import pandas as pd
for i in range(50):
print(i)
# bibliotecas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Normalizer
import numpy as np
# remove warnings
import warnings
warnings.filterwarnings("ignore")
# bibliotecas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import Normalizer
import numpy as np
# remove warnings
import warnings
warnings.filterwarnings("ignore")
# bibliotecas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
# remove warnings
import warnings
warnings.filterwarnings("ignore")
# bibliotecas
from sklearn.datasets import load_iris
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# dataset
iris = load_iris()
# dataset to pandas dataframe
# bibliotecas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report
# remove warnings
import warnings
warnings.filterwarnings("ignore")
# bibliotecas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
# remove warnings
import warnings
warnings.filterwarnings("ignore")
# bibliotecas
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
# dataset
iris = load_iris()
# features e target
X = iris.data
print("Hello, world!")