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import sys
print('Python: {}'.format(sys.version))

import scipy
print('Scipy: {}'.format(scipy.__version__))

import numpy
print('numpy: {}'.format(numpy.__version__))

import matplotlib
print('matplotlib: {}'.format(matplotlib.__version__))

import pandas
print('pandas: {}'.format(pandas.__version__))

import sklearn
print('sklearn: {}'.format(sklearn.__version__))
# -

import pandas as pd
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pd.read_csv("C:/Users/Gray/Desktop/dataset/IRIS.csv")

print(dataset.shape)

print(dataset)

print(dataset.describe())

print(dataset.groupby('class').size())

dataset.plot(kind='box', subplots=True, layout=(5,1), sharex=False, sharey=False)
plt.show()

dataset.hist()
plt.show()

scatter_matrix(dataset)
plt.show()

array = dataset.values
X = array[:, 0:4]
Y = array[:, 4]
validation_size = 0.20
seed = 6
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size = validation_size, random_state = seed)

seed = 6
scoring = 'accuracy'


print(X_train.shape)





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