model1-checkpoint.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas
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


# In[2]:


url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pandas.read_csv(url, names=names)


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print(dataset.head())


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print(dataset.shape)


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print(dataset.describe())


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


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dataset.plot(kind='box',subplots=True, layout=(2,2), sharex='True', sharey='True')
plt.show()


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dataset.hist()
plt.show()


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array= dataset.values
X=array[:,0:4]
Y=array[:,4]
validation_size=0.20
seed=7
X_train,X_test,Y_train,Y_test=model_selection.train_test_split(X,Y,test_size=validation_size,random_state=seed)


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seed=7
scoring='accuracy'


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models=[]
models.append(('LR',LogisticRegression()))
models.append(('LDA',LinearDiscriminantAnalysis()))
models.append(('KNN',KNeighborsClassifier()))
models.append(('CART',DecisionTreeClassifier()))
models.append(('NB',GaussianNB()))
models.append(('SVM',SVC()))

results=[]
names=[]
for name, model in models:
    kfold=model_selection.KFold(n_splits=10,random_state=seed)
    cv_results=model_selection.cross_val_score(model,X_train,Y_train,cv=kfold,scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg="%s: %f (%f)" % (name,cv_results.mean(),cv_results.std())
    print(msg)
    


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print(results)


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knn=KNeighborsClassifier()
knn.fit(X_train,Y_train)
predictions=knn.predict(X_test)
print((accuracy_score(Y_test,predictions))*100)
print(confusion_matrix(Y_test,predictions))
print(classification_report(Y_test,predictions))


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