magixer

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

#Classifiers
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.tree import DecisionTreeClassifier

magixer

'''
Evaluating accuracy of two different classifiers - Decision Tree and GaussianNB
from same prepared dataset
'''

from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
from sklearn.tree import tree

magixer

'''
Program to evaluate iris flower into its types - setosa, versicolor, virginica
using GaussianNB and rating the accuracy of the classifer
'''

from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

magixer

'''
A simple program to calculate if a tumor is malignant or benign and judging our trained gaussianNB classifiers accuracy
using scikit learn module accuracy_score
'''
from sklearn.datasets import load_breast_cancer # Importing cancer dataset
from sklearn.model_selection import train_test_split # Importing train_test_split module to split our bulk data into training data and testing data
from sklearn.naive_bayes import GaussianNB # Importing classifier called 'GaussianNaiveBayes'
from sklearn.metrics import accuracy_score # Importing scikit learn module to evaluate accuracy of our classifier model

#Storing imported data into 'data'

magixer

'''
The program predicts if a fruit is an apple or an orange from examining a
given data set of four fruits with two labels - apple or orange.
'''


import sklearn # Import SciKit Learn
from sklearn import tree # Import a classifier called 'DescisionTreeClassifier'

# Dataset of 4 fruits with 2 labels, apple and orange