import pandas as pd
import numpy as np                     # For mathematical calculations
import seaborn as sns                  # For data visualization
import matplotlib.pyplot as plt        # For plotting graphs
import warnings                        # To ignore any warnings
warnings.filterwarnings("ignore")

dataset=pd.read_csv("cancer.csv")
X = dataset.iloc[:, 1:31].values
Y = dataset.iloc[:, 31].values

#Encoding categorical data values
from sklearn.preprocessing import LabelEncoder
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)




# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)



#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)


#Using Logistic Regression Algorithm to the Training Set
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, Y_train)
#Using KNeighborsClassifier Method of neighbors class to use Nearest Neighbor algorithm
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, Y_train)

#Using SVC method of svm class to use Support Vector Machine Algorithm

from sklearn.svm import SVC
classifier = SVC(kernel = 'linear', random_state = 0)
classifier.fit(X_train, Y_train)

#Using SVC method of svm class to use Kernel SVM Algorithm

from sklearn.svm import SVC
classifier = SVC(kernel = 'rbf', random_state = 0)
classifier.fit(X_train, Y_train)

#Using GaussianNB method of naïve_bayes class to use Naïve Bayes Algorithm

from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, Y_train)

#Using DecisionTreeClassifier of tree class to use Decision Tree Algorithm

from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, Y_train)

#Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm

from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, Y_train)



Y_pred = classifier.predict(X_test)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_test, Y_pred)