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May 13, 2019 14:21
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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) | |
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