b-15-Random_Forest_Classification.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 15 04:18:20 2018

@author: regkr
"""

#1. kutuphaneler
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#2. Veri Onisleme

#2.1. Veri Yukleme
veriler = pd.read_csv('veriler.csv')
#pd.read_csv("veriler.csv")

x = veriler.iloc[:,1:4].values #bağımsız değişkenler
y = veriler.iloc[:,4:].values #bağımlı değişken
print(y)

#verilerin egitim ve test icin bolunmesi
from sklearn.cross_validation import train_test_split
x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=0.33, 
                                                  random_state=0)

#verilerin olceklenmesi
from sklearn.preprocessing import StandardScaler

sc = StandardScaler()
X_train = sc.fit_transform(x_train)
X_test = sc.transform(x_test)

from sklearn.linear_model import LogisticRegression
logr = LogisticRegression(random_state=0)
logr.fit(X_train,y_train)

y_pred = logr.predict(X_test)
print(y_pred)
print(y_test)

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test,y_pred)
print(cm)

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=1, metric='minkowski')
knn.fit(X_train,y_train)

y_pred = knn.predict(X_test)

cm = confusion_matrix(y_test,y_pred)
print(cm)


from sklearn.svm import SVC
svc = SVC(kernel='poly')
svc.fit(X_train,y_train)

y_pred = svc.predict(X_test)

cm = confusion_matrix(y_test,y_pred)
print('SVC')
print(cm)


from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)

y_pred = gnb.predict(X_test)

cm = confusion_matrix(y_test,y_pred)
print('GNB')
print(cm)


from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(criterion = 'entropy')

dtc.fit(X_train,y_train)
y_pred = dtc.predict(X_test)

cm = confusion_matrix(y_test,y_pred)
print('DTC')
print(cm)



"""
Random Forest birçok karar ağacını bir arada kullanarak karar veren bir
algoritmadır.
"""
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=10, criterion = 'entropy')
"""
# criterion için gini ölçüm yöntemi de kullanılabilir.
# n_estimators kaç tane karar ağacı oluşturacağını belirler. Daha sonra bu 
# karar ağaçlarının ortalamasından (ki burada farklı yöntemler kullanabiliriz)
# bir sonuç döndürür.
"""
rfc.fit(X_train,y_train)

y_pred = rfc.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
print('RFC')
print(cm)