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

# In[1]:


# k-Fold Cross Validation
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd


# In[2]:


# Veri Setimiz
dataset = pd.read_csv('https://raw.githubusercontent.com/cagriemreakin/Machine-Learning/master/5%20-Model%20Selection/1%20-%20K-%20Fold%20Cross%20Validation/sosyal_ag_reklamlari.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values


# In[3]:


# Eğitim ve Veri Setine Ayırma
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)


# In[4]:


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


# In[5]:


# Kernel SVM
from sklearn.svm import SVC
classifier = SVC(kernel = 'rbf', random_state = 0)
classifier.fit(X_train, y_train)


# In[6]:


# Test Set Tahmini
y_pred = classifier.predict(X_test)


# In[7]:


# Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm


# In[8]:


# K-Fold Cross Validation
from sklearn.model_selection import cross_val_score
#Estimator'ımıza oluşturduğumuz modeli gönderiyoruz, cv : kaç parçaya bölüneceğini belirler.Genellikle 10 alınır.
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)
print("Ortalama değer (mean): %",accuracies.mean()*100)
print("std: %",accuracies.std()*100)


# In[9]:


# Eğitim Seti Görselleştirme
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Kernel SVM (Training set)')
plt.xlabel('Yas')
plt.ylabel('Maas')
plt.legend()
plt.show()


# In[10]:


#Test Set Görselleştirme
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Kernel SVM (Test set)')
plt.xlabel('Yas')
plt.ylabel('Maas')
plt.legend()
plt.show()


# In[ ]: