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

# In[1]:


# Kod 1
 
import pandas as pd
from sklearn.datasets import load_breast_cancer
 
from sklearn.neighbors import KNeighborsClassifier
 
from sklearn.model_selection import train_test_split
  
breast_cancer = load_breast_cancer()
X = pd.DataFrame(breast_cancer["data"], 
                 columns = breast_cancer["feature_names"])
y = pd.Series(breast_cancer["target"])
 
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size = 0.25, 
                                                    random_state = 42)


# In[2]:


# Kod 2

knn = KNeighborsClassifier()
knn.fit(X = X_train, y = y_train)

train_score = knn.score(X = X_train, y = y_train)
test_score = knn.score(X = X_test, y = y_test)

train_score, test_score


# In[3]:


get_ipython().run_cell_magic('time', '', '# Kod 3\n\n\nfrom sklearn.model_selection import GridSearchCV\n\nparam_grid = {\'n_neighbors\': range(1, 101),\n              \'weights\': ["uniform", "distance"],\n              \'p\': [1, 2]\n             }\n\nknn_grid_search = GridSearchCV(estimator = KNeighborsClassifier(), param_grid = param_grid, cv = 5, iid = False)\n\nknn_grid_search.fit(X = X_train, y = y_train)\n\ngrid_train_score = knn_grid_search.score(X = X_train, y = y_train)\ngrid_test_score = knn_grid_search.score(X = X_test, y = y_test)\n\nprint(grid_train_score, grid_test_score)\nprint(knn_grid_search.best_params_)\n')


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