1-kendi-calismam-siniflandirma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1582c16",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f2163d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>long_hair</th>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>15.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>11.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
       "0          1               11.8                 6.1          1          0   \n",
       "1          0               14.0                 5.4          0          0   \n",
       "2          0               11.8                 6.3          1          1   \n",
       "3          0               14.4                 6.1          0          1   \n",
       "4          1               13.5                 5.9          0          0   \n",
       "5          1               13.0                 6.8          1          1   \n",
       "6          1               15.3                 6.2          1          1   \n",
       "7          0               13.0                 5.2          0          0   \n",
       "8          1               11.9                 5.4          1          0   \n",
       "9          1               12.1                 5.4          0          0   \n",
       "\n",
       "   lips_thin  distance_nose_to_lip_long  gender  \n",
       "0          1                          1    Male  \n",
       "1          1                          0  Female  \n",
       "2          1                          1    Male  \n",
       "3          1                          1    Male  \n",
       "4          0                          0  Female  \n",
       "5          1                          1    Male  \n",
       "6          1                          0    Male  \n",
       "7          0                          0  Female  \n",
       "8          1                          1  Female  \n",
       "9          0                          0  Female  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1.adım: veri okuma\n",
    "df_gender=pd.read_csv(\"resources/resources/gender_classification_v7.csv\") \n",
    "df_gender.head(10) #ilk 10 satırını gösterir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e004f775",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5001 entries, 0 to 5000\n",
      "Data columns (total 8 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   long_hair                  5001 non-null   int64  \n",
      " 1   forehead_width_cm          5001 non-null   float64\n",
      " 2   forehead_height_cm         5001 non-null   float64\n",
      " 3   nose_wide                  5001 non-null   int64  \n",
      " 4   nose_long                  5001 non-null   int64  \n",
      " 5   lips_thin                  5001 non-null   int64  \n",
      " 6   distance_nose_to_lip_long  5001 non-null   int64  \n",
      " 7   gender                     5001 non-null   object \n",
      "dtypes: float64(2), int64(5), object(1)\n",
      "memory usage: 312.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# dataframe hakkında genel bir bilgi sahibi olalım\n",
    "df_gender.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93b3e73b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>long_hair</th>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
       "0         False              False               False      False      False   \n",
       "1         False              False               False      False      False   \n",
       "2         False              False               False      False      False   \n",
       "3         False              False               False      False      False   \n",
       "4         False              False               False      False      False   \n",
       "...         ...                ...                 ...        ...        ...   \n",
       "4996      False              False               False      False      False   \n",
       "4997      False              False               False      False      False   \n",
       "4998      False              False               False      False      False   \n",
       "4999      False              False               False      False      False   \n",
       "5000      False              False               False      False      False   \n",
       "\n",
       "      lips_thin  distance_nose_to_lip_long  gender  \n",
       "0         False                      False   False  \n",
       "1         False                      False   False  \n",
       "2         False                      False   False  \n",
       "3         False                      False   False  \n",
       "4         False                      False   False  \n",
       "...         ...                        ...     ...  \n",
       "4996      False                      False   False  \n",
       "4997      False                      False   False  \n",
       "4998      False                      False   False  \n",
       "4999      False                      False   False  \n",
       "5000      False                      False   False  \n",
       "\n",
       "[5001 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#nan alan kontrolü yapalım\n",
    "df_gender.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b1b844d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "long_hair                    0\n",
       "forehead_width_cm            0\n",
       "forehead_height_cm           0\n",
       "nose_wide                    0\n",
       "nose_long                    0\n",
       "lips_thin                    0\n",
       "distance_nose_to_lip_long    0\n",
       "gender                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender.isna().sum() #nan alan olmadığını gördük"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b58d2fba",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>long_hair</th>\n",
       "      <td>5001.0</td>\n",
       "      <td>0.869626</td>\n",
       "      <td>0.336748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <td>5001.0</td>\n",
       "      <td>13.181484</td>\n",
       "      <td>1.107128</td>\n",
       "      <td>11.4</td>\n",
       "      <td>12.2</td>\n",
       "      <td>13.1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <td>5001.0</td>\n",
       "      <td>5.946311</td>\n",
       "      <td>0.541268</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.4</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nose_wide</th>\n",
       "      <td>5001.0</td>\n",
       "      <td>0.493901</td>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nose_long</th>\n",
       "      <td>5001.0</td>\n",
       "      <td>0.507898</td>\n",
       "      <td>0.499988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lips_thin</th>\n",
       "      <td>5001.0</td>\n",
       "      <td>0.493101</td>\n",
       "      <td>0.500002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <td>5001.0</td>\n",
       "      <td>0.498900</td>\n",
       "      <td>0.500049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count       mean       std   min   25%   50%  \\\n",
       "long_hair                  5001.0   0.869626  0.336748   0.0   1.0   1.0   \n",
       "forehead_width_cm          5001.0  13.181484  1.107128  11.4  12.2  13.1   \n",
       "forehead_height_cm         5001.0   5.946311  0.541268   5.1   5.5   5.9   \n",
       "nose_wide                  5001.0   0.493901  0.500013   0.0   0.0   0.0   \n",
       "nose_long                  5001.0   0.507898  0.499988   0.0   0.0   1.0   \n",
       "lips_thin                  5001.0   0.493101  0.500002   0.0   0.0   0.0   \n",
       "distance_nose_to_lip_long  5001.0   0.498900  0.500049   0.0   0.0   0.0   \n",
       "\n",
       "                            75%   max  \n",
       "long_hair                   1.0   1.0  \n",
       "forehead_width_cm          14.0  15.5  \n",
       "forehead_height_cm          6.4   7.1  \n",
       "nose_wide                   1.0   1.0  \n",
       "nose_long                   1.0   1.0  \n",
       "lips_thin                   1.0   1.0  \n",
       "distance_nose_to_lip_long   1.0   1.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dataframe in matematiksel sonuçlarına bakalım\n",
    "df_gender.describe().T\n",
    "#min max değerler genel olarak yakın birbirlerine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a79c5dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns #İlgi çekici ve bilgilendirici grafikler çizmeyi sağlar\n",
    "import matplotlib.pyplot as plt #2D grafikler çizmek için kullanılır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cefa5015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Saç Uzunluğu</th>\n",
       "      <th>Saç Uzunluğu Miktarı</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Saç Uzunluğu  Saç Uzunluğu Miktarı\n",
       "0             1                  4349\n",
       "1             0                   652"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_graph=pd.DataFrame({\n",
    "    \"Saç Uzunluğu\":df_gender[\"long_hair\"].value_counts().index,\n",
    "    \"Saç Uzunluğu Miktarı\":df_gender[\"long_hair\"].value_counts().values\n",
    "})\n",
    "df_graph #dengesiz veriseti : İki veri arasındaki farkın büyük olması durumuunda ortaya çıkar.\n",
    "# ya 4349 u 652 yapacağız **burada veri kaybına uğrayabiliriz mantıklı değil**\n",
    "#ya da 652 yi 4349 yapacağız"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4800073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Saç Uzunluğu Dağılımı'}, xlabel='Saç Uzunluğu', ylabel='Saç Uzunluğu Miktarı'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Saç Uzunluğu Dağılımı\") #grafik başlığı\n",
    "sns.barplot(data=df_graph,x=\"Saç Uzunluğu\",y=\"Saç Uzunluğu Miktarı\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5377162b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>long_hair</th>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <th>gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>14.4</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>1</td>\n",
       "      <td>13.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>1</td>\n",
       "      <td>11.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>13.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>1</td>\n",
       "      <td>15.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
       "0             1               11.8                 6.1          1          0   \n",
       "1             0               14.0                 5.4          0          0   \n",
       "2             0               11.8                 6.3          1          1   \n",
       "3             0               14.4                 6.1          0          1   \n",
       "4             1               13.5                 5.9          0          0   \n",
       "...         ...                ...                 ...        ...        ...   \n",
       "4996          1               13.6                 5.1          0          0   \n",
       "4997          1               11.9                 5.4          0          0   \n",
       "4998          1               12.9                 5.7          0          0   \n",
       "4999          1               13.2                 6.2          0          0   \n",
       "5000          1               15.4                 5.4          1          1   \n",
       "\n",
       "      lips_thin  distance_nose_to_lip_long  gender  Female  Male  \n",
       "0             1                          1    Male       0     1  \n",
       "1             1                          0  Female       1     0  \n",
       "2             1                          1    Male       0     1  \n",
       "3             1                          1    Male       0     1  \n",
       "4             0                          0  Female       1     0  \n",
       "...         ...                        ...     ...     ...   ...  \n",
       "4996          0                          0  Female       1     0  \n",
       "4997          0                          0  Female       1     0  \n",
       "4998          0                          0  Female       1     0  \n",
       "4999          0                          0  Female       1     0  \n",
       "5000          1                          1    Male       0     1  \n",
       "\n",
       "[5001 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sözel verilerimizi sayısal veriye çevirelim\n",
    "df_gender[\"gender\"].unique()\n",
    "df_newGender=pd.get_dummies(df_gender.gender)\n",
    "df_gender=df_gender.join(df_newGender)\n",
    "df_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80d13b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>long_hair</th>\n",
       "      <th>forehead_width_cm</th>\n",
       "      <th>forehead_height_cm</th>\n",
       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>1</td>\n",
       "      <td>11.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>1</td>\n",
       "      <td>13.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>1</td>\n",
       "      <td>15.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      long_hair  forehead_width_cm  forehead_height_cm  nose_wide  nose_long  \\\n",
       "0             1               11.8                 6.1          1          0   \n",
       "1             0               14.0                 5.4          0          0   \n",
       "2             0               11.8                 6.3          1          1   \n",
       "3             0               14.4                 6.1          0          1   \n",
       "4             1               13.5                 5.9          0          0   \n",
       "...         ...                ...                 ...        ...        ...   \n",
       "4996          1               13.6                 5.1          0          0   \n",
       "4997          1               11.9                 5.4          0          0   \n",
       "4998          1               12.9                 5.7          0          0   \n",
       "4999          1               13.2                 6.2          0          0   \n",
       "5000          1               15.4                 5.4          1          1   \n",
       "\n",
       "      lips_thin  distance_nose_to_lip_long  Female  Male  \n",
       "0             1                          1       0     1  \n",
       "1             1                          0       1     0  \n",
       "2             1                          1       0     1  \n",
       "3             1                          1       0     1  \n",
       "4             0                          0       1     0  \n",
       "...         ...                        ...     ...   ...  \n",
       "4996          0                          0       1     0  \n",
       "4997          0                          0       1     0  \n",
       "4998          0                          0       1     0  \n",
       "4999          0                          0       1     0  \n",
       "5000          1                          1       0     1  \n",
       "\n",
       "[5001 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gender.drop(\"gender\",axis=1,inplace=True)\n",
    "df_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47113a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>nose_wide</th>\n",
       "      <th>nose_long</th>\n",
       "      <th>lips_thin</th>\n",
       "      <th>distance_nose_to_lip_long</th>\n",
       "      <th>Female</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>4997</th>\n",
       "      <td>11.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>12.9</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>13.2</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5000</th>\n",
       "      <td>15.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5001 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      forehead_width_cm  forehead_height_cm  nose_wide  nose_long  lips_thin  \\\n",
       "0                  11.8                 6.1          1          0          1   \n",
       "1                  14.0                 5.4          0          0          1   \n",
       "2                  11.8                 6.3          1          1          1   \n",
       "3                  14.4                 6.1          0          1          1   \n",
       "4                  13.5                 5.9          0          0          0   \n",
       "...                 ...                 ...        ...        ...        ...   \n",
       "4996               13.6                 5.1          0          0          0   \n",
       "4997               11.9                 5.4          0          0          0   \n",
       "4998               12.9                 5.7          0          0          0   \n",
       "4999               13.2                 6.2          0          0          0   \n",
       "5000               15.4                 5.4          1          1          1   \n",
       "\n",
       "      distance_nose_to_lip_long  Female  Male  long_hair  \n",
       "0                             1       0     1          1  \n",
       "1                             0       1     0          0  \n",
       "2                             1       0     1          0  \n",
       "3                             1       0     1          0  \n",
       "4                             0       1     0          1  \n",
       "...                         ...     ...   ...        ...  \n",
       "4996                          0       1     0          1  \n",
       "4997                          0       1     0          1  \n",
       "4998                          0       1     0          1  \n",
       "4999                          0       1     0          1  \n",
       "5000                          1       0     1          1  \n",
       "\n",
       "[5001 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#X ve y değerlerimizi ayarlayalım\n",
    "X=df_gender.drop(\"long_hair\",axis=1)\n",
    "y=df_gender[\"long_hair\"]\n",
    "df_gender=X.join(y)\n",
    "df_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f14f770",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler #varolan verileri kullanarak yeni veriler üretmemizi sağlar\n",
    "from collections import Counter #adet miktarını veren python sınıfı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98c4b669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eski Durum: Counter({1: 4349, 0: 652})\n",
      "Yeni Durum: Counter({1: 4349, 0: 4349})\n"
     ]
    }
   ],
   "source": [
    "sampler=RandomOverSampler()\n",
    "X_new,y_new=sampler.fit_resample(X,y) #verilerin sayılarındaki dengesizliği ortadan kaldırmaya yarar.Verileri birbirine uyumlu hale getirir.\n",
    "print(f\"Eski Durum: {Counter(y)}\")\n",
    "print(f\"Yeni Durum: {Counter(y_new)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e7da7f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#korelasyon durumlarını sıcaklık haritasıyla gösterelim\n",
    "df_gender=X_new.join(y_new) \n",
    "plt.figure(figsize=(14,8))\n",
    "sns.heatmap(df_gender.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7c82e6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#yeni verileri train ve test olarak bölmemizi sağlar\n",
    "#train: modeli eğitmek için kullanılan orijinal verilerin alt kümesidir\n",
    "#test: modelin doğruluğunu kontrol etmede kullandığımız veri kümesidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "afcffd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X_new,y_new,test_size=0.2,random_state=42)\n",
    "#test_size: verilerin yüzde kaçının test verisi olarak kullanılacağını belirlememizi sağlar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0bf921a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier #sınıflandırma yapmada kullanıyoruz\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "#accuracy_score: sınıflandırmada alt küme doğruluğunu hesaplar\n",
    "#confusion matrix: bir sınıflandırmanın doğruluğunu değerlendirmek için karışıklık matrisi yöntemi kullanılır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4bcbd3a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tree=DecisionTreeClassifier() #modelimizde DecisionTreeClassifier metodunun özelliklerini kullanabilmeyi sağladık\n",
    "model_tree.fit(X_train,y_train) #model_tree yi eğittik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "55969931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy Score=0.9192296636964645\n",
      "Test Accuracy Score=0.882183908045977\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train Accuracy Score={accuracy_score(y_train,model_tree.predict(X_train))}\")\n",
    "#train verilerle eğittiğimiz modeldeki verileri karşılaştırıp modelimizin doğruluk oranını veriyor\n",
    "print(f\"Test Accuracy Score={accuracy_score(y_test,model_tree.predict(X_test))}\")\n",
    "#test verilerle eğittiğimiz modeldeki test verileri karşılaştırıp modelin doğruluk oranını veriyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "48b176a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Çıkan veriler çok da yakın değil, karar ağacına bakalım\n",
    "from sklearn import tree #karar ağcını çizebilmek için "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a36a03b6",
   "metadata": {},
   "outputs": [
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       " Text(0.4225900681596884, 0.19642857142857142, 'gini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(0.42356377799415773, 0.23214285714285715, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.42453748782862705, 0.26785714285714285, 'gini = 0.0\\nsamples = 8\\nvalue = [0, 8]'),\n",
       " ...]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(model_tree) #karar ağacını çizer.\n",
    "#karar ağacı okunamadığından prune(budama) yapacağız"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c788cc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ccp_alpha: min maliyet karmaşıklık budaması için kullanılan karmaşıklık parametresi\n",
    "#en büyük maliyet karmaşıklığına sahip olan ve ccp_alpha dan küçük olan alt ağaç seçilecektir\n",
    "#prune (budama) işlemi default olarak yapılmaz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d083947f",
   "metadata": {},
   "outputs": [],
   "source": [
    "path=model_tree.cost_complexity_pruning_path(X_train,y_train) #herbir dallanma için kullanılan alpha değerlerini aldık\n",
    "alphas=path[\"ccp_alphas\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c60817d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_train,accuracy_test=[],[]\n",
    "for i in alphas:\n",
    "    model_prune=DecisionTreeClassifier(ccp_alpha=i)\n",
    "    #prune için gerekli ccp_alpha parametresine eğitme sınıfının kurucusunu aldı\n",
    "    model_prune.fit(X_train,y_train)\n",
    "    #prune edilmiş modeli eğittik\n",
    "    y_train_pred=model_prune.predict(X_train)\n",
    "    #X_train verileriyle y_train i tahmin ettiriyor\n",
    "    y_test_pred=model_prune.predict(X_test)\n",
    "    #X_test verileriyle y_test i tahmin ettiriyor\n",
    "    accuracy_train.append(accuracy_score(y_train,y_train_pred)) #bu dizilerin içerisine doğruluk skorlarını kaydettiriyoruz\n",
    "    accuracy_test.append(accuracy_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "46457e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jft1t58hEpDC5uibTzbarv+zmRHBwMJMnT85Suav3c3d3Jzg4mO7du/PPP//k6L4DBgzgww8/xN3dnfXr19OrV68cXSe35Ld48prdEh4XFxcaNGjA0qVL0x1funQpzZs3v+V7nZ2dKVOmDBaLhblz53LfffdhNtu9scqhhBT35Mdnm/FF7wYA/LD5OFGXM+8nJSKS28LDw9O2yZMn4+Pjk+7YRx99ZJM4xo4dS3h4OKGhocyZMwc/Pz/at2/PO++8k+1rXV3dPSoqip07d1Ks2K3nQUtMTMxp2HkST0Fn1yxh+PDhfPXVV8yYMYO9e/fywgsvEBYWxsCBAwFr/5vrFyvbv38/33zzDQcOHGDjxo307NmTXbt28e6779qrCg7NZDLRqca10VpHI2PtGI2IFCYBAQFpm6+vLyaTKd2xVatW0aBBA9zc3ChfvjxvvfUWycnXBlqMGTOGsmXL4urqSqlSpRgyZAgAbdq04dixY7zwwgtZWmnc29ubgIAAypYty1133cWXX37JqFGjGD16NKGhoWnlVq5cSePGjXF1dSUwMJCRI0emiycmJobHH38cT09PqlSpwqRJk2jTpg3Dhg1LKxMcHMy4cePo27cvvr6+DBgwgBUrVmAymbh48WJaue3bt2MymTh69GimMfft25du3bqlOzZs2DDatGmTrXhMJhMLFixIdx0/Pz9mzZp1yz+z/MquCU+PHj2YPHkyY8eOpW7duqxatYrFixdTrlw5wJrhXz8nT0pKChMmTKBOnTp06NCB+Ph41q1bR3BwsJ1qULh0/WwtoREx9g5DRO6UYUBirH22XBgE8eeff/LEE08wZMgQ9uzZwxdffMGsWbPSWl1+/vlnJk2axBdffMGBAwdYsGABtWrVAmD+/PmUKVMmreUmPDw82/cfOnQohmGkLWFx8uRJ7rnnHho1asR///3H1KlTmT59eroVyYcPH87atWv59ddfWbp0KatXr2br1q0Zrv3BBx9Qs2ZNtmzZwqhRo3Lyx5MlWY3Hkdh98dBBgwYxaNCgTM/dmEVWq1aNbdu22SAqud7LnarwwZ/W32Q6TV7FosEtqVna185RiUiOJcXBu6VuXy4vvHYKXDzv6BLvvPMOI0eO5MknnwSgfPnyvP3227zyyiu8+eabhIWFERAQQPv27XF2dqZs2bI0btwYgKJFi2KxWNJabnKiaNGilCxZMq2FZcqUKQQFBfHpp59iMpmoWrUqp06dYsSIEYwePZrY2Fhmz57Nd999x9133w3AzJkzKVUq499Bu3bteOmll9L2T5w4kaMYbyUmJibL8TgSdXyR23qubUXql/VL2+/55QbikzRqS0TsY8uWLYwdOxYvL6+0bcCAAYSHhxMXF8ejjz7K5cuXKV++PAMGDOCXX35J93gpN1w/onjv3r00a9Ys3eOxFi1acOnSJU6cOMHhw4dJSkpKS7oAfH19qVKlSobrNmzYMFfjzEx24nEkdm/hkYLhp4HNGf7jdhZuP8WlhGTWHz5H2yol7R2WiOSEs4e1pcVe975DqampvPXWWzz00EMZzrm5uREUFERoaChLly5l2bJlDBo0iA8++ICVK1fi7OycyRWz59y5c5w9e5aQkBAg8+lUrs5fZjKZ0r3OrMz1PD3Tt35dHZBzfdmbTbR7/XtuvPb178lqPNfHntV752dq4clNsefg5JVnoDERELETLhyFM/vSl7t6rgCxmE181LMevZta+1c9NXMTKamakFCkQDKZrI+V7LHdppNwVtSvX5/Q0FAqVqyYYbuaILi7u/PAAw/w8ccfs2LFCtavX8/OndbPXRcXF1JSct5K/dFHH2E2m9M6BlevXp1169alSw7WrVuHt7c3pUuXpkKFCjg7O7Nx48a089HR0Rw4cOC29ypRogRAur5G27dvv+17buybdP17shrPjdc5cOAAcXFxt405v1ILT26aXAuSYsErAC5lMlv0C3tgy0xYdWUtsEH/Qsmqto3xDjUKKcrXG44BMOGvUF7pXLDiF5GCb/To0dx3330EBQXx6KOPYjab2bFjBzt37mTcuHHMmjWLlJQUmjRpgoeHB19//TXu7u5pA2KCg4NZtWoVPXv2xNXV9ZbzuMXExBAREUFSUhJHjhzhm2++4auvvmL8+PFUrFgRsPZFnTx5MoMHD+b5558nNDSUN998k+HDh2M2m/H29ubJJ5/k5ZdfpkSJEpQsWZJRo0ZlaZRYxYoVCQoKYsyYMYwbN44DBw4wYcKEW76nXbt2fPDBB8yZM4dmzZrxzTffsGvXLurVqweQ5XjatWvHp59+StOmTUlNTWXEiBG50kJmL2rhyS2XzliTHcg82QGYVP1asgMwpUnex5XL7qsVSNUAbwC+WHWYiKh4O0ckIoVNp06dWLRoEUuXLqVRo0Y0bdqUiRMnpiU0fn5+TJs2jRYtWlC7dm3+/vtvfvvtt7R5ZsaOHcvRo0epUKFCWgvKzYwePZrAwEAqVqxI7969iYqK4u+//2bEiBFpZUqXLs3ixYvZuHEjderUYeDAgfTr14833ngjrczEiRNp1qwZXbp0oW3btjRr1ozq1avfdjFXZ2dnvv/+e/bt20edOnV4//33043+utmfz6hRo3jllVdo1KgRMTEx6aZ4yWo8EyZMICgoiLvuuovHHnuMl156CQ+PO38kaS92XS3dHu50tdWbijkNEyrn7L2Dt8L+JeBRHCp1AI+i1uOGASmJ4OSae3HmknYfruBwZCytKhVn9lONMZvvvJlaRPKGVkvPf2JjYyldujQTJkygX79+9g4n38STl6ul65FWbrmTYZaf1E+/3+ApSIiBg1dmoe4+B0Ja58qz79xSJ8iPw5GxrD4Qycj5O/jfI3Vu/yYRkUJq27Zt7Nu3j8aNGxMVFcXYsWMB6Nq1q+KxET3Syi2uXrcvU/fxrF1ry0zY9TPER1m3OV1h2zd3Fl8ue6trDVpUtDYP/7j5BPtPa0JCEZFb+fDDD6lTpw7t27cnNjaW1atX23UdyPwWT15TwpOb3jgDPb8Dj+vWI+lxXaJSudO1169HQP0ns37tX5+HBc9ZW37yAR83Z77t35SGVxYY3Xz0vJ0jEhHJv+rVq8eWLVu4dOkS58+fZ+nSpWmzPyse29Ajrdzk5ApV74UKd8OeBVCmERSrACYLGClQriUM2Q5GKji7wwMfQ8OnoGh5OBtq7cez+ha977d/AxePwRPz8k2/noblirL56AV2nYq2dygiIiI3pYQnLzi7QZ2e1/ZHHoPEOPAsZt2uV8o6TJCgxtbNtwwsegEe+MQ6p8+WmenLH10Nvw2FWo/A0jFQogo8Mj1Pq3Mr5YpZe+x/928YHar507R8MdxdLHaLR0RurpCNUZECKC//jSrhsQVXb+uWFQ2fhhoPgnsRKN/mWsLTdBCE/gEXjsB/31s3gNM7wc0XOo4DF9sPF6xZ6tqaWk/N2oSrk5mh7Svxf60r3HZ+CRGxjatzp8TFxeHu7m7naERuLjExEQCLJfd/cVbCkx+5W/vF4FcWHpkBniUhpBVUvQ9m3ZOx/ObpkJoElTpC8SpQIofD43OgVhlfFg1uyYh5O9h9KpqE5FT+tySUmqV8uavyree3EBHbsFgs+Pn5cebMGQA8PDz0C4nkO6mpqZw9exYPDw+cnHI/PdE8PAVJShK8faUHfad34c/XMi9Xpxc8+Lnt4gJSUg2ORMbSfuLKtGM7xnTEx63gzsop4kgMwyAiIoKLFy/aOxSRmzKbzYSEhODi4pLh3J1+fyvhKWg2z4Sw9dY+PuNusXhnz++sHahtbMG2kwz7YTsAlUp6MX9Qc7yV9IjkGykpKQV6AUhxbC4uLmnrod1ICU82FfiE53oHlkHoYkhJyDhPT+kG1gkLv+0OD30BAbYbbrjmQCRPTP8XgDH3V6dvixCb3VtERByTEp5scqiE53pRJ8C7FIy90v/HpxREn7p2fkyUTcN57499fL7yEABju9agT7Ngm95fREQcy51+f2viQUfhWwbMZmj1knX/+mQH4GIYpKbYLJx+La+16oxeuJsZa47Y7N4iIiI3UsLjaMo2y/z45Fqw7hObhVHC25VJPerg7WrtaT9t9WGSUlJtdn8REZHrKeFxNOXbQN0noPUIuPeGWZuXvWnTUB6sV4bfBrcEICIqntCI/LEshoiIFD5KeByNxQm6fQZtX4NG/TOet3GXrXLFPKgT5IcB3PfJGl75+T/ik2z3aE1ERASU8BQ+n7e0LnNhIyaTiWl9GqTt/7j5BMPmbrfZ/UVEREAJj+Mbvi/9/uld8El96ySGNlLS242/X2ydtr9kdwTPzNlMamqhGiAoIiJ2pITH0fkEwugL6Y/FhEP4dpuGUaGEF9tGdUjb/2vPaT76+4BNYxARkcJLCU9hkNmslRE7bR5GEU8XZj/dKG1/9YGzNo9BREQKJyU8hcWbF6HF0Gv7Zvss99C6cklG31cdgK1hFwmPumyXOEREpHBRwlNYmEzQYSxU7mzdXzPRbqF0qO6f9rrZ+H84ccF2nahFRKRwUsJT2MReeYx0/jAcW2+XEIKKelC7jG/afsv3lzNy3g67xCIiIoWDEp7CpkHfa69ndoYLRyHmNPz+Epw7ZLMwfn2+Jc+2Lp+2P3fTccLOqaVHRETyhhKewqbmI+n3P6oDEyrDpmnW4eo2XG/rmVblGdu1Rtr+lBUHbXZvEREpXJTwFDaW23RW/vZRm83GXMzLlT7Ngpn3f9b1v+ZuOs6In3cwedl+EpI1G7OIiOQeJTyFjcUZ7nrl5ucP/Q3zMlmSIg81KFeUZuWLAfDD5uNMXnaAP3ZG2DQGERFxbEp4CqMmzwKmm5/f9TMkxdssHICPetZN16dn2A/bbXp/ERFxbEp4CiPP4jDiCLR9HUwWeGI+WFzSlzmx0dqZ2UaPt0r6uPFql2rULOWTdqzTpFWcvKh5ekRE5M4p4Sms3ItA61dg1FmoeDc89y/c8+G187Pvt3ZmXvk/m4Y1qUfdtNehp2NYvCPcpvcXERHHpISnsDNbrD+LlofGA6BM4/TnV7wLXz8IF47ZJJxK/t78PqRl2v47i/dqYkIREbljSngkve6zwbtU+mOH/oEfnrBZCDVK+fJxr3pp+10mr+ajZQdITE61WQwiIuJYlPBIej6l4MW9UL5N+uMRO2zWnwegS80ABraugL+PKzEJyUxatp/+czbb7P4iIuJYlPBI5ho/k/FYYqzNbu9sMTOyS1XWjbybmqWtHZlX7T9LSqrtki4REXEcSngkc75lMh5LvGTzMCxmE4PaVEzbX3Mw0uYxiIhIwaeERzIXWAe6z4GBa68d++MWExbmoXtqBdK9oTUB23jknF1iEBGRgs3J3gFIPla9a/r9PQvh/BEoGmLzUMoU8QDgs+WHSEoxeO2eajaPQURECi618Ej2HN9ol9tazNdmhv5y1WF2noiySxwiIlIwKeGR2+v+9bXXO3+ySwhNr6y1ddX9n67h3KUEu8QiIiIFjxIeub2yTa+9PrgULl+weQgNyhXhz2F3Max9pWvHxi3juW+3sunoeZvHIyIiBYsSHrk9r5Lp998PhktnbR5GlQBvht5diUolvdKO/b4znO6fr+fYOdsNmRcRkYJHCY9kzf0fp98PXWyXMEwmE9OfbMRHPeuyaHBLinu5YACntMioiIjcgkZpSdaUqJJ+P/qkfeIAyhbzoGwx66itQF93Ii8lsutkNM0qFLdbTCIikr+phUeypnjl9PtRJyEl2T6xXKeYlwtgXWRUj7VERORmlPBI1ngUhZcPXdvf/g28XQzi7Nth+IX21xKx33eG2zESERHJz5TwSNZ5Fod6N6yavnoCJCfaJx6gTpAfz9xVHoD/LQllzK+77RaLiIjkX0p4JHs8bxixtf5T+OsN+8RyRf9W12Z+nrXuKHGJ9n/UJiIi+YsSHskecyb93Dd+Yfs4rlPS242fBzZL25+26ogdoxERkfxICY9kT61HMjloyuSYbTUMLkrLitZRWpOW7WfXSS09ISIi1yjhkewpUQVeDL3hoAERu+wSzvWu9uUBGPf7HgzDsGM0IiKSnyjhkezzDoDmQ8DN79qx73vafcRWq0rFeaBOKQA2HD7P0Lnb2XLsAvtPx3Dq4mUuJahvj4hIYWUyCtmvwdHR0fj6+hIVFYWPj4+9wynYUlNhens4ueXasTq94MHP7RcT8PWGY7z1626SU9P/0zaZYHj7ygy+u9JN3ikiIvnVnX5/q4VHcs5szjhM/b/vYYyvdVv3qV3C6t20HN/0b0Kj4CKULepBUU8XnMwmDAMmLN3P7lPq3yMiUtgo4ZE7Y3a++bm/XrddHDdoWr4YPw1szqpX2rJ1VAfef7h22rmPlh2wW1wiImIfSnjkzrj73fp8QoxNwrgdJ8u1kWQeLhY7RiIiIvaghEfuTOUu0HokPDwd3Hwzno/cny/W3DKbriU8C7afYucJPdYSESlMlPDInbE4QdtXrfPzjAyDQRugyj3Xzk9rBx/VhssX4behcHSNXcK8sWd+7xn/8uGfoVyItd+yGCIiYjsapSV548MqcCki83NjbN+6EhWXRJ2xf2V67vMnGtC5ZoCNIxIRkezQKC3Jn24cvXW94xttF8cVvh7ObHz9bib3qMtPA5vxWJOyaeeGzN1m83hERMS2lPBI3mg9AjxLZH5uegdIuGTbeLCuudWtXmkaBRdlROeqacdTUgtVI6eISKGkhEfyhpMLDNt58/NxkbaLJRO+7s482qAMALXLZNLZWkREHIoSHsk7zu7Qd3Hm5/LBcPV7agcCkJicaudIREQkrynhkbwV3AK6fgaNn4UGT107/utgsHN/eVeL9Z//7lPRdo1DRETynt0TnilTphASEoKbmxsNGjRg9erVtyz/7bffUqdOHTw8PAgMDOSpp57i3LlzNopWcqTeE9BhLNzzAZSsYT12ahuE3qT1x0ZM183NE3kpwY6RiIhIXrNrwvPDDz8wbNgwXn/9dbZt20arVq3o0qULYWFhmZZfs2YNffr0oV+/fuzevZuffvqJTZs20b9/fxtHLtnm7AYWZ3Avcu3Yyv9ZFyD9rqd1jh4bK1PEPe31yQuXbX5/ERGxHbsmPBMnTqRfv37079+fatWqMXnyZIKCgpg6dWqm5Tds2EBwcDBDhgwhJCSEli1b8uyzz7J582YbRy455l/j2uvw7TCvH+z/A7bMgpQkm4YSVNQj7XViivrxiIg4MrslPImJiWzZsoWOHTumO96xY0fWrVuX6XuaN2/OiRMnWLx4MYZhcPr0aX7++Wfuvffem94nISGB6OjodJvYUad3oFjFa/u75197ffmizcOpE+QHwLFzcTa/t4iI2I7dEp7IyEhSUlLw9/dPd9zf35+IiMxn6G3evDnffvstPXr0wMXFhYCAAPz8/Pjkk09uep/x48fj6+ubtgUFBeVqPSSbLM5Q97HMz12+YNtYgNqlrUPSX/rpP6LibNvCJCIitmP3TsvXdxwFMAwjw7Gr9uzZw5AhQxg9ejRbtmxhyZIlHDlyhIEDB970+q+++ipRUVFp2/Hjx3M1fskBZ4/Mj1++YO3MbMMh6w/WL532evGucJvdV0REbMtuCU/x4sWxWCwZWnPOnDmTodXnqvHjx9OiRQtefvllateuTadOnZgyZQozZswgPDzzLytXV1d8fHzSbWJn9Z/M/Pise+HLNrDwOduFUrYII7tYZ13+YdNxCtnSciIihYbdEh4XFxcaNGjA0qVL0x1funQpzZs3z/Q9cXFxmM3pQ7ZYLAD6oipIXDxg9AUYsByeXQWNroyyS73ySGnPQps+3upatxQWs4ntxy/Sb/ZmTl7UiC0REUdj10daw4cP56uvvmLGjBns3buXF154gbCwsLRHVK+++ip9+vRJK3///fczf/58pk6dyuHDh1m7di1DhgyhcePGlCpVyl7VkJwwm6F0fQisA4F1M57//ib9fPJAoK87Y7vWwNli4p99Z+j66VpCI+w/E7SIiOQeJ3vevEePHpw7d46xY8cSHh5OzZo1Wbx4MeXKlQMgPDw83Zw8ffv2JSYmhk8//ZQXX3wRPz8/2rVrx/vvv2+vKkhuCKyT8VhY5iP18srjTcrRJKQoz3+3jX0RMXSavIpXu1Tl2dYVbBqHiIjkDZNRyJ4FRUdH4+vrS1RUlPrz5BcpSfB28YzHx0TZPJSouCTqjP0rbf/wu/dgNmfeiV5ERGznTr+/7T5KSwSLM7Qfk/6Yk3umRfOar4czLSteS77Kv7aYNQfsu7K7iIjcOSU8kj+0GAb9lsIzK68cMCAl2S6hfNKrXrr9Z7/WTN4iIgWdEh7JH0wmCGoMAbWt620lx8ORlbd/Xx4o4ulCx+rXpkaITUzh2LlYu8QiIiK5Q314JP+Z+zjsW2R9bXayrrbe/i0wUq3J0E0mpsxNcYnJ9Jm+kc3HrMPj3ZzNlC/uhdkMvRqXxcvViUbBRSnlZ59HbyIihc2dfn8r4ZH8Z/9f8N2jmZ9r9rx1PS4bOX4+jhHzdrDu0LlMzx997+bruImISO5Rp2VxPJU7Qt/fMz+3/lObhhJU1INv+zdh/qDmzH66cYbzZ2LibRqPiIjkjBIeyZ+CW8KTi+Clg/aOBJPJRP2yRWhduQQvd6qS7tzCbafsFJWIiGSHEh7Jv0JagVcJaPNa+uMzOtttBNdzbSvy+j3V0vbfWbyXLcfOk5paqJ4Mi4gUOEp4JP+762Xwvm7pkLD18HYxOHfILuE80bRcuv2Hp67n438O2CUWERHJGiU8kv+ZzTBsB9R4KP3xX56FPb9aR3X9NhQuX7RJOO4uFla81CbdsaORGrYuIpKfKeGRgsHiDC2HpT92YhP82Ns6hH3LLAhdbLNwgot7cvS9e+nRMAiA83FJJCan2uz+IiKSPUp4pOAIrAMPT7+271kS/Mpe20+0fStLg+AiAKzaf5ZHv1hPTHySzWMQEZHbU8IjBUutR2DIdhiyDV4+AIO3QpV7rOe2zoHUFJuG80CdUgxuVxFfd2f+O36Rp2dtIj7JtjGIiMjtKeGRgqdoCBQtb31tcQZXb+vriB3wzzibhuLmbOHFjlX4tn8TvN2c2HT0An/ujrBpDCIicntKeMQBXLfUxJqJYIfJw2uW9uX+OtaRZAfPXLL5/UVE5NaU8EjBl3rDnDwbp9kljGoB1pam7/4Ns8v9RUTk5pTwSMFXv3f6/f++t0sYLSoWB+BcbCKHzqqVR0QkP1HCIwVf+Tbwxlko08i6f2obRNt+yYeQ4p5pr++esJIDp2NsHoOIiGROCY84BicX6L8MgpoCBuxeYPMQTCYTEx6tk7Y//o99pGjJCRGRfEEJjziW8q2tPyND7XL7hxuU4akWwQD8s+8M9368mrUHI+0Si4iIXKOERxyLs4f1Z3Ji+uPJida1t2wwT8/wDpV5vm1FfNyc2BcRw+Nf/cuMNUfy/L4iInJzTvYOQCRXOblaf57aZl1u4twhOL7Rup+SAM2HQMe38zQEbzdnXupUhX4tQ3h/yT7mbjrO2EV78HF35pEGZfL03iIikjklPOJYfEpbf57da11Q9EZnbfeoq4inC+MfqsXlpBQWbj/Fl6sO0blmAF6u+m8nImJr+uQVx1LtfnjsJziyEiJ2WmdlLtMYok7AinfBsO2yDyaTiSF3V+L3HeHsP32JRuOWUb2UDz0bBdEouCjB143sEhGRvKOERxyLyQSVO1q36/031/rTxmttAVQo4cXknnWZ8Nd+jkTGsuXYBbYcu4CT2cTHverRpWYAJpPp9hcSEZEcU6dlKRxMFuvPw8vh9O5rxy+dhdTUPL/9fbVL8c+Lrfnfw7Xp0TCIMkXcSU41GPTtVrp9tpaLcYm3v4iIiOSYEh4pfKY2hz9Gwr7f4cOK8NfrNrmtyWSie6Mg3n+kNsuGt2ZAqxAA/jsRxfjF+2wSg4hIYaWERwqHC0fT7/87FeY+Zn29YYrNw3FztvD6vdX5pl8TAH7YfFzz9YiI5CElPFI4OLnYO4JMtaxUnF6NgwBYsO2knaMREXFcSnikcGj4NBQJvrZ/78Rrr128bR7O9TpU9wfgtx2nCDsXZ9dYREQclRIeKRxcvWHwNmg9Ep6YD436QZ0rj7Qs9h2s2LJiCUKKexKflMoPm8O4lJCMYWgNLhGR3KSERwoPsxnavgoV77buN3za+vPyBTi9x25huTiZuadWAACfLT9EzTf/pN7bS9ly7ILdYhIRcTRKeKTwCqh57XXoYvvFAdxTK5AKJTxxc7b+l7wYl8SSXeF2jUlExJEo4ZHCy9n92utd8+0XB1CjlC9/v9iGfW934YX2lQE4d0lz84iI5BYlPCIAZ3ZDPuk34+lqnSQxNZ/EIyLiCJTwSOHW45trr88dtF8c1zFfWWYiRfmOiEiuUcIjhVu1+6+tsL5pun1jueJqC09kTIKdIxERcRy5nvBcvnw5ty8pkrcsztaf/06FNZPtGgpA3aAiAGw+dp7v/g3TEHURkVyQo4Tnueeey/R4bGwsXbp0uaOARGyu0YBrr5e9CZ81gd9fslufnsr+XnSs7k9SisFrv+zkm3/D7BKHiIgjyVHC89dff/HGG2+kOxYbG0vnzp1JSUnJlcBEbKb58zAmCtqPse6f3QebpsHe3+wSjslk4vMnGvBs6/IATF992C5xiIg4khwnPDNnzmTSpEkAxMTE0KFDB0wmE0uWLMnVAEVspuUL8NwmqNDOur9hqt1CMZtNDGpdEYCj5+KoN/YvGr+zjJHzdnApIdlucYmIFFQ5mlM/JCSEP//8kzZt2mA2m5k7dy6urq78/vvveHp65naMIrZTojK0HgGH/oGwdbDkVeg83i6h+Ho407BcETYfu8CFuCQA5m46jruLhTfvr2GXmERECiqTcQc9Ijds2ED79u1p0qQJixYtwt3d/fZvsrPo6Gh8fX2JiorCx8fH3uFIfpSaAu+HQEKUdf/Ni3BlqLitJaWkcjQyFoDloWd4d/E+AL7t34QWFYvbJSYREXu40+/vLLfw1KtXD1MmH/qurq6cOnWKFi1apB3bunVrtgMRyTfMFhi8GT6sZN0/thaCW9olFGeLmUr+1tXcK5b0YvKyA8QlpvDV6sNKeEREsiHLCU+3bt3yMAyRfMarJJRtCmEb4JuHYeBaKF7RriGZTCbGP1SLoXO3c+xcnF1jEREpaLKc8Lz55pt5GYdI/vPQNPj6ITh3AJaOgq6fwf4/IagxFKtgl5AalLPO0XP8QhwpqQYWs30etYmIFDSaaVnkZvzKQs/vwGSxrqY+oSosGAjzB9z+vXkk0NcdZ4uJpBSD8ChN8ikiklU5SnjMZjMWi+Wmm4jDKFEZGl9JcFKuLPVwcitcvggJMZBo20dLFrOJoKIeAExbdZj9p2M0E7OISBbkaFj6L7/8km4/KSmJbdu2MXv2bN56661cCUwk32j/FhSrCP41YcH/wYUj8H4567nSDaHbFCgSAk4uNgmndmlfDp+NZfb6Y8xef4wS3q4MaBXCM3fZ5zGbiEhBcEfD0m/03Xff8cMPP7Bw4cLcumSu07B0uSMbp8HilzIeb/s6tH7FJiFcTkzht/9O8ceucNYePEdiSioAk3rU4cF6ZWwSg4iIrd3p93euJjyHDh2idu3axMbG5tYlc50SHrljG6bCkpHpjzl7wOvhNg8lPimFScv288XKw7g5m/llUAuqBerftYg4njv9/s61TsuXL1/mk08+oUwZ/YYpDq5+H/Aolv5Y6QZ2CcXN2cIrnapyV+USxCelMvCbLURdTrJLLCIi+VmOEp4iRYpQtGjRtK1IkSJ4e3szY8YMPvjgg9yOUSR/cfGEVw7Dy4cg5C7rsaOrYdNXdgnHYjbxUY+6lCnizrFzcQz/YTupqerILCJyvRw90po1a1a6WZfNZjMlSpSgSZMmFClSJFcDzG16pCW5bsV7sGI8mMzQ4xuoeq9dwth1MoqHpq4jMTmVlztV4bm29p0oUUQkN+WrPjwFgRIeyXWGAb8Nga1zwMkN+i+DgFp2CeX7jWG8On8nxb1c2fxGe7vEICKSF2y2ltaOHTuyfNHatWtnOxCRAstkgnsnwcXjcHg5bPsGurxvl1DaVS0JwIW4RAzDyHT9OxGRwijLCU/dunUxmUy3neTMZDKRkpJyx4GJFCgWJ6j1qDXhOb3bbmH4uDkDkJJqEJuYgpdrjqbaEhFxOFn+NDxy5EhexiFS8JWsZv0ZnvXW0Nzm5mzG1clMQnIqT8/cxJgHalC9lB7diohkOeEpV65cXsYhUvC5X+mwnxAFl86CVwmbh2AymXjtnmqM/2MvG4+e575PVvNYk7K82KEKRTxtMxO0iEh+lKP27jlz5tzyfJ8+fXIUjEiB5lf22uvTu8CrrV3CeLJ5MO2r+/Pu4r38viOcbzaE8dt/4bzQvhK1g/wI9HUj0NfdLrGJiNhLjkZp3Tj0PCkpibi4OFxcXPDw8OD8+fO5FmBu0ygtyVMz74Fja6HrZ1DvCXtHw/pD53jrt93si4hJO+ZiMbNseGvKFvOwY2QiItljl5mWL1y4kG67dOkSoaGhtGzZku+//z4nlxRxDP41rD//GAHnD9s3FqBZhWIsGtySsV1rUNnfC4DElFS++feYnSMTEbGtXFtaolKlSrz33nsMHTo0ty4pUvC0GwX+tSDxEhxYZu9oAHCymOnTLJi/XmjNF72tS2BMW32YNQci7RyZiIjt5FrCA2CxWDh16lRuXlKkYHHzgeAW1tfrPoF9v0N81LXzCZesm510qhFAr8ZBGAY8991WXv9lJ4t3hnMhNtFuMYmI2EKO+vD8+uuv6fYNwyA8PJxPP/2UoKAg/vjjj1wLMLepD4/kuYvHYc4D1x5pOXvA3aOhYT/4tCFcOg3lWoDZYl2EtH4fKNfcZuFdTkzhkc/XsftUdLrjL3aozOC7K9ksDhGR7LDL0hJmc/qGIZPJRIkSJWjXrh0TJkwgMDAw24HYihIesYm487B6AmyaDsmXb1/+6T+hbNO8j+uK+KQUVu0/y7pD5/ju3zASU1IBeO+hWvRsXPY27xYRsT2tpZVNSnjEplKS4IfesP+GVs/q3SAxFg4ute7fOwEa9bd5eADR8UncPWElZ2MSAHjnwZo83kTzbolI/mLzUVpJSUmUL1+ePXv2ZPtmIoWOxRl6fQ/dv7YmOU0GwtD/oPtseOJnaDHMWm7VhxATYZcQfdyc+ffVu3mqRTAAr/+yi683aBSXiDiWbE886OzsTEJCghYlFMkqkwmqP2DdbtTqRdi3CM4dhAlVoN8yCGpk8xDNZhOj76uOk9nEtNVHGLVgF85mkx5viYjDyNEorcGDB/P++++TnJx8xwFMmTKFkJAQ3NzcaNCgAatXr75p2b59+2IymTJsNWrUuOM4ROzCzcfa+nPV0VV2C+XqshTP3lUegAlL99stFhGR3JajhOfff/9l/vz5lC1blk6dOvHQQw+l27Lqhx9+YNiwYbz++uts27aNVq1a0aVLF8LCwjIt/9FHHxEeHp62HT9+nKJFi/Loo4/mpBoi+YN/dWj6nPX1kdUQsQvs1LXOZDIxqE1FAM7GJBCflGKXOEREcluO1tLy8/Pj4YcfvuObT5w4kX79+tG/v7Wz5uTJk/nzzz+ZOnUq48ePz1De19cXX1/ftP0FCxZw4cIFnnrqqTuORcSuiluTDA4vh89bQPHK0GsuFKtg81B83J3wdLEQm5jCyYuXqVDCy+YxiIjkthwlPDNnzrzjGycmJrJlyxZGjhyZ7njHjh1Zt25dlq4xffp02rdvf8uV3BMSEkhISEjbj46OvmlZEbup/yQ4e8KehRD6O0Tuhw1TrKO3bMxkMlGumCd7wqM5GhmrhEdEHEKuzrScHZGRkaSkpODv75/uuL+/PxERtx+tEh4ezh9//JHWOnQz48ePT2sZ8vX1JSgo6I7iFskTZgvU6QG9voO2r1uPJcTc+j15KKSEJwBHImPtFoOISG7KUQtPvXr1Mh2lZTKZcHNzo2LFivTt25e2bdve9lo3XscwjCyNAJs1axZ+fn5069btluVeffVVhg8fnrYfHR2tpEfyN5crLSpJWZiwMI+UL25NeA4r4RERB5HlFp7u3buzYcMGADp37szhw4fx9PSkbdu2tGnTBi8vLw4ePEijRo0IDw+nffv2LFy48KbXK168OBaLJUNrzpkzZzK0+tzIMAxmzJhB7969cXFxuWVZV1dXfHx80m0i+Zp7EevPvb/C1w9C1Ambh1D+SgvP4bP2W/dLRCQ3ZTnhue+++3jgAes8IpGRkbz44ousXr2aCRMmMHHiRFatWsWIESOIjY3lr7/+4o033uDtt9++6fVcXFxo0KABS5cuTXd86dKlNG9+63WFVq5cycGDB+nXr19WwxcpOKp3hfJtrK8P/QPbv7N5CCHFra1Mh87GkppaqCZjFxEHla0WnnPnznHp0iV+/PFHevXqlaHMY489xo8//ghAr169CA0NveU1hw8fzldffcWMGTPYu3cvL7zwAmFhYQwcOBCwPo7q06dPhvdNnz6dJk2aULNmzayGL1JwuHhAn4XQ+kqH/nOHbB5C+RKeOJlNnI1J4MGp69gadsHmMYiI5KYsJzx9+/alVatWeHl54ebmlulIqjVr1uDm5gZAamoqrq6ut7xmjx49mDx5MmPHjqVu3bqsWrWKxYsXp426Cg8PzzAnT1RUFPPmzVPrjji+ElWsP0/vtq67lZxw6/K5yMfNmXcfrIWni4X/jl/koSnrmLfF9o/WRERyS5Y7LT/xxBN06NABsM60PHDgQLZs2UKjRo0wmUxs3LiR6dOn8+qrrwLw559/Uq9evdted9CgQQwaNCjTc7NmzcpwzNfXl7i4uKyGLVJwBdax/jy9E94tBZisj7vumwQeRfP89t0bBdGmagk6TFxF1OUkFu04xcMNyuT5fUVE8kKOV0v/9ttv+fTTT9MeW1WpUoXBgwfz2GOPAXD58uW0UVv5iVZLlwLl+14Qujj9sZC7oPdCMNtmVolV+8/SZ8ZGXJ3MBBfzzLSMyQTVS/nQunIJWlUqQVHPWw8mEBHJrjv9/s5xwlNQKeGRAifxytDwY+vg20cBA5o9D53escntz8cm0vy9v4lPSs1SeZMJWlcuwdC7K1GvbJE8jk5ECos7/f7O0Tw8V23ZsoW9e/diMpmoXr16lh5hiUg2uVxpVanUAco2g7B1cGKTzW5f1NOFv19sw7FbzMlzOSmFjUfOs3L/WfZFxLAi9Cybjpxn/Wt34+PmbLNYRURuJkctPGfOnKFnz56sWLECPz8/DMMgKiqKtm3bMnfuXEqUKJEXseYKtfBIgXZqG3zZBjxLwssH7B1Npo5ExtJv1iYOR8bSv2UIr99bLUuTiYqI3Mqdfn/nqBPA4MGDiY6OZvfu3Zw/f54LFy6wa9cuoqOjGTJkSE4uKSJZUSTE+jP2zLVHXflMSHFPnm9nXQz1qzVHePPX3ZrLR0TsLkcJz5IlS5g6dSrVqlVLO1a9enU+++wz/vjjj1wLTkRu4O4Hbn7W1xeO2jGQW3uofhne7loDkwnmrD/GsB+2k5SStT5AIiJ5IUcJT2pqKs7OGZ/LOzs7k5qqDzWRPFX0SivPoX/SH4+PssskhTfTu1kwk3vUxcls4tf/TvHMnM1cTkyxd1giUkjlKOFp164dQ4cO5dSpU2nHTp48yQsvvMDdd9+da8GJSCZqPGj9uXQ07PkVUpJgw+cwuRZ8Uh+Wj4d88otH17qlmdanIW7OZpaHnuWZrzerpUdE7CJHnZaPHz9O165d2bVrF0FBQZhMJsLCwqhVqxYLFy6kTJn8OzmZOi1LgWcY8NsQ2DoHzE7g6gOXz6cv41EcilUEz+LWzaM4eBSzlnd2g5oPXxv9ZQObjp7nyRkbiUtMoUfDIN57uJY6MotItth1Hp5ly5axd+9eDMOgevXqtG/fPqeXshklPOIQUlNgXj/Y/Yt136MYtBsFZgv8/iKkJN76/XV6wYOf532c1/l772kGzNlMqgGvdK7CoDYVbXp/ESnYbJ7wpKamMmvWLObPn8/Ro0cxmUyEhITwyCOP0Lt373z/W5sSHnEYhgFnQyEpzrru1tUWm0tnIXI/xEVC7FmIPWd9HXfO+vhr72+AAU/+Zp212YZmrzvKm7/uBqBsUQ9u/LhwtpipWcqHBuWK0LR8MSr5e9s0PhHJv2ya8BiGwf3338/ixYupU6cOVatWxTAM9u7dy86dO3nggQdYsGBBtoOwJSU8UugtGg6bp4NvWajRDXxKg08pKFoeAmrm+e3f+X0P01YfyVLZz59oQOeaAXkckYgUBDadaXnWrFmsWrWKv//+m7Zt26Y7988//9CtWzfmzJlDnz59sh2IiNjI3aNh3yKICoN1H6c/d//H0ODJPL396/dWp0ejskRdTspw7lJCMtvCLrBg20mOnotjT3i0Eh4RyRXZSni+//57XnvttQzJDlhHbo0cOZJvv/1WCY9IfubuB8+sgF3zIPoURJ+Ec4etq7IvGwPVHwD3vF0Dq2JJr5uea125BAnJqUxdcYiY+IxJkYhITmRrWPqOHTvo3LnzTc936dKF//77746DEpE85lMKmg+GzuOh+xxrAlSimnW01/Lx9o4Obzfr72Ix8cl2jkREHEW2Ep7z58/j7+9/0/P+/v5cuHDhjoMSERuzOEGX96yvN30Fp/fYNRzvKwuOqoVHRHJLthKelJQUnJxu/hTMYrGQnKzfyEQKpPJtoOp9YKTAkpHWUWB24qMWHhHJZdnqw2MYBn379sXV1TXT8wkJCbkSlIjYSad34MBSOLISPmsMg/4Fc44mZL8jeqQlIrktWwnPk0/efvSGOiyLFGBFgq19e1Z/aJ3L59gam8/VA3qkJSK5L1sJz8yZM/MqDhHJL1oNtyY8AIeW2yXh8XJVC4+I5C7bt1WLSP7m4gkPfmF9feOK7DZy9ZFWdHwSF2Jvs0yGiEgWKOERkYzKX5lrK3w7nN5t89uX9HbD38eVpBSDblPWKukRkTumhEdEMvL2h+rdrK//ftvmt3dxMjPn6SYE+Lhx7FwcS3ZH2DwGEXEsSnhEJHPt3gCTBfb/AWEbbH77KgHetK1aAoCle06Tkmq/YfIiUvAp4RGRzBWvBPWesL5eNsYu8/L0aFQWF4uZf/adYexvu8nGWsciIuko4RGRm2s9AiyuELYeTu+y+e3rBvkxsUcdAGavP8a01YdtHoOIOAYlPCJyc76loWiI9XXcebuEcF/tUrxxbzUA3l28j1//O2WXOESkYFPCIyK3ZnGx/kyx30ip/q3K83QLa+L10o//sf7QObvFIiIFkxIeEbk1pytLySTbd+mYN+6txj21AkhMSeWZrzcTGhFj13hEpGBRwiMit2a5kvCk2DfhMZtNTOxel0bBRYiJT6bvzI2cvHjZrjGJSMGhhEdEbs1iXdeK/X/aNw7AzdnCtD4NqVDCk/CoeLp+ukaPt0QkS5TwiMitJcdbfybkj0dIfh4uzOnXhGqBPkReSuSJ6f8ybdVhDVkXkVtSwiMit9b0/6w/YyPtG8d1Svu5M///mvNQvdKkpBq8s3gvz3+3jUsJWmxURDKXrdXSRaQQ8vK3/jyzB+Y+fvvyTq7Q4CnwLQNFgsFkypOw3F0sTOheh3pl/Ri7aA+/7wwn9HQMX/RuQIUSXnlyTxEpuExGIWsHjo6OxtfXl6ioKHx8fOwdjkj+FxMBE6uDkZL999bpBV0/A7Ml9+O6zpZjFxj07RZORycQ4OPG4qGtKOrpkqf3FBHbutPvbyU8InJ7YRvgzN6slT21FbbOubZf8xF48Auw5G2D8tmYBHp8sZ7DkbHUKePLtwOa4uWqRmwRR6GEJ5uU8IjYwJl9cOgfWDoKUpOtK68//NW1EV955OCZGB79fD0X4pJoElKU2U83xs05b1uXRMQ27vT7W52WRST3lawKzQZB96/B7Ax7FsBPfSHqJESHQ1J8nty2Yklv5jzdBC9XJ/49cp4nvvqXFaFnSNVK6yKFnlp4RCRv7f8Lfngi/cSFHsXguY3gWTxPbvnv4XM8OXMj8UmpAPi4OeHqbKF7wzK83KlqntxTRPKWHmllkxIeETs4+Df8OhgunbY+4gIIqGVNfK5ncYWQVlDlHihW4Y5ueSQyltnrjjJv6wli4q33dHe2sPutTpjNeTNyTETyjhKebFLCI2Jnaz+CpaNvX65kdXh0NpSofEe3u5yYwtFzsTzw6RqSUgxWv9KWoKIed3RNEbG9O/3+1hAGEbGtJv8HxSpCYmzGc7GRcOBPOLrGOu/PnoXQ+uU7up27i4VqgT6UL+5F6OkYfv3vFL0al6WIhzOmPJojSETyH7XwiEj+s3S0tSWo6XPQ+d1cueSUFQf535LQtH1vNyfKFfOgir8Pw9pXUquPSD6nUVoi4njci1h/Xr6Qa5f8v9YVGHp3Jfx9rKu/x8Qns+tkNPO2nqDbZ2vZcux8rt1LRPIfPdISkfwnDxIek8nECx0q80KHysQnpRB2Po6jkbF89PcBdp+K5tHP1+N+Zc6eEt6udKjuT+eagdQL8lMnZxEHoIRHRPIfNz/rz1xMeNJd3tlCZX9vKvt707JScV766T8W74wgNtG6fEbsuTimrT7CtNVHCPBxo1MNf/q1LE/ZYnrsJVJQKeERkfwnD1p4bsbDxYkpjzcgIiqexORUDAz2hkfzx64I/t57hojoeGavP8Yv204y9YkGtKiYN3MHiUjeUsIjIvnP1YQn/qLNbhng65b2ulwxTzrXDCQhOYU1ByL55J+DbD9+kT4zNjK2aw0eb1LOZnGJSO5Qp2URyX+uJjyxZ+GPkZBwyS5huDpZuLuaP3OfaUq3uqVISTV4/ZddvPXbblK0XIVIgaKER0TyH+9AKFYJjFT4dyq8Vxb+GgXH1kNqis3DcXO2MKlHXV7sYJ0Ecebao/SbvYmY+CSbxyIiOaN5eEQkf0pOhH/ehg1TIfW6xMK9KFTqCFW6QNV783wF9hv9viOcF3/aTnxSKj5uTni75ez+PRoFMeTuSrkcnYjj0tIS2aSER6SAiY+Cg8sgdAkc+Ct9v54yjaH7bPApZdOQdpy4SP/ZmzkTk3D7wjfh4WJh1xit6yWSVUp4skkJj0gBlpIMx/+F0MWw9WtIiALPEvDoLAhuadNQLiemsP90TLbfZwDdv1hPYnIqK19uQ7linrkfnIgDUsKTTUp4RBzEuUPwYx84vQtMFugwFpo9BwVgfax7P17N7lPR9GsZQv2yRfB2c7puc8bbzQl3Z4vW+hK5jhKebFLCI+JAEuNg0TDY8YN1v9oD0PY1KF4ZzBa7hnYrr/z8Hz9uPnHLMp4uFl67t5qGwItcoYQnm5TwiDgYw4BNX8GSkZCabD3m5AYlqkLFu6HlcHD1sm+MNzgaGctnyw9yLjaRS/HJRMcncSkhmZj4ZC4lJKcb8v5Ui2AGtCpPKT93O0YsYn9KeLJJCY+Igwr7Fxa9AOcOQsp1nYl9SlsfdwXWBYsTFAm2V4RZYhgGl5NSmLzsAF+uOgyAxWyilJ8bfu4u+Hk44+vuTHEvV3o1LkuVAG87RyxiG0p4skkJj4iDS02BC0fhxGZY8a719VVuvvDCbnDN/0mCYRisCD3Ll6sOs/7wuZuW8/dxxUT6vj7BxT2Y1qdhjofMi+RHSniySQmPSCGSdBnWTIJt30L0lT4zvRdAhbZ2DSu7TlyI43R0AlGXE7kYl0TU5SSW7T3N2oM3T4R6Ny3H291q2jBKkbylhCeblPCIFEKGAbPug2NroGR1qNfbOnePT2koGgKeBW9BUMMwOHjmEgnJqemOHzp7iaFztwPwUL3SlPJzx9/XjQAfN/x9XAnwcaO4l6vm/5ECRwlPNinhESmkYiLgs8bWiQzTMUHjZ6D9m+DiGHPivDp/B99vPH7T82WLevDr8y3w83CxYVQid0YJTzYp4REpxM7shW3fQPSpK9tJiLqSGPgGWVt7wNrXJ7AOBNaDUnULXAtQSqrBn7sjOBIZS0RUPBHR8Zy+sp2NSSDVgL7NgxnzQA17hyqSZUp4skkJj4ikc/Bv+HWwNfm5GZ8y1iHuNR6E4FbW0V4F1NqDkTz+1b9YzCb+HNaKiiXzfwduEVDCk21KeEQkg/hoOLwCUhKt+zERcGobhG+3DnO/nkcxqHa/Nfkp17JAJj/9Z29m2d7TtK1SgplPNbZ3OCJZooQnm5TwiEi2xEfDiU2w91fY8ytcPn9DgRx0/jWZoOHTcO+EXAkxu45ExtJx0kqSUgxmP92Y1pVL2CUOkey40+9vcx7EJCLiONx8rI+z7v8IXjoAvX+B+n3AvciVAkb2NyMV/ptrHT1mByHFPenTLBiAcYv2kJySeus3iDiAgtcWKyJiLxYnqNDOut07CeJuPg/OTaUkwuRakHgJYiPByz6tK0PaVWL+1hMcOHOJ7zeG0ftKAiTiqJTwiIjkhMUJvP1z9l6fUtZO0heO2i3h8fVwZniHyoxauJuJS/dzT61Ainm52iUWEVvQIy0REVsrcmX4e+hiCN8Bceft8nirV+OyVCrpxYW4JDpOWsWq/WdtHoOIrdg94ZkyZQohISG4ubnRoEEDVq9efcvyCQkJvP7665QrVw5XV1cqVKjAjBkzbBStiEguKFbe+nPNRPiiFfwvBN4JhE8awOwHYMEgWPkBxJzO0zCcLGY+eaweFUt6cS42kZd//o/EZPXnEcdk10daP/zwA8OGDWPKlCm0aNGCL774gi5durBnzx7Kli2b6Xu6d+/O6dOnmT59OhUrVuTMmTMkJyfbOHIRkTvQbLB1xucLRyHqJMRFQvJl6xD464fBb54Bd70IZRpd10n6CrMzeAdYR3zdgaoBPiwa3JK7/rec09EJLNpxiofql7mja4rkR3Ydlt6kSRPq16/P1KlT045Vq1aNbt26MX78+AzllyxZQs+ePTl8+DBFixbN0j0SEhJISEhI24+OjiYoKEjD0kUk/0iKt/bpiT5pTYCiT8COHyFy/63f1+pFuHt0roTw2fKDfPBnKC5OZrxd7/x3YXcXC/1ahtCnWTAWrdsluaDAzsOTmJiIh4cHP/30Ew8++GDa8aFDh7J9+3ZWrlyZ4T2DBg1i//79NGzYkK+//hpPT08eeOAB3n77bdzd3TO9z5gxY3jrrbcyHFfCIyL5WnwU/PsFHFsLp7ZDcnz688nxYLJA2WYZW3kCalsTIWe3298nKR7Ct3PRrzrtPvqX87GJuVYFgPpl/Xj/4dpU8teMznJn7jThsdsjrcjISFJSUvD3Tz/Kwd/fn4iIiEzfc/jwYdasWYObmxu//PILkZGRDBo0iPPnz9+0H8+rr77K8OHD0/avtvCIiORrbr7Q+pWbn/+pL+z+xboC/I2OrrYmRPdNvPU9ok7C9z0gYid+rj6sazaQsBqDwHznXw3/Hj7H+0tC2Rp2kXs/XsPz7SoysHUFXJzs3nVUCim7D0s33fCbiWEYGY5dlZqaislk4ttvv8XX1xeAiRMn8sgjj/DZZ59l2srj6uqKq6uGWoqIg+k6BWo+cm05jKtiwuHP12HzdGsrUe0eUKEtWJzTlzu1Hb7vaS2PCRKicVvzPypv+iJj2Ryo7F6Eex5+m5e3luSffWeYuHQ/i3eG8/7DtakT5HfH1xfJLrslPMWLF8disWRozTlz5kyGVp+rAgMDKV26dFqyA9Y+P4ZhcOLECSpVqpSnMYuI5BsuHlDtvszPJVyCFe/Crp+tm3tRqN4Vaj0CZZvD/j9gXn9IioMSVaHXXDi+EX4fDglRuRNfXCTFFvVn+lOL+bVuXd76bQ/7ImJ4cMpa+rUMYXiHKri7WHLnXiJZYPdOyw0aNGDKlClpx6pXr07Xrl0z7bT85ZdfMmzYMM6cOYOXlxcACxcu5KGHHuLSpUs37cdzPa2lJSKFwvGNsPMn62Ov2Ovm1/EKgEunAcM6Y/Sjs6yPz8C6bljUiVy4uQFLRsKRVeBdCgb8zXlLccb+tpsF208BULaoB+89VIvmFYvnwv2kMCiwnZbBOiy9d+/efP755zRr1owvv/ySadOmsXv3bsqVK8err77KyZMnmTNnDgCXLl2iWrVqNG3alLfeeovIyEj69+9P69atmTZtWpbuqYRHRAqVlGQ4ugp2zoO9v11rwWn4NHT5IO9We798EaZ3hMhQayfqp/4AVy+W7zvDa7/sJDzK2gm7Z6MgXr2nGr7ud/4YTRxbgU54wDrx4P/+9z/Cw8OpWbMmkyZN4q677gKgb9++HD16lBUrVqSV37dvH4MHD2bt2rUUK1aM7t27M27cuCy17oASHhEpxJLi4dA/gAFV7rnjOXxu68JRmHa3dZ6hyp2h53dgthATn8T/loTy9YZjAJT0duXtbjXpVCMgb+ORAq3AJzy2poRHRMSGjm+C2fdZR401GQhd3k87tfHIeUbO28HhyFgA7q0VyJgHalDCWwNNJKM7/f7W+EAREck7QY3gwc+tr//93Dq30BWNQ4qyeGgrBrWpgMVs4ved4bSfuJKft5ygkP0uLjaghEdERPJWjQeh/Rjr6yUjIXRJ2ik3ZwuvdK7KwudaUKOUD1GXk3jpp//oM2Mjx8/H2SdecUhKeEREJO+1GAb1eoORCj8/DeH/pTtds7QvC59rwYjOVXFxMrP6QCSdJq9i5tojpKSqtUfunBIeERHJeyYT3DcJQlpDUix818M60/N1nCxm/q9NBZYMbUXjkKLEJabw1m97ePTzdRw4HWOnwMVRKOERERHbsDhD9znWyQ5jwq3LWiRkTGTKl/Bi7oCmjOtWEy9Xp7TlKT7++wCJyal2CFwcgRIeERGxHXc/eOxH8CwBETutj7dSkjMUM5tNPNG0HEuH30W7qiVJTEll4tL9PPDpGv47ftHmYUvBp2HpIiJieyc2w6x7rcPVGz8D93xw06KGYfDrf6d467c9nI9NxGyCop6ZD133dLVQ3MuV4l4uFPdypZiXKyWuvC7u7UoxTxeKe7vi7ep003UbJX/SPDzZpIRHRCSf2LMQfuxjfd35fWg68JbFz8cmplue4k64OJkpfiX5uZog+Xm4YLZTElQ1wJuudUspCbsFJTzZpIRHRCQfWTMZlr0JmKwzMVe957ZvOXnxMjHxSRmOGwZcSkgmMiaByEsJnL2UyLlL1teRaa8TuZSQ8RFafvBK5yoMalPR3mHkW3f6/W231dJFRERoMRTOH4ats2FeP+uaW6Xq3vItpf3cgawtJ5SZ+KQUzsYkcC42MS05OhebyMW4ROzRBHA+NpH5207ywZ+hVC7pTfvq/rYPohBQwiMiIvZjMsG9E+BiGBxebh2uPuBv8C2TZ7d0c7YQVNSDoKIeeXaP7PJwtfDNhjCGzt3GL8+1oLK/t71DcjgapSUiIvZlcYbus6FENbgUYU16Mhmu7sjevL8GTUKKEpuYQv/Zm7kQm2jvkByOEh4REbE/N1947AfwLAmnd8FPT2U6XN1ROVvMTHm8PmWKuBN2Po7nv99KcormHMpNSnhERCR/KFIOes0FJ3c4uBSWjMAunWrspJiXK9P6NMTDxcLag+cY9/tee4fkUJTwiIhI/lGmATz0JWCCTV/Bhqn2jsimqgX6MLF7XQBmrTvK3I1h9g3IgSjhERGR/KX6A9BhrPX1n6/Bvt/tG4+Nda4ZwAvtKwMwauEuNh09b+eIHIMSHhERyX+aD4YGTwEGzOsPp7bZOyKbGtyuIvfUCiApxWDg11s4efGyvUMq8JTwiIhI/mMyWZebqNAOkuKsI7cuHrd3VDZjNpv48NE6VAv04VxsIgNmbyYusfB04s4LSnhERCR/sjjDo7OgZHW4dNqa9MRH2zsqm/FwcWJanwYU83RhT3g0L/+0g0K2OEKuUsIjIiL5l5uvdXV1L384sxt+LlzD1csU8eDz3g1wtpj4fWc4n/5z0N4hFVhKeEREJH/zC7puuPoy+OPlQjVcvVFwUcZ2rQnAhKX7WbIrws4RFUxaPFRERAqGvb/BD70BA7wDwZTJ7+wuntbJC71KWH96lrj22qskeBa3vnbJP8tKZNWbC3cxe/0xPFwszB/UnKoBhes7TKulZ5MSHhGRAmz9FOtQde7wq8vF61ry43UlMXIvAmZLroSZbcWrQK1HrJ21byIpJZUnZ2xk3aFzBBV1Z+FzLSnq6WLDIO1LCU82KeERESngLh6HuMiMxw3DugZX7Fm4dMb6M/YMXLryMzbSejwlwfYxZ0XDfnDPh2C+eW+TC7GJdP1sLWHn42havihf92uCs6Vw9E5RwpNNSnhERAoxw4CE6CtJ0NWE6EoydPkCd9xylBOJcbD9W+u96/WG+z+6ZUvT/tMxPPjZWmITU+jdtBxvd6tpu1jt6E6/v53yICYREZH8yWSyjvxy84XiFe0dzTUhd8GCgbDta0hJgm5Tbpr0VPb3ZnLPejzz9Wa+3nCMqoHePN6knI0DLngKRzuYiIhIflanBzz8FZgssGMuzB9gTXxuokN1f17qWAWANxfu5t/D52wVaYGlhEdERCQ/qPkwdJ8NZmfYNc8651By4k2LD2pTgftqB5KcavB/327l+Pk4GwZb8CjhERERyS+q3Q89vgGLi3UY/o99IDnzTtYmk4kPHqlDzdI+nI9NZMCczcQmFJ5JGbNLCY+IiEh+UqUz9PoenNxg/x/wfS9IynzxUHcXC1/2bkhxL1f2RcQw/MftpKYWqrFIWaaER0REJL+p2N66pIazBxz6G77rDomxmRYt5efOF73r42wx8efu03z09wEbB1swKOERERHJj8q3hifmWSdJPLIKvn3UOs9QJhqUK8o73WoB8NHfB/hjZ7gtIy0QlPCIiIjkV+WaQ+9fwNUHjq2Frx+C+KhMi3ZvFMTTLUIAGP7jf+w5VXhWls8KJTwiIiL5WVBj6LPQOnfQiY0wp9uVSRIzeu2eqrSqVJzLSSkMmLOZyEv5dFZpO1DCIyIikt+Vrg9PLgL3onBqK8y+H2Izzr3jZDHzaa/6BBfz4OTFywz6ZiuJyal2CDj/UcIjIiJSEATWhr6/Wxc6jdgJs++zLpFxA18PZ756siFerk5sPHqeN3/dTSFbRSpTSnhEREQKCv/q0HcxeAXAmT0w616IichQrGJJbz7uVReTCb7fGMY3G47ZIdj8RQmPiIhIQVKiMjy1GHxKQ2QozLwHok5mKNauqj8jOlcFYMxve1h3MJMV5gsRJTwiIiIFTbEK1qTHtyycPwQzu8CFjK04z95Vnm51S5GSajDou62EnSu8y08o4RERESmIigRbk54iIXDxmPXx1vnD6YqYTCbee7g2tcv4cjEuif5zNnGpkC4/oYRHRESkoPILsiY9xSpB1HHr463I9DMtuzlbl58o4e3K/tOXeOGHwrn8hBIeERGRgsynlHX0VomqEBNuTXrO7EtXJMDXjS97N8DFyczSPaeZtGy/nYK1HyU8IiIiBZ23vzXp8a8FsWesj7cidqUrUq9sEcY/aF1+4pN/DvLbf6fsEandKOERERFxBJ7F4clfIbAuxEVa5+k5tT1dkYcblGFAK+vyEy///B+7Tma+TIUjUsIjIiLiKDyKWpehKN3QuvzE7AfgxOZ0RUZ2qUbryiWIT0rlmTmbORtTOJafUMIjIiLiSNz9rAuOlm0GCVHWtbeOrU87bTGb+LhXPcoX9+RUVDwDv9lCQnKK3cK1FSU8IiIijsbNBx7/GYJbQWIMfPMwHFmddtrX3ZlpTzbE282JLccuMGrBLodffkIJj4iIiCNy9YLHfoTybSEpFr59FA4tTztdoYQXn/Sqh9kEP24+wax1R+0Xqw0o4REREXFULh7Qay5U6gjJl+G7HrD/r7TTbaqU5NUu1QAY9/te1hxw3OUnlPCIiIg4Mmc36PENVLkXUhJg7mOw7/e00/1bhfBQ/dKkpBo8991WjkbG2jHYvKOER0RExNE5uUL32VC9G6QmwY99YPcvgHX5iXcfrEXdID+iLifRf85mYuKT7BtvHlDCIyIiUhhYnOHh6VCrO6Qmw89Pw46fgKvLTzTA38eVg2cuMXTudlIcbPkJJTwiIiKFhcUJHvwc6j4ORirMHwDbvwOgpI8bX/ZuiIuTmX/2neHDv0LtHGzuUsIjIiJSmJgt8MCn0KAvYMCCQbBlFgB1gvz438O1AZi64hALt5+0W5i5TQmPiIhIYWM2w32TofEzgAG/DYWN0wDoVq80A1tXAOCVn3ew48RFu4WZm5TwiIiIFEYmE3T5HzR73rq/+CVY9ykAL3eqQruqJUlITuWZOVs4Ex1vx0BzhxIeERGRwspkgo7joNWL1v2/XofVE7GYTUzuWZcKJTyJiI7nma+3EJ9UsJefUMIjIiJSmJlM0G4UtHnVuv/3W7DifXxcnfjqyUb4uDmx/fhFXv+lYC8/oYRHRESksDOZoM1IuHu0dX/Fu/DP24QU8+Czx+tjNsG8rSeYvuaIfeO8A0p4RERExKrVi9DxHevr1RPgrzdoVbE4b9xbHYB3F+9l5f6zdgww55TwiIiIyDXNn4cuH1hfr/8U/hjBU83L0b1hGVINeP67rRw+e8m+MeaAEh4RERFJr8kz1mHrmGDjF5h+H87bXavToFwRYuKT6T9nM9EFbPkJJTwiIiKSUcOnoOtngAm2zMT192FMfawOgb5uHD4by5DvtxWo5SeU8IiIiEjm6j0OD30JJjNs/4aSy4Yx7Ym6uDmbWRF6lv8t2WfvCLNMCY+IiIjcXO3u8MgMMDvBzh+puX44Hz5k7cT8xarDzN96ws4BZo0SHhEREbm1Gg9C9zlgdoY9C7gv9DWGti4LwMj5O9kWdsHOAd6eEh4RERG5var3Qs/vwOIK+xYx7NxYOlcpQmJyKs9+vYXT+Xz5CSU8IiIikjWVO8Jjc8HJDdPBv/jU9D61SjpzJiaBZ+ZsztfLT9g94ZkyZQohISG4ubnRoEEDVq9efdOyK1aswGQyZdj27Ss4naZEREQKtArt4PGfwNkDp6Mr+MlnMgHuKfx3IopX5+/Mt8tP2DXh+eGHHxg2bBivv/4627Zto1WrVnTp0oWwsLBbvi80NJTw8PC0rVKlSjaKWERERAi5C56YDy7euJ1Yy1/FP8LXfJlftp3ky1WH7R1dpuya8EycOJF+/frRv39/qlWrxuTJkwkKCmLq1Km3fF/JkiUJCAhI2ywWi40iFhEREQDKNYM+C8DVF5+zm1lWYjI+xPLekn0s33fG3tFlYLeEJzExkS1bttCxY8d0xzt27Mi6detu+d569eoRGBjI3XffzfLly29ZNiEhgejo6HSbiIiI5IIyDeHJheDmR4monSwu8iE+xiWGfL+N8KjL9o4uHbslPJGRkaSkpODv75/uuL+/PxEREZm+JzAwkC+//JJ58+Yxf/58qlSpwt13382qVatuep/x48fj6+ubtgUFBeVqPURERAq1UvWg7yLwKEaZy6Es9HqPV1oVI8DHzd6RpeNk7wBMJlO6fcMwMhy7qkqVKlSpUiVtv1mzZhw/fpwPP/yQu+66K9P3vPrqqwwfPjxtPzo6WkmPiIhIbgqoBX1/h9kPEBx7mODQ56HZIvAsbu/I0tithad48eJYLJYMrTlnzpzJ0OpzK02bNuXAgQM3Pe/q6oqPj0+6TURERHJZyWrw1GLwDgSfUuDiZe+I0rFbwuPi4kKDBg1YunRpuuNLly6lefPmWb7Otm3bCAwMzO3wREREJLuKV4Kn/4Se34KzHmmlGT58OL1796Zhw4Y0a9aML7/8krCwMAYOHAhYH0edPHmSOXPmADB58mSCg4OpUaMGiYmJfPPNN8ybN4958+bZsxoiIiJyVZFy9o4gU3ZNeHr06MG5c+cYO3Ys4eHh1KxZk8WLF1OunPUPKzw8PN2cPImJibz00kucPHkSd3d3atSowe+//84999xjryqIiIhIAWAy8uuUiHkkOjoaX19foqKi1J9HRESkgLjT72+7Ly0hIiIikteU8IiIiIjDU8IjIiIiDk8Jj4iIiDg8JTwiIiLi8JTwiIiIiMNTwiMiIiIOTwmPiIiIODwlPCIiIuLwlPCIiIiIw1PCIyIiIg7ProuH2sPVpcOio6PtHImIiIhk1dXv7ZwuAVroEp6YmBgAgoKC7ByJiIiIZFdMTAy+vr7Zfl+hWy09NTWVU6dO4e3tjclkytVrR0dHExQUxPHjxx12JXZHr6Oj1w9UR0fh6HV09PqB6phdhmEQExNDqVKlMJuz3yOn0LXwmM1mypQpk6f38PHxcdh/vFc5eh0dvX6gOjoKR6+jo9cPVMfsyEnLzlXqtCwiIiIOTwmPiIiIODwlPLnI1dWVN998E1dXV3uHkmccvY6OXj9QHR2Fo9fR0esHqqOtFbpOyyIiIlL4qIVHREREHJ4SHhEREXF4SnhERETE4SnhEREREYdXaBOeKVOmEBISgpubGw0aNGD16tW3LL9y5UoaNGiAm5sb5cuX5/PPP89QZt68eVSvXh1XV1eqV6/OL7/8ku37GobBmDFjKFWqFO7u7rRp04bdu3c7TB2TkpIYMWIEtWrVwtPTk1KlStGnTx9OnTrlMHW80bPPPovJZGLy5MnZrl927wW2rePevXt54IEH8PX1xdvbm6ZNmxIWFuYQ9bt06RLPP/88ZcqUwd3dnWrVqjF16tRs1c2edVy1ahX3338/pUqVwmQysWDBggzXKOifN7eroyN83mTl7/F6d/J5k5/rlxufNRiF0Ny5cw1nZ2dj2rRpxp49e4yhQ4canp6exrFjxzItf/jwYcPDw8MYOnSosWfPHmPatGmGs7Oz8fPPP6eVWbdunWGxWIx3333X2Lt3r/Huu+8aTk5OxoYNG7J13/fee8/w9vY25s2bZ+zcudPo0aOHERgYaERHRztEHS9evGi0b9/e+OGHH4x9+/YZ69evN5o0aWI0aNAgW/XLz3W83i+//GLUqVPHKFWqlDFp0iSHquPBgweNokWLGi+//LKxdetW49ChQ8aiRYuM06dPO0T9+vfvb1SoUMFYvny5ceTIEeOLL74wLBaLsWDBgizXz551XLx4sfH6668b8+bNMwDjl19+yXCvgv55c7s6OsLnTVb+Hq+6k8+b/Fy/3PisMQzDKJQJT+PGjY2BAwemO1a1alVj5MiRmZZ/5ZVXjKpVq6Y79uyzzxpNmzZN2+/evbvRuXPndGU6depk9OzZM8v3TU1NNQICAoz33nsv7Xx8fLzh6+trfP7559moYf6tY2Y2btxoADf9j3Uz+b2OJ06cMEqXLm3s2rXLKFeuXI4Snvxcxx49ehhPPPFE9ip0g/xcvxo1ahhjx45NV6Z+/frGG2+8kYWaXWOvOl4vsy8SR/i8ud7tkoGrCtrnzfVuVcc7/bzJz/XLjc8awzCMQvdIKzExkS1bttCxY8d0xzt27Mi6desyfc/69eszlO/UqRObN28mKSnplmWuXjMr9z1y5AgRERHpyri6utK6deubxlbQ6piZqKgoTCYTfn5+WapfTu9lyzqmpqbSu3dvXn75ZWrUqJHlehWUOqampvL7779TuXJlOnXqRMmSJWnSpMltm9sLSv0AWrZsya+//srJkycxDIPly5ezf/9+OnXqlO/rmBUF/fMmpwrS501W3ennTX6uX2581lxV6BKeyMhIUlJS8Pf3T3fc39+fiIiITN8TERGRafnk5GQiIyNvWebqNbNy36s/sxNbQavjjeLj4xk5ciSPPfZYthaWy+91fP/993FycmLIkCFZrtON8nMdz5w5w6VLl3jvvffo3Lkzf/31Fw8++CAPPfQQK1euLPD1A/j444+pXr06ZcqUwcXFhc6dOzNlyhRatmyZpfrZs45ZUdA/b3KioH3eZNWdft7k5/rlxmfNVYVutfSrTCZTun3DMDIcu135G49n5Zq5VSYr8nMdwdqhsGfPnqSmpjJlypRb1OTm8mMdt2zZwkcffcTWrVtz9PeWlZjtXcfU1FQAunbtygsvvABA3bp1WbduHZ9//jmtW7e+bb2yE8vtyt94PDf+nX788cds2LCBX3/9lXLlyrFq1SoGDRpEYGAg7du3z0LNsn6vrJS/8bi9Pidy6zq2rCMU7M+bW8nNz5v8WL/c/KwpdC08xYsXx2KxZMgwz5w5kyETvSogICDT8k5OThQrVuyWZa5eMyv3DQgIAMhWbAWtjlclJSXRvXt3jhw5wtKlS7P121Z+r+Pq1as5c+YMZcuWxcnJCScnJ44dO8aLL75IcHCwQ9SxePHiODk5Ub169XRlqlWrluWRE/m5fpcvX+a1115j4sSJ3H///dSuXZvnn3+eHj168OGHH2apfvasY1YU9M+b7CionzdZkRufN/m5frnxWXNVoUt4XFxcaNCgAUuXLk13fOnSpTRv3jzT9zRr1ixD+b/++ouGDRvi7Ox8yzJXr5mV+4aEhBAQEJCuTGJiIitXrrxpbAWtjnDtw+fAgQMsW7Ys7T9HduTnOvbu3ZsdO3awffv2tK1UqVK8/PLL/Pnnnw5RRxcXFxo1akRoaGi6Mvv376dcuXIFvn5JSUkkJSVhNqf/iLRYLGm/cebnOmZFQf+8yaqC/HmTFbnxeZOf65cbnzVp7rjbcwF0dfjd9OnTjT179hjDhg0zPD09jaNHjxqGYRgjR440evfunVb+6vC7F154wdizZ48xffr0DMPv1q5da1gsFuO9994z9u7da7z33ns3HQp7s/sahnWYqK+vrzF//nxj586dRq9eve5omGh+q2NSUpLxwAMPGGXKlDG2b99uhIeHp20JCQkOUcfM5HSUVn6u4/z58w1nZ2fjyy+/NA4cOGB88sknhsViMVavXu0Q9WvdurVRo0YNY/ny5cbhw4eNmTNnGm5ubsaUKVOyXD971jEmJsbYtm2bsW3bNgMwJk6caGzbti3DNBgF+fPmdnV0hM+brPw93ignnzf5uX658VljGIV0WLphGMZnn31mlCtXznBxcTHq169vrFy5Mu3ck08+abRu3Tpd+RUrVhj16tUzXFxcjODgYGPq1KkZrvnTTz8ZVapUMZydnY2qVasa8+bNy9Z9DcM6VPTNN980AgICDFdXV+Ouu+4ydu7c6TB1PHLkiAFkui1fvtwh6piZnCY8+b2O06dPNypWrGi4ubkZderUyfYcNfm5fuHh4Ubfvn2NUqVKGW5ubkaVKlWMCRMmGKmpqQWijsuXL8/0/9mTTz6ZVqagf97cro6O8HmTlb/HG+X08yY/1y83PmtMhnGll5GIiIiIgyp0fXhERESk8FHCIyIiIg5PCY+IiIg4PCU8IiIi4vCU8IiIiIjDU8IjIiIiDk8Jj4iIiDg8JTwiIiLi8JTwiMgtrVixApPJxMWLF7P8njFjxlC3bt08iym3/Pbbb/To0YPLly8zZ84cHnnkkVy57qxZs/Dz87vj6+Tkz15EMqeER0RYt24dFouFzp072zuUW+rbty8mkwmTyYSzszP+/v506NCBGTNmZGtRz6s6dOhAeHg4Hh4eDB8+nBdeeCEPos655s2bEx4ejq+vr71DESnwlPCICDNmzGDw4MGsWbOGsLAwe4dzS507dyY8PJyjR4/yxx9/0LZtW4YOHcp9991HcnJytq7l5ubGqlWriI6O5syZM7Ro0eKO40tKSrrja1y9jouLCwEBAZhMply5pkhhpoRHpJCLjY3lxx9/5P/+7/+47777mDVr1i3LX31cs2DBAipXroybmxsdOnTg+PHjGcp+/fXXBAcH4+vrS8+ePYmJiUk7t2TJElq2bImfnx/FihXjvvvu49ChQ7eN19XVlYCAAEqXLk39+vV57bXXWLhwIX/88Ue62KOionjmmWcoWbIkPj4+tGvXjv/++y/dtcaNG0fJkiUpVaoUzzzzDCNHjszwKG7mzJlUq1YNNzc3qlatypQpU9LOHT16FJPJxI8//kibNm1wc3Pjm2++yTTu3377jQYNGuDm5kb58uV566230iVoJpOJzz//nK5du+Lp6cm4ceP0SEskFynhESnkfvjhB6pUqUKVKlV44oknmDlzJrdbUzguLo533nmH2bNns3btWqKjo+nZs2e6MocOHWLBggUsWrSIRYsWsXLlSt57772087GxsQwfPpxNmzbx999/YzabefDBB3P0aKpdu3bUqVOH+fPnA2AYBvfeey8REREsXryYLVu2UL9+fe6++27Onz8PwLfffss777zD+++/z5YtWyhbtixTp05Nd91p06bx+uuv884777B3717effddRo0axezZs9OVGzFiBEOGDGHv3r106tQpQ3x//vknTzzxBEOGDGHPnj188cUXzJo1i3feeSdduTfffJOuXbuyc+dOnn766Wz/OYjILWR7fXURcSjNmzc3Jk+ebBiGYSQlJRnFixc3li5dmnZ++fLlBmBcuHDBMAzDmDlzpgEYGzZsSCuzd+9eAzD+/fdfwzAM48033zQ8PDyM6OjotDIvv/yy0aRJk5vGcebMGQMwdu7cedMyTz75pNG1a9dMz/Xo0cOoVq2aYRiG8ffffxs+Pj5GfHx8ujIVKlQwvvjiC8MwDKNJkybGc889l+58ixYtjDp16qTtBwUFGd999126Mm+//bbRrFkzwzAM48iRIwaQ9ud31cyZMw1fX9+0/VatWhnvvvtuujJff/21ERgYmLYPGMOGDUtX5sY/exHJObXwiBRioaGhbNy4Ma11xsnJiR49ejBjxoxbvs/JyYmGDRum7VetWhU/Pz/27t2bdiw4OBhvb++0/cDAQM6cOZO2f+jQIR577DHKly+Pj48PISEhADnuQ2QYRlpfly1btnDp0iWKFSuGl5dX2nbkyJG0x2ahoaE0btw43TWu3z979izHjx+nX79+6a4xbty4DI/erv+zyMyWLVsYO3ZsuusMGDCA8PBw4uLisnwdEck5J3sHICL2M336dJKTkyldunTaMcMwcHZ25sKFCxQpUuSm782sI+31x5ydnTOcu/5x1f33309QUBDTpk2jVKlSpKamUrNmTRITE3NUl71796YlTampqQQGBrJixYoM5a4fLn5jHYzrHuVdjXXatGk0adIkXTmLxZJu39PT85axpaam8tZbb/HQQw9lOOfm5pbl64hIzinhESmkkpOTmTNnDhMmTKBjx47pzj388MN8++23PP/88zd97+bNm9NaREJDQ7l48SJVq1bN0r3PnTvH3r17+eKLL2jVqhUAa9asyXFd/vnnH3bu3Jk2rLx+/fpERETg5OREcHBwpu+pUqUKGzdupHfv3mnHNm/enPba39+f0qVLc/jwYR5//PEcx3Y1ntDQUCpWrHhH1xGRnFPCI1JILVq0iAsXLtCvX78M87w88sgjTJ8+/aYJj7OzM4MHD+bjjz/G2dmZ559/nqZNm2Z4RHQzRYoUoVixYnz55ZcEBgYSFhbGyJEjs/TehIQEIiIiSElJ4fTp0yxZsoTx48dz33330adPHwDat29Ps2bN6NatG++//z5VqlTh1KlTLF68mG7dutGwYUMGDx7MgAEDaNy4MS1btuT777/nv//+o0KFCmn3GjNmDEOGDMHHx4cuXbqQkJDA5s2buXDhAsOHD89SvACjR4/mvvvuIygoiEcffRSz2cyOHTvYuXMn48aNy/J1RCTn1IdHpJCaPn067du3z3RSu4cffpjt27ezdevWTN/r4eHBiBEjeOyxx2jWrBnu7u7MnTs3y/c2m83MnTuXLVu2ULNmTV544QU++OCDLL13yZIlBAYGEhwcTOfOnVm+fDkff/wxCxcuTHvUZDKZWLx4MXfddRdPP/00lStXpmfPnhw9ehR/f38AHn/8cV599VWGDx9O7dq1OXToEE899VS6R0z9+/fnq6++YtasWdSqVYvWrVsza9astEdnWdWpUycWLVrE0qVLadSoEU2bNmXixImUK1cuW9cRkZwzGcZtxp+KiFxn1qxZDBs2zCHnhunQoQMBAQF8/fXX9g5FRHKZHmmJSKEUFxfH559/TqdOnbBYLHz//fcsW7aMpUuX2js0EckDSnhEpFC6+thr3LhxJCQkUKVKFebNm0f79u3tHZqI5AE90hIRERGHp07LIiIi4vCU8IiIiIjDU8IjIiIiDk8Jj4iIiDg8JTwiIiLi8JTwiIiIiMNTwiMiIiIOTwmPiIiIOLz/B3oURxDyeqSFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#.lineplot ile eğitim ve test verilerinin doğruluk dizilerinin çizgi grafiğini çizdiriyoruz\n",
    "sns.lineplot(y=accuracy_train,x=alphas,label=\"Eğitim Doğruluğu\")\n",
    "sns.lineplot(y=accuracy_test,x=alphas,label=\"Test Doğruluğu\")\n",
    "#x ve ye eksenlerinin isimlerini değiştirmek için .xlabel() ve .ylabel kullanırız.\n",
    "plt.xlabel(\"Alpha Değerleri\")\n",
    "plt.ylabel(\"Doğruluk\")\n",
    "plt.xticks()\n",
    "plt.show()\n",
    "#İki değerin paralel olmaya başladığı nokta ccp_alpha değerimizdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bf1d5d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(ccp_alpha=0.0012)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(ccp_alpha=0.0012)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0012)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tree_new=DecisionTreeClassifier(ccp_alpha=0.0012)\n",
    "model_tree_new.fit(X_train,y_train) #modelimizi train verilerle eğittik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b9aa1dee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy Score= 0.6003161828111526\n",
      "Test Accuracy Score= 0.596551724137931\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train Accuracy Score= {accuracy_score(y_train,model_tree_new.predict(X_train))}\")\n",
    "print(f\"Test Accuracy Score= {accuracy_score(y_test,model_tree_new.predict(X_test))}\")\n",
    "#iki sonuç birbirine yakın ve gerçekçi çıktığından aşırı öğrenme yok deriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "406e488c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(model_tree_new)\n",
    "#yeni modelimizin karar ağacını çizdirdik. Aşırı öğrenme durmunu ortadan kaldırdık ve daha okunaklı bir ağaç elde etmiş olduk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2117a005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JCf0lSQ0NTfroo48UEhKin21apWE3pOmOO2cqJCTEM+f06TNqa2vzrHXllZdr1KgRmpQ3Iyg/C7pBLznFEEx+JQgLFy7UvHnzVFVVpXHjxikhIUE2m00ul0tlZWX6yU9+ouXLl3dTqPik0q69Wsud/6Onn1uj59a8pEsTHXr04bmaOH6s32u9+qsdGtA/Tpn/dWM3RAr0nP+eN1OStOuNV7zuf3n2I1q3/mcaODBReZM+PqXzu31lXnOysu/Wnjff9nz9pVn36Phxl3aU7enmqNFj/Dx98J/IZhiGX2nSpk2bVFxcrKqqKrW3t0v6+Ix8enq6CgoKPOcu/dV28vCFJwGfMRFJPKES8OVca/e2pc8+MT1ga0X+78YLT+qF/D7mOG3aNE2bNk1tbW06efLjtkB8fLzCwsICHhwAAAiOT/wkxbCwsC7tNwAA4FOHUww8ahkAAAs2KfKoZQAAYEUFAQAAM04xkCAAAGBBi4EWAwAAsKKCAACACe9iIEEAAMCKFgMtBgAAYEUFAQAAMyoIJAgAAFhwzJEEAQAACyoI7EEAAABWVBAAADAxqCCQIAAAYEGCQIsBAABYUUEAAMCMJymSIAAAYEGLgRYDAACwooIAAIAZFQQSBAAAzAyDBIEWAwAAsKCCAACAGS0GEgQAACxIEEgQAAAw41HL7EEAAAA+UEEAAMCMCgIVBAAALDoCePlhyZIlstlsXpfD4fCMG4ahJUuWKCkpSREREbrtttt08OBBrzXcbrfmz5+v+Ph4RUZGKi8vTzU1NX7/CkgQAADoRa6//nrV1tZ6rurqas/Y0qVL9dRTT2nFihWqrKyUw+HQuHHj1NTU5JmTn5+v0tJSlZSUqLy8XM3NzZo4caLa29v9ioMWAwAAJsHcpBgaGupVNfgHwzC0fPlyPfbYY5oyZYokae3atUpISNBLL72kuXPnqqGhQatXr9b69euVnZ0tSdqwYYOSk5O1c+dOjR8/vstxUEEAAMCswwjY5Xa71djY6HW53e5Ov/WhQ4eUlJSkQYMG6Z577tHhw4clSUeOHJHL5VJOTo5nrt1u1+jRo1VRUSFJqqqqUltbm9ecpKQkpaameuZ0FQkCAADdyOl0KiYmxutyOp0+5w4fPlzr1q3T9u3b9fzzz8vlcikzM1OnTp2Sy+WSJCUkJHh9JiEhwTPmcrkUHh6ufv36dTqnq2gxAABg5ufmwvMpLCxUQUGB1z273e5zbm5urufPaWlpGjlypK688kqtXbtWI0aMkCTZbDavzxiGYbln1pU5ZlQQAAAwMTqMgF12u13R0dFeV2cJgllkZKTS0tJ06NAhz74EcyWgrq7OU1VwOBxqbW1VfX19p3O6igQBAIBeyu12609/+pMSExM1aNAgORwOlZWVecZbW1u1Z88eZWZmSpLS09MVFhbmNae2tlYHDhzwzOkqWgwAAJgFsMXgj4ULF2rSpEm67LLLVFdXp+985ztqbGzUzJkzZbPZlJ+fr6KiIg0ePFiDBw9WUVGR+vbtq/vuu0+SFBMTo9mzZ2vBggWKi4tTbGysFi5cqLS0NM+phq4iQQAAwCRYxxxramp077336uTJk+rfv79GjBihvXv3KiUlRZK0aNEitbS06IEHHlB9fb2GDx+uHTt2KCoqyrNGcXGxQkNDNXXqVLW0tCgrK0tr1qxRSEiIX7HYDMPoFc+TbDt5ONghAL1ORNKoYIcA9ErnWo936/qn7xgdsLVif7EnYGv1JPYgAAAAC1oMAACYGEHag9CbkCAAAGBGgkCLAQAAWFFBAADAhBYDCQIAAFYkCLQYAACAFRUEAABMaDGQIAAAYEGCQIIAAIAFCQJ7EAAAgA9UEAAAMDNswY4g6EgQAAAwocVAiwEAAPhABQEAABOjgxYDCQIAACa0GGgxAAAAH6ggAABgYnCKgQQBAAAzWgy0GAAAgA9UEAAAMOEUAwkCAAAWhhHsCIKPBAEAABMqCOxBAAAAPlBBAADAhAoCCQIAABbsQaDFAAAAfKCCAACACS0GEgQAACx41DItBgAA4AMVBAAATHgXAwkCAAAWHbQYaDEAAAArEgQAAEwMwxaw65NyOp2y2WzKz8/33Js1a5ZsNpvXNWLECK/Pud1uzZ8/X/Hx8YqMjFReXp5qamr8/v4kCAAAmBgdtoBdn0RlZaVWrVqloUOHWsYmTJig2tpaz7Vlyxav8fz8fJWWlqqkpETl5eVqbm7WxIkT1d7e7lcMJAgAAJgYRuAufzU3N2v69Ol6/vnn1a9fP8u43W6Xw+HwXLGxsZ6xhoYGrV69WsuWLVN2draGDRumDRs2qLq6Wjt37vQrDhIEAAC6kdvtVmNjo9fldrs7nf/ggw/q9ttvV3Z2ts/x3bt3a8CAARoyZIjmzJmjuro6z1hVVZXa2tqUk5PjuZeUlKTU1FRVVFT4FTcJAgAAJoFsMTidTsXExHhdTqfT5/ctKSlRVVVVp+O5ubnauHGjdu3apWXLlqmyslJjx471JBwul0vh4eGWykNCQoJcLpdfvwOOOQIAYBLIY46FhYUqKCjwume32y3zjh07pocfflg7duxQnz59fK41bdo0z59TU1OVkZGhlJQUbd68WVOmTOk0BsMwZLP59zORIAAA0I3sdrvPhMCsqqpKdXV1Sk9P99xrb2/Xm2++qRUrVsjtdiskJMTrM4mJiUpJSdGhQ4ckSQ6HQ62traqvr/eqItTV1SkzM9OvuGkxAABgEoxjjllZWaqurtb+/fs9V0ZGhqZPn679+/dbkgNJOnXqlI4dO6bExERJUnp6usLCwlRWVuaZU1tbqwMHDvidIFBBAADA5JOcPvh3RUVFKTU11eteZGSk4uLilJqaqubmZi1ZskR33XWXEhMTdfToUS1evFjx8fG68847JUkxMTGaPXu2FixYoLi4OMXGxmrhwoVKS0vrdNNjZ0gQAAD4FAgJCVF1dbXWrVunM2fOKDExUWPGjNGmTZsUFRXlmVdcXKzQ0FBNnTpVLS0tysrK0po1a3xWIM7HZhjByJOs2k4eDnYIQK8TkTQq2CEAvdK51uPduv7+lLyArXXD314P2Fo9iQoCAAAm/84jkv9TsEkRAABYUEEAAMCkdzTfg4sEAQAAk0A+KOnTqtckCCcnzw52CECv8+MBY4IdAvCZxB4E9iAAAAAfek0FAQCA3oIWAwkCAAAW7FGkxQAAAHygggAAgAktBhIEAAAsOMVAiwEAAPhABQEAAJOOYAfQC5AgAABgYogWAy0GAABgQQUBAACTDh6EQIIAAIBZBy0GEgQAAMzYg8AeBAAA4AMVBAAATDjmSIIAAIAFLQZaDAAAwAcqCAAAmNBiIEEAAMCCBIEWAwAA8IEKAgAAJmxSJEEAAMCig/yAFgMAALCiggAAgAnvYiBBAADAgpc5kiAAAGDBMUf2IAAAAB+oIAAAYNJhYw8CFQQAAEyMAF6flNPplM1mU35+/j/jMgwtWbJESUlJioiI0G233aaDBw96fc7tdmv+/PmKj49XZGSk8vLyVFNT4/f3J0EAAKCXqays1KpVqzR06FCv+0uXLtVTTz2lFStWqLKyUg6HQ+PGjVNTU5NnTn5+vkpLS1VSUqLy8nI1Nzdr4sSJam9v9ysGEgQAAEw6Anj5q7m5WdOnT9fzzz+vfv36ee4bhqHly5frscce05QpU5Samqq1a9fqww8/1EsvvSRJamho0OrVq7Vs2TJlZ2dr2LBh2rBhg6qrq7Vz506/4iBBAADApMMWuMvtdquxsdHrcrvdnX7vBx98ULfffruys7O97h85ckQul0s5OTmee3a7XaNHj1ZFRYUkqaqqSm1tbV5zkpKSlJqa6pnTVSQIAAB0I6fTqZiYGK/L6XT6nFtSUqKqqiqf4y6XS5KUkJDgdT8hIcEz5nK5FB4e7lV5MM/pKk4xAABgEsgnKRYWFqqgoMDrnt1ut8w7duyYHn74Ye3YsUN9+vTpdD2b6YSFYRiWe2ZdmWNGBQEAAJNAnmKw2+2Kjo72unwlCFVVVaqrq1N6erpCQ0MVGhqqPXv26Ic//KFCQ0M9lQNzJaCurs4z5nA41Nraqvr6+k7ndBUJAgAAvUBWVpaqq6u1f/9+z5WRkaHp06dr//79uuKKK+RwOFRWVub5TGtrq/bs2aPMzExJUnp6usLCwrzm1NbW6sCBA545XUWLAQAAk2C87jkqKkqpqale9yIjIxUXF+e5n5+fr6KiIg0ePFiDBw9WUVGR+vbtq/vuu0+SFBMTo9mzZ2vBggWKi4tTbGysFi5cqLS0NMumxwshQQAAwKS3voth0aJFamlp0QMPPKD6+noNHz5cO3bsUFRUlGdOcXGxQkNDNXXqVLW0tCgrK0tr1qxRSEiIX9/LZhhGr3hpVe0tY4IdAtDrbDl6abBDAHql2TUbunX9Fy+9P2Brfel498baXdiDAAAALGgxAABgEow9CL0NCQIAACa9dQ9CT6LFAAAALKggAABgQgWBBAEAAAuDPQi0GAAAgBUVBAAATGgxkCAAAGBBgkCLAQAA+EAFAQAAk17xDoIgI0EAAMCEJymSIAAAYMEeBPYgAAAAH6ggAABgQgWBBAEAAAs2KdJiAAAAPlBBAADAhFMMJAgAAFiwB4EWAwAA8IEKAgAAJmxSJEEAAMCigxSBFgMAALCiggAAgAmbFEkQAACwoMFAggAAgAUVBPYgAAAAH6ggAABgwpMUSRAAALDgmCMtBgAA4AMVBAAATKgfkCAAAGDBKQZaDAAAwAcSBAAATDpkBOzyx8qVKzV06FBFR0crOjpaI0eO1NatWz3js2bNks1m87pGjBjhtYbb7db8+fMVHx+vyMhI5eXlqaamxu/fAQkCAAAmRgAvfwwcOFDf/e53tW/fPu3bt09jx47VHXfcoYMHD3rmTJgwQbW1tZ5ry5YtXmvk5+ertLRUJSUlKi8vV3NzsyZOnKj29na/YmEPAgAAvcSkSZO8vn7yySe1cuVK7d27V9dff70kyW63y+Fw+Px8Q0ODVq9erfXr1ys7O1uStGHDBiUnJ2vnzp0aP358l2OhggAAgElHAC+3263Gxkavy+12XzCG9vZ2lZSU6OzZsxo5cqTn/u7duzVgwAANGTJEc+bMUV1dnWesqqpKbW1tysnJ8dxLSkpSamqqKioq/PodkCAAAGASyD0ITqdTMTExXpfT6ez0e1dXV+viiy+W3W7XvHnzVFpaquuuu06SlJubq40bN2rXrl1atmyZKisrNXbsWE/C4XK5FB4ern79+nmtmZCQIJfL5dfvgBYDAAAmgXwOQmFhoQoKCrzu2e32TudfffXV2r9/v86cOaNXXnlFM2fO1J49e3Tddddp2rRpnnmpqanKyMhQSkqKNm/erClTpnS6pmEYstn8e340CQIAAN3IbrefNyEwCw8P11VXXSVJysjIUGVlpZ5++mn9+Mc/tsxNTExUSkqKDh06JElyOBxqbW1VfX29VxWhrq5OmZmZfsVNiwEAAJNA7kH4dxmG0emehVOnTunYsWNKTEyUJKWnpyssLExlZWWeObW1tTpw4IDfCQIVBAAATIwgPWx58eLFys3NVXJyspqamlRSUqLdu3dr27Ztam5u1pIlS3TXXXcpMTFRR48e1eLFixUfH68777xTkhQTE6PZs2drwYIFiouLU2xsrBYuXKi0tDTPqYauIkEAAKCX+OCDDzRjxgzV1tYqJiZGQ4cO1bZt2zRu3Di1tLSourpa69at05kzZ5SYmKgxY8Zo06ZNioqK8qxRXFys0NBQTZ06VS0tLcrKytKaNWsUEhLiVyw2wzB6xTspam8ZE+wQgF5ny9FLgx0C0CvNrtnQres/dPm0C0/qohVHNwVsrZ5EBQEAABN/H5H8n4hNigAAwIIKAgAAJtQPSBA+cyLvv0/R8+bo7M9+rsYf/kiSlFj+a59zG3/0nM7+9OPeWewzxbIPu8FrvGXnLp1Z8u1ujRfoLtfMyNK1X8zSxQP7S5LOvFej3y8vVc2v35Ek9YmP1k2L79Glt6bJHtNXrt++q7f/Z60aj3zgWePq6WN05eRMxaVervCoCK2/7qtqbfwwKD8PAosWAwnCZ0rYNVerb95Etf3lr173P8jzfvqWfcRwxXzj6/poz5te9z98/Vdq+skLnq8Nd2v3BQt0s7O1p1Xp3OT5C3/wF0Ype3WBXpvwmM68d1zjVj+ijrZ27ZxdrLamFqV+NVe5Py3UK2Me1bmWj8+kh/YJV83ud1Sz+x3dVBi4TW1Ab0CC8Blhi+ijSx5/TA1Lf6CLZ87wGus4Xe/1dZ9bblbr7/ar/USt133jo48sc4FPq2M7f+/1ddXSl3XtF7M04Mar1NHWrgHpg/XK2Ed15r3jkqSKxS/qvj88qysmj9R7P90tSTq4erskyTHy2h6NHd0vEA84+rRjk+JnRHRBvj6q2KvWfb8777yL+vWTPXOEPty8xTLWZ1y2En71muLXv6ioB+fJFhHRXeECPcp2kU1X5I1QaIRddVWHFGL/+L+d2t1tnjlGh6GO1nYl3DQkWGGiBxkB/OfTigrCZ0CfrDEKu3qITn5l7gXnRuSOl/Hhh5b2QsuOnWqvrVXHqdMKvWKQoubOUdhVV+r0I1/vrrCBbtfvmoGa9IslCrGHqe3sR9o5Z7nOHDohW2iImo79XRnfmKbffGO1zn3oVupXP6++CZeo74BLgh02egAVhG5IEI4dO6bHH39cL7zwQqdz3G635bnS7o4O2S+ioBFoFw3or+iHH9LpgkVSa9sF5/e9PVctO3Za5rb8crPnz+eOHNW5muPqv/rHCh0yWOfeOxTwuIGe0PDXWpWOf0z26L66/PM36dbiudpy93d05tAJvfHVpzXqB3M04+AqdZxr14nygzq2a3+wQwZ6TMD/Rj59+rTWrl173jm+3o39TM3fAh0KJIVdPUQhsbGK/8mP5di9U47dO2UfdoP63j1Fjt07pX9JysKGpik05TJ9+Ctre8Hs3LvvyWhrU+jAgd0ZPtCtOtra1XT0A51854j2ffdnOv3H93X97AmSpFPVR/Xa+Me07to5+umND2n7/Utl73exmt7/e5CjRk+gxfAJKgivv/76eccPHz58wTV8vRv79IRJ/oaCLmjd9zv9fcaXvO7FLH5U5/72vs5u/KnU8c9CWt+Jn1frn9/VOdMpB19CB10uW1iYOk6dCnjMQLDYbDZdFO79f4ttTS1qkxQ9KEHxQ6/Q777/8+AEhx5Fi+ETJAiTJ0+WzWbT+V7hYLPZzruGr3djn6W90C2MlhadO3LU+95HH8lobPS6b+vbV33GjFbTipWWNUKSkhSRky3323vV0dCg0MsvV/RD/622d99Ta/WBbv4JgO6R/uhU1fz6Dzp74pTCLu6jK/JGyjHyWm2/f6kk6fLb/0sfnW7S2eMn1e+aZI341gz9bfs+HX/zn/+bj+gfo4j+MYq+PEGS1O+aZLU1t6j5xCm1njkblJ8LCBS/E4TExET96Ec/0uTJk32O79+/X+np6f9uXOhhfbLHymazqWXnLsuYca5N4ek3KvILU2SLiFB73d/lfnuvml5Y61WBAD5NIvpHa/TT89R3wCVqbfpQp/90TNvvX6oTb32cAPRNuETDH5+uiPgYtdSd0aGfl2v/06Vea1wzI0s3FvzzOSITX/0fSdKbj/xYh15+q+d+GARcR+94j2FQ+f02x7y8PN1www164oknfI7/4Q9/0LBhw9Th518cvM0RsOJtjoBv3f02x/tTplx4Uhdt+NurAVurJ/ldQfj617+us2c7L51dddVV+vWvfT+6FwAAfDr4nSCMGjXqvOORkZEaPXr0Jw4IAIBg410MPCgJAACLT/PxxEDh6AAAALCgggAAgAnns0gQAACwYA8CCQIAABbsQWAPAgAA8IEKAgAAJuxBIEEAAMDCz4cM/0eixQAAACyoIAAAYMIpBhIEAAAs2INAiwEAAPhABQEAABOeg0CCAACABXsQaDEAAAAfqCAAAGDCcxBIEAAAsOAUAy0GAAAsjAD+44+VK1dq6NChio6OVnR0tEaOHKmtW7f+My7D0JIlS5SUlKSIiAjddtttOnjwoNcabrdb8+fPV3x8vCIjI5WXl6eamhq/fwckCAAA9BIDBw7Ud7/7Xe3bt0/79u3T2LFjdccdd3iSgKVLl+qpp57SihUrVFlZKYfDoXHjxqmpqcmzRn5+vkpLS1VSUqLy8nI1Nzdr4sSJam9v9ysWm9FLGi21t4wJdghAr7Pl6KXBDgHolWbXbOjW9bOTxwdsrZ3Htv9bn4+NjdX3v/99ffnLX1ZSUpLy8/P16KOPSvq4WpCQkKDvfe97mjt3rhoaGtS/f3+tX79e06ZNkySdOHFCycnJ2rJli8aP7/rPRQUBAAATwzACdrndbjU2Nnpdbrf7gjG0t7erpKREZ8+e1ciRI3XkyBG5XC7l5OR45tjtdo0ePVoVFRWSpKqqKrW1tXnNSUpKUmpqqmdOV5EgAADQjZxOp2JiYrwup9PZ6fzq6mpdfPHFstvtmjdvnkpLS3XdddfJ5XJJkhISErzmJyQkeMZcLpfCw8PVr1+/Tud0FacYAAAwCeSDkgoLC1VQUOB1z263dzr/6quv1v79+3XmzBm98sormjlzpvbs2eMZt9lsXvMNw7DcM+vKHDMSBAAATAL5qGW73X7ehMAsPDxcV111lSQpIyNDlZWVevrppz37DlwulxITEz3z6+rqPFUFh8Oh1tZW1dfXe1UR6urqlJmZ6VfctBgAAOjF/rGPYdCgQXI4HCorK/OMtba2as+ePZ6//NPT0xUWFuY1p7a2VgcOHPA7QaCCAACASUeQDvgtXrxYubm5Sk5OVlNTk0pKSrR7925t27ZNNptN+fn5Kioq0uDBgzV48GAVFRWpb9++uu+++yRJMTExmj17thYsWKC4uDjFxsZq4cKFSktLU3Z2tl+xkCAAAGASrPP/H3zwgWbMmKHa2lrFxMRo6NCh2rZtm8aNGydJWrRokVpaWvTAAw+ovr5ew4cP144dOxQVFeVZo7i4WKGhoZo6dapaWlqUlZWlNWvWKCQkxK9YeA4C0IvxHATAt+5+DsKoS7MCttZbx98I2Fo9iQoCAAAmvO6ZBAEAAAsSBBIEAAAsekn3Pag45ggAACyoIAAAYEKLgQQBAACLQD5J8dOKFgMAALCgggAAgAmbFEkQAACwYA8CLQYAAOADFQQAAExoMZAgAABgQYuBFgMAAPCBCgIAACY8B4EEAQAAiw72IJAgAABgRgWBPQgAAMAHKggAAJjQYiBBAADAghYDLQYAAOADFQQAAExoMZAgAABgQYuBFgMAAPCBCgIAACa0GEgQAACwoMVAiwEAAPhABQEAABPD6Ah2CEFHggAAgEkHLQYSBAAAzAw2KbIHAQAAWFFBAADAhBYDCQIAABa0GGgxAAAAH0gQAAAw6TCMgF3+cDqduummmxQVFaUBAwZo8uTJevfdd73mzJo1SzabzesaMWKE1xy326358+crPj5ekZGRysvLU01NjV+xkCAAAGBiBPAff+zZs0cPPvig9u7dq7KyMp07d045OTk6e/as17wJEyaotrbWc23ZssVrPD8/X6WlpSopKVF5ebmam5s1ceJEtbe3dzkW9iAAANBLbNu2zevrF198UQMGDFBVVZVuvfVWz3273S6Hw+FzjYaGBq1evVrr169Xdna2JGnDhg1KTk7Wzp07NX78+C7FQgUBAAATwzACdrndbjU2Nnpdbre7S3E0NDRIkmJjY73u7969WwMGDNCQIUM0Z84c1dXVecaqqqrU1tamnJwcz72kpCSlpqaqoqKiy78DEgQAAEw6ZATscjqdiomJ8bqcTucFYzAMQwUFBbrllluUmprquZ+bm6uNGzdq165dWrZsmSorKzV27FhP0uFyuRQeHq5+/fp5rZeQkCCXy9Xl3wEtBgAAulFhYaEKCgq87tnt9gt+7qGHHtI777yj8vJyr/vTpk3z/Dk1NVUZGRlKSUnR5s2bNWXKlE7XMwxDNputy3GTIAAAYBLI5yDY7fYuJQT/av78+Xr99df15ptvauDAgeedm5iYqJSUFB06dEiS5HA41Nraqvr6eq8qQl1dnTIzM7scAy0GAABMgnXM0TAMPfTQQ3r11Ve1a9cuDRo06IKfOXXqlI4dO6bExERJUnp6usLCwlRWVuaZU1tbqwMHDviVIFBBAADAJFhPUnzwwQf10ksv6Re/+IWioqI8ewZiYmIUERGh5uZmLVmyRHfddZcSExN19OhRLV68WPHx8brzzjs9c2fPnq0FCxYoLi5OsbGxWrhwodLS0jynGrqCBAEAgF5i5cqVkqTbbrvN6/6LL76oWbNmKSQkRNXV1Vq3bp3OnDmjxMREjRkzRps2bVJUVJRnfnFxsUJDQzV16lS1tLQoKytLa9asUUhISJdjsRm95IHTtbeMCXYIQK+z5eilwQ4B6JVm12zo1vVjLr4yYGs1NP81YGv1JCoIAACY9JL/dg4qNikCAAALKggAAJj4e/rgPxEJAgAAJv6+ZOk/ES0GAABgQQUBAAATWgwkCAAAWHCKgRYDAADwgQoCAAAmbFIkQQAAwIIWAwkCAAAWJAjsQQAAAD5QQQAAwIT6QS96myN6B7fbLafTqcLCQtnt9mCHA/QK/HuBzyISBHhpbGxUTEyMGhoaFB0dHexwgF6Bfy/wWcQeBAAAYEGCAAAALEgQAACABQkCvNjtdj3++ONsxAL+Bf9e4LOITYoAAMCCCgIAALAgQQAAABYkCAAAwIIEAQAAWJAgwOPZZ5/VoEGD1KdPH6Wnp+utt94KdkhAUL355puaNGmSkpKSZLPZ9NprrwU7JKDHkCBAkrRp0ybl5+frscce0+9//3uNGjVKubm5ev/994MdGhA0Z8+e1ec+9zmtWLEi2KEAPY5jjpAkDR8+XDfeeKNWrlzpuXfttddq8uTJcjqdQYwM6B1sNptKS0s1efLkYIcC9AgqCFBra6uqqqqUk5PjdT8nJ0cVFRVBigoAEEwkCNDJkyfV3t6uhIQEr/sJCQlyuVxBigoAEEwkCPCw2WxeXxuGYbkHAPhsIEGA4uPjFRISYqkW1NXVWaoKAIDPBhIEKDw8XOnp6SorK/O6X1ZWpszMzCBFBQAIptBgB4DeoaCgQDNmzFBGRoZGjhypVatW6f3339e8efOCHRoQNM3NzfrLX/7i+frIkSPav3+/YmNjddlllwUxMqD7ccwRHs8++6yWLl2q2tpapaamqri4WLfeemuwwwKCZvfu3RozZozl/syZM7VmzZqeDwjoQSQIAADAgj0IAADAggQBAABYkCAAAAALEgQAAGBBggAAACxIEAAAgAUJAgAAsCBBAAAAFiQIAADAggQBAABYkCAAAAALEgQAAGDx/wC2zmbJoTDpYgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,model_tree_new.predict(X_test)),annot=True,fmt='g') \n",
    "#fmt=g sayıları daha düzgün şekilde gösterir\n",
    "#sol yukarıdan sağ aşağıya olan köşegen doğru tahmin, diğer köşe yanlış tahminleri gösterir\n",
    "#y_test de bulunan gerçek değerlerle y_test modelimizi karşılaştırdı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d428d09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1740"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "211744e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.596551724137931"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(647+391)/1740 #doğru tahmin olan köşeleri toplayıp tüm verilerin toplamına böldük "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4342733e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score= 0.596551724137931\n"
     ]
    }
   ],
   "source": [
    "print(f\"Test Accuracy Score= {accuracy_score(y_test,model_tree_new.predict(X_test))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "79555e4c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tree_new.predict(X_test) #model_tree_new i X_test e göre tahmin ediyor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "775e5af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7581    0\n",
       "3909    1\n",
       "6560    0\n",
       "675     1\n",
       "1253    1\n",
       "       ..\n",
       "5224    0\n",
       "6729    0\n",
       "7563    0\n",
       "26      1\n",
       "6583    0\n",
       "Name: long_hair, Length: 1740, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test #gerçek veriler"
   ]
  }
 ],
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