titanic-veri-seti.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gerekli kütüphanelerin import edilmesi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"titanic.csv\", sep = \",\")\n",
    "test_df = pd.read_csv(\"test.csv\", sep = \",\")\n",
    "\n",
    "full_df = pd.concat([train_df, test_df])\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Veri Setine Genel Bir Bakış"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10692"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>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>PassengerId</th>\n",
       "      <td>891.0</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>1.00</td>\n",
       "      <td>223.5000</td>\n",
       "      <td>446.0000</td>\n",
       "      <td>668.5</td>\n",
       "      <td>891.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        mean         std   min       25%       50%    75%  \\\n",
       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
       "\n",
       "                  max  \n",
       "PassengerId  891.0000  \n",
       "Survived       1.0000  \n",
       "Pclass         3.0000  \n",
       "Age           80.0000  \n",
       "SibSp          8.0000  \n",
       "Parch          6.0000  \n",
       "Fare         512.3292  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Cabin', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns[train_df.isnull().any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Missing values heatmap')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train_df.isnull(), cbar = False).set_title(\"Missing values heatmap\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988c2f8e48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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06bkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# !pip install missingno\n",
    "import missingno as msno\n",
    "msno.matrix(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C</td>\n",
       "      <td>C85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>C123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.0</td>\n",
       "      <td>S</td>\n",
       "      <td>E46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Embarked Cabin\n",
       "0  22.0        S   NaN\n",
       "1  38.0        C   C85\n",
       "2  26.0        S   NaN\n",
       "3  35.0        S  C123\n",
       "4  35.0        S   NaN\n",
       "5   NaN        Q   NaN\n",
       "6  54.0        S   E46\n",
       "7   2.0        S   NaN\n",
       "8  27.0        S   NaN\n",
       "9  14.0        C   NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Age\",\"Embarked\",\"Cabin\"]].iloc[0:10]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VERİ SETİNİN GÖRSELLEŞTİRİLMESİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+wxvHXcJiseReO37Bv7v6LLnXL285q9fve0+3o8sgGFZhnUMfqmo3sHs+ilrskkxV1cS469DZ87Vb2lp+/bocGpoG1va11wD3na5PkhXAdwAPdFiTJGlAl0GwH9iQZH2SlcDVwORAn0ng5LjCVcAnq+qUKwJJUnc6GxrqjflvBfYBy4EPVtWBJDuAqaqaBH4H+FCSw8xeCVzdVT1LSBNDYE9SvnZLW7OvX/wDXJLa5sxiSWqcQSBJjTMIFokk70xyIMkdSW5PMjjnQotYktVJ9iT5uyQHk+xN8pxx16W5JVmT5I+T3JXkSJL3JHnKuOtaSAbBIpDkh4GfAF5QVZcAl/Htk/G0iPVmw38c+HRVPauqNgL/Fvie8VamufReu/8K/FFVbQA2AE8Ddo61sAXW5YQyje6ZwP1V9U2AqlpSsxvFy4FHq+p9JzdU1e1jrEej+1Hg4ar6XYCqeizJzwH3JnlnVX1tvOUtDK8IFoc/A9Ym+WKS/5zkpeMuSGflucBt4y5C5+QHGHjtquofgHuAZ4+joHEwCBaB3l8dP8Ts/ZSOAx9Jct1Yi5LaEIbc1obht7950jIIFomqeqyqPl1VvwBsBV497po0sgPMBrmWngPAt91fKMkFzL6/82S+ueW3MQgWgSTfl2RD36bnAfeOqx6dtU8CT0nyUyc3JHmhQ3xLwieA85JcC99aR+U3gPdU1TfGWtkCMggWh2cAN/Y+dngHswv5/OJ4S9KoevfH+lfAK3ofHz3A7Os3eJNFLTJ9r91VSe4Cvgw8XlXvGm9lC8tbTEhST5J/DtwEvKqqmvkAgEEgSY1zaEiSGmcQSFLjDAJJapxBIEmNMwjUjCSP9e7sevLr+rM49mVJ/vQJPv+nk5zT4ujz8fzS6XjTObXkG1X1vHE8cW+ikrQoeUWg5iW5J8mvJPnrJFNJXpBkX29y2Jv7ul6Q5OO9iX/vS7Ksd/x7e8cdSPJLA+fdnuQzwGv6ti9LcmOSf9drv7L33H+b5KNJntHbvjnJF3rHv2pBfhhqkkGgljxtYGjodX37jlbVDwN/CfwecBXwYmBHX59NwM8DPwg8i3/85fzOqpoALgFemuSSvmMerqqXVNWeXnsF8F+AL1bVDUkuBG4ALquqFwBTwFuTPBX4APCTwL8AVs/Tz0A6hUNDasmZhoYme98/Dzyjqh4CHkrycJLv7O37m6o6ApDkJuAlwMeA1ybZwuz/p2cye4uQO3rHfGTged4P3Nx3C4MX9/rfMrtGCiuBvwa+H7i7qu7qPd8fMHt3WmneGQTSrG/2vj/e9/hk++T/k8Fp+JVkPfA24IVV9WCS3wOe2tfn6wPH/BXw8iS/UVUPM3u74/9ZVdf0d0ryvCHPJ3XCoSFpdJuSrO+9N/A64DPABcz+sv9qku8BLp/jHL8D7AU+mmQFcCtwaZJnAyQ5r7fW8ReA9Ume1TvumqFnk+aBVwRqydOS9C8h+T+qauSPkDI7ZPPvmX2P4H8BH6+qx5N8jtn72h8BbpnrJFX17iTfAXwIeANwHXBT34LpN1TVF3vDTf8tyf3Mhs5zz6JWaWTedE6SGufQkCQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjfv/u0vU9usJwEkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(train_df['Embarked'], train_df['Survived']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H3/MDzJ4ykiSNiHcWS1LjDAJJapxBIEmNMwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIaZxBIUuMMAklqnEEgSY0zCCSpcQaBJDWu0yBIsinJkSRHk9w0YPvfSfLx3va7klzWZT2SpNN1FgRJVgG7gdcClwPXJbl8zrA3Ad+uqhcDHwD+a1f1SJIG6/KIYCNwtKqOVdVJYC+wec6YzcCHe89vBX4kSTqsSZI0x/kd7vsS4Hhfewp45ZnGVNWpJN8Bng880D8oyVZga6/5SJIjnVR8bljDnM9/rssv/vSoSzhXrLjvjv/o3119Vtz3l7cv6vt74Zk2dBkEgyqssxhDVe0B9ixFUee6JJNVNT7qOrR4fncrW8vfX5enhqaAdX3tS4H7zzQmyfnA9wEPdliTJGmOLoPgALAhyfokq4EtwMScMRPAk+cVXg/cVlWnHRFIkrrT2amh3jn/bcB+YBVwc1UdSrITmKyqCeA3gY8kOcrskcCWrupZQZo4BfYM5Xe3sjX7/cU/wCWpbd5ZLEmNMwgkqXEGwTkiyc1Jvpnky6OuRYuTZF2S25Pcm+RQkneMuiYNL8kFSf48yRd73997Rl3TcvMawTkiyauAR4DfrqqXj7oeDS/JxcDFVfX5JBcBdwM/XlWHR1yahtCbzeDCqnokyfcAfwq8o6ruHHFpy8YjgnNEVX0G76FYkarqG1X1+d7zh4F7mb1rXitAzXqk1/ye3qOpv5ANAmkJ9WbQvRK4a7SVaDGSrEpyEPgm8MdV1dT3ZxBISyTJc4BPAP+6qr476no0vKp6vKpewewMCBuTNHV61iCQlkDv3PIngN+pqt8bdT06O1X1EPApYNOIS1lWBoH0NPUuNv4mcG9VvX/U9WhxkowleW7v+bOAq4GvjLaq5WUQnCOSfAz4HPDSJFNJ3jTqmjS0q4CfBF6T5GDv8c9HXZSGdjFwe5J7mJ0j7Y+r6g9HXNOy8uejktQ4jwgkqXEGgSQ1ziCQpMYZBJLUOINAkhpnEEhzJHm89xPQLyf53STPnmfsu5PcuJz1SUvNIJBO92hVvaI3C+xJ4C2jLkjqkkEgze+zwIsBkvxUknt689Z/ZO7AJG9OcqC3/RNPHkkkeUPv6OKLST7T63tZbw78g719bljWTyX18YYyaY4kj1TVc5Kcz+z8QX8EfAb4PeCqqnogyfOq6sEk7wYeqapfTPL8qvpWbx//Cfirqvpgki8Bm6rqRJLnVtVDST4I3FlVv5NkNbCqqh4dyQdW8zwikE73rN6UxJPA15mdR+g1wK1V9QBAVQ1aO+LlST7b+4//euBlvf47gN9K8mZgVa/vc8C/S/JzwAsNAY3S+aMuQDoHPdqbkvgpvYnlFjp8/i1mVyb7YpIbgFcDVNVbkrwS+DHgYJJXVNVHk9zV69uf5Geq6rYl/hzSUDwikIbzSeCNSZ4PkOR5A8ZcBHyjNyX19U92JnlRVd1VVTuAB4B1Sb4fOFZVvwRMAFd0/gmkM/CIQBpCVR1K8l7g00keB74A3DBn2M8zuzLZ14AvMRsMAO/rXQwOs4HyReAm4CeSPAZMAzs7/xDSGXixWJIa56khSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIa9/8AxesgOxT+NlUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(train_df['Pclass'], train_df['Survived']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(train_df['Sex'], train_df['Survived']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988c504ec8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(train_df['Cabin'], train_df['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "Survived = train_df[\"Survived\"].value_counts()\n",
    "sns.barplot(y=Survived.values, x=Survived.index, alpha=0.6)\n",
    "plt.title('Distribution of Passenger survival')\n",
    "plt.xlabel('Passenger survival', fontsize=10)\n",
    "plt.ylabel('Count', fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))\n",
    "ax1.hist(train_df[\"Age\"].dropna(),bins = 12)\n",
    "ax1.set_xlabel = (\"Age\")\n",
    "\n",
    "ax2.hist(train_df[\"Fare\"], bins = 12)\n",
    "ax2.set_xlabel(\"Fare\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988cdb2ec8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#titanic_dataset.dropna(inplace = True)\n",
    "sns.distplot(train_df[\"Age\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kategorik değişkeni sayısal değerlere dönüştürme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.CİNSİYET DEĞİŞKENİ İÇİN  ANALİZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Binary encoding\n",
    "\n",
    "train_df[\"Sex\"] = train_df[\"Sex\"].map({\"male\":0, \"female\":1})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Cabin Değişkeni İçin Analiz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin değişkeninin çok büyük oranda eksik değer içermesinden dolayı veri setinden silindi.\n",
    "\n",
    "train_df.drop(\"Cabin\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.EMBARKED Değişkeni İçin Analiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Southampton(S)'dan binen yolcu sayısı: 644\n",
      "Cherbourg (C)'dan binen yolcu sayısı: 168\n",
      "Queenstown(Q)'dan binen yolcu sayısı: 77\n"
     ]
    }
   ],
   "source": [
    "southampton = train_df[train_df[\"Embarked\"] == \"S\"].shape[0]\n",
    "print(\"Southampton(S)'dan binen yolcu sayısı:\",southampton)\n",
    "\n",
    "cherbourg = train_df[train_df[\"Embarked\"] == \"C\"].shape[0]\n",
    "print(\"Cherbourg (C)'dan binen yolcu sayısı:\",cherbourg)\n",
    "\n",
    "queenstown = train_df[train_df[\"Embarked\"] == \"Q\"].shape[0]\n",
    "print(\"Queenstown(Q)'dan binen yolcu sayısı:\",queenstown )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# her farklı kategorideki embark için değer atama (MAPPING)\n",
    "\n",
    "embarked_mapping = {\"S\": 1, \"C\": 2, \"Q\": 3}\n",
    "train_df['Embarked'] = train_df['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Embarked  \n",
       "0         A/5 21171   7.2500       1.0  \n",
       "1          PC 17599  71.2833       2.0  \n",
       "2  STON/O2. 3101282   7.9250       1.0  \n",
       "3            113803  53.1000       1.0  \n",
       "4            373450   8.0500       1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ticket Değişkeni İçin Analiz\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bilet değişkeni doğrudan target değerine etki etmeyeceği için silebiliriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop([\"Ticket\"], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "      Fare  Embarked  \n",
       "0   7.2500       1.0  \n",
       "1  71.2833       2.0  \n",
       "2   7.9250       1.0  \n",
       "3  53.1000       1.0  \n",
       "4   8.0500       1.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pclass Değişkeni İçin Analiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass  Survived\n",
      "0       1  0.629630\n",
      "1       2  0.472826\n",
      "2       3  0.242363\n"
     ]
    }
   ],
   "source": [
    "print(train_df[[\"Pclass\",\"Survived\"]].groupby([\"Pclass\"], as_index = False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Name Değişkeni İçin Analiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re as re  # for regular expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>Col</th>\n",
       "      <td>2</td>\n",
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       "      <th>Countess</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>517</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex         0    1\n",
       "Title             \n",
       "Capt        1    0\n",
       "Col         2    0\n",
       "Countess    0    1\n",
       "Don         1    0\n",
       "Dr          6    1\n",
       "Jonkheer    1    0\n",
       "Lady        0    1\n",
       "Major       2    0\n",
       "Master     40    0\n",
       "Miss        0  182\n",
       "Mlle        0    2\n",
       "Mme         0    1\n",
       "Mr        517    0\n",
       "Mrs         0  125\n",
       "Ms          0    1\n",
       "Rev         6    0\n",
       "Sir         1    0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in [train_df]:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veri setinde yer alan name değişkeninde bulanan unvanları daha genel kullanımına \n",
    "# göre replace metoduyla değiştirelim ve hayatta kalma durumuna göre gruplayalım\n",
    "\n",
    "for dataset in [train_df]:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EMBARKED DEĞİŞKENİ İÇİN EKSİK VERİYİ MOD İLE DOLDURMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_categ = train_df[[\"PassengerId\",\"Name\",\"Embarked\",\"Pclass\",\"Sex\",\"Title\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# Embarked değişkeninde yer alan kayıp değerleri mod ile dolduruyoruz.\n",
    "\n",
    "embarked = train_df_categ[\"Embarked\"].mode()[0]\n",
    "print(embarked) # mod değeri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ysdmn\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "train_df_categ[\"Embarked\"] = train_df_categ[\"Embarked\"].fillna(embarked)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Name           0\n",
       "Embarked       0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "Title          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_categ.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = train_df[\"Survived\"]   # hedef değişken belirlendi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop([\"Survived\",\"PassengerId\",\"Name\",\"Embarked\",\"Sex\",\"Pclass\",\"Title\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age      177\n",
       "SibSp      0\n",
       "Parch      0\n",
       "Fare       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_num = train_df\n",
    "train_df_num.isnull().sum()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eksik Veri Doldurma Yöntemleri "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. ORTALAMA (MEAN) İLE DOLDURMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age değişkeni için çeşitli veri doldurma yöntemlerini uygulayalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_mean = train_df_num.copy() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.7\n"
     ]
    }
   ],
   "source": [
    "mean_age = train_df_num[\"Age\"].mean() # ortalama yaş değeri\n",
    "print(round(mean_age,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch   Fare\n",
       "886  27.0      0      0  13.00\n",
       "887  19.0      0      0  30.00\n",
       "888   NaN      1      2  23.45\n",
       "889  26.0      0      0  30.00\n",
       "890  32.0      0      0   7.75"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_mean.tail()  # mean ile doldurulmadan önce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_mean['Age'] = train_df_mean[\"Age\"].fillna(np.round(mean_age,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>29.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch   Fare\n",
       "886  27.0      0      0  13.00\n",
       "887  19.0      0      0  30.00\n",
       "888  29.7      1      2  23.45\n",
       "889  26.0      0      0  30.00\n",
       "890  32.0      0      0   7.75"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_mean.tail()  # mean ile doldurulduktan sonra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df_num[\"Age\"] , color=\"olive\", label=\"original-dataset\")\n",
    "sns.distplot( train_df_mean[\"Age\"] , color=\"teal\", label=\"replacement-by-mean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. MEDYAN (ORTANCA) İLE DOLDURMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_median = train_df_num.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median age: 28.0\n"
     ]
    }
   ],
   "source": [
    "median_age = train_df_num[\"Age\"].median()\n",
    "print(\"median age:\",median_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch   Fare\n",
       "886  27.0      0      0  13.00\n",
       "887  19.0      0      0  30.00\n",
       "888   NaN      1      2  23.45\n",
       "889  26.0      0      0  30.00\n",
       "890  32.0      0      0   7.75"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_median.tail() # medyan ile doldurulmadan önce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_median[\"Age\"] = train_df_median[\"Age\"].fillna(median_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch   Fare\n",
       "886  27.0      0      0  13.00\n",
       "887  19.0      0      0  30.00\n",
       "888  28.0      1      2  23.45\n",
       "889  26.0      0      0  30.00\n",
       "890  32.0      0      0   7.75"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_median.tail() # medyan ile doldurulduktan sonra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df_num[\"Age\"] , color=\"red\", label=\"original-dataset\")\n",
    "sns.distplot( train_df_median[\"Age\"] , color=\"skyblue\", label=\"replacement-by-median\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Mod ile Doldurma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_mode = train_df_num.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_mode = train_df_mode[\"Age\"].mode()[0] # modunu bulma\n",
    "age_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_mode[\"Age\"] = train_df_mode[\"Age\"].fillna(age_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NQVFVSkpKiI8/sGNdlroxpgsNHDiQwsJC7LoL5mDFx8c3nSAWKQv0xnQhr9fL4MGDu7sbppex1I0xxsQ4C/TGGBPjLNAbY0yMs0BvjDExzgK9McbEOAv0xhgT4yzQG2NMjLNAb4wxMc4CvTHGxDgL9MYYE+Ms0BtjTIyzQG+MMTHOAr0xxsQ4C/TGGBPjLNAbY0yMs0BvjDExzgK9McbEOAv0xhgT4yIK9CJyhoisFZH1InJdK+vjRGReeP1HIpIfXu4VkSdE5HMRWSMiv4hu940xxnSkw0AvIm7gfuBMYCQwS0RGtmh2BbBXVYcCfwLuCC+/CIhT1dHAeOAHjV8CxhhjukYkI/pJwHpV3aiqDcDfgXNbtDkXeCJ8/zngZBERQIEkEfEACUADUBGVnhtjjIlIJIE+F9jW7HFheFmrbVQ1AJQDmThBvxrYCWwF7lTV0pYvICJzRGSpiCwtKio64J0wxhjTtkgCvbSyTCNsMwkIAgOAwcD/isiQ/RqqPqKqE1R1QnZ2dgRdMsYYE6lIAn0hMKjZ44HAjrbahNM0aUApcCnwqqr6VXUPsBiYcKidNsYYE7lIAv0SYJiIDBYRH3AJsKBFmwXA7PD9C4GFqqo46ZqTxJEEHAd8EZ2uG2OMiUSHgT6cc78aeA1YA/xDVVeJyM0ick642WNApoisB/4HaJyCeT+QDKzE+cKYq6qfRXkfjDHGtMMTSSNVfRl4ucWy65vdr8OZStnyeVWtLTfGGNN17MxYY4yJcRbojTEmxlmgN8aYGGeB3hhjYpwFemOMiXEW6I0xJsZZoDfGmBhngd4YY2KcBXpjjIlxFuiNMSbGWaA3xpgYZ4HeGGNinAV6Y4yJcRbojTEmxlmgN8aYGGeB3hhjYpwFemOMiXEW6I0xJsZZoDfGmBhngd4YY2KcBXpjjIlxFuiNMSbGWaA3xpgYZ4HeGGNinAV6Y4yJcRbojTEmxlmgN8aYGGeB3hhjYpwFemOMiXEW6I0xJsZFFOhF5AwRWSsi60XkulbWx4nIvPD6j0Qkv9m6MSLygYisEpHPRSQ+et03xhjTkQ4DvYi4gfuBM4GRwCwRGdmi2RXAXlUdCvwJuCP8XA/wFPBDVT0GmAH4o9Z7Y4wxHYpkRD8JWK+qG1W1Afg7cG6LNucCT4TvPwecLCICnAZ8pqqfAqhqiaoGo9N1Y4wxkYgk0OcC25o9Lgwva7WNqgaAciATOApQEXlNRD4RkZ+39gIiMkdElorI0qKiogPdB2OMMe2IJNBLK8s0wjYeYBpwWfjneSJy8n4NVR9R1QmqOiE7OzuCLhljjIlUJIG+EBjU7PFAYEdbbcJ5+TSgNLz8bVUtVtUa4GWg4FA7bYwxJnKRBPolwDARGSwiPuASYEGLNguA2eH7FwILVVWB14AxIpIY/gI4EVgdna4bY4yJhKejBqoaEJGrcYK2G/iLqq4SkZuBpaq6AHgMeFJE1uOM5C8JP3eviNyF82WhwMuq+u9O2hdjjDGt6DDQA6jqyzhpl+bLrm92vw64qI3nPoUzxdIYY0w3sDNjjTEmxlmgN8aYGGeB3nQJVWV3TYC6QKi7u2JMrxNRjt6YQxEIKZ+V1LGnLkhZQ5BvHpmKc+K0MaYr2IjedKr6YIgPd9dSVBekX4KbTZV+PtpT293dMqZXsRG96VTbqgJUBUJMyI4nM87Nlio/7+yoYVCyl9wkb3d3z5hewUb0ptOoKjtr/PSJc5EV70FEOGNQMkleF+/urOnu7hnTa1igN52mwh+iOqAMSPxq5B7vcXFMnzi2VvrtwKwxXcQCvek0O6oDCNA/cd8M4bB0HyFgQ0VDt/TLmN7GAr3pFKrKrpoA2QluvK59Z9gMSPSQ5BHWlVugN6YrWKA3naK0Pkh9aN+0TSMRYVhaHBsr/ARCLSteG2OizQK96RS7a4O4BbLj3a2uH5bmoyGkbKm0K0sa09ks0JtOUVofpE+cG7er9ROj8lK8+FyWvjGmK1igN1HnDylV/hB9fK2P5gE8LmFwqpcNFQ04ly4wxnQWC/Qm6vbWO9d/T49r/+N1RLKXSn+I8gabZmlMZ7JAb6Jub30QAdLbGdEDDAyfGbu92vL0xnQmC/Qm6srqg6T6XG3m5xtlJ7jxuYTt1YEu6pkxvZMFehNVQVXKGkL0iWt/NA/gEmFAkodCG9Eb06ks0JuoqmgIoRBRoAfITfJQVBukPmh5emM6iwV6E1WNB2Lbm3HT3MAkLwrstPSNMZ3GAr2Jqr31QZI8gs8d2YVFcpKcOjiFFuiN6TQW6E3UqCplDUHSI0zbAMS7XWTHu23mjTGdyAK9iZryhhD+EKRFmLZpNDDZy/bqACE7ccqYTmGB3kTNrhon/ZLmO7CPVW6Sh4aQUlwX7IxuGdPrWaA3UbOzxqk/n+I9sI9VY4XLnTWWpzemM1igN1GzsyZAiteFSyI7ENuoT5yLOLeww/L0xnQKC/QmKlSV3TWBA07bgFOffkCix0b0xnQSC/QmKhovNHKgB2Ib5YRPnGoI2gFZY6LNAr2Jip0HeSC20YBE58Sp3bU2qjcm2izQm6jYVRPA64KkAzwQ2ygnfAFxy9MbE30R/VWKyBkislZE1ovIda2sjxOReeH1H4lIfov1R4hIlYhcE51um55mZ02AfgmeAz4Q2yjJ6yLN57I8vTGdoMNALyJu4H7gTGAkMEtERrZodgWwV1WHAn8C7mix/k/AK4feXdMThcIHYvuHR+UHKyfRww4L9MZEXSQj+knAelXdqKoNwN+Bc1u0ORd4Inz/OeBkEWdoJyLfADYCq6LTZdPTFNcFCehX6ZeDNSDJS0VDiGq/VbI0JpoiCfS5wLZmjwvDy1pto6oBoBzIFJEk4FrgpvZeQETmiMhSEVlaVFQUad9ND9GYbskJn/h0sJry9DWWpzcmmiIJ9K0lXVvOgWurzU3An1S1qr0XUNVHVHWCqk7Izs6OoEumJ9lVEyDOLfTp4BqxHemf6MEFdsUpY6Iskv+1C4FBzR4PBHa00aZQRDxAGlAKTAYuFJHfA+lASETqVPW+Q+656TF2Vgfon+BBDvJAbCOvS+iX6KGwykb0xkRTJEOwJcAwERksIj7gEmBBizYLgNnh+xcCC9Vxgqrmq2o+cDfwWwvysSUQUvbUBQ45P99oYJKHXTUBgiE7ccqYaOkw0Idz7lcDrwFrgH+o6ioRuVlEzgk3ewwnJ78e+B9gvymYJjYV1QYIKfRPik6gz03yElA7ccqYaIror1NVXwZebrHs+mb364CLOtjGjQfRP9PDfXUgNkqBPvmrK04NSDq0g7vGGIedGWsOyc6aAIkeIfUgz4htKcXrJs3nsitOGRNFFujNIdkVPlHqUA/ENjcwyUthlR+1K04ZExUW6M1Bawg6V4WKVtqmUW6Sh+qAUt5gJ04ZEw0W6M1B210bQOGQSx+0lBvOzRda+saYqLBAbw5aYx499xDPiG0pO8FNnEsorLKZN8ZEQ3SHYqZXKawKkFFbSeLcF75amJ3f/pPOO63D7bpEGJTiZVNlA6oa1fy/Mb2RjejNQVFVCqv9DKwq6ZTtD0nxUt4QYm+95emNOVQW6M1BKakPUhdUcjsp0A9O9QGwqbKhU7ZvTG9igd4clO3h/Hlnjej7xLlJ97nYWGGB3phDZYHeHJTCaj8JHiGjrt3CpIdkcKqPrVV+q3tjzCGyQG8OSmG1n4FJ3lbrU0fL4BQv/pBNszTmUNmsG3PAqv3OQdJxmZF/fFw11XjL9sLixeD3Q1ISpKVBXh7ExbX6nLwULy5gU4WfvBRflHpvTO9jgd4csMYRdm57RceCQVKXfUTaxx+QtHY18dsLW2/ncsERR8D48TBtGsyYAWPHgghxbhe5yR42VDQwIzcp+jtiTC9hgd4csG1VfjzS+hmxrro6shc8T+Z//o2vtIRAcgrVI46h9MRT8Gf3I+/MmeDzQXU17N0L69fDl1/CRx/B8887G8nPh/PPh9mzGd5/GG9sr2ZPbYC+CfZxNeZg2F+OOWCbK/0MSvbicTXL0KvC8uWMeP4FfMVFVBRMpHDO1VSMnwxud1OzvC2b991Ybq5zmznTCfyrV8Py5XDvvXDXXRx9ymm8eceTrCquoe+g1K7ZQWNijAV6c0AqGoIU1wUZndEsr+73w1NPwYcfEswbzLqfXUv1yNEHvvE+feD4451bVRV8+CFJ777LkPfeYPWIMZz4yztwTZ26zxfHfubMOfDXNSbGWaA3B2RzpZOfbzyhibIyePBB2LwZvvY11s7+cfuBOFLJyXDKKXDSSYyqLuOf2f3ZunYz+a+/7qR1wnl8Y0zHbHqlOSCbK/0keYTseDds2wa//z3s3Ak//CF8/evRCfLNuVwMTfXhC/pZ9b/XOwdvH3wQ7rkH9uyJ7msZE6NsRG/298gjrS5WYNO4sxlSvgt59S9w553OQdX/+R/nAGon8WqI4aXbWXvkKE654SbiFi2E+fPhppvg7LPh9NOj/wVjTAyxQG8itjsxnVpvHJ66Wmr/fD++igo2XP9bakYcE7XXWNFG9ctkfx0Nbi/Lc4Zx3EyFY4+FefPgn/+Ezz6D734X+vWLWj+MiSWWujER25TaF4Bxd99K/I5CNl13Y1SDfHvSGmrIqinno/7DaHC5IT0dfvADuOIK2L0bbr0VPvywS/pizOHGRvQmYhvT+pG9ays5C1+j8MofUzXm2C59/SPLd/FRznD+PXg8gyvC+fmz8/FOnknePb8nee5c5yzbu+9u82xbY3ojC/QmItWeOAqTM5n69z9SOuMUis88p8v70Ke+mszaCjal9eOIyiLc4YuH+7OyWX/j7eQ8PZd+Dz0Er7zijPYzMzveqE3HNL2ApW5MRL5MzEBdLoavXMK2H/yk26Y2Di3bSYPby/q0nH1XuN3s/M6Vzuyf3bvhtttg5cpu6aMxPY0FetMxVdYGfPTZupHsmdPQbkyL9KmvZmBlMZvS+lEWl7h/g2OPhV/+0snh33cfvP66c9auMb2YBXrTodrPV7H1qDEM37YWGTiwu7vDiNJC4oMNfJ6VT7C1/yz69YPrrnOC/nPPwdNPQzDY9R01poewQG/aV13Nut0VhLxehqe2U62yC3k0xOjirVR741mdMYhWx+s+H3z/+3DGGfDuu07tnOrqru6qMT2CBXrTvgULWDvtVFJrKuhfV9HdvWmSWVfJkWU72Z6SxYa0/q03crngvPNg9mxYt845i7eoqGs7akwPYIHetK2wkNrln7J56kkMr9jdqVeTOhhDy3YyoKqE9X0GsC25nRk2U6fCz34GlZXwu985Qd+YXsQCvWmdKsybx5qvfZOgx8sxJVu7u0f7EWBU8VayastZlZXH5pTsthsfdRRce61TLO3uu+3kKtOrRBToReQMEVkrIutF5LpW1seJyLzw+o9EJD+8/FQRWSYin4d/nhTd7ptO88kn8OWXfH7p98muKaNfTXl396hVLpRj92ykb3UZX2QO4p3cka3n7ME5SHvttXDkkTB3rlMvJxTqyu4a0y06DPQi4gbuB84ERgKzRGRki2ZXAHtVdSjwJ+CO8PJi4OuqOhqYDTwZrY6bTtTQAM8/T/GU6ezMyWN08dYel7Zpzq3KuKKNDKws5v0BI3h+6BTq3G0cOE5Kgp/8xKl5/8orcNFFdpDWxLxIRvSTgPWqulFVG4C/A+e2aHMu8ET4/nPAySIiqrpcVXeEl68C4kXEzk3v6f7zHygp4fMf/x+uUIhjSrZ1d4865AKOKdnKKVtWsDGtH4+PnMnuhLTWG3s88O1vO0H+xRdh+nTYvr1L+2tMV4ok0OcCzf/SC8PLWm2jqgGgHGh5dOwCYLmq1h9cV02X2LoVXn2V0MSJrBo2liHlu0gKHB5vmQAT9mzksi/eIehy8+TIGXyWeUQbjcW5sMm//uVcs3biRFi6tEv7a0xXiSTQt/Zfe8s0aLttROQYnHTOD1p9AZE5IrJURJYW2fS37vXznwOwYc5PqfIlMLq45x2E7UhudSmXr1pIblUJLw+ZwMv5BU7FS5wyyPvcJp/MFy8vot7rI3TCCfCPf3Rz742JvkgCfSEwqNnjgcCOttqIiAdIA0rDjwcCLwLfUdUNrb2Aqj6iqhNUdUJ2djszJ0zneucdp6SiBGgAABcnSURBVMb76aez/MixJDfUMrR8Z3f36qAkBeq5eO17TN3xBZ9l5fHXkTMpik9ptW3d0cew7tV3qBlzLFx8Mdx8s5VNMDElkkC/BBgmIoNFxAdcAixo0WYBzsFWgAuBhaqqIpIO/Bv4haoujlanTScIBp2DlIMGsffcC9iY3p9xRZuaKkQejlzA9O2rueTL96j1+Hhi5Ey2JWe2OisnkN2XDS+8At/5DtxwA1xwgXM9XGNiQIeBPpxzvxp4DVgD/ENVV4nIzSLSWKv2MSBTRNYD/wM0TsG8GhgK/EZEVoRvfaO+F+bQPfoofPop3HknywcchWiIsUWbu7tXUZFfUcR3V77JwKoSVmXl8WlWPgHZ/6OvcXHw+OPwxz86ufuCAsvbm5gg2sNGbBMmTNCl9sfVtfbuhWHDYNQo/G8u5P5PdpJXUcR5Gz5utXlbl/vrCca18+WkwAtDJ7MufQCJgXrGFm0ibfLEfZ+fFe/c+eADJ42ze7cT+K+6qttKMxsTCRFZpqoTWltnZ8YaJ1Wxdy/ccw9ryhqo88RRsGdjd/cq6gQ4snw3k3d9SUhcfJgznC2VDbQ62JkyBZYvh1NPhf/6L/jmN53fkTGHIbvCVG+3ciU88ADMmYOOGcOSL8pIbqilND6ZvfHJ3d27TtGnvpqpO9bweVY+a8RFaX2QURnxeF0tRuyZmbBggTOi/8Uv4P334bHHnIqYxhxGbETfm6k6xb5SU+GWW9hS5aeoLkh+DyxgFm2+UJCCPRsYnuZjT22Q93fVUN4QZEVx3b630gZWfPe/WPvq29SmpMGZZ8L3vgclJd29C8ZEzEb0vdn8+fDmm/DnP0NWFks2lJPkEQZU9Y4UhQCDP/2Q9LgkPs0ezIe7ggzfu528iqL9vuhqgS9v+j1jl78Hd97pHKy9807nDFuXjZdMz2af0N6qqgp++lMYPRp++EOK6wJsqPBTkJ2Aq+2yYDGpMZWTVVvJFxmDWNJvGDUe337t1OeD2293Cr4NGwaXX+7k8hfbzGHTs1mg761uvBG2bYOHHgKPhyV7avEIHJsZ39096xaNqZxRxVsoj0tk8YCj25xzz5gx8N578MQTUFgI06bBOefARx91dbeNiYilbjrDI48cWPs5czqnH2359FOnJvv3vw9Tp1LREOTz0nrGZsaT6D28v/sPZeqnAAOrSsioq2RlZh6rsvLYnZjOyNJtJAYa9m3scjknV11wgfO7vOsuOO44p37Or3/tFEqz6Zimhzi8/6rNgQsE4Ac/gIwMJw0BfLynFlWY3DehmzvXMyQGGpi4ex1Hl2xjb3wy7+aO5Mv0Aa2eZEVSEvzqV7BlC/zhD/D55zBjBpxwglNOoqFh/+cY08Us0Pc2v/+9k2K4+27IyKDaH2JFcR2jMuJIj3N3d+96DAHyKos4Yftqcqr3sjG9P+/mjmRlaV3r8+6Tk+Gaa2DTJrjvPqfs8SWXwMCBTqG49eu7fB+MaWRnxnaCFS/+p931+5292VWpm08+gcmTnXTDM8+ACIt2VPPh7lq+f3Q6mfFOJq+j/vdGZXGJrMkYRHlcEn0T3Eztl8hR6T5cbaVnQiF4/XV4+GFnLn4wCDNnOgdwL7jA+U/AmChq78xYy9H3FrW18K1vQd++fH7LXQRL6mkIKkv21NI/wcO2qgDbqgLd3cseK72+huN2rsUXDLJ4wAjm1wbJrK1gys61jCwpbHum0hlnfDUzZ/FimD3b+WIvKHCWDxv21fTMrj5WY3oNC/S9gaoTRNasgddeI9gnA4D1FQ2EFIam7T+V0OxPgGNKt3F06Ta+7JPL4gHDeWnIRN7JHcmxezYxpngL6/rk7P/EbGDoWPjOjxj34ZtOHZ2lS52fmZnOf1nHHdfVu2N6EQv0vcGFF8ILLzhTADdvhmqo8fjYljuS3KoSkjf3/EsF9iQuYMTe7Qzfu531af1Z0n8Ybw8axbu5I8msqySnupTs2gq8oeC+TxSBoUOd28UXw4oVTrB/5RV4+WXn5+zZzrr09G7ZNxObLNDHupdecq6LOmECnHVW0+J16QMQhaFlu7qxc4c3AYaV72JY+S5K4pP5LCufT7PzKUocDKqk1VeTVVdJVm0FafXV+8588Plg0iTnVlbmHCBfuxZ++EPnRLZzzoHvfhdOP93OvDWHzAJ9LPv3v53R/KBBzkgxfOBwb1wSO5MzGFK2i/igv5s7GRsy66qYWbiS9PoqyuKSKE5IpTghlQ1p/dmQnoM7FCStvoYKXyIDqkrJrSolIRieepme7gT0005zrtn7wQfOCP/ZZyE725muOXUqJCbu+6KW0zcRskAfq1580UkBjBkDl13mjCCBgLhYlXkE8YF6hpTbaD7aBKekQp/6aoaV7cTvclMSn0JpfDJlccl8kHMUGp6Pn1FbSW5VKbnVJQyoKiWrtgJXXh7k5Tlf0MuXw1tvOQH/n/908vgzZ8KAAd27k+awY4E+1gQCcOutcMstzkG+V15xTtwJ+zDnKKp8CYzfvR6Phrqxo4enAz3z1hsK0r+mjP41zmUJA+KiwpdIWXwSe+OS+CJjAJ9n5wEQF/CTU13KwKoShpbtot/EicjEic4o/623nDLJ77wDw4fDSSc5Uzbddu6D6ZgF+liyerVT1uD9952qig884JzIE1aUkMoHOcPJqXIOFpqu59EQGfVVZNRXAc5Vr2o8cZTFJeHRENuTM1g84Gjeyx1Jan0Nx5RsY0xcEn2OOMKZf//uu/D22/Dgg05656qr4IornDOdjWmDBfouJH4/8Zs3wtoVsGePc3p8IOAchMvMdPKxeXkweLDz0xfBtMdgED780Km18uKLkJICf/sbzJq1T7Nat5fnhx5HfNDPiNLCTtpDc6AESArUkxSobzqRrsbjY31af9ZmDOTDnKP4YMBw8sr3MLZ4M0edlYrntNOcekVffOGcdXvDDU567uqrYezYbt0f0zNZoO9kUl9P4tKPKFIPlXFJVPbNYUXB6aTs2UGfHdvI++R9+j/8MK7q6hZPFMjNdYL+kCGQn+9cICQuDurqYOdO2LABFi1yZm2kpTk1V376U8jK2mdTIYR/HjmJSl8Cl37xLkWJqV22/+bAJQYaGFOylTElW6nwJvB51hF8mp3PgiMnkeivY1zRZo6NTyLloYfgs8+ckgtPPeVc4H38eGe2zqxZNso3TawEQidY8eJ/8JSVkfD2QlYNL+DLGWcSiE/AW1dDYm01CYSoTEql2ucUEUtwC8NT3IwMVTFoxyZk40anZkrz2/btzolPjeLjnToq06c71zU980wn2LcQVOWVNz5hZVYeZ21axpjiLT364t69WUcXNt+c2pdlfYewPj0HlyojMhOYkB3PgCQvlJY6wX7uXGd+vs8H3/iGk8I75RTn82JiWnslECzQR1tVFdu/fxWFvlTem3MN6naTt2MTfX2Q6q9FYJ9/0Ten9mX9hBNYV16PPwSpXhcjM+I4pk8c2QnN/uHy+6GmxhnN+3zOlLwOyuDWBUPM31TJ5ko/07avZtqOL4BDK+Vruk/j52ZvXBLL+g7h8wFHUR9SBiR6GJ8dT31QcYmQ8PmnZDzzV/o8Pw9PaQnBxCQqZ55C+sUXwNln20g/Rlmg7yr/+hf1/3sNC66+kQ0nnEq/0l0Mrynev5Z5SydMJxBS9tQG2FEToKQuiALZ8W5G9oljSKqPvgluJBzYVxTXtbu5sZlxrC1rYNGOaioaQpwRHsk3skAfGwLHn8D2aj9bqvzUBBSvC/oleMhJ9JAR58bl95P83tukvfIvUl99Cd+unc4sncmTnbn5jfPzrcBaTLBA39l27YKf/ISKdxbz7MMvUDxwMEfvLWRQZXFkF9k+Yfo+D4el+VhTVs/q0np21DiFxhI9woBELzlJHio/XUmiv574oB+3BhGFBrebWk8cJfEpVPoS2Z2UTlZNOadt/ZQjKov32b4F+tiiQHFCKjuSMtiTmEbQ5SYu0EC/mjIy6yrpU1eFL+Bn3BGZznz8N9+EJUucA/keD0yc6Fwla+pU59a3b3fvkjkIFug7iyr85S9wzTXsPGIozz30HIHEJEbtWEd2XeVBb7Z5rrbSG8/m1L5sTs1mZ1IGpQkpHfapf00ZBXs2Mqp4S6sXHLBAH7uCIuxJSGdnch+KE1IJhU/OSm6o5agBfeif6CEr3k1moI64D993pmouWgTLln11kZShQ52Af/zxzs+RI60Mw2HAAn1naKxLsmgRX151DQuu+DmJPjcXDUll+6sLD2nT7R2Uq3d5+DBnGLWeOOrdHgIuNyB4QwHign761FUxafeGdrdvgb53CCGUxSWyN3xmboUvEb/7q+M+KQ21ZNRWkt5QTVpNJWnbt5C+djVpny4jaflSpDI8WElLc0oqN474J0/e5/wM0zNYPfpoamiAO+6AW29FExL4+J8LeWvQaAYkerhgSCpJXhfbD/ElOgrEaQ21pDXUHuKrmFjnQsmoryajvpojy2FM0ZZwHZ4USuJTKElIoTQ+hfXpOVRnD4a8MTD1685zQyES66pJKd1D+rZNZH6xiuxFH9H/gYdJ3b0DGTgQjjzyq1tGhnOJStMjWaA/EO+911TXPXDpZbz+m7v4tNbFiHQfZ+el4HXZxaBNz+UE/sazcnfus87vcvNR/2HUeHzUeuKo9fio9fiozs6hKCePNVNOaWobX11J/3Uryf14MYM+eZ/cp55Gk5LwvfnmVymfcePA6+3iPTRtsUAfiQ0b4Ne/hr//HY44grJXX+ef+RPYWRNgSr8EpuckNs2IMaan6ug/xWR/Hcn+1md0+V1uqj1xVMQlUhaXRNHIcWweexzINbiCAfpt+pIhS98j78lnGfDza/F43E4J5sY8f0EB9O/f4ZRg0zks0LdnzRqntMDjj4PPh/7qV6z88f/xZnEArQ9y3uAUhqfHdXcvjel03lCQ9IYa0htqmmZx+cVFWXyyU51zUB6Lh45k8SVz8ISC5O7ayhEfv0Pev56j/x/uxONvcEp8jBnj3EaPdg76Hnkk5OTYF0Ans0DfUk2NMwXtr3+FV191ziicM4cdP/8NC+vjKdztJzfJw9fyUugTZ5UDTe/l1RDZtRVNBfL8x5/A3vogJXVBSuOOZMuAwbz7jdmIKmmVexmwawvpX64h7bNPiF/xHPGV5cRXlBNXV40vMYHaPlmEUlMI9MnEn5mFPyMTf0YW/owMgskpjDv/9G7e48NXRLNuROQM4B7ADTyqqre3WB8H/BUYD5QAF6vq5vC6XwBXAEHgJ6r6Wnuv1eWzboJBpzjU22/D66/DG29AVRXk5lJ71X+xYdb3WN7gZXt1gAS3MCM3iTEZce2mala8+J+u678xPVSDy83e+GTKfUlU+BLwuz1U+BLRdv52JBjEW1eLp64Gb20Nvtpqkor3kFyyh5SGWpJDfpJdSopHSE6IIyktCXd2tjP3v29f57+GCM4aj0WHNOtGRNzA/cCpQCGwREQWqOrqZs2uAPaq6lARuQS4A7hYREYClwDHAAOAN0TkKFVtcTHNKFF1ZsU03urrobYW9u51aoE03nbuJLhlC/4t2whs3IQfoT4llaqRo6m47R6KJh3P7j592VkTRPdCui/EKblJjM6MI85t84mNiYQvFKRfTTn9asoBZ9pwUIRKbwJ1Hi/1bi91bh91Hi9+l4etqVkExUVQQQN+VEIE4+OoGjyUopFjqU1OI9RK/f3E0iKSC3eTvGI58eV7iaupIj7oJy7ox6uKy+fFFedDfD5cPh+u+Dhcbhfi8SAeDy63C1f4vng8uLweXF6vc9/n3He73U47txuXx7nvdnsQjxu3x43L48HldiNej3MSmtvt/PR4esQ5CJGkbiYB61V1I4CI/B04F2ge6M8Fbgzffw64T5wh77nA31W1HtgkIuvD2/sgOt1v5uOPnfm9Efjje5vxJ7Z92necW+jrEqb2T2Boqo/+iR472GpMFLhVSW+ogVaqgnjaGv8FgboidC80uDzUe7zUub3Uixt/IESDgifRR9Ww4ZR646j3xVMfn0DI3QmZ6RCt9r2RBOpxhWr2LUAInPrHXzNu/tMdb/+b34SnI2h3gCL5TeQC25o9LgRaRtSmNqoaEJFyIDO8/MMWz81t+QIiMgdovABmlYisjaj3B2tafmduPQso7rBV7OhN+9ub9hV61/72jH3929+c28HJa2tFJIG+taFsy8R+W20ieS6q+gjwSAR96fFEZGlbebJY1Jv2tzftK/Su/Y31fY0keVQIDGr2eCCwo602IuIB0oDSCJ9rjDGmE0US6JcAw0RksIj4cA6uLmjRZgEwO3z/QmChOtN5FgCXiEiciAwGhgEfR6frxhhjItFh6iacc78aeA1neuVfVHWViNwMLFXVBcBjwJPhg62lOF8GhNv9A+fAbQC4qtNm3PQcMZGCOgC9aX97075C79rfmN7XHle90hhjTHR1/wRPY4wxncoCvTHGxDgL9FEiImeIyFoRWS8i13V3f6JNRAaJyFsiskZEVonIT8PLM0TkdRFZF/7Zp7v7Gi0i4haR5SLyUvjxYBH5KLyv88KTE2KCiKSLyHMi8kX4PZ4S4+/tf4c/xytF5BkRiY/l99cCfRQ0KxNxJjASmBUu/xBLAsD/qurRwHHAVeF9vA54U1WHAW+GH8eKnwJrmj2+A/hTeF/34pT+iBX3AK+q6ghgLM5+x+R7KyK5wE+ACao6CmeSSWPplph8fy3QR0dTmQhVbQAay0TEDFXdqaqfhO9X4gSCXJz9fCLc7AngG93Tw+gSkYHA2cCj4ccCnIRT4gNia19Tgek4s+dQ1QZVLSNG39swD5AQPu8nEedKLDH5/oIF+mhprUzEfqUeYoWI5APHAh8B/VR1JzhfBkDf7utZVN0N/Bynugk4JT3KVDUQfhxL7/EQoAiYG05VPSoiScToe6uq24E7ga04Ab4cWEbsvr8W6KMkolIPsUBEkoHngZ+pakV396cziMjXgD2quqz54laaxsp77AEKgAdV9VigmhhJ07QmfKzhXGAwTlXdJJy0a0ux8v5aoI+SXlHqQUS8OEH+aVV9Ibx4t4jkhNfnAHu6q39RdDxwjohsxknDnYQzwk8P/6sPsfUeFwKFqvpR+PFzOIE/Ft9bgFOATapapKp+4AVgKrH7/lqgj5JIykQc1sI56seANap6V7NVzctfzAb+2dV9izZV/YWqDlTVfJz3cqGqXga8hVPiA2JkXwFUdRewTUSGhxedjHM2e8y9t2FbgeNEJDH8uW7c35h8f8HOjI0aETkLZ9TXWCbitm7uUlSJyDTgXeBzvspb/xInT/8P4AicP6CLVLW0WzrZCURkBnCNqn5NRIbgjPAzgOXAt8LXWjjsicg4nAPPPmAj8F2cgWBMvrcichNwMc5ssuXAlTg5+dh8fy3QG2NMbLPUjTHGxDgL9MYYE+Ms0BtjTIyzQG+MMTHOAr0xxsQ4C/TGNCMi54mIisiI7u6LMdFigd6Yfc0C3iN8OUxjYoEFemPCwnV8jscpT3tJeJlLRB4I1y5/SUReFpELw+vGi8jbIrJMRF5rLBdgTE9jgd6Yr3wDpyb7l0CpiBQA5wP5wGicsyenQFPdnz8DF6rqeOAvQEydDW1ih6fjJsb0GrNwyliAcyr8LMALPKuqIWCXiLwVXj8cGAW87pRLwY1T8taYHscCvTGAiGTiVKkcJSKKE7gVeLGtpwCrVHVKF3XRmINmqRtjHBcCf1XVPFXNV9VBwCagGLggnKvvB8wIt18LZItIUypHRI7pjo4b0xEL9MY4ZrH/6P15nAtTFAIrgYdxqnWWhy8ZeSFwh4h8CqzAqWluTI9j1SuN6YCIJKtqVTi98zFwfLiGuzGHBcvRG9Oxl0QkHadW+y0W5M3hxkb0xhgT4yxHb4wxMc4CvTHGxDgL9MYYE+Ms0BtjTIyzQG+MMTHu/wMypojHkEP5nQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df_num[\"Age\"] , color=\"red\", label=\"original-dataset\")\n",
    "sns.distplot(train_df_mode[\"Age\"] , color=\"skyblue\", label=\"replacement-by-mode\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. KNN-Imputer Metodu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch     Fare\n",
       "0    22.0      1      0   7.2500\n",
       "1    38.0      1      0  71.2833\n",
       "2    26.0      0      0   7.9250\n",
       "3    35.0      1      0  53.1000\n",
       "4    35.0      0      0   8.0500\n",
       "..    ...    ...    ...      ...\n",
       "886  27.0      0      0  13.0000\n",
       "887  19.0      0      0  30.0000\n",
       "888   NaN      1      2  23.4500\n",
       "889  26.0      0      0  30.0000\n",
       "890  32.0      0      0   7.7500\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_knn = train_df_num.copy()\n",
    "train_df_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833],\n",
       "       [26.    ,  0.    ,  0.    ,  7.925 ],\n",
       "       ...,\n",
       "       [    nan,  1.    ,  2.    , 23.45  ],\n",
       "       [26.    ,  0.    ,  0.    , 30.    ],\n",
       "       [32.    ,  0.    ,  0.    ,  7.75  ]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_df_knn = np.array(train_df_knn)\n",
    "np_df_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = KNNImputer(n_neighbors= 4) # komşu sayısı belirtildi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_df_knn = imputer.fit_transform(train_df_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_names = [\"Age\",\"SibSp\",\"Parch\",\"Fare\"]\n",
    "train_df_knn = pd.DataFrame(np_df_knn, columns = var_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age      0\n",
       "SibSp    0\n",
       "Parch    0\n",
       "Fare     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_knn.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DiBEjaNq0KbZtH/IxJkyYQFFREePGjTvsmSy1JS0tjVWrVtG/f38yMzMrPqB/+9vfcuGFF/Lcc88dMq3z7bffznXXXUfv3r0xxpCTk8Mbb7xR6/XUNMWqQdE0xU5rcMmSJbRo0aK+q3LMRaNRcnNzefnllyuWNGzI0tPTK2ZLHUuHm6ZYu26UUg3C6tWr6dy5MyNHjmwUQb4x0a4bdUzU9pWoiax8tkuy6dGjBxs2bKjvahyW+mjNHwlt0auk0dC6KZU6EkfyOtZAr5KCz+ejsLBQg71q1IwxFBYW4vMd3hiVdt2opNC+fXsKCgoqLlFXqrHy+Xw1Xux1MA30Kim43W46duxY39VQql5o141SSiU4DfRKKZXgNNArpVSC00CvlFIJTgO9UkolOA30SimV4DTQK6VUgtNAr+qdMQZ/JEowoletKlUX9IIpVW/8kSirdgfYHYgQMU6r47h0N52auPHa2gZRqrZooFf1Ym8gwvJCP+GooV2amzSXUBSKsqk4REFJiP7ZKTT3HnrhCaVUfDTQq2NuTyDC4h1leG3h5JYpZHi+D+gdM6Is21XG8l1lDGqZSrpbW/ZKHS19F6ljKhw1fF7ox2sLg1ulHhDkAdLcFv2zUxCEpTvLCESi9VRTpRKHtujVUZk+Pb5y/mznd9MeAcoihkEtU/DYUmXZVJdF/xY+PttZxqo9AXJbpNRSbZVKTtqiV8dMODXM1tIwJzRx06yG/vdMr80JTTzsKIuwsyx8jGqoVGLSQK+OCYMh1CxAhtvihCaeuO7TMcNNqktYszdAVBcMUeqIaaBXx0QkPYxxG7pkerCk6i6bg1kidG/qpTRs2FgUquMaKpW4NNCrOmfEEGoaxApYZPsOb8pkdoqLbJ/Nhv1BQlFt1St1JDTQqzoXaOHHuAzuPR4kztZ8ZZ0zPYQNbC7WVr1SR0IDvapTBoO/TRmW38LyH9kFUJkemxY+m41FISLaqlfqsGmgV3Uq1DRI1BvFtc+DcPit+XKdMjwEo4aCEm3VK3W4NNCrOuVv6ccKWNhlR5fOoJnXoqnH4puikM7AUeowaaBXdSbiCxPODOHd6Tuq1jyAiNCpiQd/xLBd59UrdVg00Ks648/2QxS8O321crxsn02KLWzSQVmlDosGelUnjGUItgjg2ePBCtfOy0xEOD7dzZ5AlB3aqlcqbhroVZ0INgtgXKbWWvPl2qW5sQSW7fTX6nGVSmQa6FWdCDYPYAUsXEXuWj2uxxbaprpYtcePP6yZLZWKhwZ6VeuiriihzBCeQu9RD8JW5fh0N6EofLE7UOvHVioRaaBXtS7YPAACnkJvnRy/icembaqLFYV+jE61VKpGGuhVrQtkBbBLbFz+A5c7KNoLH//XYsGbFp8vEXZtP/LH6NPCxy5/hC0lOiirVE104RFVqyLeCJH0MCmbUwGIRuGTT+CtdzwUbv9hu6L3gChn/zjC2RMiZDSJ/3G6N/Xy34IS8gv9tE+v3XEApRJNXC16ERkjImtFZL2I3FLFfq+IzIrt/0xEcmLbB4pIfuxnhYiMr93qq4YmmBUAA95CLxs3wr33wnPPQXoTOHdyiCdfC/LiuwEeeSHINb8PUVoM993i5ryTvcx53iYSie9xPLbQo5mXL/cEdFBWqRrU2KIXERt4HBgFFACLRWSuMWZ1pWKXA3uMMZ1F5CLgfmAisBLIM8aERaQNsEJE/m2M0e/bCSrYLICr2MWGNTaPPAKpqXDllXDSmCAikDekvKRh2Jlw+XURVi4T/ny7m7uudzPneZv7ngzS9riaH6tvCx/5hX5W7QnQP1uXG1SqOvG06AcC640xG4wxQeAl4NyDypwLPBP7ezYwUkTEGFNaKaj7AB05S2ARb4RIaoS9a7088gg0bQo33wx5eXCo7MQ9cw3/eCPIvU8E+eYr4eIzvPzvg5pfmq1TXbRKscnfpYOySh1KPIG+HbC50u2C2LYqy8QC+z4gC0BEBonIKuAL4BdVteZFZKqILBGRJTt37jz8s1ANQrCZM93xqT96aN4cbrgBmjWL774icPYFUZ6fH6RFK8PVF7p59bmaE6H1beFjpz/CtlL9kqhUdeIZjK2qLXZw86naMsaYz4CTRKQ78IyI/McYc8BljcaY6cB0gLy8PG2aNVLBpkF2fe1i33c206ZBZuYPy3y4sKajGKbeHuTpB9zc/Rs3kQhMmFJ9x32PZl4WbClhRaGftmk6KKtUVeIJ9AVA5R7T9sDWasoUiIgLyAR2Vy5gjFkjIiVAT2DJEddYNUhRd4RIRpilz6Zy4YWQlXXkx/L64IqbQ/zj/8Efb3Szbh0MHXNgsF9U8cXPwsrxsiIUYNM7afz8Cp0xrNTB4nlXLAa6iEhHEfEAFwFzDyozF5gc+/sCYIExxsTu4wIQkQ5AV2BjrdRcNSiFdhCA0GYPp5xy9MdzueGyG0P0zIsw+ykXK5dU/1L17vSBDYGs4NE/sFIJqMZAH+tTvwZ4G1gD/MsYs0pE7hKRcbFiM4AsEVkP/AYon4I5FGemTT4wB7jKGLOrtk9C1b9ddpBd39qce4brkAOvh8Plhp/9JkT7joZnHnSz5ZuqD2yXuLBLbQLZmuhMqapIQ5utkJeXZ5Ys0Z6dxmL6dNi2M4rrzN1s+TiFvqlpVZbzH0UQ3rcbHrzZSadw4wMB0jPBd1BWTH/LMko7lDCla1Nap+p1gCr5iMhSY0xeVfu0Q1MdteWbQtguZ13XupDZHK78XZDi/fD8o26iVVwf5Sn0QhRWFGqrXqmDaaBXR2XbNrBbBwmXCRmRumtJt+9kOG9KmDXLbD5444fTLq2IhWe3l1W7AwQjDetbqlL1Tb/jqmrl76q5dfzWW17OuDuEZ7+7TlISVzZ0TIS1Kyz+/byL7m0gJ+fA/d6dPopaBFizN0CfrNpd8ESpxkxb9OqI7SmErUURMrKjpJTUTbdNZSIw6eoQ6U1g5kwIHbR0rKvYRZbPZkUcH1BKJRMN9OqIvfWyTedTnCmN7n11H+gB0jJg4i9CbNsG8+YduE8Q+mT52Foa1jVllapEu27UD0yf7vz2Z1dfxhh4frrNxPtLsEtctbYAeDxOyosycCD85z+QmwvtKiXk6NXcywdbnStlR7VPP2Z1Uqoh0xa9OiLffiXs3w+tTgzj3nvsUw9MnAgpKfDssxwwCyfFZdG1qZeVuwOEojooqxRooFdH6NN3bU48JYhYx67bprL0dJgwATZuhE8/PXBfnywvgYhh7V5dU1Yp0K4bdQQCZbDsY5tLHyxFwoKrpH5eRoMGwQcfwJw5ThdOSiwl/fHpbpp5LfJ3+Yl3TZK+LXSWjkpc2qJXh23VUougX2jfK4SrqO6nVVZHBC66CIqL4Y03Km8X+mb5KCgJUxzS1aeU0ha9Omyf/8+mXdcwrjQD/qNLb3C0OnSAIUNgwQIYNuz77T2b+/hgWykFxSG6NfPWW/2Uagi0Ra8OSygIq5ZZDLvA6f+2/fXfVjjvPPB44NVXv9+W5rY4MdPDltIQ0QaWz0mpY00DvTosX+Y73TadB4WQsCCh+um2qSwjA0aPhhUr4OOPv9/eJ8tHKArbdU69SnIa6NVh+fwzm5T0KGmtw1h+u9765w82cqSzotVNNzlz/AFyMtyk2MLmYg30KrlpoFdxi4Rh5WKLIeNC4AK7rOY1XY8Vrxd+9CP45BN4/XVnm4jQPt3N7kCEEh2UVUms/jtYVaOxbqVFabHQ5wwn7YHlbziBHpxB2Xffhauugu++A8uCstYuaB/k07UhPHt+OCg7fFgVB1IqwWiLXsVt5WILj9eQ3TmMhAQr0rBePrYNY8c6qZOXLXO2ScTCLrUJp4cxP1jTXqnk0LDeqapBW5Nv0aVXBJMaaXCt+XL9+0ObNs68+vLUCK5iN9iGSGrk0HdWKkFpoFdx2fWdsGubRb+RYbDAbqCB3rLgnHMObNVbZTYSFsIZoUPfWakEpYFexeXLfOelcsIAJ1g21BY9fN+qf/NNp1UvCK4iN9GUCFGXDsqq5KOBXsXly3yL5tmG1FYNs3++MsuCs8+GrVthxf+cetrFLjAQTtdWvUo+OutG1SgShq++sOg/NEzUF8Euq/+XTU1pF3qOgVbzPMyb5aLPyUEsLKwym0h6GLPX02Dm/yt1LDTcZplqMDZ+JQTKhN6nhsFuuP3zlVk2jL4wzHebrYpWvavIjXEZIik6KKuSiwZ6VaMv820sy9Chr3OFaUPun6+s3ylRWrWLMm+Wi2jUucBLB2VVMtJAr2q0ZrlFhxMNdpOIk98m3Di6PQ5u1QuCXexyBmVtHZRVyUMDvTqkshIo2CB07R0h6os0qPw28eh3SpSW7aLMn+3CmNicetBWvUoqGujVIW1ca2GM0KV/BOMyWIHG9ZKxbBh1fpgtGy1WLbWwwhaWPzYoq1fKqiTRuN616pj7eo2FZRna9XBawHagcfTPV9Z/WJTmLSu16mODslEdlFVJQgO9OqQNayzadzLYGVGIggQb30vGdsHI8yJs/Mpi3Uon9w0RHZRVyaPxvWvVMRMOwbfrhE7do0S8EaxA4+qfr2zQ6RGaNDO8M9s5B1exi0hKBH9EB2VV4tNAr6q1ab0QDgmde0cwnmij65+vzO2BEePCrPvC5pu1TkoEBLaU6KIkKvE13neuqnMb1jgvj459QiCNs3++siFnRkjLMLwz2+UMypbZFBSHMLqmrEpwGuhVtb5eY9GqXRRvltO9YTXyQO9NgVN/FGb1UpuCbwRXkYuyiKEwoIOyKrFpoFdVikbhmy+tiv55CVpItHH2z1c27OwIvlTD/Fdc2KUu3BZsLtZBWZXYNNCrKm3bBmUlQqfuEaLeSKPun68sNQ2Gjomw4lOLHQUW7dLc7CjTNWVVYkuMd6+qdevWOb8750acRGaNvNumshFjw7jc8O4cF+3T3Bjgi92HzoapVGOmgV5Vaf16yGxuyGjr9F839v75ytIz4ZRREZZ8YLF/m00zr8WKQr8OyqqEpYFe/YAxTqDv1D2K8UacC6VCjb9/vrLTzw0jFsx8zOa4NDd7AlE2aV+9SlAa6NUPFBbCnj3EBmKjjfpCqeo0bQEDR0R47QUba78Lny3k79LuG5WY4gr0IjJGRNaKyHoRuaWK/V4RmRXb/5mI5MS2jxKRpSLyRez36bVbfVUX1q93fp9wUuO/UOpQzhgfIRqBF55wc1JzL1/tC1Ia1kFZlXhqfAeLiA08DpwF9AAmiUiPg4pdDuwxxnQGHgLuj23fBYw1xvQCJgPP1VbFVd1Zvx5SUqBV5whIYvXPV9aitWHM+VFmP2PTwfIRMbByd6C+q6VUrYunqTYQWG+M2WCMCQIvAeceVOZc4JnY37OBkSIixpjlxpitse2rAJ+IeGuj4qrurF8PJ5wAJpbd0W6Eiczidemvw/hLheeecNEuzcWKXTooqxJPPO/gdsDmSrcLYtuqLGOMCQP7gKyDyvwYWG6M+UGTSUSmisgSEVmyc+fOeOuu6kBhoTOHvnNniHqjzopSkcQN9Cd0NZx+ToRHH4UTU30UBiIUaP4blWDieQdXNQp3cJPnkGVE5CSc7pyfV/UAxpjpxpg8Y0xednZ2HFVSdeWjj5zfnTtD1JM4F0odyuXXh9m3Dxa86MVr6aCsSjzxvIsLgOMq3W4PbK2ujIi4gExgd+x2e2AO8DNjzNdHW2FVtz76CFwuOL5zFOM2Cds/X1mPPoYxY+ChB4QTm3hZuzeAXwdlVQKJJ9AvBrqISEcR8QAXAXMPKjMXZ7AV4AJggTHGiEhT4E3gd8aYj2ur0qruLFwIOTkgTZ3ui2QI9AC//z3s3Alf/ddH2MDKPTooqxJHjYE+1ud+DfA2sAb4lzFmlYjcJSLjYsVmAFkish74DVA+BfMaoDNwu4jkx35a1vpZqFpRWgpLlzrdNuG0EBiwEnggtrKhQ2H4cHjgDy5a+XRQViUWVzyFjDFvAW8dtG1apb/9wIQq7ncPcM9R1lEdI599BuFwLNCnh5GQhZjEulDqUG67Dc48E4rW+CjtWMzW0jDt0tx19njxjgX0beGrszqo5JAczTUVl4ULQQQ6nWCIpIWTYiC2sjPOgAED4K+3e3DroKxKIHG16FVy+Ogj6NULfM2iBFwmoTJW1qQ8qKLKYwMAAB71SURBVE/6lcVvfuYhsM3FahOgZYoLt/X9txptXavGKLmabKpa4TB8+ikMGwbhdCe5V7K16AFOHR3lxJOivPJAChED20p1Tr1q/JLvnayqtGIFFBc7g5LhtDBEBAkl38vDsuCaW8Ms+6+bSJHFZl1TViWA5HsnqyotXOj8Lg/0rhJXwmWsjNfQUVH6DDC890wKRaEo+4I6p141bhroFfD9/PnWbQ2RVCfQJysRuOb3IRa+5CUaQvPUq0ZPA73CGGcgdtgw2FEWBoukDvQAeUMM/Qcalr/hY1tpmEBEW/Wq8dJAr1i3DnbscAL91lhCL1dxcgd6gF9PC7PwBS8GNNGZatQ00KuKRGZDhzqzTCRoYYWSZ2pldbr2MgwaKGxY4mLjXh2UVY2XBnrFwoWQlQXdusHW0lDSd9tUdvWtYZbM8RESw46ySH1XR6kjooFesXCh05r3R6LsCUS126aSlm1gQE+bPVstVm7TQVnVOGmgT3LbtsHXXzv98+UXB2mL/kCTr4rw+Zs+Qp4IBUUa7FXjo4E+yZX3zx8wEKuB/gCp6XBank1ZkfDS/8rquzpKHTYN9Enuo48gNRX69YNtpSFa+Gwkqi+Lg5051rB5kYdgsyAbtmlfvWpctOmW5BYuhJNPBpfLsLUkTJdMDwX1Xalj6MOF8Zdtk+HCmAB/n1fG/Zem112llKpl2nRLYvv3Ozluhg6FvcEoZRFDmzT97K9Oq5ZCtMBLejc/b87XC6hU46GBPol9+ilEo7GB2Fj/fNvUultoIxFcOTIV2w3Pf1JKUVF910ap+GigT2ILF4JtO103W0tDuASyU/RCqUNp3cSmjfHS40w/v79LW/WqcdBAn8Tefx/y8iA93Zla2TrVhSXJmbHycJzXOxWXBzaYUubPr+/aKFUzDfRJqqQEFi2C006DSNTwXWmYtnW4Pmoiaea1OamZl8ET/FxzY4SdO+u7Rkodmgb6JPXJJxAKwYgRTsbKiIG2qToQG6/T2qfi8cCgn5Vw6aVOBlClGioN9Enq/fed/vkhQ2BL+UCszriJWxOPzZA2qfQYEeTLnUEefri+a6RU9TTQJ6n33oMBA5z++S0lIZq4LZp4dCD2cAxsmUKmx+Ind5Vw8y2G996r7xopVTVtwtWB6dMPr/zUqXVTj+oUF8PixXDjjc7tLSVhbc0fAZcljGyXxqvBIsbfUMaFF6ayZAl06FDfNVPqQNqiT0KffALhsDMQWxSMsD8UpZ0OxB6RLpkeejTzkjuhlOzOIc47D51frxocDfRJ6L33wOWCU075vn++nbboj4iIcOZxaTTxWFzxSBFfbYgyYYIz0K1UQ6GBPgm9/z4MHPh9/7xLoFWKBvoj5bMtxnbIIOSKcvfrRcz/r+HKK3Umjmo4NNAnmb17nf75ESOc21tKnAulbEsvlDoa7dPdjGqfRjAzxF1zinj2OcNvfqPBXjUMGuiTzLvvQiQCZ50F4ajhu7Kw9s/XktzsFEa0TcVuF+T2WcU89oTh5ps12Kv6p9/Xk8x//gNNm8KgQfBdaZio0f752jSoVSphAwsp5fa3wvztlxlYlos//Qk0u4SqL9qiTyLGwLx5MGqUMxi7pcQZMdQWfe0a0jqViSc0oVl2lF+9sJeNGUX8+o4Q4bA27VX90KZcEvniC9i6FcaMcW5vKg7R3GuT5tbP+3jFf42EB7erGb42pfQ8PYDLF+DeT4TUsAu334UVtIj4IkjIQsKCIAwfVpc1V8lMA30SmTfP+T16NESNoaA4TPdm3vqtVAKzwhZpm9NJ3ZLG8q0BdoZDnNAvTPPjyw78Lh0Fu9RFod9Fc6+NaB+PqmXalEsi8+ZB797Qrh3sKIsQiBqOS9fP+romUSG3tY8mmzP48wXN+PuELNwfNce7LQX3Li92iYtISpjFO/18sr2MoqCuSatqlwb6JFFU5CwEXrnbBuD4dO2fP1YGD4Zf/AK2FAj/716LvZtduIvdeAt9pBSk0au5l0DE8On2MjYWBTE6XUfVEg30SWL+fOdqzcqBvpnXIkMTmR1TffrAddc5H7wP3+Kh4Bunm0aM0C7NzZDWKWT5bL7cG2T9/mA911YlCg30SWL2bMjKctaHNcZQUBziOG3N14vOnZ2EcpYNj9zm4asvvn8bem2L3BY+2qa6+Hp/iI1FGuzV0dNAnwTKymDuXDj/fGda5Y6yCP6I0W6betS2LVz/pwDNsw1/u9vNso++fyuKCD2be2mZ4rTsv9wbqMeaqkSgI3FJ4PrrnaUD09Kc6YH+ViE4HhbOdfOJNhjrTdMWcO09QZ66z8MzD3rIbhbi4l84A7GWCH2yfCzaUca8TcW0S3VpN5s6YnG16EVkjIisFZH1InJLFfu9IjIrtv8zEcmJbc8SkfdEpFhEHqvdqqt4LVvmJDA78UTndigjhOW3sIMaOOpbajr8clqQ3idHeOB2Nw/d4SIadfbZIvRu7iNiDG9uKtbBWXXEamzRi4gNPA6MAgqAxSIy1xizulKxy4E9xpjOInIRcD8wEfADtwM9Yz/qGCsrg88/d7JV2jYYMYQzQnj2eOq7ao2SP9sfVznfTl/cx3R74NIbQnz6luHZx13sLYRpD4exbUhzW4xsl868zcUs2elnQMuUI626SmLxtOgHAuuNMRuMMUHgJeDcg8qcCzwT+3s2MFJExBhTYoz5CCfgq3rwn/9AIAD9+zu3w+lhjMvg3quBviGxbLjl/jC/uCnE3Jdc/OFaN5HYdPo+WV5OaOLmw20lOsdeHZF4+ujbAZsr3S4ABlVXxhgTFpF9QBawqzYq2djURavvSP3znwd122QGwUDEE427nurYWPgRdD85wtmT4M1/utm+HS6+JsSi3ULEm06o5x6e/LCU9A0ZwLFfglI1XvEE+qquxz64szCeMtU/gMhUYCrA8ccfH+/dVA22bYPXXoNTT3W6bcAJ9JbfRoxeZt9QjZ4QQQTefNGNicLlk8AO2Pi2peBvV0Zohw93sc6YUvGLJ9AXAMdVut0e2FpNmQIRcQGZwO54K2GMmQ5MB8jLy9MRp1oyfbqzNuyppzq3I54IkdQI7t3abVPXjvbb0pkXOMH+jRfcWH649FJI+S6VYIsApR2KabKqKVW3r5T6oXj66BcDXUSko4h4gIuAuQeVmQtMjv19AbDA6BSBehUKwd//7lwJ26pVbFumk/bALtNZtY3BqB9HGHtJiMWL4dlnwYSFlM1pRFIjBFvo3HoVvxrf8bE+92uAtwEb+IcxZpWI3AUsMcbMBWYAz4nIepyW/EXl9xeRjUATwCMi5wFnHjRjR9WBOXOcrpvp053UxBDrtglYSEhbgo3FGedHsIrcvP46eL1w0SQPdrGLsnalhKJe3LoEpIpDXE07Y8xbwFsHbZtW6W8/MKGa++YcRf3UEXr8cejY0VkycMYMZ1plqEkIb6EX0a/8jcpZZznTZN95B7xeYWxGGsXd97F0Zxknt0qt7+qpRkC/wyegjz+GDz+E//u/AwdhsZ1plVFPtH4rqA6LiJO+wu93gn1Kipthv3bzqV1GnywfKS7NZKIOTV8hCSYadVIetG0LV131/fZAVgAJCe59OlujMRKBSZOctX5ffx2+eCWtIqWxUjXRQJ9gXnwRFi+GP/3JyW0DYKwooaZBPLu126YxsyyYPBn69oVnHnfh2+1l6c4y9ulFVKoGGugTSEkJ3HIL5OXBJZd8vz3YLAgWeAt12cDGzrbhiiuge3e466epRCOwcFtpfVdLNXAa6BPIrbfCli3w0ENO669cICvgJDEr0SGZROB2wy9/CT062Sx8IYWVhQF2lIXru1qqAdNAnyD+/nd45BG49loYOvT77cWhKOEmIe22STBeL7z5JuxakkJZkfDKypL6rpJqwLSJd4wV74dlH9ms+NRm/05n2twf/+gs2t23L5xzDgwYcGCLvCbz58PVV8PZZ8ODDx6474tCP4h22ySizEz49ysWv7gvlZRLSnhrcZCzB+hVz+qHtEV/jIRD8PozLm6/3MsrT7kpLYYOHaBfPzjlFPj6ayfgn3wyHHccXHMNLFjgpDCo9phhuP9+GDsWevSAl176fjolQDhqWLKzDNc+N7ZfP9MTUXY2PPwbH3u32Ly3tYRFS/SCdPVD+u4/Br7bLDz7kJstGy0GjQxz6jkR2uWYiuyV5VkI9+xxvo6/+ir84x/ORU/Nm8O4cTB+PHTt6qz7umOHM0/+H/9wZticfz488QRkZBz4uF/s9lMSNmRs0xzmiey4dsK40jQ+LN7PrY/5uefKFE4+ub5rpRoSDfR1bPPXwmN/8OBywZW/C9JzQPUXKzVr5syWueQSKC2Ft992gv6cOTBz5g/LH3ec04q/8EJnnnVlUWP4bHsZbVJd+It07nyiG9zZzbpVboZdWsp5E7289IzFaafVd61UQ6GBvg5t/Vb4650eUtPgV/cEaJ594P7yDIf51WTt7zgMbhgGM2b4+PRTKCiA3bud/PLDh0OnTj8M8OXW7g2yNxhlRLs0PtBB2IQnIoztnM5Ta/Yw/tZiRo9uwnPPOY0ApTTQ15HC7cLjd3hwe+DqO4M0zwZjGcKpYaKpYYwABqyQxW6/i6ZeC6uaqO3xfJ9qOB6hqGHhtlKae226ZHr4oHZOSTVwzX02w9umEh1YyrgrA1x0kZeCAudK6eoaBCo5aKCvA+EQPP2Am0gIrr07SFbrKMFmQcJNQiAgIUGiggHCKSEW7QzhtYWOGW6OS3NjH2VGwg+3lrA7EOGiE5pU++GhEtPAlil8uSfI0KnFyF43N9xg8fnn8Le/ga/uFzRTDZTOuqkDr810sflri4uvDdGyQ4RA6zLCmSHsEhe+rSn4tqTi25ZKyrZUUjal0TfLR6pL+HJvkA+3lVJQHOJI0/lvLg6xeKeffi185DTRqXbJxhLh7A7pBKKGCXcW84c/GJ55xunq27ixvmun6osG+lr28suw8D8uRowLc9LgMP42ZUQ9UTw7vXh3+bCC9gEXLokRWqe6GNQylQHZPlJcwso9AT7ZXsZu/+HlMCkKRXjz2yIyPRYj2qbV9qmpRqJliouR7dL4en+Q0b8oY84cWLsW+vRxBu9V8tFAX4t27XIuTe/QJcqPfhoi0MqPsQy+71JwldQ88yXL52JQyxT6ZHkJRQ2LdpaxfFcZewI1B/xCf5jnvtpHadgwLicDj61dNskst4WPHs28fLitlL4jg+TnO9daTJoEF18MO3fWdw3VsaR99LXohhtg3z644rYg4bZ+jDuKd7vTio+XiNAm1U1Ln4uNxSE27A8yffUeTmzqIS87hXZprgP63UNRw6rdAT7YVoIAP+mSSetUfVqTwfTph9orGCsdq0eYWV8WkbE2k4ULXdx7L9x7r5PX/qGHnKCvwziJTxra0q55eXlmyZIl9V2Nw/buuzBqFPz+99B05D7CTUN4dnlxFdfckh8+rPp9/kgUf9iwvNBPIGLw2kK00IVEBGMbwulhjMtgl9ikf90EO1Dzh8rRLlytjq3yC+tqUtXzGrWjBNqUYcQwtF0q6W4L13c+rrgCPvvMuRL7oYfQC6wSgIgsNcbkVbVPu25qQVkZ/OIX0KULXH5DyBl4LXbFFeRr4rMtTmuXxtUnNWdsh3S6NXVWiAqnhYm6o7j3uclYk0mT1U3jCvIquVgRC+93KWCExTuc3PU9ezqrkM2Y4QzQDh4MP/kJfPttfddW1RVt0deCW291FvqYv8Dwdas97C0x+LakIqZ2vhPH26KLl7boG5ejadGXi7qjcHwZwahhXIcMujVzktwVFTn5kv78Z6fc9dfDTTdB06ZHXW11jB2qRa+B/ih9/jn07w8//SlcdGcxy3b58W5LqZfWdW0EBJW4Bp0SZfkuP3uDUfpn+zi1TVrFoP2mTU6D5YUXnPxKt97qZETVufeNhwb6OhKJwJAhTubJD1eEeH37Pvpn+1izuH4GQzXQq5oYDNGUCIFWfiy/Req36bj3uyum/G7a5ORWWr3ayaV0111OI8bWXsEGT/vo68gTTzgDWg89bPh0fzHpbovhbVLru1pKVUsQ7DIX3m0pGNtQ3HU/+3rupaRdCf5sPy37+/n5PX6uvjNIq1Zw6aXO/Pt//xsaWJtQHQYN9EeooMD5envmmXDiGX52lEU4o30aXlv/parhswM2vi2puAudwf1A2zL8rUsJp4YxGE7sFWXRIvjXvyAYdFJln3oqLF9e3zVXR0InXB+hX/3KWfjjz49HePu7Ek5o4qZrpqYcUI2HILiLPLiK3YQzQoQzQgRb+pGQ4Cpy8/d/eLAiFtdfDx995LTq+/eHYcPg3HOdLKqVla+roBoeDfRHYM4ceO01Z7bCWqsEY2BU+3RErzxRjZAYwb3fg2u/m0hqhHCTIKHmQfZm7sa7y4dvh49TT3UxYIAT7N9/H5YscYL98OGHt+ylqh8a6A/Tnj3OMn99+sDYywO8vinIaW1TaerV0SrVuAmCq9SFq9RF1BNxunSy/QRa+XHvdePdnsKFE90MHSrMmgX//CcsXAgXXeRcQ6IaLg30h+m662D7dnh1bpT3tpWQ7bMZ0FKX6lOJxQrapG5JI7o5jUBLP/7sMkJd92OX2GRnpnLd9R6WLxNefhkeeMBZ0P7ss6F9+/quuaqKfuk6DG+8Ac8+C7/7HexrWcr+UJQxx6dja5eNSkD+bD/BZkEkZOHbmopnl5eoJ0rxiUXs67OHruOL+d1fyzjnHGeQtmtXJ4+OX2fvNjga6OO0ezf8/OfQqxdc+psQy3b5ycv20S5N12NViU8QXMVufFucgI9lCLbyY3LKOOuSIHfeCaNHw223OVkyX3tNp2M2JBro42AMTJnipHadMdMwf1sRTT0Ww9tozneVXA4O+MY2FHXdj+eUfTz+XIh334XUVBg/3knyt3RpfddYgQb6uPz5z85sgwcegD3ZJewJRDnr+PQGl/Pdn+2P60epo1UR8AtSSf02jUhqmGe+2sf+TvtZ8FmYRx6B/HzIy4MJE2DlyvqucXLTQF+Djz+GW26BH/8YRv00wLJdfgZk++iQoXPmlRIE344Umn7ejCGtU/hmf4inv9pL53OL+PyrCNOmwbx5TpfnuHHOfHzt0jn2NNAfwtq1cN550LEjPPi3CP/ZVEzbVBen6TJ9SlXwZ/sJZAXJcNsMaZ3C8eluPi8M8Ny3e+hy8V5eXlLKL24O8fHHzsVWffs66UMKC+u75slDp1dWY+tWZ3BJBF5/K8r8nfsRgXM7ZmBbDavLRqmGwmtbdG/mpUOGm2/2B9lSEqaAMP0n2/zyulT+94aHJx4XrrrKuR6lWzene6dvX0irof2kV94eOQ30Vdi2zQnyhYXw3/cNy8x+CgMRJnRqQqZHL4xSqiapLouTmvvonBllU3GIgpIwb20rwjtQuPsMD/ZuD3+9x83/Flo8+yw8/7wT9Hv0gO7doV07XeKwNmmgP8jq1XDWWU6Qn/O64dum+ynYH+a8nAw6NtF+eaWq8+HCqrZagBcLD+lBm2DzAKsCQYwrwJDbYHipTdkOFxtXuFj+oYvX33Axe7bQpIkT+Lt1g86doWXLY3wyCUYDfSX//jf87GfOYgvzP4yyLm0/W/eHGX1cWsWKPEqpwycInn0ePPs8GDGE08KEM0KEMkJ42wc5sWOAE88DDAT22Oz42mb9Mhf/+8LFq2+6sEIW8+c7ffxDhzopSFwaveKm/yqci6F+/Wvn62Pv3vDk7BCLA0X4/VHGd8yga1MN8krVFjGCu9iNu9hNyrbYYiieKJHUMOHUMHbTIMf3DXHcgGDFffxFwvb1Nv/Jd/PkXBd7Nrro2dli6FBh2DDIzYUmTerxpBq4pA70u3bBI4/Ao49CcTFM+2OEfhNLWbAvQKbH4pIuTWmVmtT/IqXqnCDYQRs7aOPZ68UfcsbBjGWIuqNEPRFsT5ReA6Pk9CujfHamf5/w9RcuPnjJRcHtbrxBm64dLPr1E/r2dRKtdegAbr14Pb5ALyJjgL8ANvCUMea+g/Z7gWeB/kAhMNEYszG273fA5UAEuNYY83at1f4IFBbCO+/AK6/AW29B2Bgu/lWI4ZP87CDIuv0wpHUKJ7dKxa2za5SqNYd7sZ5EBTtgV6y/fEoPiBhDUTBKhsfiu+Zh2rQIs2dIGUgZAMEyYetGixXLXOx6zaZol0WqLbRoYtGyqUVWhkWrbGjdSmjVClq0gMxM5yclJXEHgGsM9CJiA48Do4ACYLGIzDXGrK5U7HJgjzGms4hcBNwPTBSRHsBFwElAW+BdETnRGBOp7ROJRmHv3u9/9u2DPXsNO/cYNm01FGw3bNgUZXeRoWmrKMf3iXDz5WF8rcMYoMglDGyeQr8WPk05rFQD5Az2Ck57E8ANuEkRp+sn6onickdp1z5Kqw5B7BRTZeAuAgrLYLlfCH0tBMucn3BAiIacDxjLCDaCyxI8NrHfzo/XBT6PkOIRUr1CqhfSU52/vW7B5waPW3C7nW8Tntjv8r8tcS4aE5GKi8fKf4vUzfq88bToBwLrjTEbnIrIS8C5QOVAfy5wR+zv2cBj4qzCcS7wkjEmAHwjIutjx/u0dqr/vUWLYPDgA7f1PzfABX8oJqMfdMf5Kee1hBYpNselpXB8hpsO6W6dH69UIyTmwJZ/+dw4g8HYzg+x3xExhIKGaAhMGMSARwytWgthY4iKwVhOt5HlMlhug+ugIbowUBz7OYABgrGfOGxbZ/PIxGYHbJs4EV566bBOPy7xBPp2wOZKtwuAQdWVMcaERWQfkBXb/r+D7tvu4AcQkalA+eUQxSKyNq7a12Dp687PMdYC2HXMH7X+JNP5JtO5QnKdb4M411mznJ8j1KG6HfEE+qqauQdnq6iuTDz3xRgzHZgeR10aPBFZYozJq+96HCvJdL7JdK6QXOeb6OcaT66bAuC4SrfbA1urKyMiLiAT2B3nfZVSStWheAL9YqCLiHQUEQ/O4Orcg8rMBSbH/r4AWGCMMbHtF4mIV0Q6Al2ARbVTdaWUUvGosesm1ud+DfA2znD3P4wxq0TkLmCJMWYuMAN4LjbYuhvnw4BYuX/hDNyGgavrYsZNA5MQXVCHIZnON5nOFZLrfBP6XMVocmillEpomo9eKaUSnAZ6pZRKcBroa4mIjBGRtSKyXkRuqe/61DYROU5E3hORNSKySkR+HdveXETmi8i62O9mNR2rsRARW0SWi8gbsdsdReSz2LnOik1OSAgi0lREZovIl7HneHCCP7fXx17HK0XknyLiS+TnVwN9LaiUJuIsoAcwKZb+IZGEgRuMMd2Bk4GrY+d4C/BfY0wX4L+x24ni18CaSrfvBx6KnesenNQfieIvwDxjTDegD855J+RzKyLtgGuBPGNMT5xJJuWpWxLy+dVAXzsq0kQYY4JAeZqIhGGM2WaMWRb7uwgnELTDOc9nYsWeAc6rnxrWLhFpD5wDPBW7LcDpOCk+ILHOtQkwHGf2HMaYoDFmLwn63Ma4gJTYdT+pwDYS9PkFDfS1pao0ET9I9ZAoRCQH6Ad8BrQyxmwD58MASJS1gB4GbgKisdtZwF5jTDh2O5Ge407ATuDpWFfVUyKSRoI+t8aYLcADwCacAL8PWEriPr8a6GtJXKkeEoGIpAOvANcZY/bXd33qgoj8CNhhjFlaeXMVRRPlOXYBucATxph+QAkJ0k1TldhYw7lAR5ysumk43a4HS5TnVwN9LUmKVA8i4sYJ8i8YY16Nbd4uIm1i+9sAO+qrfrVoCDBORDbidMOdjtPCbxr7qg+J9RwXAAXGmM9it2fjBP5EfG4BzgC+McbsNMaEgFeBU0jc51cDfS2JJ01Eoxbro54BrDHGPFhpV+X0F5OBY58vtJYZY35njGlvjMnBeS4XGGMuBt7DSfEBCXKuAMaY74DNItI1tmkkztXsCffcxmwCThaR1Njruvx8E/L5Bb0yttaIyNk4rb7yNBH31nOVapWIDAUWAl/wfb/1rTj99P8Cjsd5A00wxuyul0rWARE5DfitMeZHItIJp4XfHFgOXBJba6HRE5G+OAPPHmADcClOQzAhn1sRuROYiDObbDlwBU6ffGI+vxrolVIqsWnXjVJKJTgN9EopleA00CulVILTQK+UUglOA71SSiU4DfRKVSIi40XEiEi3+q6LUrVFA71SB5oEfERsOUylEoEGeqViYnl8huCkp70ots0Skb/Gcpe/ISJvicgFsX39ReQDEVkqIm+XpwtQqqHRQK/U987Dycn+FbBbRHKB84EcoBfO1ZODoSLvz6PABcaY/sA/gIS6GlolDlfNRZRKGpNw0liAcyn8JMANvGyMiQLfich7sf1dgZ7AfCddCjZOylulGhwN9EoBIpKFk6Wyp4gYnMBtgDnV3QVYZYwZfIyqqNQR064bpRwXAM8aYzoYY3KMMccB3wC7gB/H+upbAafFyq8FskWkoitHRE6qj4orVRMN9Eo5JvHD1vsrOAtTFAArgb/jZOvcF1sy8gLgfhFZAeTj5DRXqsHR7JVK1UBE0o0xxbHunUXAkFgOd6UaBe2jV6pmb4hIU5xc7XdrkFeNjbbolVIqwWkfvVJKJTgN9EopleA00CulVILTQK+UUglOA71SSiW4/w/JKCGXQvFjmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train_df_num[\"Age\"] , color=\"blue\", label=\"original-dataset\")\n",
    "sns.distplot(train_df_knn[\"Age\"] , color=\"skyblue\", label=\"replacement-by-knn-imputer\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EKSİK VERİ DOLDURMA YÖNTEMLERİNİN GRAFİKLE GENEL GÖSTERİMİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots\n",
    "\n",
    "f, axes = plt.subplots(2, 2, figsize=(15, 7), sharex=True)\n",
    "\n",
    "sns.distplot( train_df_mean[\"Age\"] , color=\"skyblue\", ax=axes[0, 0])  # yaş değişkeninin ortalama ile doldurulması\n",
    "\n",
    "sns.distplot( train_df_median[\"Age\"] , color=\"olive\", ax=axes[0, 1]) # yaş değişkeninin medyan ile doldurulması\n",
    "\n",
    "sns.distplot( train_df_mode[\"Age\"] , color=\"gold\", ax=axes[1, 0]) # yaş değişkeninin mod ile doldurulması\n",
    "\n",
    "sns.distplot( train_df_knn[\"Age\"] , color=\"teal\", ax=axes[1, 1]) # yaş değişkeninin knn-imputer ile doldurulması\n",
    "\n",
    "f.suptitle(\" Mean-Median-Mode-Knn imputer Methods for Age variable\", fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AYKIRI GÖZLEM ANALİZİ YÖNTEMLERİ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### YÖNTEM 1 : KUTU GRAFİĞİ KULLANMA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age değişkeni için aykırı değer var mı ? Bu sorunun cevabı için box plot ile görselleştirme yapalım."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988ce70a48>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y = train_df_num[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_show = train_df_num.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare\n",
       "0  22.0      1      0   7.2500\n",
       "1  38.0      1      0  71.2833\n",
       "2  26.0      0      0   7.9250\n",
       "3  35.0      1      0  53.1000\n",
       "4  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_show.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Nümerik Verilerde Aykırı Değer olup Olmadığını Görselleştirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare\n",
       "0  22.0      1      0   7.2500\n",
       "1  38.0      1      0  71.2833\n",
       "2  26.0      0      0   7.9250\n",
       "3  35.0      1      0  53.1000\n",
       "4  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_show.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fare = [train_df_num.Fare]\n",
    "Age = [train_df_num.Age]\n",
    "SibSp = [train_df_num.SibSp]\n",
    "Parch = [train_df_num.Parch]\n",
    "\n",
    "df = pd.DataFrame(data = np.random.random(size=(10,4)), columns = [\"Age\",\"Fare\",\"SibSp\",\"Parch\"])\n",
    "\n",
    "sns.boxplot(data=train_df_show, width = 0.8)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plot grafiğinde kolaylıkla görüldüğü gibi özellikle Age ve Fare değişkenlerinde aykırı değerler mevcuttur.\n",
    "Bu değerleri çeşitli aykırı değer analiz yöntemlerini kullanarak değerlendirelim:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### İlk Olarak Age Değişkeni Üzerinde Aykırı Değerleri Kaldıralım "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_Age = pd.DataFrame(train_df[\"Age\"])\n",
    "train_df_Fare = pd.DataFrame(train_df[\"Fare\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988e7aa908>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OYAcw0JBngKSTF+BG4ImICEmrgb8Gbo+If6hljog3gJOS3puO/vk08OV5lmVeZloOGqCQkzeGN7PMmDEApG36twKPAy8AX4qIvZLulPTRNNt9wBpJg8BvArWhorcClwOfkbQnfV2YfvarwOeBQeAl4KsLVai5qO0HfLbRqIUclL0YnJllRCt9AETEY8BjDWl31B2XgE80Oe+zwGenueZu4J2zudnFVI3pO4BrChIldwKbWUZ4JnCqGjHtHICaYs59AGaWHQ4AqZZqADm5CcjMMsMBIFWhlSYgKFfxpjBmlgkOAKlWmoAKuWRTmEmvBmFmGeAAkKrG2ecAQBIAwOsBmVk2OACkasNAz6aYZvByEGaWBQ4AqZaagOQloc0sOxwAUq2OAgLXAMwsGxwAUq0FgOSnA4CZZYEDQKpKCxPB5BqAmWWHA0CqMqsmII8CMrPlzwEgVY0gP0MNoDZM1DUAM8sCB4BUK8NAJVGQRwGZWTY4AKRa6QSGpBnINQAzywIHAJLmn+DM7SCbKToAmFlGOACQdABDqzUA9wGYWTa0tCFM1lWqtQ3hZ85bkDg2Xjlts/iaq9Z2LfStmZktGtcAgPJUDWDmCLCikOPkZJUJ1wLMbJlrKQBIukHSi5IGJd3W5PNOSQ+nnz8paXOavkbStyQNS/pcwznfTq/ZuFfwOVeeRQ1gXU+BAH48Wl7cmzIzW2QzBgBJeeAe4MPAFcAnJV3RkO0W4GhEXA7cDdyVppeAzwC/Nc3lPxURV6WvA3MpwEKopBu8zLQcNEBvMUdvMcfro5OLfFdmZourlRrANcBgROyLiAngIWB7Q57twAPp8SPAtZIUESMR8fckgWDJKqcTe3PMHAEksa6nwPGJKicnK4t8Z2Zmi6eVALAOeK3u/VCa1jRPRJSB48CaFq79Z2nzz2ekFhrgF0mtBtBKExDAJSsKCNg/4mYgM1u+WgkAzR6LjT2greRp9KmI+Engg+nrl5v+cmmnpN2Sdh88eHDGm52LqRpAiwGgM5+jvzvP/pEyVe8PbGbLVCsBYAjYUPd+PbB/ujySCsAq4MjZLhoRr6c/TwJfJGlqapbv3ojYFhHb+vv7W7jd2StP1QBar4SsW1FkvBocKrkZyMyWp1YCwC5gq6QtkjqAHcBAQ54B4Ob0+EbgiYjpvxpLKkhamx4XgY8Az8325hfKbJuAAPq78xRz8PqIO4PNbHmacSJYRJQl3Qo8DuSB+yNir6Q7gd0RMQDcBzwoaZDkm/+O2vmSXgH6gA5JHwOuB14FHk8f/nngG8CfLmjJZmG2TUBJXnHpiiI/Gp5kohJ0tDKEyMxsCWlpJnBEPAY81pB2R91xCfjENOdunuayV7d2i4vv1DDQ2T3E1/UUeHV4kjdGJ9nU27EYt2Zmtmg8E5j6YaCz09eRT+YEeDSQmS1DDgDMrRO4Zl1PgROTVU5OuDPYzJYXBwBmtxhco0tXFJM5AV4awsyWGQcA6heDm/25HXnRW8wxPOl9gs1seXEA4FQNYK7jeDrzouTVQc1smXEAIKkB5JWs8zMXXXlRqrgGYGbLiwMAyXLQc2n+qenK55isnlpW2sxsOXAAIJkH0MpKoNPpTCeBuR/AzJYTBwCSeQDzqgEUkpNPOACY2TLiAEBaA5hnExDA8IQDgJktHw4AJJ3Ac5kEVtOVNgF5gxgzW04cAEiGgc6nBlDIibzgpJuAzGwZcQAgWQpiPgEAklqAA4CZLScOAEClOr8mIEj6AU66D8DMlhEHAJIaQH6e1+jMy8NAzWxZcQBgoWoASROQ9wg2s+XCAYCF6gPIEcBo2QHAzJYHBwBqNYD5XaM2Gcz7ApjZctFSAJB0g6QXJQ1Kuq3J552SHk4/f1LS5jR9jaRvSRqW9LmGc66W9Gx6zh9qriuxLYCkBjC/X985NRfA/QBmtjzMGAAk5YF7gA8DVwCflHRFQ7ZbgKMRcTlwN3BXml4CPgP8VpNL/zGwE9iavm6YSwEWwnxnAkP9ZDAHADNbHlqpAVwDDEbEvoiYAB4Ctjfk2Q48kB4/AlwrSRExEhF/TxIIpki6BOiLiO9ERABfAD42n4LMVURQribLQc9HR07kcAAws+WjlQCwDnit7v1QmtY0T0SUgePAmhmuOTTDNQGQtFPSbkm7Dx482MLtzk4VCJjXaqCQ7CWwsui5AGa2fLQSAJo9GRuHurSSZ075I+LeiNgWEdv6+/vPcsm5qe3jMt8mIIDejpxrAGa2bLQSAIaADXXv1wP7p8sjqQCsAo7McM31M1zznCjH3DeEb+S9gc1sOWklAOwCtkraIqkD2AEMNOQZAG5Oj28Enkjb9puKiDeAk5Lem47++TTw5Vnf/QKo7Qc831FAkASAk5MVzlJ0M7MlozBThogoS7oVeBzIA/dHxF5JdwK7I2IAuA94UNIgyTf/HbXzJb0C9AEdkj4GXB8RzwO/Cvw50A18NX2dc7V5WwtRA1hZTLaGHK/E1LwAM7OlasYAABARjwGPNaTdUXdcAj4xzbmbp0nfDbyz1RtdLOXqwjUBrepIVhQ6MVmlq+A5dma2tLX9U6qS1gDyC9AE1NeR/HOe8EggM1sG2j4ALGQNoHcqAHg5CDNb+to+AFRqo4DmOQ8AYGUhR07eHN7Mloe2DwDlBZwHIIneYs5NQGa2LDgALOA8AEj6AdwEZGbLQdsHgMrUMNCFiQB9xbybgMxsWWj7ALCQncCQ1ACGJ7wzmJktfW0fACqL0ARUBUZcCzCzJa7tA0CtE3gh5gFA0gQEHglkZktf2weAU8NAF0avJ4OZ2TLR9gFgIYeBQv1sYI8EMrOlre0DQG07yIXakrgrn6MjJzcBmdmS1/YBoFwNCgu8H30yF8ABwMyWtrYPAJWA/AL/K/R1eGtIM1v62j4ALEoNoJjnxKT7AMxsaXMACMgv8N4tfR05RsvBZNWTwcxs6Wr7ADBartJTXNh/ht70em4GMrOlrKUnn6QbJL0oaVDSbU0+75T0cPr5k5I21312e5r+oqR/VZf+iqRnJe2RtHshCjMXI5NVehZ4966poaBuBjKzJWzGLSEl5YF7gOuAIWCXpIF0X9+aW4CjEXG5pB3AXcBNkq4g2R/4SuBS4BuS3hoRtSfjz0XEoQUsz6wNl6usX1lc0GtObQ3pGoCZLWGtfPW9BhiMiH0RMQE8BGxvyLMdeCA9fgS4VsnA+u3AQxExHhEvA4Pp9ZaESgRj5ViwGsCeQyX2HCqx78QEOcHeI+PsOVRakGubmS20Vp5864DX6t4PpWlN80REGTgOrJnh3AC+JukpSTtnf+vzN5pO1lq5wH0AOYlVHTmOeTawmS1hMzYBQdO9EhuHt0yX52znvj8i9ku6EPi6pB9ExN+e8cuT4LATYOPGjS3cbutGysmt9BTFyOTCjthZ3ZHnlZOTXhbazJasVr76DgEb6t6vB/ZPl0dSAVgFHDnbuRFR+3kAeJRpmoYi4t6I2BYR2/r7+1u43dYNpzWAhe4EhiQABHDc/QBmtkS18uTbBWyVtEVSB0mn7kBDngHg5vT4RuCJiIg0fUc6SmgLsBX4rqQeSb0AknqA64Hn5l+c2amt2b/Qw0ABVnUm1zzuZiAzW6JmbAKKiLKkW4HHgTxwf0TslXQnsDsiBoD7gAclDZJ889+RnrtX0peA54Ey8OsRUZF0EfBougBbAfhiRPzNIpTvrIbLi1cD6Mrn6MqLY+OuAZjZ0tRKHwAR8RjwWEPaHXXHJeAT05z7u8DvNqTtA94125tdaCOTVbryorBQa0E3WN2RP2tHcLMRQlet7VqUezEza9TWM4FHytUFHwFUb3VnjlIlOHmWIBDuJDaz86S9A8AizAKutzqdEPb6aPmMz8rV4LkjJf7ux6NMVBwEzOzca+sAMDy58OsA1evryCFg/8jpAWBksspfDB5naKTMaDn44fHxRbsHM7PptHUAGClX6SksTvs/nJoQ9vKJiam9h0vlKv/rh8d4c7TMu9Z0smllkddGyh4tZGbnXNsGgIlKMFld+FnAjTauLHKwVOGJ10eICL7yo2GOj1e56fJVXLKiyOWrOujIieePjs/YH1AqV/nugTG+NHicu79/mL1HvMyEmc1dS6OAsmikvHhzAOpd2lOkIy92HyxxtFRh38lJrl3Xw4aVRQ6XKhRz4m2rO3j2yDj7R8u8e5rrPHVgjO8eHOP4RFJryQu++qNhNvV2LHoQM7Nsatsnx2LOAm70oXU9bFpZZN/JSd6+uoNt/acP9bx0RYHeYo6XTkw0XTqiGsEzR0ocn6jy7rVdfPCSHq5e200VePy1YY8kMrM5adsAsJizgBvlJD62pZcPrevhwxtXooYtKCXxE30djJaDHxydOOP8b70+woGxCm9f3cFF3YWp+97a18EPj0/wg2NnnmNmNpO2DQC1WcDnqvmku5Djmgu76ZxmB/qLuvOsLOT4xzdHT/tG//TBMXYdLLFpZZHNvR2nnbOpt8jFKwp8Y2iY8YpnHJvZ7LRtABiZrJIDuhd6Q+A5ksRlfUUOlSpT3+gHj0/w9aERLu/r4O2rO844Jydx/foeRsrBd94cO9e3bGbLXPt2AqdzABqbY86nS1YUGBop8+VXTvK1oWEmK8GF3Xk+urmX5482nytwaU+RKy/oZNeBMa5a08Xqzvw5vmszW67aNwCUF3cW8FxI4p1v6eTHo2VGy1WqAW9d3THtwx+S9YT6u/L8AHj05RO8e2034DWFzGxmbRsAhicXdx2gerPZFnJFIcdlfWc295xNVyHHZb0d/PDEBAfGylzY3bb/Wc1sFpbWV+BzaKQc52QE0Lmypa9IbzHHs0dKlNwhbGYtyM4TcBaqEYxMVlm5xJqA5iMn8a41XVQCnj0y86xiM7PsPAFn4eUTkwRkrqlkZTHH21d3crhU4bsHPCrIzM4uW0/AFj19aIyegti6anZt7cvBhp4Ch0tlvr1/lEt7imxYWTwjT0TwT2+O8Q8/HqWcVhQu6y3yofU9rO1auD+JagS5JTTKysxO13YB4Nh4hZdOTPK+i7vJL9JOYOdTMpKoi6cPjfHll0/yb9+++rTO7t0HxnjuyDg/HivT35WnryPHWzrzfP/IOPe9cIyr+7v4wMUr6JqmeexIqcJzR0r0deS58i2dFOv+DfccKjFRCfaPTvL6SJnhySo/1d/F+y9ewYoMNbfNZLSc7DTn4GdLXUsBQNINwB+Q7An8+Yj4vYbPO4EvAFcDh4GbIuKV9LPbgVuACvDvI+LxVq65WPYcKiHgqjXZHSZZzImPb+njCy8e46/2neD6DSu5eEWBl09M8I9vjjJaDt66qoMtvcWpeRDvvyjPD4+Ps/tgiWcOl9i6qpMPb1xJMScigldPTrLr4BgvnZic+j3ffH2YS1cUWduVp7eY49XhSX40PEklYFVHjotXFHj6YInnDo/zMxd3c3V/92kB41yYacmPagSlSjBZDfrmMS9kz6ESJyYqvHRigjfHKnTnxZa+jql/Q7OlaMYAICkP3ANcBwwBuyQNRMTzddluAY5GxOWSdgB3ATdJuoJkg/grgUuBb0h6a3rOTNdccOVqpA+3Dvo6sj1h6sLuAh/Z1MtXfzTMn794jDVdeQ6XKqwoiJ/u72JNQ1NPR15c+ZYuNqys8MKxcZ4/Os6Lx8bZuLLIyckqh9Jz339xNx05MVpOgsKPhid5dfhUULhkRYHL+or0FpN/31/YVODbr4/y7f2jPH2wxLYLu7mgM0dvMc/arvyi7Mf81IExfjxWZv9ImcPjyT4LPQVx+aoOLujM01fM8+ZYmX0nJjhYOrUPQ18xx6beIpesKJCTWp5L8cbIJE8dHONgqUJBsHllkaMTFZ4/Os5rw5Ncv6GHras6F7yctvwNT1Y5XCpzfKJKQWJLX5Huc1hbbqUGcA0wmG7kjqSHgO1A/cN6O/Db6fEjwOeUfJXaDjwUEePAy5IG0+vRwjUXzEQlGDwxwbOHS4xVgp9qk0lSb7+gky19RfYcKvHC0Qk+cPEKugsif5ZvuX0dea7p7+ZI+uB8+eQkxZz4hY0reccFnRRyYs+hEp15uKAzT6UaHJ2ocHyiyoVdeXobAuvargI3/kQfr56c4Fuvj/LE6yNTn+UFF3UXuLSnwKU9RS7uLrCiIF8TrScAAAd0SURBVDrzmtU38YhguFzl+HiVwRMTPHVwjMlqsszHT/QVKUgcGa/wz8cmGEu338wJNvQUed9FHRwdrxDAa8OTPHtknBeOjnNRd4GOnKgSjFeCw6UKB8bKjJSrCCEAQQQcGa9QzMHWvg429hanak1Hxiu8cnKSv9x3kreuGuftF3ROrfyaE0tqFrotroigEnByssquA2McG69wsFTh5OTpQ7YFrOspsLG3yIaeImu68qwo5BblixK0FgDWAa/VvR8C3jNdnogoSzoOrEnT/6nh3HXp8UzXXBARwed/cJQTE8mwz/dd3M2m3jM7RrOmfvJZZz43q5nBkljTVWjpnHxOrO0qMFPWTb0d3Py2pDYxPFnlmcMlTkxUOTZR4elDJXYfPH2yXP0STY1/+vXPzWpA/ZbKAvq782xaWeQtnfmph+yW9PPJalAqV+mu+5+qVhvctLLI4fEK+0fKvDlWZuDVk1PX7ciJ/u78qcX80t8ZSprTNq4snvY/ae3f8Gcv7eHJA2N8581R/vn4mau25pQMxXMsyKZKJH+jjQSs7szxtlUd9HXkuLq/m9FylcHjE7x0YoLv/HiMf+TUSL7OvPi1Ky+YdjHJuWolADT702ws0nR5pktvVoqmA9cl7QR2pm+HJb04zX3O11rg0CJdeylw+Za/rJfR5TuL35zf797ULLGVADAEbKh7vx7YP02eIUkFYBVwZIZzZ7omABFxL3BvC/c5L5J2R8S2xf4954vLt/xlvYwu37nXSn1iF7BV0hZJHSSdugMNeQaAm9PjG4EnIpmKOgDskNQpaQuwFfhui9c0M7NFNGMNIG3TvxV4nGTI5v0RsVfSncDuiBgA7gMeTDt5j5A80EnzfYmkc7cM/HpEVACaXXPhi2dmZtOR14xJSNqZNjdlksu3/GW9jC7fuecAYGbWptpnfr6ZmZ2m7QOApBskvShpUNJt5/t+5krS/ZIOSHquLu0tkr4u6YfpzwvSdEn6w7TM35f0U+fvzlsjaYOkb0l6QdJeSb+RpmeijJK6JH1X0jNp+X4nTd8i6cm0fA+ngyZIB1Y8nJbvSUmbz+f9t0pSXtL3JH0lfZ+18r0i6VlJeyTtTtOW7N9oWwcAnVrm4sPAFcAnlSxfsRz9OXBDQ9ptwDcjYivwzfQ9JOXdmr52An98ju5xPsrAf4qIdwDvBX49/W+VlTKOAx+KiHcBVwE3SHovybIqd6flO0qy7ArULb8C3J3mWw5+A3ih7n3WygfwcxFxVd2Qz6X7NxoRbfsCfgZ4vO797cDt5/u+5lGezcBzde9fBC5Jjy8BXkyP/wT4ZLN8y+UFfJlkLanMlRFYATxNMjv+EFBI06f+XklG0P1MelxI8+l83/sM5VpP8gD8EPAVkomimSlfeq+vAGsb0pbs32hb1wBovszFumnyLkcXRcQbAOnPC9P0ZV3utDng3cCTZKiMafPIHuAA8HXgJeBYRJTTLPVlOG35FaC2/MpS9vvAfwZqC+CsIVvlg2RFg69JeipdxQCW8N9o2+0H0KCVZS6yaNmWW9JK4C+B/xARJ86yoNqyK2Mkc2SukrQaeBR4R7Ns6c9lVT5JHwEORMRTkn62ltwk67IsX533R8R+SRcCX5f0g7PkPe9lbPcaQCvLXCxnb0q6BCD9eSBNX5blllQkefj/74j4qzQ5U2UEiIhjwLdJ+jpWK1leBU4vw1T5dPryK0vV+4GPSnoFeIikGej3yU75AIiI/enPAyRB/BqW8N9ouweArC9JUb9Ex80k7ea19E+noxDeCxyvVVGXKiVf9e8DXoiI/1n3USbKKKk//eaPpG7gX5J0ln6LZHkVOLN8zZZfWZIi4vaIWB8Rm0n+P3siIj5FRsoHIKlHUm/tGLgeeI6l/Dd6vjtNzvcL+Hngn0naW//r+b6feZTjL4A3gEmSbxa3kLSZfhP4YfrzLWlekYx+egl4Fth2vu+/hfJ9gKR6/H1gT/r6+ayUEfgXwPfS8j0H3JGmX0ayftYg8H+AzjS9K30/mH5+2fkuwyzK+rPAV7JWvrQsz6SvvbXnyVL+G/VMYDOzNtXuTUBmZm3LAcDMrE05AJiZtSkHADOzNuUAYGbWptp9JrDZWUmqkAzRq/lYRLxynm7HbEF5GKjZWUgajoiVczgvH+n2p2ZLlZuAzGZJ0mZJfyfp6fT1vjT9Z9M9C75IWmuQ9G/Sdf73SPqTdAlysyXBTUBmZ9edrtAJ8HJEfJxkLZfrIqIkaSvJLOza2u/XAO+MiJclvQO4iWSBsElJfwR8CvjCOS6DWVMOAGZnNxYRVzWkFYHPSboKqABvrfvsuxHxcnp8LXA1sCtdtbSbUwuBmZ13DgBms/cfgTeBd5E0o5bqPhupOxbwQETcfg7vzaxl7gMwm71VwBsRUQV+GZiuXf+bwI3p2vC1vWE3naN7NJuRA4DZ7P0RcLOkfyJp/hlplikingf+G8kOUd8n2eXrknN2l2Yz8DBQM7M25RqAmVmbcgAwM2tTDgBmZm3KAcDMrE05AJiZtSkHADOzNuUAYGbWphwAzMza1P8H8v1oEu5P88QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot( train_df_num[\"Fare\"] , color=\"skyblue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age için birinci ve üçüncü kartil değeerlerini hesaplayalım:\n",
    "\n",
    "AgeQ1 = train_df[\"Age\"] .quantile(0.25)\n",
    "AgeQ3 = train_df[\"Age\"].quantile(0.75)\n",
    "AgeIQR = AgeQ3 - AgeQ1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AgeQ1: 20.125 \t AgeQ3: 38.0 \t AgeIQR: 17.875\n"
     ]
    }
   ],
   "source": [
    "print(\"AgeQ1:\", AgeQ1,\"\\t\",\"AgeQ3:\", AgeQ3, \"\\t\",\"AgeIQR:\", AgeIQR) # yaşın Q1, Q3 ve interquantile range değerleri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lower_bound_age:  -6.6875 \t upper_bound_age:  64.8125\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lower_bound_age = AgeQ1 - 1.5 * AgeIQR #  yaş değişkenine ait üst sınır\n",
    "upper_bound_age = AgeQ3 + 1.5 * AgeIQR # yaş değişkenine ait alt sınır\n",
    "\n",
    "print(\"lower_bound_age: \", lower_bound_age, \"\\t\", \"upper_bound_age: \", upper_bound_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "without_outlier_df_age = train_df_Age[~ ((train_df_Age< (lower_bound_age)) | (train_df_Age> (upper_bound_age))).any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891,)\n",
      "(880, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_df_num[\"Age\"].shape)      #  yaş değişkeninde aykırı değerler atılmadan önce\n",
    "print(without_outlier_df_age.shape)  #  yaş değişkeninde aykırı değerler atıldığında "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988e8e7688>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# aykırı değerlerin atılarak Age değişkeninin görselleştirilmesi\n",
    "sns.boxplot(data = without_outlier_df_age ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aynı Yöntemi Kullanarak Fare Değişkeni Üzerinde Aykırı Değerleri Kaldıralım"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988eb43308>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aykırı Değerler ile Birlikte Fare Değişkenini Görselleştirelim\n",
    "\n",
    "sns.boxplot(y = train_df_num[\"Fare\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "Fare_Q1 = train_df[\"Fare\"].quantile(0.25)\n",
    "Fare_Q3 = train_df[\"Fare\"].quantile(0.75)\n",
    "Fare_IQR = Fare_Q3 - Fare_Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fare_Q1: 7.9104 \t Fare_Q3: 31.0 \t Fare_IQR: 23.0896\n"
     ]
    }
   ],
   "source": [
    "print(\"Fare_Q1:\", Fare_Q1,\"\\t\",\"Fare_Q3:\", Fare_Q3, \"\\t\",\"Fare_IQR:\", Fare_IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bound_fare = Fare_Q1 - 1 * Fare_IQR\n",
    "upper_bound_fare = Fare_Q3 + 1* Fare_IQR\n",
    "\n",
    "without_outlier_df_fare = train_df_Fare[~((train_df_Fare < (lower_bound_fare)) | (train_df_Fare> (upper_bound_fare))).any(axis = 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891,)\n",
      "(750, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_df_num[\"Fare\"].shape)  #   fare değişkeninde aykırı değerler atılmadan önce\n",
    "print(without_outlier_df_fare.shape) #  yaş değişkeninde aykırı değerler atıldıktan sonra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988e7cd548>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = without_outlier_df_fare ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 2. BASKILAMA YÖNTEMİ İLE AYKIRI DEĞERLERİ DOLDURMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_printing_method = train_df_num.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "AgeQ1 = train_df[\"Age\"] .quantile(0.25)\n",
    "AgeQ3 = train_df[\"Age\"].quantile(0.75)\n",
    "AgeIQR = AgeQ3 - AgeQ1\n",
    "\n",
    "\n",
    "Fare_Q1 = train_df_Fare[\"Fare\"].quantile(0.25)\n",
    "Fare_Q3 = train_df_Fare[\"Fare\"].quantile(0.75)\n",
    "Fare_IQR = Fare_Q3 - Fare_Q1\n",
    "\n",
    "lower_bound_age = AgeQ1 - 1.5 * AgeIQR\n",
    "upper_bound_age = AgeQ3 + 1.5 * AgeIQR\n",
    "\n",
    "lower_bound_fare = Fare_Q1 - 1.5 * Fare_IQR\n",
    "upper_bound_fare = Fare_Q3 + 1.5 * Fare_IQR\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_outlier_df_age = train_df_Age < lower_bound_age\n",
    "high_outlier_df_age = train_df_Age > upper_bound_age\n",
    "\n",
    "train_df_printing_method[low_outlier_df_age ] =  lower_bound_age\n",
    "train_df_printing_method[high_outlier_df_age  ] =  upper_bound_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "low_outlier_df_fare = train_df_Fare < lower_bound_fare  # True veya False olarak değer döndürür\n",
    "high_outlier_df_fare = train_df_Fare > upper_bound_fare # True veya False olarak değer döndürür\n",
    "\n",
    "train_df_printing_method[low_outlier_df_fare] =  lower_bound_fare\n",
    "train_df_printing_method[high_outlier_df_fare] =  upper_bound_fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988d3e9b88>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = train_df_printing_method[\"Fare\"] ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1988d140f88>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = train_df_printing_method[\"Age\"] ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. LOCAL OUTLIER FACTOR (LOF) YÖNTEMİ İLE AYKIRI DEĞER ANALİZİ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gözlemleri bulundukları konumda yoğunluk tabanlı skorlayarak buna göre aykırı değer olabilecek değerleri tanımlayabilmemize imkan sağlayan bir yöntemdir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import LocalOutlierFactor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_lof = train_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_lof.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 4)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(714, 4)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lof.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LocalOutlierFactor(n_neighbors=5, contamination = 0.1 ) # Buradaki contamination değeri yoğunluğu ifade etmektedir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       -1, -1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,\n",
       "        1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,\n",
       "       -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "       -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,\n",
       "        1,  1,  1,  1,  1, -1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,\n",
       "        1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1,  1, -1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,\n",
       "        1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,\n",
       "       -1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1, -1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       -1,  1, -1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1, -1,  1, -1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1, -1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1, -1,  1, -1,  1,  1,\n",
       "        1,  1, -1,  1,  1,  1, -1,  1,  1,  1,  1, -1, -1,  1,  1,  1,  1,\n",
       "        1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       -1, -1,  1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit_predict(train_df_lof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = clf.negative_outlier_factor_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8.50220478, -1.15077246, -0.99842549, -0.9498919 , -0.95788633,\n",
       "       -1.18025531, -0.94633733, -1.43255278, -1.24619935, -0.97995713])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores[0:10]  # gözlemlerin yoğunluk skoru"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.74442583, -9.74375607, -9.74375607, -8.50220478, -8.26972604,\n",
       "       -7.14713761, -5.48535379, -4.65813462, -4.00954468, -3.67645063,\n",
       "       -3.62486909, -3.45635313, -3.42554105, -3.25100432, -3.15907858,\n",
       "       -3.06652918, -3.0201967 , -2.83052015, -2.78757349, -2.78623947,\n",
       "       -2.73314803, -2.68778881, -2.56316176, -2.54674445, -2.53180556,\n",
       "       -2.50848131, -2.46959191, -2.43916385, -2.39735859, -2.33160418])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(df_scores)[0:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5.4853537914012716"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keskin düşüşün 7.değer olduğu göz önüne alınarak eşik değer belirlenir.\n",
    "\n",
    "np.sort(df_scores)[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "esik_deger = np.sort(df_scores)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aykiri_tf = df_scores > esik_deger\n",
    "aykiri_tf[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>8.0500</td>\n",
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       "      <th>6</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>885</th>\n",
       "      <td>39.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>710 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch     Fare\n",
       "1    38.0      1      0  71.2833\n",
       "2    26.0      0      0   7.9250\n",
       "3    35.0      1      0  53.1000\n",
       "4    35.0      0      0   8.0500\n",
       "6    54.0      0      0  51.8625\n",
       "..    ...    ...    ...      ...\n",
       "885  39.0      0      5  29.1250\n",
       "886  27.0      0      0  13.0000\n",
       "887  19.0      0      0  30.0000\n",
       "889  26.0      0      0  30.0000\n",
       "890  32.0      0      0   7.7500\n",
       "\n",
       "[710 rows x 4 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lof[df_scores > esik_deger]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>714 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch     Fare\n",
       "0    22.0      1      0   7.2500\n",
       "1    38.0      1      0  71.2833\n",
       "2    26.0      0      0   7.9250\n",
       "3    35.0      1      0  53.1000\n",
       "4    35.0      0      0   8.0500\n",
       "..    ...    ...    ...      ...\n",
       "885  39.0      0      5  29.1250\n",
       "886  27.0      0      0  13.0000\n",
       "887  19.0      0      0  30.0000\n",
       "889  26.0      0      0  30.0000\n",
       "890  32.0      0      0   7.7500\n",
       "\n",
       "[714 rows x 4 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lof # aykırı olmayan gözlemler ( eşik değerinin üzerindeki değerler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  SibSp  Parch      Fare\n",
       "258  35.0      0      0  512.3292\n",
       "679  36.0      0      1  512.3292\n",
       "737  35.0      0      0  512.3292"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_lof[df_scores < esik_deger]  # aykırı gözlemler (eşik değerinin altındaki gözlemler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOF YÖNTEMİNİ KULLANARAK FARKLI SINIF SAYISI PARAMETRESİNE GÖRE GÖRSELLEŞTİRME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LOF(k):\n",
    "\n",
    "    clf = LocalOutlierFactor(n_neighbors=k, contamination=.1)\n",
    "    y_pred = clf.fit_predict(train_df_lof)\n",
    "    LOF_Scores = clf.negative_outlier_factor_\n",
    " \n",
    "    plt.title(\"Local Outlier Factor (LOF), K={}\".format(k))\n",
    "    plt.scatter(train_df.iloc[:,train_df.columns.get_loc(\"Age\")], target, color=\"k\", s=3., label=\"Data points\")\n",
    "    radius =(LOF_Scores.max() - LOF_Scores) / (LOF_Scores.max() - LOF_Scores.min())\n",
    "    plt.scatter(train_df.iloc[:,train_df.columns.get_loc(\"Age\")], target, s=1000 * radius, edgecolors=\"r\",\n",
    "    facecolors=\"none\", label=\"Outlier scores\")\n",
    "    plt.axis(\"tight\")\n",
    "    plt.xlabel(\"{}\".format(train_df.columns[train_df.columns.get_loc(\"Age\")]))\n",
    "    legend = plt.legend(loc=\"center\")\n",
    "    legend.legendHandles[0]._sizes = [50]\n",
    "    legend.legendHandles[1]._sizes = [50]\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LOF(5) # Age Değişkenini göz önüne aldığımızda hayatta kalma ve kalmama durumu için lof yöntemi ile görselleştirme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LOF(10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UyczUsrN0qY7uyM0V6dFD5Pvv6zSbDli++05HokQ7TLYuOOUUkddf33fpxRg1CfrB7XJZuFA74t56S1/5U1JgxYpQb/2IEfrKD+rL9fvVZVET5eU6qaeoSDty6vqVfPBgOOYYuOmm0L6lS+HZZ9WN4PfDkUfCwIHqTrjnHp148/LLsG2bXuOQITBqlM4a/fBD9V0//zycd57O0Nu2TX3nAwaoz/rxx+GXX9Rv3Lat+uGD+RINIvo6/vTTMG+e2tiwodoybZq6O9q2Vd/5tm3qcjnnHJ1csnGj+mbnz9frAnU5tW+vo3tattRZhx98oJOnduxQv/jMmepiyc2F/v013849FwoK1K2zcaPmW3Ky5s2oUTqDdsWK0EiK4mK1MSlJ88M5mD1b86pDB82/d95R98tXX2n6fj+89hrcdpu6fo49tk5u+24joveusFA7zut6otWIERrv6NGV0127Vl1XjRppXtUVb7+tfvTp0+suzkOI2HS5fPCBjnX+05/0FbtTJ215HX64tv4aNtTX6eOP11aXxyPi9YocdpjIo4+KFBZWjK+4ONRCy83V+OrVE7n4Ym3F1AXr16tNmzbp76IidTNkZ+vY8Q8/1FblffeJpKfr9fh8IomJ2qK86SaR/v3VrRIXp9edlaWumEieeUZbzmecIfLKKxrfgw/qNaWmVj6nsFDk+efVtdG8ubZ2hw7VCSIZGWrL3XfrxKVjj9WWbb16+pKXmirSrZv+/utfRX77TV0eGRkiaWmahy1a6DmZmfp94ED9DnrdDzwgcvrpeq1NmmhcSUnqXgG9f40a6ZuGx6P74uLUXZCYqOFTUkSGD9fx+G+/rXmakaF55PHo95QUDZuYqHZ9+aXI7beL3HijtuB//VXkl190DHWjRiLLlmn+FBSITJyoZeett6JzGUT7NlRWJrJyZUW3WHm5yLPPqtuocWOdNJaerjZ/80108Ubi94usWiWyZo1+LyjQPFm1KhSmuFiv8YgjNN+aNtXP224TWbtWw5SWirz7rrrPJk7UeHaF0lJ9K/jxx927jkMcYs7l8v33KgYjR+pDre0J3ZzTgp+UpA9tcL/Pp+KSnBwSyT59RP77X5EnnlA/dbt2FWe2bd4sMnasFuhPPqnall9+UbdGVa6CSN59V+S00/R7ebm6A844o/IsyrlzQ0IWFydy//0i27bpsbIykRde0AoLVBxnz9aK7Z57RCZP1goimBeHHaYC6ZwK1O9+p3EmJorceqvIsGEqGD6fujOefVbdG82ahYQzuMXFqV3t24v8/vcaZzCdY4+tKL4nnKD57fWqgD70kIrtZZepTeHxgoYL/56Sot8bNapox1FHaaUNIm3binTpErq/N9+sfRNt2+r9vO027Vs4/ngNk5kZuk+bN4cquHPOUXdO/fp6jXFx+v3UU9WH/8wzKnzNm2u8OTka1/jxFe9bfr5W2o8/rgLsnFaEN90UEsPgvf/6ay1rd9+tcSck6DUceaQ2VoYOFenVS+S66/QaW7QQOfNMbQCkpWlllZ+v4jxlipbB6ioQv1/kuec0X7Kz1abu3bXMtG0rsnixhisqEunbVyvW6dNDfQlLlojccIPet+ee0+s/5hjtsznzTM2LRx+tvfyHM2yYlrVYprRUGxcDB2rD6IEH6qQva48EHXgR+A2YX81xBzwGLAG+B7rXFqfsqaAPHqwtQo9HBdrr1ULqXGWBBxWs9u3VlxopUqDi0a6dyEUXqVB+8UXF9D77TEU9XHi3btWblJioaScmqt+6uhma06bpQ+n16kPbtavakpWlItGrV6jlHbQxKJiJiWrXqaeGhLZBg8rXEb6FV2agaXi9GmdwCz/u8YTSDXaq1sUWaUdNm8ej+VJXaQevJSlJK3nQN5wOHbQyr19fW8DB646P1/uRna19EsE88vn0/Jtv1lm9N9ygZSYuTsVt6FC9p/Hxek5KiopwWprGc9ppWlmuXatC3rq1lsfgG0piotoQF6fpeL1aeXTtKnL++SqiL72krXPntELz+fTaEhO1z6RVK403/G1ywQK1tUMHLTeff64iXVamSysEGz+NG6uNI0borNyysqrLcLAxMG1axf0//6wt+qee0t9+vzZ0li7V7yUlIgsXVqzUHntMn5dYpbhY9aF3b713U6ZoJdasmchPP+1R1Hsq6CcA3WsQ9LOB9wPCfjTwdW1xyp4I+rp1KuING+oDFBenhbN5c21tORd6QD0efWiDrcvwFmCkoN18s746vvuuvmYGR0bMmqU3pkkTkbvu0n0lJSoMKSlayCdN0gchLU0f+Ei+/16FomdPnd4/YIDalpio15CUpPaMGBFqmYLI0UeHWuKRrdguXTTO6oTM5wvlAag4BCuLqiq13d3C8zUhQV1V4ccj87kuNq9XRaimMC1aVKwo7r8/9L1Xr1Cl1rNnZXuD+Rx0KcXFqVvJ6w3lYWamXm9Ojua116sNhtTU0LkjRmiZOeIIFczrrtM3jrfe0s77hIRQ/qWnV135nXqqlvfkZLXtkUe0vDRrppW6x6Mt52bNtJKqV0/L4h13qI0XXqj2deyo9ojoG1i3bmpvUpK6yG65RW1ZsaL6Z69PH30Opk+vfOzHH7WsFhZqp3XQTXfaaZpHwTeQUaM0/MyZmvexyiOP6NtOZAPvqadEjjtuj6LeY5cL0LIGQX8WGBz2exHQpLY4d1vQ//UvfSiys0Vat5ZSn0/KQMpB/IHP771e8YOUgeQHHpiSwHE/iGRkSGHge37gwfGDrAM5/LDDZElmpjwGctUll4g0bCifX365jE9OltK4OJHRo3em83lurgwdOlS8Xq8MHTJEC2i9eqFX1WXLZGOTJpLn9crs884TOe44effmm2Wzc5peSoocFrSpZ0/5ISVFfgv+BhmWnS2XBn87J+Ugm0BKQYoC1+cPs99fxe+SwFYe2KRbNymuJmxVv/2B9GoLWx74XRawLXx/eNjI36uqSE9ApofZXNXx7VXsD4+3FGRW2O/7U1P1eFKSFINsCYQpiDi3DGRl4BqC524Iy4PlwbBer/zcu7dsd05KvV6R7Gwp9HhkB8jipCSRQNn6BeShpCRtPScl6eu3iCzs00d2BOM6+WR5NS5OXCD9yHwuAlkbzOO0NNmQlLTz/ktmpqzz+eRXkI1xcVIaFyfjUlOlOCFB5KSTZLvHI1tAFiQnizRoIP944AGZ7/PJv//wB5G2baU8cE1jjz9eRXfBAi27JSXya0KCbAG5pFMn7VvIytJ+m1tuEREJlf2hQ/Wc449XN1fLlrIuJUV+BNnh9YrEx8sDJ58sT4CUeDzawFm6VN8aY5XOnXe+yYwbN05ycnJk3LhxKvBNmoTcXLtBTYJeF1P/mwErw37nBfZVwjl3rXNujnNuTn5+/u6lVlCgPe9lZeDxsL68HA+6hoELbMXl5QCUA77A1HcJj2PLFn4F/GH7HZANbPnlF7Zv2MBw4OuJE8HnY8gnn/BUYSGurAzeeIPtgfBvL1/OhAkTKC8vZ8Ibb+hElq1bdaQMwPTp5Ofn06i8nDemTYPSUt5+7TUKtOIjrqCA1gE7cY7UgoLQDfF4aJKfT254/gU2D+Cl4roNFa4v8LssEL4w8AlA3754A78jz6mKUmpeND8Yx47ANQTvw87LqCZ8kIyw71WtoB25L5CztS7k/wVwWNjv5tu3a9pFRQCkAhuAqsY8JQDB0umAZEJlpV1YuI8XLKBcBFdeDnFx5Pv9rAF27NgBQBZ6/ck7dugSszt2wBFHALBqzhzNM4CLL6ZxWdnO+xukJPD5FTA3kD7bt/Pmjh1IIG42bGBxaSmvA8VlZazx+/l++3Y2l5ZCfj6lfj/pQJvCQmjViskPP0yj0lLunjAB1q1jIdAQWD59us60XbVKE12zhpziYtKAI374QSd8NWqk27ZtAKGyP2GCntOsmY7ISUrifwsLuRc0b7KyuGvaNB4E/H6/TmTy+XSyV6yybp3OWgbGjBlDXl4eY8aM0fX/W7bU43uBuhD0qp7DKrVCRMaJSE8R6Zmdnb17qWVlaaYkJUFxMdnOsSmQYHBLCwT1ANsDw60qGHnYYbQJHA8+0AL8C0g/7DAap6ZyuXN0GzwYmjRhWsOGPB8XR0laGjzzDEUpKZQCN2ZmMmTQILxeL8MHDNDhdq1bq30AAwey45RT+DwhgWvat4eEBPpffjku8OcNkpxMEirObNvGsvr1Q3b6/XzdpAmLQ5lHKRBPSFyC4lbp+gK/48LyozTwneeeozjwPfKcqm6aL5BebQQXLyiJ2F8e8TuyEtpUTfrByefBtIPxeAGcozYpOA74Nez3nHr19HrT09kQKDMNgeXBexFm/49AUwCvFwG2AeLczsoRgORkzmvaVPMnLg62bqVJ4Lxsnw+AVc6RCBQkJuowzSZNdNauCPVPPZWUYFx/+hM/xMWRE3EN8QG7jgP6BncecQRXeTzEAaUeD2Rn0xO4Gkjxemnm9zMgIYEGXi80bkyCx0Mp8HFmJixZwnl3380PCQmMPeMMaN+ets7xbyDnpJNg/frQTNEWLZjYqBHvA1N79NAhvHl5unBXjlo6ePBgvF4vgwcP1uGec+fCaadBfj4PeL28BIjXC+vWMbV5c/4LOK9XZy8XFR1Ya/fXNZ06weefAzB69GhycnIYPXq0VowLF+6s2Ouc6pru4RsHksulqEj94unp6lNMSFD/8O9+pz67cD8zhIYr1tY599xzusTnLbfoq2PQbbJli45aOOwwHUES5P771WfeooXIkCGadkqKzoSLZP16HU2Qm6t+1H79NM3GjXW0QfPm+rtz54o+9MhrCfrFfT7tbAv6eKPZLrigcnxVbXvq827Zcs/Oj3arrVM4vJ/A59PRIcFrC3b6pqaqbzryvKBfO5jfXq+GDfZnNGmi9yk40SkhQcO0axfyzTdurP7ikSPVVdG9u076Ouookauu0s7R+Piay2VCgsjf/qa+cOc0/OWXa3xt2+q5Xm9oJNGxx+rokxdf1KWHO3dWX7nPp/YGJ/PMmKEuy+BQ3y++0HMzMqr2jwe5+GJ1GwWHcoYzaZKm5/drOu3bq42PPSZy9dWaTm6udsyKiLz/vk4wilXefVevN7xPoqRE+/uuumqPomYv+9D7UbFTdFY0ce7RKJc77lDRDQpUVaM2wrf0dBXfYcOqfoASE0OjVc49VwU4iN+vwwG7davY++/36/DB4APdtavexOrYuFGHx+Xm6nDBv/wlNATw+uu1QunTRx+KjIyK15WUpJXGSy9px1ZcXHTiHFkRJCSEOo2DQwyDx4LHGzSoeqRQ+BY8N1I4a9tqC+v1ipx8ct0Kf69eKq4ej177Bx/oOP9//EPvR3JyyK7gsMH27VUggw2BpCRtRPTooeIUFMLkZB2K+e9/a2d4mzbaOZmUpJ2Zxx+vnYKtW+vSDWVlOlzyxhtVlOPjNe3gEgQZGZpmWpoev+Ya7S+6/XZtVJx6amgggMejHZ0dOoTOufHG0PBWEfXXvv++DnFs0EArmFmztEOyf/9QZdC2rQ7PfflltXXlysrlt6hIh9impGg8wREr27apbVlZWolEy333qb89lgnOxbjoIh3R07SpNqy2b9+jaPdI0IEJwBr0rT0PuAq4Hrg+cNwBTwJLgR+AnrXFKXsq6Bs36oN366368MXFaUHLzNSWR3B0S1C8gy351NTQUK++fXXyid+vtej112uhvOce7RhauVKHGp1+uj7Aq1fvvr1Bpk+v2LP/xBPa8v/004rrhxQXayswKLKvvVYxng8+UFud00qgvFwf3tJSfcBuvTU0eub3v9dWYmamjq4ZN06PNW8usmiRitGNN2oLMi1NK4zHH9eWXmSlkJmpedG6tb6JBFvJmZkqBqeconl/+unaYktNDY3/v/NObaE+/LC+oYRXwD5faKQP6Pejj9b94eESEkKTi5xTsQyOQ09K0olR69frmPd779WW5LRpoTeg9HQdZTRzpo4PPuMMFeC//U3DXHKJlp/sbH0ITz9d38Tmz9fRIklJeq3JyRq2uo6tRYv0Hpx1lg5p/PDD6teH+f57TatZMx2OOHq0XsMrr2haw4bp28Wll2oZPeUUFeAffqhYXmqbxLR4sZaFLl1CY9A//VQbJOE89JCWrdtvV4YPPj8AACAASURBVIGeNy80rr5/fx2TftVVWuE1b6739sILNdyucP75+m9Usc769doQe+KJOltOoiZBP3in/q9apdPLk5N1aviaNTpF3uPRzqf339cVDS+5RKekf/qp+vmOPVbXfW7dunKc8+fr9PYPPlAfX6tWcM01uhb3rkyVr47iYvVFfvFFKP3Jk3XatXO6EqHXq9PQFy3SZUzr19clYDMydNu6Vae9d+wI48fDpZfq1P9Royqm9dprOqU9Pl7/SPrdd3X6+8cf6+qFkybpKoTh5OXpeb/+qh1k/fppvr7xhi4XcPzxGtf06eoLDnT+7ewzOPxwuPZaXS7h9dd1Ncnyck3X59MOM9Ap9qWlGs/27fq/lS1a6LT8N96AL79U20TU51haqp3grVpp/n31ld6fXr102eCHHtJOx/R0Peess9SmGTN0ZcWiIj0/MVFXFSwq0ny95BJdHmHQIM2TW29V+0T0TzWmT9dyE1zeePNm9TM3bLh3lpiNZPlyXRLi3//WwQCHHaZ/FnHxxXVTHsvLNU8nT9YVLIMsXqzPwdSpmm8dOmhZCi6fALoUQX6+lsl69XYt3U2btKwsWqR5aewSsTn1X0RbpG+/rS21YAuvUSNtiezh4P29xh//qK23cPx+nbw0dqy2YP/zH/Xn9+unr/Rr1qib6cor9fzwqdq//qq+22uu0ckcItpays7WOFeu1JZimzbaQt3VGX1BCgrUN3vTTdpf8fDD6rZo3lzjffhhnfB15pnqa33tNb2GDRv02JFHhmZhHnmkXkdWlh7Lztax2UHy8vT3hAnqay4v11bhqafqG8L55+uEjSDffqut2auvVvdK0Dedmqr516SJzuqcNEnTevppfZPx+3XVx+OPD/1F2vz52mLu1UvHZ8c699+v5Wpf8sgj6kI0dgtibup/dRwMy54uXaris3lz7WG3bdNJCBdfXHntmXA2btSJUQ0aaOdWWpq+Mr/5psj//I+6QY4+WgW+rtm4UQX09NNrtlFE74/fr66nRo1CU+fnzFHXU58+WklErpMyZ46uzBhcnuC449QlEc5HH2kF8dZbmm+bNmnF1qKFVhpBZs1SP2aDBuqua9FC3VCPPSZy4okq/qNH77Gf86Bh3Tr184bP4tybFBery27mzH2TXgxy6Aj6wcJ110XfKtqxQ/2wjRqpH7qqmXylperTPuUUbS2fcorObh0wQP2l555bt/ZH8tRTKpANG6qNwTeFcMrK1F9/1lkqvB98UPka3n5bW+H162tLvHt3FfrDDtOW5OrV2vfh9arfP/ItbPZsFegOHbSFnZFReb2VVavUv96okb4pXHqpVpjXXacLTe3LZWQPFEaNUj/4vmgQjR6tb54HQ+PrAKUmQT94fegHM9u26TjVp59Wf280/PQTPPOM+ri7dKn4j0Vff62+5ZEjddnd4Pjezz9XH/sPP1T2l9clQb91cGzt+PH6D0nNm1e0MTtbbRw0qOZljNeu1YkXJSXq687N1b6F1as13pde0uV1X3hBl+3NzdVlcrds0SWICwvV771hg/aj1K+vfvi1a2HOHF2Kd8SIvfvvPAcTxcXat3D33dqvsLf49ls44wxdgnl3/4rQsL+gOyD59FNd13zq1F2bZBBcD3z9+tB/irZrV1mcVqzQTsynntJ1xPc2K1dqx9rLL2vn2aefqo1FRWrjkUfqhJLdpaAA+vbVLfgfqcXF+l+m69bp8eB/ih51lHbeLV4M33+vnXDx8VqpnXDCvunQPNiYPVs7wffW3xWuWaPl8c9/1nJv7Dax2yl6sPPCC9qpuHBh3ca7bJkOgQv+i9C+4osvtNPx/ffrNt6tW3V8+hVXHNj/RnWwM3mydirv7nrr1bFqlfZX7Ku/uYtx2MtruRi7y5VXwpgx+ofJb79dN3G+9566GW69FW68sW7ijJZjjtF/+Rk2TIcSlkdO/N8Nvv9eW3atW+s/DXmsyO41BgyAJ55Qt8hrr6krbU/57DMdKnzZZfCnP+15fEbNVKf0e3uzFnoYM2fqZJFBg6L7o4yq2LBBW7C5uRX/pGN/sGyZyEkn6cia3f1XmqIi/d/QrCx9k7FOtH3HN9/ojOVzz9UhpLvDli06oa1ZMx2Ga9QZWAv9AOfYY7WjKCdHO/muuEJ9mtHw7bc6+alVK/0vze+/10ky+5PcXJ2IdNll6rPu108nNkXTYv/1V7jrLp1EM3u2dnJeeWVoQoux9+nWTfO+WzedwHbppTpJK5oW+48/6sS9li21/+SHH/T+G/sE6xQ90MjP11Eczzyjozx69oQePXQmZUKC7lu5Ule2mztXH7Lrr9cZhI0a7W/rK7NjB0ycCE8+CcuW6WiKHj20IzgpSWci/vabCvfcudrBefnlek3BP5U29h+bN8Mrr2jn+tateu969tRKO1geV68OlceiIm1gXHPNzlUZjbrFRrkcjIjo1O/gg7J6tY7qSEhQ4Q4+WIcffvD4ldeuDV3PsmUq9j6fDivs1k2vqX173WccWIjo21Pw/uXlqXjHx+v0/WBF3bbtwVMeD1JM0A3DMGKEmgTdqlLDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRohK0J1zZzrnFjnnljjnRlVxvIVz7jPn3LfOue+dc2fXvamGYRhGTdQq6M45L/AkcBbQHhjsnGsfEexPwJsi0g24BHiqrg01DMMwaiaaFnpvYImILBOREuAN4PyIMAKkB77XA1bXnYmGYRhGNEQj6M2AlWG/8wL7wrkXGOqcywPeA26sKiLn3LXOuTnOuTn5+fm7Ya5hGIZRHdEIelV/tx75v3WDgZdFJAc4G3jNOVcpbhEZJyI9RaRndnb2rltrGIZhVEs0gp4HNA/7nUNll8pVwJsAIvIlkAhk1YWBhmEYRnREI+izgTbOuVznXDza6flORJhfgVMBnHPtUEE3n4phGMY+pFZBF5Ey4AbgQ2AhOprlR+fcGOfceYFgtwLXOOe+AyYAV4hIpFvGMAzD2IvERRNIRN5DOzvD940O+74A6FO3phmGYRi7gs0UNQzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGKEqATdOXemc26Rc26Jc25UNWEuds4tcM796Jx7vW7NNAzDMGojrrYAzjkv8CRwOpAHzHbOvSMiC8LCtAHuAPqIyCbnXMO9ZbBhGIZRNbUKOtAbWCIiywCcc28A5wMLwsJcAzwpIpsAROS3ujbUOHApLS0lLy+PoqKi/W2KUQWJiYnk5OTg8/n2tynGXiYaQW8GrAz7nQccFRGmLYBzbibgBe4VkQ8iI3LOXQtcC9CiRYvdsdc4AMnLyyMtLY2WLVvinNvf5hhhiAgbNmwgLy+P3Nzc/W2OsZeJxode1RMqEb/jgDbAScBg4HnnXEalk0TGiUhPEemZnZ29q7YaByhFRUVkZmaamB+AOOfIzMy0t6dDhGgEPQ9oHvY7B1hdRZh/iUipiCwHFqECbxwimJgfuNi9OXSIRtBnA22cc7nOuXjgEuCdiDBTgJMBnHNZqAtmWV0aahg14fV66dq1Kx06dKBLly488sgj+P3+Gs9ZsWIFr7++9wdkXX311SxYsKDGMFOmTKk1jGHURq2CLiJlwA3Ah8BC4E0R+dE5N8Y5d14g2IfABufcAuAz4DYR2bC3jDYObrZt28bzzz/P7bffzvPPP8+2bdv2OM6kpCTmzZvHjz/+yEcffcR7773Hn//85xrP2VeC/vzzz9O+ffsaw5igG3WCiOyXrUePHmLEBgsWLIg67PTp0yUtLU1SUlIEkJSUFElLS5Pp06fvkQ0pKSkVfi9dulQaNGggfr9fli9fLscdd5x069ZNunXrJjNnzhQRkaOOOkrS09OlS5cu8sgjj1QbLpzly5fLEUccIZdffrl06tRJBgwYIAUFBSIi8vHHH0vXrl2lY8eOMnz4cCkqKhIRkRNPPFFmz569084777xTOnfuLEcddZSsXbtWZs6cKfXr15eWLVtKly5dZMmSJfLoo49Ku3btpFOnTjJo0KA9yhuRXbtHxoENMEeq0VUTdGOPiVYstm7dKmlpaYJ2qlfY0tLSZNu2bbttQ6Sgi4hkZGTI2rVrpaCgQHbs2CEiIj///LMEy95nn30m/fr12xm+unDhLF++XACZMWOGiIgMHz5cHnroIdmxY4fk5OTIokWLRETksssuk7Fjx4pIRUEH5J133hERkdtuu03uu+8+EREZNmyYTJo0aWc6TZo02VkhbNq0aXezZScm6LFDTYJuU/+NfcbEiROr9Wv7/X4mTpxYp+lp2ddx8tdccw2dOnXioosuqta1EW245s2b06dPHwCGDh3KjBkzWLRoEbm5ubRt2xaAYcOGMW3atErnxsfHc8455wDQo0cPVqxYUWUanTt3ZsiQIYwfP564uGhGFxuGreVi7EMWL15MQUFBlccKCgpYsmRJnaW1bNkyvF4vDRs2ZOzYsTRq1IjvvvuOOXPmUFJSUuU50YaLHDXinNtZedSGz+fbeb7X66WsrKzKcO+++y6/+93vmDt3Lj169Kg2nGGEY4Ju7DPatGlDSkpKlcdSUlJo3bp1naSTn5/P9ddfzw033IBzji1bttCkSRM8Hg+vvfYa5eXlAKSlpVXokK0uXCS//vorX375JQATJkzguOOO48gjj2TFihU7K6XXXnuNE088MWqbw23x+/2sXLmSk08+mQcffJDNmzezffv23coL49DCBN3YZwwaNAiPp+oi5/F4GDRo0G7HvWPHjp3DFk877TT69u3LPffcA8DIkSN55ZVXOProo/n55593ViqdO3cmLi6OLl26MHbs2GrDRdKuXTteeeUVOnfuzMaNGxkxYgSJiYm89NJLXHTRRXTq1AmPx8P1118ftf2XXHIJDz30EN26dWPx4sUMHTqUTp060a1bN2655RYyMirN0zOMSrhoXxXrmp49e8qcOXP2S9pG3bJw4ULatWsXVdgZM2Zw9tln4/f7KSgoICUlBY/Hw3vvvcdxxx23ly3dc1asWME555zD/Pnz97cpu8Su3CPjwMY5N1dEelZ1zHpbjH3Kcccdx+rVq5k4cSJLliyhdevWDBo0iNTU1P1tmmEc9JigG/uc1NRUrrrqqv1txm7RsmXLg651bhw6mA/dMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTd2PeIwJYtUId/upCXl8f5559PmzZtaNWqFTfffHO1Mz3D+etf/1rhd3C0zerVqxk4cGCd2WcY+wITdGPf8vrr0LEj5ORAZiZccgn88sseRSkiXHjhhVxwwQUsXryYn3/+me3bt3PXXXfVem6koAdp2rQpkydPjtqG6maV1hV7O34jNjBBN/Yd48bB6NHwxBOwdSusXg0dOsDxx8Nvu/+/4p9++imJiYkMHz4c0DVSxo4dy4svvkhhYSEvv/wyN9xww87w55xzDlOnTmXUqFE7Z5gOGTKkQpwrVqygY8eOgIrpbbfdRq9evejcuTPPPvssAFOnTuXkk0/m0ksvpVOnThXOLy8v54orrqBjx4506tSJsWPHArBkyRJOO+00unTpQvfu3Vm6dCkiwm233bYzbHCRsqriHz9+PL1796Zr165cd911lJeXV5uWcehh49CNfUNJiYr5xx9rCx2gXj24+27Iy4Mnn4Ra/pCiOn788Ud69OhRYV96ejotWrSoccGvBx54gCeeeIJ58+bVGP8LL7xAvXr1mD17NsXFxfTp04e+ffsCMGvWLObPn1/pD5jnzZvHqlWrdo5Z37x5MwBDhgxh1KhR9O/fn6KiIvx+P2+//Tbz5s3ju+++Y/369fTq1YsTTjihUvwLFy5k4sSJzJw5E5/Px8iRI/nHP/5Bhw4dqkzLOPSwFrqxb/j2W2jSJCTm4QwdCu+/v9tRi0iV/5tZ3f5d5b///S+vvvoqXbt25aijjmLDhg0sXrwYgN69e1cSc4DDDz+cZcuWceONN/LBBx+Qnp7Otm3bWLVqFf379wcgMTGR5ORkZsyYweDBg/F6vTRq1IgTTzyR2bNnV4r/k08+Ye7cufTq1YuuXbvyySefsGzZsirTMg5NTNCNfYPXC6WlVR8rK9Pju0mHDh2IXBdo69atrFy5klatWhEXF1dhHfaiXeyMFREef/xx5s2bx7x581i+fPnOFnp1C3jVr1+f7777jpNOOoknn3ySq6++utoldmtaTyk8fhFh2LBhO+1YtGgR9957b5VpGYcmJujGvqFrV/Wbf/VV5WPPPw8XXLDbUZ966qkUFhby6quvAuq/vvXWW7niiitITk6mZcuWzJs3b+eytLNmzdp5rs/no7S6iibAGWecwdNPP70z3M8//1ztuu5B1q9fj9/vZ8CAAdx333188803pKenk5OTw5QpUwAoLi6msLCQE044gYkTJ1JeXk5+fj7Tpk2jd+/eVV7n5MmT+S3Q37Bx40Z++eWXKtMyDk3Mh27sG+Li4O9/hwED9PO887Qj9O9/h2++UR/6buKc45///CcjR47kvvvuw+/3c/bZZ+8cwdKnTx9yc3Pp1KkTHTt2pHv37jvPvfbaa+ncuTPdu3fnH//4R5XxX3311axYsYLu3bsjImRnZ+8U5epYtWoVw4cP3/lmcP/99wO6Tvp1113H6NGj8fl8TJo0if79+/Pll1/SpUsXnHM8+OCDNG7cmJ9++qlCnO3bt+cvf/kLffv2xe/34/P5ePLJJ0lKSqoyLePQw5bPNfaYXVqa9aOP4P774YsvID0dBg/WjtGsrL1r5CGOLZ8bO9jyucaBw+mn62YYRp1jPnTDMIwYwQTdMAwjRjBBN+qE/dUXY9SO3ZtDBxN0Y49JTExkw4YNJhwHICLChg0bSExM3N+mGPuAqDpFnXNnAo8CXuB5EXmgmnADgUlALxGxISyHCDk5OeTl5ZGfn7+/TTGqIDExkZycnP1thrEPqFXQnXNe4EngdCAPmO2ce0dEFkSESwNuAr7eG4YaBy4+n6/K6e+GYexbonG59AaWiMgyESkB3gDOryLcfcCDQN0tcm0YhmFETTSC3gxYGfY7L7BvJ865bkBzEflPTRE55651zs1xzs2x13PDMIy6JRpBr2q5up29X845DzAWuLW2iERknIj0FJGe2dnZ0VtpGIZh1Eo0gp4HNA/7nQOsDvudBnQEpjrnVgBHA+8456qcmmoYhmHsHaIR9NlAG+dcrnMuHrgEeCd4UES2iEiWiLQUkZbAV8B5NsrFMAxj31KroItIGXAD8CGwEHhTRH50zo1xzp23tw00DMMwoiOqcegi8h7wXsS+0dWEPWnPzTIMwzB2FZspahiGESOYoBuGYcQIJuiGYRgxggm6YRhGjGCCbhiGESOYoBuGYcQIJuiGYRgxggm6YRhGjGCCbhiGESOYoBuGYcQIJuiGYRgxggm6YRhGjGCCbhiGESOYoBuGYcQIJuiGYRgxggm6YRhGjGCCbhiGESOYoBuGYcQIJuiGYRgxggm6YRhGjGCCbhiGESOYoBuGYcQIJuiGYRgxggm6YRhGjGCCbhiGESOYoBuGYcQIUQm6c+5M59wi59wS59yoKo7/wTm3wDn3vXPuE+fcYXVvqmEYhlETtQq6c84LPAmcBbQHBjvn2kcE+xboKSKdgcnAg3VtqGEYhlEz0bTQewNLRGSZiJQAbwDnhwcQkc9EpDDw8ysgp27NNAzDMGojGkFvBqwM+50X2FcdVwHvV3XAOXetc26Oc25Ofn5+9FYahmEYtRKNoLsq9kmVAZ0bCvQEHqrquIiME5GeItIzOzs7eisNwzCMWomLIkwe0Dzsdw6wOjKQc+404C7gRBEprhvzDMMwjGiJpoU+G2jjnMt1zsUDlwDvhAdwznUDngXOE5Hf6t5MwzAMozZqFXQRKQNuAD4EFgJvisiPzrkxzrnzAsEeAlKBSc65ec65d6qJzjAMw9hLRONyQUTeA96L2Dc67PtpdWyXYRiGsYvYTFHDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBFM0A3DMGIEE3TDMIwYwQTdMAwjRjBBNwzDiBGi+k9R4wBGBDZvhk2bYMMG+OAD+PVX3ZeYCA0bwoABcMwx4Fzdpev3w8aNmo7PB5mZkJpad/EbhrHLmKAfrBQWwoQJ8NRT8NNPKtaFheD1QvPmcNZZ0LUrrFsHw4ZBSgqMHAlDhuj33WX9enjpJXjmGa1E6teH0lKtTE48EUaMgLPPVjuMQ4+8PHjnHcjP10o/M1PLQ+vW+9uyQwJzuewJ69fD1Knw7rswfTps37530tm6FaZN03SmToU//xlyclTQmzfXh+a++9SeoiK4/3748Ue47TZtsTdqpLbdcQe0aKG27iolJXDTTfpgzp+vaW/YAEuWwIsvQt++MGsW9O8PCQnQqxf85z9QXl7n2WEcYIjAxx/rve/cGWbPDt33+fP17fDMM1Xo/f79a2uMYy303eHrr+HJJ7WAduqkroZNm+Dnn2HoUG0JH3nknqfzww+azsSJ0K4dxMfDnDlQUKDfZ85UoT3xRBXqjz6CZ5+FL7/UB6e0VL8fcQQ89hgkJanwn3QSHH88PPccZGTAypXaoj78cEhLq2xHQQGcf7627JcsgawsPee662DyZIiLg/POgxkz9Ninn8K118KNN+rD/sgjcOGFtV9vSQlMmQLjxsHChWp/Zia0aQO5uVCvnl7LhRfqtRj7nt9+03IzaRJs2QLJyVp2tm7VBsOrr1YuQ48+quHvvhtefx1eflndgUbdIyL7ZevRo4fsE9auFfnf/xW56CKRAQNE7r5b5Jdfdi8uv1/Pz8kR+fvfRdavr3j8l19E7rpLJCtL5MUX98zuxx8XadhQ5M9/Flm1SuSxx0QSE0WaNBH58EOR008XiYsTuecekUceEUlKEklJERk5UiQzU+TVV0V++klkyBARr1cERJo1E7nvPpF77xWpV08kPl7Pad1aJDdXJCND5OqrRX74IWRHWZlIv34ixxwj0quXnuOcxuecSHq6SKNGIi1aaLqjR4uUlIgsXarpjRkj0rixyAUXiFxxhW6PPCKyYYPGP3OmyLHHarwg4vGIdOok8uabIoMHi6SlibRqpdfep49I376av7ffLrJjh8ZRWCjy22+a7p6wbJnIW2/ptmRJ5eN+v0hxsX7uCzZv1vt+/vl63cOGiXzyScX0/X69/rKyyufPn6/l549/FHn99T3Ln5ISkWuv1fuQkiLSsqXI0UdrOfJ4tPydeabIpk3Vx7Fjhz6D55yj3//5Ty3HKSlalg87TMvPypW7b+chADBHqtHV2BX0sjKRW28NidSECSITJ4rcfLNIgwYiw4eHBCHI8uUio0aJdOggcvjhImedpYUu+LDccIOK03nniQwcqJ/Z2SI+X6gwrlkjsnChSPPmIs8+K/L55yJffKFCEC1PP60itmyZ/r7jDhXOpCS1f9o0fQgee0zt8Xr1QQuK4sMPq9BnZYn8z/+I/Pe/IvXr68OXlqZhqtuCD1enTioiSUm6PzFRBTYjIxSHz6ei3qCBHvd4VNjPPFPz9tZbQ+IPeg39+4sMGqTfc3P1eKNGmmbLliLduuk1gUi7dirUIiL5+SroRx+tFarXq+k1b65pN2igcZ5zjlZAAwdq/jzwgFZqw4eLTJpUtah9+63e66ws/TzrLL2vffuKzJkjMm+eyGmnqY3O6XX36yeyYEF097O8vPK+LVu0QRAuzgUFIs8/L3L88WqL1yvSpYs2Dt5/X+TRR7Vsduok8tVXWjE3a6b3PS5O7X7vPZFvvtFK2uvV+5mRod+TkrR872qFVFoqcsYZIqmpKsjffKP7n3hCpGdPrTh+/3uR5GSRNm1qFvWSEpGTTlK7jz5aZPx4rbiKikS++07kd7/Te/n667tm4yHEoSPowdaT3y9y5ZUiJ59cuRU9a5bIiSfqQ5CYqGK5davI22/rQ/SHP+hD/PPPIq+9pgW2b1+RoUNVQEaMELntNi28ycm6Ly5OBaBnTxWjmTNFLr5Ys7dzZ30ImzTRlmlVD9O2bdri79JFpG1btWvWLH2QBg5UEWndWuSUU7RFExSVhg1rFufERH1o0tNrDhcfrw9iYmLN4ao6L1iJJCRoXkBIbCNtCX4PCmP48Vtu0crv2WdVeBo21HiGDhV56CG9l8G0gm8cwc3j0fvdoEFoXzCMcyJNm2rlEF6ZeTyhNMIrp/Dj2dmhfRkZIn/5i8grr+hbTv36ur9DB30ru/NOkY4dtUJ64QWRyZPV5mC4Jk207Dz7rNoTTCc1Vc/97ju999266XGvVxsVhx0War0OGKBhO3RQu3r1Evn6ay1DhYUiL72klZ3Ho9f7+edamZSVaYV4880a16BBNT9H5eVagQwfrpX40UerPT6fPiMjRogsXqwV8qxZofMmT9Znol+/6uMuLNR8SkzUSq28XBsoU6aIzJ6tz1/r1mrnRReJrF5d+3MfSUmJyFNPacXRooVI797aSKquUbVihb7xnXCC2v7aa3v+trcX2WNBB84EFgFLgFFVHE8AJgaOfw20rC3OOhX0KVO0peacPkDHHqvfvV4tHAsXargXX9RC2bSpyHXXaVivVx/szMxQyyOcJEOjxAAAF6VJREFU77/Xh9Hr1dbDggXa0m3WTKRrVxUyn08f+Hr1QgKQmqqFOylJvx91lBbku+6qGH9+vj6EPp/Gl50dEsD+/dXWzp1D4gQaZ6NGuya+B8PWpYuKYHJyxf3BFua+sMHn0/sVuT+yAgqvtILHPJ7KFVm4eyoyjqq2xES9/o8+UuEOvhHFxel3n08rtgYNVFw7ddIyJKKNhcxMjePFF0XOPruiu+2TT0TeeCNUGZxzjj4Hb74ZKo+lpSIXXqhl7vHHtaIM2tasmcbfvLk2Ejp2rPy8jByp6S9fXvmY3y9yySX6zMTF6XVlZWlaffqoXb17q7A/+aQ+T02bauOqNtatU9dUQkLoXiQmqqvwuee0MXTmmZVFfcYMfeZuuSWUP8cdpw2noqLa090P7JGgA15gKXA4EA98B7SPCDMSeCbw/RJgYm3x1pmgT50a8undeaeKY3grK/i5YoU+CAMHqlvkoYfUt9itmxb6rl0rx/3551qAg4UkPV3TCroX2rYVefllFdecHC2Q9epVbIEmJmprqVUrrVzq1avY6hg4UAX6s89Ezj1XzznvvFBrNC5u/wvt/tj21nV7vbXHXZ0oR/5u0yb6dCPjbNVKW4SRtnk8WqE88ICKSlD8Iiu0oCvsjDO0xV1cLPLBBxpu3DiNIzVV3yjmzVNB83q1kZCcHHqbcU7T7NVL+w5OPVWke3dtgQ8cGLL75JO1H6S0VN804+O1XIfj96v4Bl2DmzdXPP7CC3o9d9+t/QFerz5fwYZQr176PI4bp3F17ap++zPPrFkDSku1JZ6QoFvQBRUfrxVGXJzGefLJ+oYUpKxM3zL+/e+K8ZWVaWX44IO1qc9+YU8F/Rjgw7DfdwB3RIT5EDgm8D0OWA+4muKtM0E/5RRtuWzZIkOHDpXtIP7AVhT4FJDVhx8uJSCv/P3vIkceKf/JypL5IEVB90VCws4ox40bJzk5OZLfvLnIJZdIGUg5yFafTyQuTrbGx8tmkO1xcVIOsjGYzpgxssPrla0gOwKumBLnpB7IjIwMLXR9+4r83/9pQsXFWuh+/3uRk06SAo9HCkBWxMXJUud2XkfwGvzVbJHHagq7t84t28N0d8UGCdyPXT23BEQyM3f+jowjeG5ZFeeWB84vBSkEEZ+v4rkJCVIcdm7QxmAapRHplIJsC8u3cpBvQbYEBHRdcrJMA9mUkCCbI/K4DGQHSDGIxMeLH+Q3kOn16mnDZvVqfbQnT5YL0tPlHpCGDRqINGwoZR6PPJqaKn6Qv6Wlyag2beTrQJzFXq+UgxR5vSIdOsjcZs2kIGjzggWyPT5eGns8MnToUJHkZCn0eiXH45GhQ4bImoAdLzVpItKsmZQ5J5tB/nLaaaFn9cgjtTLJypKF2dniBymMi5N1Pp+sAPmyXj2Ra6+V9Tk5kpOTI1/37y9y001a+axaVb0G/Otf+gyfeKL80LixjHZONns8UgpyVteusg6kwOdTv/yxx4bO+/RTrbwinvtx48Zpv1dkhXWAUJOgRzMOvRmwMux3XmBflWFEpAzYAmRGRuScu9Y5N8c5Nyc/Pz+KpKNgxQpo2xbS05kwYQLhg9lOCPvuWbGCIuBvjzwCK1dywfr1PAGUiejwuOLinWNnx4wZQ15eHqxaBevWsQooBHylpVBeTkpJCeXAprIytgJ/CibStCne8nIdC+r3Q9OmlIuQBLyzeTOkp+tww+C1b92qj+hRR8G33+Lz+1kN1C8rI0uEyBG72/YgmyLniLoaju0JNaVTF+dKxO/qRrkXVRd/p07Vx+/1IujkjMh0JJCWF/U9VsU2oLgK+4Jx7sTjwQ9EPgFxwFa/H5wjubCQTODn4mKWRsRRij5gXtChnkAGkLhli070ysvTgK1bc/XWrdwLpG3cCE2bgt9P78B8ib9v28b4xYvJAQoAT3k5xUBBeTn89BNTVq3aaT/5+fxSUkKm38+ECRPA52NreTn1/X7emTCBRiUlOOD0NWtAhEIRJgKeTz4JXWBeng437dGDifn5CLC9rIy00lKWAq9s2QKFhbjVq8nLy2PKZ5/pc9m0aeiZqYqlS/UZ3r6dtWvXsliEFL+fYmDVvHnMB9aWluoQy3XrQuetWaNDYgMEn/sxY8bo/rVrq0/zACUaQY/muYomDCIyTkR6ikjP7OzsaOyrnaOO0vHaCxYwePBgNoQd+gp2iuKW7t1JBEZffz2ccQaLExP5CxDnnE6EqVdv5+zG0aNHk5OTQ9GRR0JWFk2cIwkoSkiAuDg2JScDUN/rJRl4PJjgZ59R7PNRDIjHA7/+itc5EoDrkpK0gGzZEipE9evreNxJk2DQIMo9HloC3ycmstLjqXRzqhghrkQxpb8qgaru2O4QtKCmdGojmnNdxGd181GrGuUsoHMIqqO8HAeUUblAewAfUAJ0hMoTZMrKqA8VGhQuYJ8LnLcTvx+hcquoHMgM3Mvf6tfnV6CLx0O2czhClZcPSCVQtuvVQ4CpwG+ZmTo/oEULDfjxx9yRkcGpQEH9+vDzz4jXyw+B8js5Pp7bc3JYGYiv3OvFC8T7fNCvH1fVr09CMC9SUmgRH89Kj4chgwbBjh3U83pZ5fFw3qWX8nGDBvx/e+cfXEV1xfHvSV4SXkLkJeFHgaTBaIgBpBAyEYIwbaEVEGkrINCKDiJOFadArT+oiqJ2nM5Q5McUtCW2tuMoRaQ6gUFTq9UqQlHAgsgPFWqwEDEp8tuQnP7x3XUfIb8k4e3Ly/nM7Lz39u3b/b7dc8+9e+65dysBLOjdG6iuRiAQwEAAh8eN8/7gwIHA0aPA3r24PRiEAEhKSsKRxEQUAvhlUhIQCuFk797IzMzEdYMH0wmXlwO9etW9Wh5DhrChlJyMHl274koAR+PjkQQgt39/FAHISEqiAw8fH9K3L8dpOA05t9zPmzePYyr69Gn4mNFKQ013d0G0h1z27GEMLhhkp1p9mRrp6UwJS0tTzctjDH3jRmYtFBV5McS67NvHOHlCghf37NOH2ycmMnZeUsLXwkJu424LJyYq4mWRDB9OLceOece4+25+v3Ah0w3j4714PMA4aN34a7QuLe24bE6nYWssTZ3PxnS4/SkAUxnPV4MTeqj32J07qz79NDsds7IaPsciDCE8+CBtaccOfv/znzP0EhfH/pg5c7jPTp1UH32Uv3PTXd193XEH7X3ePP52zx52irod1N26qc6YwWOMH8++pLBwxVe88QY7OxcvPve711+nrU+erDp9OstsUhI7ITt39lJW//Y3Zpn07En9M2Y07gNqaxlnT0jwrm0gwA5VN612/Xr2Ya1bd/Zvhw9nrn549llFBcv5ypWNH9cn0MIYegDARwAuhtcp2rfONjNxdqfoX5rab6tmuXz4oeoNNzDmNXYsY9Rubvidd3r55lu20EEGAnTOcXE06hEjaDwlJefu++OPmeUSCLAXvaqK+x0wgIbfsSONsm9fVhjBIPeZlkZDSkvjsa65hr3pq1efvf/qahpteGeOCCuSRYtYCAsLvcrBzeRpKhWxrS2pqexLePBB/rfwwpmW5qX/ne8SXtGe7+/DO9ndjkoRr5M1PJPFfe/mf4dn7oQ7Uve9u8+kJG5/113MjIqLo7NLT2cfjHv9g0Eu06bRhlwuvZTblJYydS8nh1lc48fTUd12G485Zw6zP/buPTdFb+FC9ktNnEjbDK+E0tNZyeTns+ERPqCptpYducHguR2iLr/5Da91UhLTIceM8TpwO3Rgg+uxx5hpk5LCFOMvvmjaB1RXqz7+OMtKKES96elMe733XlZIbsUXzoEDzLIpLOTYDXdg3v33R24A2dekRQ6dv8cYALvBbJd7nXUPARjnvO8AYBWYtrgJQE5T+7zgA4ueeIIG9/LLZ1+Yo0fZ0ZKczBbQhg38fudOGlNhIQ26pIQ97KEQDWHFCi+/98c/9lrOGRlsfRcU0MEPHerl/LqjKhMSmF3w1lsN6926la11d2CF25JYssQ7zmWX8S7EzZkOz9aor9V5xRVM/7rmGhaOhpxVKMQKyv3cnAwTEa+F5TojN90vOfnslmUwyHOTkMCld2/PQbu6i4uZ333DDV6KngiziDZu5Hc5OZ5zdbOAAJ6bkhLvLsnV527brx8LbUoKnUnHjtR39908p4EAnV7Pnl7qYWYmW2nuQKv4eHbAz5jBgT/x8fyfs2ezRbl3L7Os5s+n4zx0iI5p7FjaxKRJtMX9++koQyHqKC7muIVTp1SnTuV/6duXx8zNpTPPzqZjWrqUxyoo4DnNyeHgnrffZprjtGlsAIRC/H72bA5Oq6hgjnifPtS9bFnT5efwYea1L1vG/+RmjFx7LQfpnT5Nh/z889y+poblKjWVlVFD1NZyAFSHDl665cmTqpWVrFjWrOF5DgaZmVLfoKzmcOAAbeb66zm4bvv2hrc9c4YV4H33MbvIHdAXpbTYoV+IJSJD/9esYeHIy2PNO3Mmb+suuYTGWpeaGjrSW2/lEPVHH2V4Jnx/aWls3axYwVzfCRN46z11KgtbbS3zert0YV77mTNfv6Z/800WntJSfi4rY0Hp0IEO/uhROqAFC+gAunfnq5unvHAhB4ZkZzOkNGuW1/Lr1MlrrdYNkaSmsiXTqRP/47PPstC6IaZp0+hkMjLObmF27OilbHbpwuH+qrztTknxwhRZWSzMvXqpDhrEHGc3wygQ4LHz8rzWcG4uz0F1Nac76NGDzmXoUJ6Lb36To4CLiuiYR45kBfDcc7xdrqhgJfr447ze5eX1n++qKl7r7GzvzioriyG5yko6nCVLeIzu3VlB/P73F2bwyQcfsGIYO5b/t2tXjlCeO5e2FgrRlo8dY5bG5MmsyIYN4384dIgDdmbM4PV2K6P4eFYEr712froeeYTXskcPVlSVlbwm3/gGW939+tEOrruucSd88CBTBUeP5n+55RbVF1+kjS9dyrz6/PzGHXA7p/06dFU60zfeoLEsXsxW0vnW+qp0+BkZvBVdv9675Tx1ii2XYcPoGLZsaZnuDRtYeEaMYJjm9ddZeBISOHovP59O9P776RgTE+kYp0xhS3PSJKZzTZzotaYnTmS+++jRdJ5ZWWwtJyczzFFczDlg6k6JMH8+j3frrdSTm8vfuHO59OzpDbsvK+NvSkt5nmbN4rHcCvDJJzlwxKW6mnPt5OV5I2+HDaNjvu02agwE6Ez692dFN2qU6tq1LTu/9VFTw1ZjRUXLbKQ12bKFFffDD/MupLFh9XX58kvOy7N5Mx19S1m5kncFqalePn94GHDBgsYbLx99xOs8fz4/f/opK//Ro9kqD28UGQ3SmEMXfh95CgsLdfPmzb4cu8UcP85Z45YvB7ZtYyrWqVOcwXDmTM5MmJDQ8uOcPg2sXs05z99+m9k4J04wq+Wii4C4OGYN3Hwz8MAD7K1fsYK/2b+fU+aqAsOHU9Pnn3NfJ05wZrxf/KJ5c6OrAvPmccrcpUuBq67i+tJSft6wARg5ktOnxsUBL7wArFvnzcL4wANnpYc1Snk5p+PduZMped26AYMH80EdgQD3k519/ufUaBmqwFtvcSrnykrOrFhbC5SUAFdfzZlGi4vPzrx6913a3erVnO3z9tv90x8DiMg7qlpY73fm0FvImTPAyZN0jHEXcHr5mhpWJCkpLFAPPQS89x4rkbIyOr2pU5mTv2oVpygtKACuvJK5t5s2Ae+/z3nK165lOtjXZdUqzrV+5AgfZDFqFFMvDx9mgV63jhVJZiYwcSIrjK5dW/1UGFFIVRXw1FN03DU1nKdfhKmCJ08CP/0pMH262UMrYA49Vtm9m08OWr+eBef4cRaiSy4BBg1ibu4nn3AO9euvpxPOz2/ZMVVZOSxfzteqKh6nSxdgwgTgppus0LZnamv5cJXDh70nFl1+uT3BqhUxh95eUOXTYnbtoqMNBuloR460530aRozQmEO3JxbFEiJAUREXwzDaHfZMUcMwjBjBHLphGEaMYA7dMAwjRjCHbhiGESOYQzcMw4gRzKEbhmHECObQDcMwYgTfBhaJyGcA9rfCrjqDD9SIJkxT84lGXaap+USjrljXlK2q9T7yzTeH3lqIyOaGRk35hWlqPtGoyzQ1n2jU1Z41WcjFMAwjRjCHbhiGESPEgkP/nd8C6sE0NZ9o1GWamk806mq3mtp8DN0wDMMgsdBCNwzDMGAO3TAMI2Zosw5dREaJyC4R2Ssi9/io40kRqRCR7WHr0kWkTET2OK9pEdaUJSKvishOEdkhIrP81iUiHURkk4hsczTNd9ZfLCIbHU0rRSQxUprCtMWLyBYRKY0iTftE5N8islVENjvr/LarkIg8JyIfOLY1xGebynPOj7t8ISKz/T5PjrY5jp1vF5FnHPu/4HbVJh26iMQD+C2A0QD6AJgiIn18kvNHAKPqrLsHwCuqmgvgFedzJDkD4A5VzQcwGMBM5/z4qes0gO+q6rcADAAwSkQGA/g1gMccTVUApkdQk8ssADvDPkeDJgD4jqoOCMtf9tuuFgNYr6qXAfgWeM5806Squ5zzMwDAIAAnAKzxUxMAiEhPAD8DUKiq/QDEA5iMSNiVqra5BcAQAC+FfZ4LYK6PenoB2B72eReA7s777gB2+Xy+XgDwvWjRBSAZwLsArgBHzwXqu64R0pIJFvrvAigFIH5rco67D0DnOut8u34ALgLwMZxEimjQVEfH9wG8GQ2aAPQE8AmAdPCpcKUAroqEXbXJFjq8E+ZS7qyLFrqp6n8BwHn17anJItILwEAAG/3W5YQ2tgKoAFAG4EMA/1PVM84mflzHRQDuAlDrfM6IAk0AoABeFpF3ROQWZ52f1y8HwGcA/uCEp1aISIrPmsKZDOAZ572vmlT1AIAFAP4D4L8AjgB4BxGwq7bq0KWedZZ/WQcR6QhgNYDZqvqF33pUtUZ5e5wJoAhAfn2bRUqPiIwFUKGq74SvrmdTP2xrqKoWgGHFmSIy3AcN4QQAFABYrqoDARxH5EM+9eLEoscBWOW3FgBwYvY/AHAxgB4AUsDrWJdWt6u26tDLAWSFfc4E8KlPWurjkIh0BwDntSLSAkQkAXTmT6vq89GiCwBU9X8AXgPj+yERcR9WHunrOBTAOBHZB+BZMOyyyGdNAABV/dR5rQDjwkXw9/qVAyhX1Y3O5+dABx8NNjUawLuqesj57LemkQA+VtXPVLUawPMAihEBu2qrDv1fAHKdXuNE8HbrRZ81hfMigBud9zeCMeyIISICoATATlVdGA26RKSLiISc90HQ6HcCeBXABD80qepcVc1U1V6gDf1dVX/ipyYAEJEUEUl134Px4e3w8fqp6kEAn4hInrNqBID3/dQUxhR44RbAf03/ATBYRJKdsuieqwtvV350YLRSx8MYALvBOOy9Pup4BoyTVYOtmOlgHPYVAHuc1/QIa7oSvJ17D8BWZxnjpy4A/QFscTRtBzDPWZ8DYBOAveAtc5JP1/HbAEqjQZNz/G3OssO17yiwqwEANjvX8K8A0qJAUzKAzwF0ClvnqyZHw3wAHzi2/mcASZGwKxv6bxiGESO01ZCLYRiGUQdz6IZhGDGCOXTDMIwYwRy6YRhGjGAO3TAMI0Ywh260S0TkRyKiInKZ31oMo7Uwh260V6YA+Cc4oMgwYgJz6Ea7w5njZig4CGyysy5ORJY5c1iXisg6EZngfDdIRP7hTJT1kjus3DCiDXPoRnvkh+C83rsBVIpIAYBrwWmQLwdwMzi9qTsnzlIAE1R1EIAnAfzKD9GG0RSBpjcxjJhjCjgJF8BJuaYASACwSlVrARwUkVed7/MA9ANQxmk5EA9O9WAYUYc5dKNdISIZ4KyK/UREQQet4IyG9f4EwA5VHRIhiYZx3ljIxWhvTADwJ1XNVtVeqpoFPonnMIDxTiy9GzhZF8Cn33QRka9CMCLS1w/hhtEU5tCN9sYUnNsaXw0+iKAcnB3vCfAJT0dU9UuwEvi1iGwDZ64sjpxcw2g+NtuiYTiISEdVPeaEZTaBTw066Lcuw2guFkM3DI9S5yEciQAeNmdutDWshW4YhhEjWAzdMAwjRjCHbhiGESOYQzcMw4gRzKEbhmHECObQDcMwYoT/A9007or/mE5FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LOF(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Tahmine Dayalı Değer Atama KNN-İmputer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_knn_imp = train_df_num.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_names = list(train_df_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare\n",
       "0  22.0      1      0   7.2500\n",
       "1  38.0      1      0  71.2833\n",
       "2  26.0      0      0   7.9250\n",
       "3  35.0      1      0  53.1000\n",
       "4  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_knn_imp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833],\n",
       "       [26.    ,  0.    ,  0.    ,  7.925 ],\n",
       "       ...,\n",
       "       [    nan,  1.    ,  2.    , 23.45  ],\n",
       "       [26.    ,  0.    ,  0.    , 30.    ],\n",
       "       [32.    ,  0.    ,  0.    ,  7.75  ]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_df = np.array(train_df_knn_imp)\n",
    "np_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = KNNImputer(n_neighbors= 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "filled_knn_np = imputer.fit_transform(train_df_knn_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833],\n",
       "       [26.    ,  0.    ,  0.    ,  7.925 ]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filled_knn_np[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age      0\n",
       "SibSp    0\n",
       "Parch    0\n",
       "Fare     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filled_knn_df = pd.DataFrame(filled_knn_np, columns = var_names)\n",
    "filled_knn_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  SibSp  Parch     Fare\n",
       "0  22.0    1.0    0.0   7.2500\n",
       "1  38.0    1.0    0.0  71.2833\n",
       "2  26.0    0.0    0.0   7.9250\n",
       "3  35.0    1.0    0.0  53.1000\n",
       "4  35.0    0.0    0.0   8.0500\n",
       "5  24.8    0.0    0.0   8.4583\n",
       "6  54.0    0.0    0.0  51.8625\n",
       "7   2.0    3.0    1.0  21.0750\n",
       "8  27.0    0.0    2.0  11.1333\n",
       "9  14.0    1.0    0.0  30.0708"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filled_knn_df .head(10)"
   ]
  }
 ],
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