Week4_HW_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic = pd.read_csv(\"train.csv\")\n",
    "display(titanic.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sex değişkenini Mean encoding yaparak encode ediniz. Her kategori için Survived değişkeninin ortalamasını kategori adı ile değiştirebilirsiniz. \n",
    "\n",
    "IPUCU: pandas groupby ve map fonksiyonlarını kullanabilirsiniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "titanic.groupby(['Sex'])['Survived'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>0.188908</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.742038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.742038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.742038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.188908</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0.188908</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0.742038</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.742038</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0.188908</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.188908</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sex  Survived\n",
       "0    0.188908         0\n",
       "1    0.742038         1\n",
       "2    0.742038         1\n",
       "3    0.742038         1\n",
       "4    0.188908         0\n",
       "..        ...       ...\n",
       "886  0.188908         0\n",
       "887  0.742038         1\n",
       "888  0.742038         0\n",
       "889  0.188908         1\n",
       "890  0.188908         0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "mean_enc = titanic.groupby(['Sex'])['Survived'].mean().to_dict() \n",
    "  \n",
    "titanic[\"Sex\"] =  titanic[\"Sex\"].map(mean_enc) \n",
    "  \n",
    "titanic[[\"Sex\",\"Survived\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>0.188908</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>0.742038</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>0.742038</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>0.742038</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>0.188908</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name       Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris  0.188908  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  0.742038  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  0.742038  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  0.742038  35.0      1   \n",
       "4                             Allen, Mr. William Henry  0.188908  35.0      0   \n",
       "..                                                 ...       ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas  0.188908  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  0.742038  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  0.742038   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell  0.188908  26.0      0   \n",
       "890                                Dooley, Mr. Patrick  0.188908  32.0      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bindikleri Yer (Embarked) değişkenini sklearn OneHotEncoder objesini kullanarak encode edin.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "onehot_titanic = OneHotEncoder(sparse = False)\n",
    "\n",
    "titanic_copy = titanic.copy()\n",
    "\n",
    "titanic_copy.Embarked.fillna(titanic_copy[\"Embarked\"].mode()[0], inplace=True) #embarked değişkenindeki nan valueları mode ile doldurdum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_copy.Embarked.isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       ...,\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0],\n",
       "       [0, 1, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = onehot_titanic.fit_transform(titanic_copy[[\"Embarked\"]]).astype(np.uint8)\n",
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     C  Q  S\n",
       "0    0  0  1\n",
       "1    1  0  0\n",
       "2    0  0  1\n",
       "3    0  0  1\n",
       "4    0  0  1\n",
       "..  .. .. ..\n",
       "886  0  0  1\n",
       "887  0  0  1\n",
       "888  0  0  1\n",
       "889  1  0  0\n",
       "890  0  1  0\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dataframe = pd.DataFrame(encoded, index = titanic_copy.index, columns = [\"C\",\"Q\",\"S\"])\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cabin değişkenini sklearn LabelEncoder veya OrdinalEncoder objesini kullanarak encode edin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "label_df = titanic.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>0.188908</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>147</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>0.742038</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>81</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>0.742038</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>147</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>0.742038</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>55</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>0.188908</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>147</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>147</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>30</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>147</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>60</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>147</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name       Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris  0.188908  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  0.742038  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  0.742038  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  0.742038  35.0      1   \n",
       "4                             Allen, Mr. William Henry  0.188908  35.0      0   \n",
       "..                                                 ...       ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas  0.188908  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  0.742038  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  0.742038   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell  0.188908  26.0      0   \n",
       "890                                Dooley, Mr. Patrick  0.188908  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare  Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500    147        S  \n",
       "1        0          PC 17599  71.2833     81        C  \n",
       "2        0  STON/O2. 3101282   7.9250    147        S  \n",
       "3        0            113803  53.1000     55        S  \n",
       "4        0            373450   8.0500    147        S  \n",
       "..     ...               ...      ...    ...      ...  \n",
       "886      0            211536  13.0000    147        S  \n",
       "887      0            112053  30.0000     30        S  \n",
       "888      2        W./C. 6607  23.4500    147        S  \n",
       "889      0            111369  30.0000     60        C  \n",
       "890      0            370376   7.7500    147        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelEncoder = LabelEncoder()\n",
    "\n",
    "label_df[\"Cabin\"] =label_df[\"Cabin\"].astype(str)\n",
    "label_df[\"Cabin\"] = labelEncoder.fit_transform(label_df[\"Cabin\"])\n",
    "\n",
    "label_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([147,  81,  55, 129, 145,  49, 111,  13,  63,  41, 101,  23,  71,\n",
       "        21,  80, 142, 140, 122,  12,  91,  98,  52,  36, 116, 138, 107,\n",
       "        45, 141,  61, 123,  18,  14,  69, 144,   9,  28,  43,   8, 103,\n",
       "        93,  87,  78, 102,  83,  40, 134,  46,  57,  89,  54, 113,   3,\n",
       "        31,  90,  62,  51,  74, 125,  72,  35,  76, 124,  65,  17,  56,\n",
       "        85, 127, 146,  59, 104,  24, 131,  79,  47, 115, 128,  10,  50,\n",
       "        53,  86, 126,  97, 117, 133,   1,  25,  64,  96,  42, 121, 106,\n",
       "        39,  88,  26,  27,  20,  82,  77,   2,  48,  75,   0, 135,  29,\n",
       "         4,  95, 110, 114,   5,  33,   7, 108, 132,  58,  38,  34, 109,\n",
       "        32,  19, 139,  73, 120,  84,  66, 137,  15, 105,  67, 100, 118,\n",
       "        92, 136, 143,  22, 112,  44,  94,  11,  16,  37, 130,  68,  99,\n",
       "       119,   6,  70,  30,  60])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_df.Cabin.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeni için boxplot çizdiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26d1532c460>"
      ]
     },
     "execution_count": 17,
     "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(titanic[\"Fare\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeninedeki outlier değerleri Z-Score kullanarak tespit ediniz, sınır değerlerini ve kaç tane outlier değer olduğunu yazdırınız. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -24.954208\n",
       "1      39.079092\n",
       "2     -24.279208\n",
       "3      20.895792\n",
       "4     -24.154208\n",
       "         ...    \n",
       "886   -19.204208\n",
       "887    -2.204208\n",
       "888    -8.754208\n",
       "889    -2.204208\n",
       "890   -24.454208\n",
       "Name: Fare, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Fare\"] - titanic[\"Fare\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_score = (titanic[\"Fare\"] - titanic[\"Fare\"].mean() ) / titanic[\"Fare\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(z_score>3).sum(), (z_score<-3).sum() #sınır değer sayıları"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "       ... \n",
       "886    True\n",
       "887    True\n",
       "888    True\n",
       "889    True\n",
       "890    True\n",
       "Name: Fare, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier = ~((z_score>3) | (z_score<-3))\n",
    "outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sınır değerleri :  164.8667 0.0\n"
     ]
    }
   ],
   "source": [
    "maximum =titanic.loc[z_score<=3,\"Fare\"].max()\n",
    "minimum =titanic.loc[z_score>=-3,\"Fare\"].min()\n",
    "print(\"Sınır değerleri : \",maximum, minimum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python kodu buraya\n",
    "replace_upper = titanic[z_score<=3][\"Fare\"].max()\n",
    "replace_lower = titanic[z_score>=-3][\"Fare\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-4639b622b552>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  zscore_rep[\"Fare\"][z_score>3] = replace_upper\n",
      "<ipython-input-30-4639b622b552>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  zscore_rep[\"Fare\"][z_score<-3] = replace_lower\n"
     ]
    }
   ],
   "source": [
    "zscore_rep = titanic.copy()\n",
    "zscore_rep[\"Fare\"][z_score>3] = replace_upper\n",
    "zscore_rep[\"Fare\"][z_score<-3] = replace_lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 164.8667, (891, 12))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zscore_rep[\"Fare\"].min(), zscore_rep[\"Fare\"].max(), zscore_rep.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeninedeki outlier değerleri IQR metodunu kullanarak tespit ediniz, sınır değerlerini ve kaç tane outlier değer olduğunu yazdırınız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-26.724, 65.6344)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = titanic.Fare.quantile(q=0.25)\n",
    "Q3 = titanic.Fare.quantile(q=0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "upper_limit = Q3 + 1.5*IQR\n",
    "lower_limit = Q1 - 1.5*IQR\n",
    "\n",
    "lower_limit, upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(titanic[\"Fare\"]>upper_limit).sum() + (titanic[\"Fare\"]<lower_limit).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-d8390a2cbc21>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  IQR_replaced[\"Fare\"][titanic[\"Fare\"] > upper_limit] = upper_limit\n",
      "<ipython-input-37-d8390a2cbc21>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  IQR_replaced[\"Fare\"][titanic[\"Fare\"] < lower_limit] = lower_limit\n"
     ]
    }
   ],
   "source": [
    "IQR_replaced = titanic.copy()\n",
    "\n",
    "IQR_replaced[\"Fare\"][titanic[\"Fare\"] > upper_limit] = upper_limit\n",
    "IQR_replaced[\"Fare\"][titanic[\"Fare\"] < lower_limit] = lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 65.6344, (891, 12))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR_replaced[\"Fare\"].min(), IQR_replaced[\"Fare\"].max(), IQR_replaced.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeninedeki outlier değerleri Hard-Edges metodunu kullanarak tespit ediniz, sınır değerlerini ve kaç tane outlier değer olduğunu yazdırınız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "249.00622000000033 0.0\n"
     ]
    }
   ],
   "source": [
    "lower = titanic.Fare.quantile(0.01)\n",
    "upper = titanic.Fare.quantile(0.99)\n",
    "print(upper, lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier sayısı :  9\n"
     ]
    }
   ],
   "source": [
    "total_outlier = (titanic.Fare > upper) | (titanic.Fare < lower)\n",
    "print(\"Outlier sayısı : \",total_outlier.sum()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-44-57b16b79d8bc>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  HardEdges_replaced[\"Fare\"][titanic[\"Fare\"] > upper] = upper\n",
      "<ipython-input-44-57b16b79d8bc>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  HardEdges_replaced[\"Fare\"][titanic[\"Fare\"] < lower] = lower\n"
     ]
    }
   ],
   "source": [
    "HardEdges_replaced = titanic.copy()\n",
    "HardEdges_replaced[\"Fare\"][titanic[\"Fare\"] > upper] = upper\n",
    "HardEdges_replaced[\"Fare\"][titanic[\"Fare\"] < lower] = lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 249.00622000000033, (891, 12))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HardEdges_replaced[\"Fare\"].min(), HardEdges_replaced[\"Fare\"].max(), HardEdges_replaced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26d15aedfa0>"
      ]
     },
     "execution_count": 46,
     "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(HardEdges_replaced.Fare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeni için yukarıdaki 3 metodu kullanarak sınır değerler olarak bulduğunuz değerleri ve outlier sayılarını karşılaştırın. Bu değişken için hangisini kullanmak daha mantıklı olabilir?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Z-score ile bulunan outlier sayısı: 20, IQR ile bulunan outlier sayısı: 116, Hard Edges ile bulunan outlier sayısı:  9 Bu durumda değiştirilen veri sayısının az olduğu Hard Edges'in mantıklı olduğunu düşünüyorum\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Z-score ile bulunan outlier sayısı: 20, IQR ile bulunan outlier sayısı: 116, Hard Edges ile bulunan outlier sayısı:  9. Bu durumda değiştirilen veri sayısının en az olduğu Hard Edges'in mantıklı olduğunu düşünüyorum\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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