week4_homework_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 139,
   "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": 140,
   "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": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "titanic.groupby(\"Sex\")[\"Survived\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_encode = titanic.groupby(\"Sex\")[\"Survived\"].mean()\n",
    "mean_encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "      <th>sex_mean_encode</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",
       "      <td>0.188908</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",
       "      <td>0.742038</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",
       "      <td>0.742038</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",
       "      <td>0.742038</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",
       "      <td>0.188908</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",
       "      <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>male</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",
       "      <td>0.188908</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>female</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",
       "      <td>0.742038</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>female</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",
       "      <td>0.742038</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>male</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",
       "      <td>0.188908</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>male</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",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 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    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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  sex_mean_encode  \n",
       "0        0         A/5 21171   7.2500   NaN        S         0.188908  \n",
       "1        0          PC 17599  71.2833   C85        C         0.742038  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S         0.742038  \n",
       "3        0            113803  53.1000  C123        S         0.742038  \n",
       "4        0            373450   8.0500   NaN        S         0.188908  \n",
       "..     ...               ...      ...   ...      ...              ...  \n",
       "886      0            211536  13.0000   NaN        S         0.188908  \n",
       "887      0            112053  30.0000   B42        S         0.742038  \n",
       "888      2        W./C. 6607  23.4500   NaN        S         0.742038  \n",
       "889      0            111369  30.0000  C148        C         0.188908  \n",
       "890      0            370376   7.7500   NaN        Q         0.188908  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.loc[:, \"sex_mean_encode\"] = titanic[\"Sex\"].map(mean_encode)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bindikleri Yer (Embarked) değişkenini sklearn OneHotEncoder objesini kullanarak encode edin.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "      <th>sex_mean_encode</th>\n",
       "      <th>Emb_C</th>\n",
       "      <th>Emb_Q</th>\n",
       "      <th>Emb_S</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",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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",
       "      <td>0.742038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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",
       "      <td>0.742038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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",
       "      <td>0.742038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>male</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",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>female</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",
       "      <td>0.742038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>female</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",
       "      <td>0.742038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>male</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",
       "      <td>0.188908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>male</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",
       "      <td>0.188908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 16 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    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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  sex_mean_encode  Emb_C  \\\n",
       "0        0         A/5 21171   7.2500   NaN        S         0.188908    0.0   \n",
       "1        0          PC 17599  71.2833   C85        C         0.742038    1.0   \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S         0.742038    0.0   \n",
       "3        0            113803  53.1000  C123        S         0.742038    0.0   \n",
       "4        0            373450   8.0500   NaN        S         0.188908    0.0   \n",
       "..     ...               ...      ...   ...      ...              ...    ...   \n",
       "886      0            211536  13.0000   NaN        S         0.188908    0.0   \n",
       "887      0            112053  30.0000   B42        S         0.742038    0.0   \n",
       "888      2        W./C. 6607  23.4500   NaN        S         0.742038    0.0   \n",
       "889      0            111369  30.0000  C148        C         0.188908    1.0   \n",
       "890      0            370376   7.7500   NaN        Q         0.188908    0.0   \n",
       "\n",
       "     Emb_Q  Emb_S  \n",
       "0      0.0    1.0  \n",
       "1      0.0    0.0  \n",
       "2      0.0    1.0  \n",
       "3      0.0    1.0  \n",
       "4      0.0    1.0  \n",
       "..     ...    ...  \n",
       "886    0.0    1.0  \n",
       "887    0.0    1.0  \n",
       "888    0.0    1.0  \n",
       "889    0.0    0.0  \n",
       "890    1.0    0.0  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohc= OneHotEncoder()\n",
    "titanic.Embarked.fillna(titanic.Embarked.mode()[0], inplace=True)\n",
    "ohe = ohc.fit_transform(titanic.Embarked.values.reshape(-1,1)).toarray()\n",
    "titanicOneHot = pd.DataFrame(ohe, columns = [\"Emb_\"+str(ohc.categories_[0][i])\n",
    "                                       for i in range(len(ohc.categories_[0]))])\n",
    "\n",
    "dft = pd.concat([titanic, titanicOneHot], axis=1)\n",
    "dft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cabin değişkenini sklearn LabelEncoder veya OrdinalEncoder objesini kullanarak encode edin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>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",
       "      <th>sex_mean_encode</th>\n",
       "      <th>Cabin_label_encoded</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",
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       "      <td>147</td>\n",
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       "    <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",
       "      <td>0.742038</td>\n",
       "      <td>81</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",
       "      <td>0.742038</td>\n",
       "      <td>147</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",
       "      <td>0.742038</td>\n",
       "      <td>55</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",
       "      <td>0.188908</td>\n",
       "      <td>147</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",
       "      <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>male</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",
       "      <td>0.188908</td>\n",
       "      <td>147</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>female</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",
       "      <td>0.742038</td>\n",
       "      <td>30</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>female</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",
       "      <td>0.742038</td>\n",
       "      <td>147</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>male</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",
       "      <td>0.188908</td>\n",
       "      <td>60</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>male</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",
       "      <td>0.188908</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 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    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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  sex_mean_encode  \\\n",
       "0        0         A/5 21171   7.2500   nan        S         0.188908   \n",
       "1        0          PC 17599  71.2833   C85        C         0.742038   \n",
       "2        0  STON/O2. 3101282   7.9250   nan        S         0.742038   \n",
       "3        0            113803  53.1000  C123        S         0.742038   \n",
       "4        0            373450   8.0500   nan        S         0.188908   \n",
       "..     ...               ...      ...   ...      ...              ...   \n",
       "886      0            211536  13.0000   nan        S         0.188908   \n",
       "887      0            112053  30.0000   B42        S         0.742038   \n",
       "888      2        W./C. 6607  23.4500   nan        S         0.742038   \n",
       "889      0            111369  30.0000  C148        C         0.188908   \n",
       "890      0            370376   7.7500   nan        Q         0.188908   \n",
       "\n",
       "     Cabin_label_encoded  \n",
       "0                    147  \n",
       "1                     81  \n",
       "2                    147  \n",
       "3                     55  \n",
       "4                    147  \n",
       "..                   ...  \n",
       "886                  147  \n",
       "887                   30  \n",
       "888                  147  \n",
       "889                   60  \n",
       "890                  147  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "titanic[\"Cabin\"] = titanic[\"Cabin\"].astype(str)\n",
    "titanic[\"Cabin_label_encoded\"] = LabelEncoder().fit_transform(titanic.Cabin)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeni için boxplot çizdiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7b3e474c8>"
      ]
     },
     "execution_count": 146,
     "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": [
    "#python kodu buraya\n",
    "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": 147,
   "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": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "titanic[\"Fare\"] - titanic[\"Fare\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_score = (titanic[\"Fare\"] - titanic[\"Fare\"].mean() ) / titanic[\"Fare\"].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 0)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(z_score>3).sum(), (z_score<-3).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "886    False\n",
       "887    False\n",
       "888    False\n",
       "889    False\n",
       "890    False\n",
       "Name: Fare, Length: 891, dtype: bool"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(z_score>3) | (z_score<-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164.8667"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "titanic[z_score<=3][\"Fare\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[z_score>=-3][\"Fare\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_replace_value = titanic[z_score<=3][\"Fare\"].max()\n",
    "lower_replace_value = titanic[z_score>=-3][\"Fare\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 164.8667)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_replace_value, upper_replace_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py: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",
      "  \n",
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py: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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "titanic_zscore_replaced = titanic.copy()\n",
    "titanic_zscore_replaced[\"Fare\"][z_score>3] = upper_replace_value\n",
    "titanic_zscore_replaced[\"Fare\"][z_score<-3] = lower_replace_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 512.3292)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Fare\"].min(), titanic[\"Fare\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 164.8667, (891, 14))"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_zscore_replaced[\"Fare\"].min(), titanic_zscore_replaced[\"Fare\"].max(), titanic_zscore_replaced.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": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python kodu buraya\n",
    "Q1 = titanic[\"Fare\"].quantile(q=0.25)\n",
    "Q3 = titanic[\"Fare\"].quantile(q=0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.0896"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = Q3 - Q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-26.724, 65.6344)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_limit = Q3 + 1.5*IQR\n",
    "lower_limit = Q1 - 1.5*IQR\n",
    "lower_limit, upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(titanic.Fare[(titanic.Fare > upper_limit) | (titanic.Fare < lower_limit)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py: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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py: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",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "titanic_iqr_replaced = titanic.copy()\n",
    "titanic_iqr_replaced[\"Fare\"][titanic[\"Fare\"] > upper_limit] = upper_limit\n",
    "titanic_iqr_replaced[\"Fare\"][titanic[\"Fare\"] < lower_limit] = lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 512.3292, (891, 14))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_iqr_replaced[\"Fare\"].min(),titanic_iqr_replaced[\"Fare\"].max(), titanic_iqr_replaced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7b3cfa908>"
      ]
     },
     "execution_count": 108,
     "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_iqr_replaced.Fare)"
   ]
  },
  {
   "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": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python kodu buraya\n",
    "lower_limit = titanic_he[\"Fare\"].quantile(q=0.01)\n",
    "upper_limit = titanic_he[\"Fare\"].quantile(q=0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 249.00622000000033)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit, upper_limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py: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",
      "  \n",
      "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py: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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "titanic_he_replaced = titanic.copy()\n",
    "titanic_he_replaced[\"Fare\"][titanic[\"Fare\"] > upper_limit] = upper_limit\n",
    "titanic_he_replaced[\"Fare\"][titanic[\"Fare\"] < lower_limit] = lower_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 249.00622000000033, (891, 14))"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_he_replaced[\"Fare\"].min(), titanic_he_replaced[\"Fare\"].max(),titanic_he_replaced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier = titanic_he_replaced[(titanic_he_replaced.Fare<lower_limit)|(titanic_he_replaced.Fare>=upper_limit)]\n",
    "len(outlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d7b3c99988>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0RtKncvY9G9KfzSpJX5G0UtIf07/mr8Vqzw9DQosigNGKtkh6U9LnlL6M9r+cfSdz7pukx0II32vi2IA54xowWtGHJL0R0h8PeIvSf/qqmKcl3WxmH5Wyf8/sE00aIzArAhit6EeSBs3s90pffjhZ7KAQwl8kDSv9V0pekHRQ6b/7BcwLvA0NAJywAgYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACc/B+ECPX3FOyEUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(titanic_he_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": "raw",
   "metadata": {},
   "source": [
    "Hard-Edges metodunu kullanmak daha mantıklı.\n",
    "\n",
    "Z-skor' da  (0.0, 164.8667) -- 20 outlier\n",
    "\n",
    "IQR' da (-26.724, 65.6344) -- 116 outlier\n",
    "\n",
    "Hard-Edges' de(0.0, 249.00622000000033) -- 9 outlier"
   ]
  }
 ],
 "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",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}