week4_homework_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder,OrdinalEncoder,OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  <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",
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       "      <th>0</th>\n",
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       "      <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>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",
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       "      <th>4</th>\n",
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       "      <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",
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       "    <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_enc  \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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_mean=titanic.copy()\n",
    "means = titanic_mean.Survived.groupby(titanic_mean.Sex).mean()\n",
    "display(means)\n",
    "titanic_mean[\"Sex_enc\"] = titanic_mean.Sex.map(means)\n",
    "titanic_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <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</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</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",
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       "    </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",
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       "      <td>0</td>\n",
       "      <td>373450</td>\n",
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       "      <th>886</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>887</th>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
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       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
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       "      <td>1</td>\n",
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       "      <th>890</th>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</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_enc  \n",
       "0        0         A/5 21171   7.2500   NaN        S        1  \n",
       "1        0          PC 17599  71.2833   C85        C        0  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S        0  \n",
       "3        0            113803  53.1000  C123        S        0  \n",
       "4        0            373450   8.0500   NaN        S        1  \n",
       "..     ...               ...      ...   ...      ...      ...  \n",
       "886      0            211536  13.0000   NaN        S        1  \n",
       "887      0            112053  30.0000   B42        S        0  \n",
       "888      2        W./C. 6607  23.4500   NaN        S        0  \n",
       "889      0            111369  30.0000  C148        C        1  \n",
       "890      0            370376   7.7500   NaN        Q        1  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc = LabelEncoder()\n",
    "titanic_LabelEncoding=titanic.copy()\n",
    "\n",
    "titanic_LabelEncoding[\"Sex_enc\"] = enc.fit_transform(titanic_LabelEncoding.Sex)\n",
    "titanic_LabelEncoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bindikleri Yer (Embarked) değişkenini sklearn OneHotEncoder objesini kullanarak encode edin.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_onehot=titanic.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <td>Montvila, Rev. Juozas</td>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
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       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "  </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_C  Embarked_Q  \\\n",
       "0        0         A/5 21171   7.2500   NaN           0           0   \n",
       "1        0          PC 17599  71.2833   C85           1           0   \n",
       "2        0  STON/O2. 3101282   7.9250   NaN           0           0   \n",
       "3        0            113803  53.1000  C123           0           0   \n",
       "4        0            373450   8.0500   NaN           0           0   \n",
       "..     ...               ...      ...   ...         ...         ...   \n",
       "886      0            211536  13.0000   NaN           0           0   \n",
       "887      0            112053  30.0000   B42           0           0   \n",
       "888      2        W./C. 6607  23.4500   NaN           0           0   \n",
       "889      0            111369  30.0000  C148           1           0   \n",
       "890      0            370376   7.7500   NaN           0           1   \n",
       "\n",
       "     Embarked_S  \n",
       "0             1  \n",
       "1             0  \n",
       "2             1  \n",
       "3             1  \n",
       "4             1  \n",
       "..          ...  \n",
       "886           1  \n",
       "887           1  \n",
       "888           1  \n",
       "889           0  \n",
       "890           0  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(titanic_onehot, columns=[\"Embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc = OneHotEncoder(sparse=False)\n",
    "titanic_onehot.Embarked = titanic_onehot.Embarked.fillna(titanic_onehot.Embarked.mode()[0])\n",
    "encoded_features = enc.fit_transform(titanic_onehot[[\"Embarked\"]]).astype(np.uint8)\n",
    "encoded_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "    </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",
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       "    </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": [
       "     0  1  2\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(encoded_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['C', 'Q', 'S'], dtype=object)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C', 'Q', 'S']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories = enc.categories_[0].tolist()\n",
    "categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     C  Q  S\n",
       "0    0  0  1\n",
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       "3    0  0  1\n",
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       "888  0  0  1\n",
       "889  1  0  0\n",
       "890  0  1  0\n",
       "\n",
       "[891 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pd.DataFrame(encoded_features, columns=categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cabin değişkenini sklearn LabelEncoder veya OrdinalEncoder objesini kullanarak encode edin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # # Label cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_Cabin_Label=titanic.copy()\n",
    "titanic_Cabin_Label.Cabin = titanic_Cabin_Label.Embarked.fillna(titanic_Cabin_Label.Cabin.mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
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       "      <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",
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       "    <tr>\n",
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       "      <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",
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       "      <td>C</td>\n",
       "    </tr>\n",
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       "      <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>3</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>3</td>\n",
       "      <td>S</td>\n",
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       "    <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>3</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>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>3</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>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>3</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>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>3</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>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>1</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>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>2</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    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  \n",
       "0        0         A/5 21171   7.2500      3        S  \n",
       "1        0          PC 17599  71.2833      1        C  \n",
       "2        0  STON/O2. 3101282   7.9250      3        S  \n",
       "3        0            113803  53.1000      3        S  \n",
       "4        0            373450   8.0500      3        S  \n",
       "..     ...               ...      ...    ...      ...  \n",
       "886      0            211536  13.0000      3        S  \n",
       "887      0            112053  30.0000      3        S  \n",
       "888      2        W./C. 6607  23.4500      3        S  \n",
       "889      0            111369  30.0000      1        C  \n",
       "890      0            370376   7.7500      2        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc = LabelEncoder()\n",
    "titanic_Cabin_Label[\"Cabin\"] = enc.fit_transform(titanic_Cabin_Label.Cabin)\n",
    "titanic_Cabin_Label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeni için boxplot çizdiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x227ef81d220>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "\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": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.502163\n",
       "1      0.786404\n",
       "2     -0.488580\n",
       "3      0.420494\n",
       "4     -0.486064\n",
       "         ...   \n",
       "886   -0.386454\n",
       "887   -0.044356\n",
       "888   -0.176164\n",
       "889   -0.044356\n",
       "890   -0.492101\n",
       "Name: Fare, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_score = (titanic.Fare - titanic.Fare.mean() ) / titanic.Fare.std()\n",
    "z_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(z_score>3).sum(), (z_score<-3).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 164.8667)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_replace_value, upper_replace_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-664418159072>: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",
      "  titanic_replaced.Fare[z_score>3] = upper_replace_value\n",
      "<ipython-input-11-664418159072>: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",
      "  titanic_replaced.Fare[z_score<-3] = lower_replace_value\n"
     ]
    }
   ],
   "source": [
    "titanic_replaced = titanic.copy()\n",
    "titanic_replaced.Fare[z_score>3] = upper_replace_value\n",
    "titanic_replaced.Fare[z_score<-3] = lower_replace_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 512.3292)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.Fare.min(),titanic.Fare.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 164.8667)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_replaced.Fare.min(),titanic_replaced.Fare.max()"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.0896"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = titanic.Fare.quantile(q=0.25)\n",
    "q3 = titanic.Fare.quantile(q=0.75)\n",
    "iqr = q3-q1\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(223, 222)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1_tita=titanic.Fare<q1\n",
    "q3_tita=titanic.Fare>q3\n",
    "q1_tita.sum() , q3_tita.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-26.724, 65.6344)"
      ]
     },
     "execution_count": 16,
     "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": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-1759f1afa8d2>: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",
      "  titanic_iqr_replaced.Fare[titanic.Fare > upper_limit] = upper_limit\n",
      "<ipython-input-17-1759f1afa8d2>: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",
      "  titanic_iqr_replaced.Fare[titanic.Fare < lower_limit] = lower_limit\n"
     ]
    }
   ],
   "source": [
    "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 65.6344)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_iqr_replaced.Fare.min(), titanic_iqr_replaced.Fare.max()"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_limit = titanic.Fare.quantile(q=0.01)\n",
    "upper_limit = titanic.Fare.quantile(q=0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 9)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_hg_titan=titanic.Fare<lower_limit\n",
    "up_hg_titan=titanic.Fare>upper_limit\n",
    "low_hg_titan.sum() , up_hg_titan.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 249.00622000000033)"
      ]
     },
     "execution_count": 20,
     "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": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-53eff9ec30be>: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",
      "  titanic_he_replaced.Fare[titanic.Fare > upper_limit] = upper_limit\n",
      "<ipython-input-22-53eff9ec30be>: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",
      "  titanic_he_replaced.Fare[titanic.Fare < lower_limit] = lower_limit\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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 249.00622000000033)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_he_replaced.Fare.min(), titanic_he_replaced.Fare.max()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zscore üst sınır degerlerinde 20 tane bulurken alt sınır degeri bulmadı.\n",
    "# IQR sınır degerlerinde 223 altsınır degeri bulurken 222 tane üst sınır degeri buldu.\n",
    "# Hard Edge alt sınır değeri bulmaz iken 9 tane de üst sınır değişkeni buldu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IQR 445 tane veriyi drop ettiginden dolayı elimdeki ver hep ortalama bir veri olacak gelecek testde anlamlı ama üst bir\n",
    "#değer gördüğünde işlem yapamayacagından kullanışsız olacaktır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hard Edge 9 tane veriyi drop ediyor bu da kullanılabilir ama üst sınır bence halen çok üstte tuttugundan dolayı \n",
    "#bu sefer de anlamsız ve üst bir değer gördüğünde anlamlıymış gibi davranabilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bence bu datada hem veri silme konusunda ortalama ve anlam yakalama konusunda \n",
    "#daha iyi olacağını düşündüğüm zscore kullanılmasını önerirdim."
   ]
  }
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