HW4_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex\n",
      "female    0.742038\n",
      "male      0.188908\n",
      "Name: Survived, dtype: float64\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </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  0.188908  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  0.742038  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  0.742038  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  0.742038  35.0      1   \n",
       "4                           Allen, Mr. William Henry  0.188908  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "df_copy=titanic.copy()\n",
    "print(df_copy.groupby([\"Sex\"])[\"Survived\"].mean())\n",
    "df_copy[\"Sex\"]=df_copy[\"Sex\"].map(df_copy.groupby([\"Sex\"])[\"Survived\"].mean())\n",
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bindikleri Yer (Embarked) değişkenini sklearn OneHotEncoder objesini kullanarak encode edin.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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_C  Embarked_Q  Embarked_S  \n",
       "0      0         A/5 21171   7.2500   NaN           0           0           1  \n",
       "1      0          PC 17599  71.2833   C85           1           0           0  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN           0           0           1  \n",
       "3      0            113803  53.1000  C123           0           0           1  \n",
       "4      0            373450   8.0500   NaN           0           0           1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "df_copy=titanic.copy()\n",
    "df_copy[\"Embarked\"].fillna(df_copy[\"Embarked\"].mode()[0],inplace=True)\n",
    "\n",
    "onehot = OneHotEncoder(sparse=False)\n",
    "encoded_features = pd.DataFrame(onehot.fit_transform(df_copy[[\"Embarked\"]]).astype(np.uint8),columns=onehot.categories_[0].tolist())\n",
    "df_copy.drop([\"Embarked\"],axis=1,inplace=True)\n",
    "encoded_features.rename(columns={\"C\":\"Embarked_C\",\"Q\":\"Embarked_Q\",\"S\":\"Embarked_S\"},inplace=True)\n",
    "display(pd.concat([df_copy,encoded_features],axis=1).head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cabin değişkenini sklearn LabelEncoder veya OrdinalEncoder objesini kullanarak encode edin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Cabin_Encoder</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>S</td>\n",
       "      <td>-1</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>C</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>S</td>\n",
       "      <td>-1</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>S</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>S</td>\n",
       "      <td>-1</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 Embarked  Cabin_Encoder  \n",
       "0      0         A/5 21171   7.2500        S             -1  \n",
       "1      0          PC 17599  71.2833        C             81  \n",
       "2      0  STON/O2. 3101282   7.9250        S             -1  \n",
       "3      0            113803  53.1000        S             55  \n",
       "4      0            373450   8.0500        S             -1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "df_copy=titanic.copy()\n",
    "\n",
    "label=LabelEncoder()\n",
    "df_copy[\"Cabin_Encoder\"]=0\n",
    "df_copy.loc[df_copy[\"Cabin\"].isnull(),\"Cabin_Encoder\"]=-1\n",
    "df_copy.loc[df_copy[\"Cabin_Encoder\"]==0,\"Cabin_Encoder\"]=label.fit_transform(df_copy.loc[df_copy[\"Cabin\"].notnull(),\"Cabin\"])\n",
    "df_copy.drop([\"Cabin\"],axis=1,inplace=True)\n",
    "df_copy.head()\n",
    "\n",
    "#Nan Değerlerini -1 yaptım olmayanları Label Encoder formatına dönüştürdüm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare değişkeni için boxplot çizdiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eaad0a5108>"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sozluk={}\n",
    "data=pd.DataFrame(df_copy[\"Fare\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sınır Değerleri :  0.0 | 164.8667\n",
      "Outlier Sayısı : 20\n"
     ]
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "df_copy=titanic.copy()\n",
    "\n",
    "from scipy.stats import zscore\n",
    "z_score = zscore(df_copy[\"Fare\"])\n",
    "upper = df_copy[z_score<=3][\"Fare\"].max()\n",
    "lower= df_copy[z_score>=-3][\"Fare\"].min()\n",
    "length_outlier=len(df_copy[(df_copy[\"Fare\"]>upper)|(df_copy[\"Fare\"]<lower)][\"Fare\"])\n",
    "print(\"Sınır Değerleri : \",lower,\"|\",upper)\n",
    "print(\"Outlier Sayısı :\",length_outlier)\n",
    "\n",
    "sozluk[\"Z_Score\"]={\"Lower\":lower,\"Upper\":upper,\"Length Outlier\":length_outlier}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python kodu buraya\n",
    "df_copy.loc[z_score>3,\"Fare\"] = upper\n",
    "df_copy.loc[z_score<-3,\"Fare\"] = lower\n",
    "\n",
    "data=pd.concat([data,pd.DataFrame(data=df_copy[\"Fare\"].describe().values,columns=[\"Z_Score\"],index=df_copy[\"Fare\"].describe().index)],axis=1)"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sınır Değerleri :  -26.724 | 65.6344\n",
      "Oulier Sayısı :  116\n"
     ]
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "from scipy.stats import iqr\n",
    "df_copy=titanic.copy()\n",
    "q1 = df_copy[\"Fare\"].quantile(0.25)\n",
    "q3 = df_copy[\"Fare\"].quantile(0.75)\n",
    "iqr = q3-q1\n",
    "lower=q1-(1.5*iqr)\n",
    "upper=q3+(1.5*iqr)\n",
    "length_outlier=len(df_copy[(df_copy[\"Fare\"]<lower)|(df_copy[\"Fare\"]>upper)][\"Fare\"])\n",
    "print(\"Sınır Değerleri : \",lower,\"|\",upper)\n",
    "print(\"Oulier Sayısı : \",length_outlier)\n",
    "\n",
    "sozluk[\"IQR\"]={\"Lower\":lower,\"Upper\":upper,\"Length Outlier\":length_outlier}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python kodu buraya\n",
    "df_copy.loc[(df_copy[\"Fare\"]<lower),\"Fare\"]=lower\n",
    "df_copy.loc[(df_copy[\"Fare\"]>upper),\"Fare\"]=upper\n",
    "\n",
    "data=pd.concat([data,pd.DataFrame(data=df_copy[\"Fare\"].describe().values,columns=[\"IQR\"],index=df_copy[\"Fare\"].describe().index)],axis=1)"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sınır Değerleri :  0.0 | 249.00622000000033\n",
      "Oulier Sayısı :  9\n"
     ]
    }
   ],
   "source": [
    "#python kodu buraya\n",
    "df_copy=titanic.copy()\n",
    "lower = df_copy[\"Fare\"].quantile(q=0.01)\n",
    "upper = df_copy[\"Fare\"].quantile(q=0.99)\n",
    "length_outlier = len(df_copy[(df_copy[\"Fare\"]<lower)|(df_copy[\"Fare\"]>upper)][\"Fare\"])\n",
    "print(\"Sınır Değerleri : \",lower,\"|\",upper)\n",
    "print(\"Oulier Sayısı : \",length_outlier)\n",
    "\n",
    "sozluk[\"Hard-Edges\"]={\"Lower\":lower,\"Upper\":upper,\"Length Outlier\":length_outlier}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tespit ettiğiniz outlier'ları sınır değerler ile değiştiriniz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#python kodu buraya\n",
    "df_copy.loc[(df_copy[\"Fare\"]<lower),\"Fare\"]=lower\n",
    "df_copy.loc[(df_copy[\"Fare\"]>upper),\"Fare\"]=upper\n",
    "\n",
    "data=pd.concat([data,pd.DataFrame(data=df_copy[\"Fare\"].describe().values,columns=[\"Hard-Edges\"],index=df_copy[\"Fare\"].describe().index)],axis=1)"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Z_Score</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Hard-Edges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lower</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-26.7240</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper</th>\n",
       "      <td>164.8667</td>\n",
       "      <td>65.6344</td>\n",
       "      <td>249.00622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Length Outlier</th>\n",
       "      <td>20.0000</td>\n",
       "      <td>116.0000</td>\n",
       "      <td>9.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Z_Score       IQR  Hard-Edges\n",
       "Lower             0.0000  -26.7240     0.00000\n",
       "Upper           164.8667   65.6344   249.00622\n",
       "Length Outlier   20.0000  116.0000     9.00000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Fare</th>\n",
       "      <th>Z_Score</th>\n",
       "      <th>IQR</th>\n",
       "      <th>Hard-Edges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.204208</td>\n",
       "      <td>29.635367</td>\n",
       "      <td>24.046813</td>\n",
       "      <td>31.224767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.693429</td>\n",
       "      <td>35.532019</td>\n",
       "      <td>20.481625</td>\n",
       "      <td>42.524125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>512.329200</td>\n",
       "      <td>164.866700</td>\n",
       "      <td>65.634400</td>\n",
       "      <td>249.006220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fare     Z_Score        IQR  Hard-Edges\n",
       "mean   32.204208   29.635367  24.046813   31.224767\n",
       "std    49.693429   35.532019  20.481625   42.524125\n",
       "min     0.000000    0.000000   0.000000    0.000000\n",
       "max   512.329200  164.866700  65.634400  249.006220"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=data.loc[[\"mean\",\"std\",\"min\",\"max\"]]\n",
    "display(pd.DataFrame(sozluk))\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Z_score yönteminde outlier sayısı diğerlerine göre orta ve etkisi de orta derecede oldu.\n",
    "# IQR yönteminde outlier sayısı fazla olduğundan etkisi de fazla oldu.\n",
    "# Hard-Edges yönteminde outlier sayısı diğerlerine göre az ve etkisi de az oldu.\n",
    "\n",
    "#Bu durumda aralarında en ideal Z_score yönteminin olduğunu düşünüyorum :)"
   ]
  }
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