Kaggle_Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Competition-Titanic\n",
    "* * *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data 불러오기"
   ]
  },
  {
   "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": [],
   "source": [
    "train_df=pd.read_csv('train_titanic.csv')\n",
    "test_df=pd.read_csv('test_titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub=[]\n",
    "sub=test_df['PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. EDA(Exploratory Data Anaylsis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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",
       "    </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",
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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",
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       "    <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",
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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>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</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",
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       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
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       "      <td>893</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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",
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       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Foo, Mr. Choong</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name   Sex  Ticket        Cabin Embarked\n",
       "count               891   891     891          204      889\n",
       "unique              891     2     681          147        3\n",
       "top     Foo, Mr. Choong  male  347082  C23 C25 C27        S\n",
       "freq                  1   577       7            4      644"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418</td>\n",
       "      <td>418</td>\n",
       "      <td>418</td>\n",
       "      <td>91</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>418</td>\n",
       "      <td>2</td>\n",
       "      <td>363</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Kink, Miss. Maria</td>\n",
       "      <td>male</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>266</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name   Sex    Ticket            Cabin Embarked\n",
       "count                 418   418       418               91      418\n",
       "unique                418     2       363               76        3\n",
       "top     Kink, Miss. Maria  male  PC 17608  B57 B59 B63 B66        S\n",
       "freq                    1   266         5                3      270"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) PassengerId**\n",
    "   + 학습에 영향을 주지 않는 Feature이므로 Train과 Test Data에서 Drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=train_df.drop(['PassengerId'], axis=1)\n",
    "test_df=test_df.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**2) Cabin**\n",
    "   + NULL 값이 많아 Survived와의 관계성을 찾거나, NULL 값이 아닌 데이터를 이용하여 NULL 값을 채워넣기 어려우므로 Train과 Test Data에서 Drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=train_df.drop(['Cabin'], axis=1)\n",
    "test_df=test_df.drop(['Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**3) Ticket**\n",
    "   + 학습을 위해 object형의 Ticket Feature를 int형의 값으로 mapping을 해야 하지만, 숫자와 알파벳이 섞여있어 mapping 하기가 어려우므로 Train과 Test Data에서 Drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=train_df.drop(['Ticket'], axis=1)\n",
    "test_df=test_df.drop(['Ticket'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch     Fare Embarked  \n",
       "0    male  22.0      1      0   7.2500        S  \n",
       "1  female  38.0      1      0  71.2833        C  \n",
       "2  female  26.0      0      0   7.9250        S  \n",
       "3  female  35.0      1      0  53.1000        S  \n",
       "4    male  35.0      0      0   8.0500        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                          Name     Sex   Age  SibSp  \\\n",
       "0       3                              Kelly, Mr. James    male  34.5      0   \n",
       "1       3              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
       "2       2                     Myles, Mr. Thomas Francis    male  62.0      0   \n",
       "3       3                              Wirz, Mr. Albert    male  27.0      0   \n",
       "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1   \n",
       "\n",
       "   Parch     Fare Embarked  \n",
       "0      0   7.8292        Q  \n",
       "1      0   7.0000        S  \n",
       "2      0   9.6875        Q  \n",
       "3      0   8.6625        S  \n",
       "4      1  12.2875        S  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**4) Pclass**\n",
    "   + Survived Feature와 관계성 분석을 위해 countplot을 이용. 분석 결과 좌석등급이 낮은 3rd Class에 사람이 많이 탑승하였고 좌석등급이 높은 좌석일수록 생존율이 높은 것을 알 수 있음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Pclass\", data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Pclass\",hue=\"Survived\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**5) Sex**\n",
    "   + NULL 값이 존재하지 않지만 학습을 위해 남자를 1로 여자를 0으로 mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_mapping={\"male\":1, \"female\":0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Sex']=train_df['Sex'].map(gender_mapping)\n",
    "test_df['Sex']=test_df['Sex'].map(gender_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    1   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   \n",
       "2         1       3                             Heikkinen, Miss. Laina    0   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   \n",
       "4         0       3                           Allen, Mr. William Henry    1   \n",
       "\n",
       "    Age  SibSp  Parch     Fare Embarked  \n",
       "0  22.0      1      0   7.2500        S  \n",
       "1  38.0      1      0  71.2833        C  \n",
       "2  26.0      0      0   7.9250        S  \n",
       "3  35.0      1      0  53.1000        S  \n",
       "4  35.0      0      0   8.0500        S  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                          Name  Sex   Age  SibSp  \\\n",
       "0       3                              Kelly, Mr. James    1  34.5      0   \n",
       "1       3              Wilkes, Mrs. James (Ellen Needs)    0  47.0      1   \n",
       "2       2                     Myles, Mr. Thomas Francis    1  62.0      0   \n",
       "3       3                              Wirz, Mr. Albert    1  27.0      0   \n",
       "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0  22.0      1   \n",
       "\n",
       "   Parch     Fare Embarked  \n",
       "0      0   7.8292        Q  \n",
       "1      0   7.0000        S  \n",
       "2      0   9.6875        Q  \n",
       "3      0   8.6625        S  \n",
       "4      1  12.2875        S  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**6) Embarked**\n",
    "   + Survived Feature와의 관계를 분석한 결과, S에서 탑승한 승객이 많았고 C에서 탑승한 승객들의 생존율이 높았음을 알 수 있었음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Embarked\", data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Embarked\",hue=\"Survived\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ 2개의 NULL값이 존재하여, 승객들이 많이 탑승한 S 값으로 채워줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    data['Embarked']=data['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ Sex Feature와 마찬가지로 학습을 위해 object형을 int형의 값으로 mapping 시켜줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "embarked_mapping={\"S\":0, \"C\":1,\"Q\":2}\n",
    "train_df['Embarked']=train_df['Embarked'].map(embarked_mapping)\n",
    "test_df['Embarked']=test_df['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    1   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   \n",
       "2         1       3                             Heikkinen, Miss. Laina    0   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   \n",
       "4         0       3                           Allen, Mr. William Henry    1   \n",
       "\n",
       "    Age  SibSp  Parch     Fare  Embarked  \n",
       "0  22.0      1      0   7.2500         0  \n",
       "1  38.0      1      0  71.2833         1  \n",
       "2  26.0      0      0   7.9250         0  \n",
       "3  35.0      1      0  53.1000         0  \n",
       "4  35.0      0      0   8.0500         0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                          Name  Sex   Age  SibSp  \\\n",
       "0       3                              Kelly, Mr. James    1  34.5      0   \n",
       "1       3              Wilkes, Mrs. James (Ellen Needs)    0  47.0      1   \n",
       "2       2                     Myles, Mr. Thomas Francis    1  62.0      0   \n",
       "3       3                              Wirz, Mr. Albert    1  27.0      0   \n",
       "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0  22.0      1   \n",
       "\n",
       "   Parch     Fare  Embarked  \n",
       "0      0   7.8292         2  \n",
       "1      0   7.0000         0  \n",
       "2      0   9.6875         2  \n",
       "3      0   8.6625         0  \n",
       "4      1  12.2875         0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**7) Fare**\n",
    "   + Test Data에서 1개의 NULL값이 존재하므로 단순히 median 값으로 채워줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.265550</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.576535</td>\n",
       "      <td>0.464115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.841838</td>\n",
       "      <td>0.481622</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.850103</td>\n",
       "      <td>0.685516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.170000</td>\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>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.471875</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pclass         Sex         Age       SibSp       Parch        Fare  \\\n",
       "count  418.000000  418.000000  332.000000  418.000000  418.000000  418.000000   \n",
       "mean     2.265550    0.636364   30.272590    0.447368    0.392344   35.576535   \n",
       "std      0.841838    0.481622   14.181209    0.896760    0.981429   55.850103   \n",
       "min      1.000000    0.000000    0.170000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000    0.000000   21.000000    0.000000    0.000000    7.895800   \n",
       "50%      3.000000    1.000000   27.000000    0.000000    0.000000   14.454200   \n",
       "75%      3.000000    1.000000   39.000000    1.000000    0.000000   31.471875   \n",
       "max      3.000000    1.000000   76.000000    8.000000    9.000000  512.329200   \n",
       "\n",
       "         Embarked  \n",
       "count  418.000000  \n",
       "mean     0.464115  \n",
       "std      0.685516  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      2.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ 연속적인 숫자 값이기 때문에 범위를 정해주어 연속적이지 않게 바꿔주기 위해 5개의 범위로 나누어줌. 이때 Fare가 작은 값이 매우 많이 때문에 `cut()`을 그대로 사용하게 되면 특정 범위 안에 너무 많은 값이 존재할 수 있으므로 `qcut()`을 이용하여 백분위를 기준으로 나누어줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        (-0.001, 7.854]\n",
       "1      (39.688, 512.329]\n",
       "2          (7.854, 10.5]\n",
       "3      (39.688, 512.329]\n",
       "4          (7.854, 10.5]\n",
       "             ...        \n",
       "886       (10.5, 21.679]\n",
       "887     (21.679, 39.688]\n",
       "888     (21.679, 39.688]\n",
       "889     (21.679, 39.688]\n",
       "890      (-0.001, 7.854]\n",
       "Name: Fare, Length: 891, dtype: category\n",
       "Categories (5, interval[float64]): [(-0.001, 7.854] < (7.854, 10.5] < (10.5, 21.679] < (21.679, 39.688] < (39.688, 512.329]]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fare_band=[]\n",
    "fare_band=pd.qcut(train_df['Fare'], 5)\n",
    "fare_band"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ 범위마다 특정 숫자로 mapping을 하여 Train과 Test Data 모두 꿔준 다음 int형으로 casting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    data.loc[data['Fare']<=7.854, 'Fare']=0\n",
    "    data.loc[(data['Fare']<=10.5) & (data['Fare']>7.854), 'Fare']=1\n",
    "    data.loc[(data['Fare']<=21.679) & (data['Fare']>10.5), 'Fare']=2\n",
    "    data.loc[(data['Fare']<=39.688) & (data['Fare']>21.679), 'Fare']=3\n",
    "    data.loc[data['Fare']>39.688, 'Fare']=4\n",
    "    data['Fare']=data['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    1   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   \n",
       "2         1       3                             Heikkinen, Miss. Laina    0   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   \n",
       "4         0       3                           Allen, Mr. William Henry    1   \n",
       "\n",
       "    Age  SibSp  Parch  Fare  Embarked  \n",
       "0  22.0      1      0     0         0  \n",
       "1  38.0      1      0     4         1  \n",
       "2  26.0      0      0     1         0  \n",
       "3  35.0      1      0     4         0  \n",
       "4  35.0      0      0     1         0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                          Name  Sex   Age  SibSp  \\\n",
       "0       3                              Kelly, Mr. James    1  34.5      0   \n",
       "1       3              Wilkes, Mrs. James (Ellen Needs)    0  47.0      1   \n",
       "2       2                     Myles, Mr. Thomas Francis    1  62.0      0   \n",
       "3       3                              Wirz, Mr. Albert    1  27.0      0   \n",
       "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0  22.0      1   \n",
       "\n",
       "   Parch  Fare  Embarked  \n",
       "0      0     0         2  \n",
       "1      0     0         0  \n",
       "2      0     1         2  \n",
       "3      0     1         0  \n",
       "4      1     2         0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**8) SibSp & Parch**\n",
    "   + SibSp Feature를 countplot을 이용하여 분석한 결과, 혼자 탑승한 사람이 많았고 이들의 생존율은 낮았음을 알 수 있었음."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8-1) SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"SibSp\", data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"SibSp\",hue=\"Survived\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8-2) Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Parch\", data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Parch\", hue=\"Survived\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ SibSp Feature와 Parch Feature는 가족이라는 공통적인 특징을 가지고 있으므로 두개의 Feature를 Family라는 하나의 Feature로 합침. 이때 본인을 포함해주기 위해 뒤에 1 값을 더해줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    data[\"Family\"]=data[\"SibSp\"]+data[\"Parch\"]+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JKuDngbVV9cOMbnq5YIBSrmLv96V7gFcCn5zmgZd9KFTVJ4GvDl3HvKp6sKruaMvfYPTLu2rYqqBG/rGtHtUeg9+lkGQ18NPAu4auZVYl+T7gJcAVAFX1T1X1tWGrepz1wJeq6u+GLqRZATwtyQrg6QzweamF3peqamtVLdYMDvu07ENhliWZA14EfHrYSkbaaZo7gZ3ATVU1C3W9Ffhl4LGhCxlTwMeTbGnTsgzt+4FdwJXtNNu7kjxj6KLGXAC8b+giAKrq/wG/C9wPPAg8UlUfH7aqpWUozKgk3wt8EHhDVX196HoAqurRNtxfDZye5IeHrCfJzwA7q2rLkHUs4IyqOo3RjL+XJnnJwPWsAE4D3llVLwK+CVw2bEkj7YOqrwA+MHQtAEmOYTQR50nAc4FnJLlw2KqWlqEwg5IcxSgQ3ltVHxq6nj21Uw+3Mfy1mDOAVyS5j9Gsumcm+eNhS4Kq2tGedzI6Tz7MJPrftR3YPjayu45RSMyCs4E7quqhoQtpfhL426raVVX/DHwIePHANS0pQ2HGJAmjc79bq+r3h65nXpKVSY5uy09j9MvzxSFrqqpfrarVVTXH6BTELVU16P/qkjwjyTPnl4GfYnSBcDBV9ffAA0le0JrWA18YsKRxr2ZGTh019wPrkjy9/S6uZ/ZuYpiqZR8KSd4H/DXwgiTbk1wycElnAK9h9L/e+dv1Xj5wTQAnALcmuYvRPFU3VdVM3AI6Y44H/iLJ54DbgT+rqo8NXBPA64D3tr+/U4HfGrgekjwdeBmj/43PhDaaug64A7ib0Xvkkk95sdD7UpJ/l2Q78GPAnyX586kc22kuJEnzlv1IQZL0XYaCJKkzFCRJnaEgSeoMBUlSZyhI+5Dk0T1m8pxbhH3+5yQXteWrkpz3ZPcpLaaZ+TpOaQb9/zatx6Kpqj9azP1Ji82RgnQAkswl+T9J7miPF7f2n0jyiSTXJvm/Sd6c5Ofad1DcneQHWr/Lk/zSHvtcn+T6sfWXJZmZD3RpeTEUpH172tipo/k37Z3Ay9qEd/8eePtY/1MYfbfDjzD6VPoPVtXpjKb1ft1+jnMLcHKSlW39YuDKRfw5pIl5+kjat4VOHx0FvCPJqcCjwA+ObftMVT0IkORLwPyUy3cDL93XQaqqkrwHuDDJlYymMbhokX4G6YAYCtKB+QXgIUajgiOAb49t+87Y8mNj64/xxL9rVwIfbvv7QFXtXpRqpQNkKEgH5lmMpqF+LMkGRl/X+KRV1Y4kO4D/xmiSOGkQXlOQDsz/AjYk+RSjU0ffXMR9vxd4oKpmZVprLUPOkirNiCTvAD5bVVcMXYuWL0NBmgFJtjAadbysqr7zRP2laTEUJEmd1xQkSZ2hIEnqDAVJUmcoSJI6Q0GS1P0LehnHkIx9gOYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Family\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ Family Feature를 countplot을 이용하여 분석한 결과, 혼자 탑승한 승객이 압도적으로 많았고 이들의 생존율 또한 낮았음을 알 수 있었음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Family\", hue=\"Survived\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ 혼자 탑승한 승객이 압도적으로 많으므로 혼자 탑승한 승객과 그렇지 않은 승객으로 값을 Binary하게 바꿔줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    data[\"FamilySize\"]=1\n",
    "    data.loc[data[\"Family\"]==1, \"FamilySize\"]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"FamilySize\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ Survived Feature와의 관계성을 분석한 결과, 혼자 탑승한 승객의 생존율은 낮고 가족과 함께 탑승한 승객은 생존율이 상대적으로 높음을 알 수 있었음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"FamilySize\", hue=\"Survived\", data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=train_df.drop(['Parch', 'SibSp', 'Family'], axis=1)\n",
    "test_df=test_df.drop(['Parch', 'SibSp', 'Family'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    1   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   \n",
       "2         1       3                             Heikkinen, Miss. Laina    0   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   \n",
       "4         0       3                           Allen, Mr. William Henry    1   \n",
       "\n",
       "    Age  Fare  Embarked  FamilySize  \n",
       "0  22.0     0         0           1  \n",
       "1  38.0     4         1           1  \n",
       "2  26.0     1         0           0  \n",
       "3  35.0     4         0           1  \n",
       "4  35.0     1         0           0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                          Name  Sex   Age  Fare  \\\n",
       "0       3                              Kelly, Mr. James    1  34.5     0   \n",
       "1       3              Wilkes, Mrs. James (Ellen Needs)    0  47.0     0   \n",
       "2       2                     Myles, Mr. Thomas Francis    1  62.0     1   \n",
       "3       3                              Wirz, Mr. Albert    1  27.0     1   \n",
       "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0  22.0     2   \n",
       "\n",
       "   Embarked  FamilySize  \n",
       "0         2           0  \n",
       "1         0           1  \n",
       "2         2           0  \n",
       "3         0           0  \n",
       "4         0           1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**9) Age**\n",
    "   + Age에는 NULL 값이 많아 처음에는 성별에 따른 나이의 median값으로만 채워주려고 했지만 두 성별의 median값이 같아 모델이 학습하는데에 도움이 되지 않을 것으로 생각되어 Pclass Feature를 추가하여 두개의 Feature 값에 따른 median값으로 나이를 계산.\n",
    "   + NULL 값들을 Pclass와 성별에 맞게 계산한 median값들로 채움."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "guess_ages=np.zeros((2,3))\n",
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    for i in range(0,2):\n",
    "        for j in range(0,3):\n",
    "            temp_df=data[(data['Sex']==i)&(data['Pclass']==j+1)]['Age'].dropna()\n",
    "            guess_ages[i, j]=temp_df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[41., 24., 22.],\n",
       "       [42., 28., 24.]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    1   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   \n",
       "2         1       3                             Heikkinen, Miss. Laina    0   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   \n",
       "4         0       3                           Allen, Mr. William Henry    1   \n",
       "\n",
       "   Age  Fare  Embarked  FamilySize  \n",
       "0   22     0         0           1  \n",
       "1   38     4         1           1  \n",
       "2   26     1         0           0  \n",
       "3   35     4         0           1  \n",
       "4   35     1         0           0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0,3):\n",
    "            data.loc[(data['Sex']==i)&(data['Pclass']==j+1)&(data['Age'].isnull()), 'Age']=guess_ages[i,j]\n",
    "    data['Age']=data['Age'].astype(int)\n",
    "    \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.647587</td>\n",
       "      <td>29.093154</td>\n",
       "      <td>2.004489</td>\n",
       "      <td>0.361392</td>\n",
       "      <td>0.397306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>0.477990</td>\n",
       "      <td>13.420480</td>\n",
       "      <td>1.400635</td>\n",
       "      <td>0.635673</td>\n",
       "      <td>0.489615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\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>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Sex         Age        Fare    Embarked  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.383838    2.308642    0.647587   29.093154    2.004489    0.361392   \n",
       "std      0.486592    0.836071    0.477990   13.420480    1.400635    0.635673   \n",
       "min      0.000000    1.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    2.000000    0.000000   22.000000    1.000000    0.000000   \n",
       "50%      0.000000    3.000000    1.000000   26.000000    2.000000    0.000000   \n",
       "75%      1.000000    3.000000    1.000000   37.000000    3.000000    1.000000   \n",
       "max      1.000000    3.000000    1.000000   80.000000    4.000000    2.000000   \n",
       "\n",
       "       FamilySize  \n",
       "count  891.000000  \n",
       "mean     0.397306  \n",
       "std      0.489615  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      1.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.265550</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>29.272727</td>\n",
       "      <td>2.002392</td>\n",
       "      <td>0.464115</td>\n",
       "      <td>0.394737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.841838</td>\n",
       "      <td>0.481622</td>\n",
       "      <td>13.055582</td>\n",
       "      <td>1.436921</td>\n",
       "      <td>0.685516</td>\n",
       "      <td>0.489380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\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>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pclass         Sex         Age        Fare    Embarked  FamilySize\n",
       "count  418.000000  418.000000  418.000000  418.000000  418.000000  418.000000\n",
       "mean     2.265550    0.636364   29.272727    2.002392    0.464115    0.394737\n",
       "std      0.841838    0.481622   13.055582    1.436921    0.685516    0.489380\n",
       "min      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000\n",
       "25%      1.000000    0.000000   22.000000    1.000000    0.000000    0.000000\n",
       "50%      3.000000    1.000000   25.000000    2.000000    0.000000    0.000000\n",
       "75%      3.000000    1.000000   36.000000    3.000000    1.000000    1.000000\n",
       "max      3.000000    1.000000   76.000000    4.000000    2.000000    1.000000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ Fare Feature와 마찬가지로 Age Feature 또한 연속적인 값이기 때문에 범위를 8개로 나누어 주고, 나눈 범위에 따라 특정값에 mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (20.0, 30.0]\n",
       "1      (30.0, 40.0]\n",
       "2      (20.0, 30.0]\n",
       "3      (30.0, 40.0]\n",
       "4      (30.0, 40.0]\n",
       "           ...     \n",
       "886    (20.0, 30.0]\n",
       "887    (10.0, 20.0]\n",
       "888    (20.0, 30.0]\n",
       "889    (20.0, 30.0]\n",
       "890    (30.0, 40.0]\n",
       "Name: Age, Length: 891, dtype: category\n",
       "Categories (8, interval[float64]): [(-0.08, 10.0] < (10.0, 20.0] < (20.0, 30.0] < (30.0, 40.0] < (40.0, 50.0] < (50.0, 60.0] < (60.0, 70.0] < (70.0, 80.0]]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_band=[]\n",
    "age_band=pd.cut(train_df['Age'], 8)\n",
    "age_band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    data.loc[data['Age']<=10.0, 'Age']=0\n",
    "    data.loc[(data['Age']>10.0)&(data['Age']<=20.0), 'Age']=1\n",
    "    data.loc[(data['Age']>20.0)&(data['Age']<=30.0), 'Age']=2\n",
    "    data.loc[(data['Age']>30.0)&(data['Age']<=40.0), 'Age']=3\n",
    "    data.loc[(data['Age']>40.0)&(data['Age']<=50.0), 'Age']=4\n",
    "    data.loc[(data['Age']>50.0)&(data['Age']<=60.0), 'Age']=5\n",
    "    data.loc[(data['Age']>60.0)&(data['Age']<=70.0), 'Age']=6\n",
    "    data.loc[data['Age']>70, 'Age']=7\n",
    "    data['Age']=data['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  Sex  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris    1   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0   \n",
       "2         1       3                             Heikkinen, Miss. Laina    0   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0   \n",
       "4         0       3                           Allen, Mr. William Henry    1   \n",
       "\n",
       "   Age  Fare  Embarked  FamilySize  \n",
       "0    2     0         0           1  \n",
       "1    3     4         1           1  \n",
       "2    2     1         0           0  \n",
       "3    3     4         0           1  \n",
       "4    3     1         0           0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass                                          Name  Sex  Age  Fare  \\\n",
       "0       3                              Kelly, Mr. James    1    3     0   \n",
       "1       3              Wilkes, Mrs. James (Ellen Needs)    0    4     0   \n",
       "2       2                     Myles, Mr. Thomas Francis    1    6     1   \n",
       "3       3                              Wirz, Mr. Albert    1    2     1   \n",
       "4       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0    2     2   \n",
       "\n",
       "   Embarked  FamilySize  \n",
       "0         2           0  \n",
       "1         0           1  \n",
       "2         2           0  \n",
       "3         0           0  \n",
       "4         0           1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**10) Name**\n",
    "   + 외국 이름에서 공통적으로 들어가는 영어 호칭들에 따라 value값들을 count 해서 표로 표현."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex         0    1\n",
       "Title             \n",
       "Capt        0    1\n",
       "Col         0    2\n",
       "Countess    1    0\n",
       "Don         0    1\n",
       "Dr          1    6\n",
       "Jonkheer    0    1\n",
       "Lady        1    0\n",
       "Major       0    2\n",
       "Master      0   40\n",
       "Miss      182    0\n",
       "Mlle        2    0\n",
       "Mme         1    0\n",
       "Mr          0  517\n",
       "Mrs       125    0\n",
       "Ms          1    0\n",
       "Rev         0    6\n",
       "Sir         0    1"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    data['Title'] = data.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ Mlle, Ms, Mme는 각각 Miss와 Mrs의 프랑스식 표현이므로 같이 묶어 주고 나머지는 Rare로 묶어줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine=[train_df, test_df]\n",
    "for data in combine:\n",
    "    data['Title'] = data['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    data['Title'] = data['Title'].replace('Mlle', 'Miss')\n",
    "    data['Title'] = data['Title'].replace('Ms', 'Miss')\n",
    "    data['Title'] = data['Title'].replace('Mme', 'Mrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=\"Title\", hue=\"Survived\", data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "+ object형이기 때문에 마찬가지로 int형의 값으로 mapping을 시켜주고 Train과 Test Data에서 모두 해당 값으로 mapping 시켜줌."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_mapping={\"Mr\":0,\"Mrs\":1, \"Miss\":2, \"Master\":3, \"Rare\":4}\n",
    "train_df['Title']=train_df['Title'].map(title_mapping)\n",
    "test_df['Title']=test_df['Title'].map(title_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=train_df.drop(['Name'], axis=1)\n",
    "test_df=test_df.drop(['Name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  FamilySize  Title\n",
       "0         0       3    1    2     0         0           1      0\n",
       "1         1       1    0    3     4         1           1      1\n",
       "2         1       3    0    2     1         0           0      2\n",
       "3         1       1    0    3     4         0           1      1\n",
       "4         0       3    1    3     1         0           0      0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Embarked  FamilySize  Title\n",
       "0       3    1    3     0         2           0      0\n",
       "1       3    0    4     0         0           1      1\n",
       "2       2    1    6     1         2           0      0\n",
       "3       3    1    2     1         0           0      0\n",
       "4       3    0    2     2         0           1      1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 28), (891,), (418, 28))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train=train_df.drop('Survived', axis=1)\n",
    "y_train=train_df['Survived']\n",
    "x_test=test_df.copy()\n",
    "x_train.shape, y_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1) LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hnn02\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8282828282828283"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression()\n",
    "logreg.fit(x_train, y_train)\n",
    "y_pred_LR=logreg.predict(x_test)\n",
    "acc=logreg.score(x_train, y_train)\n",
    "acc\n",
    "#score : 0.77033"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2) Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8395061728395061"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc=SVC()\n",
    "svc.fit(x_train, y_train)\n",
    "y_pred_SVM=svc.predict(x_test)\n",
    "acc=svc.score(x_train, y_train)\n",
    "acc\n",
    "#score : 0.78468"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3) KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8597081930415263"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(x_train, y_train)\n",
    "y_pred_KNN=knn.predict(x_test)\n",
    "acc_knn=knn.score(x_train, y_train)\n",
    "acc_knn\n",
    "#score : 0.77990"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4) Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8821548821548821"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree=DecisionTreeClassifier()\n",
    "decision_tree.fit(x_train, y_train)\n",
    "y_pred_DT=decision_tree.predict(x_test)\n",
    "acc_decision_tree=decision_tree.score(x_train, y_train)\n",
    "acc_decision_tree\n",
    "#score : 0.75598"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5) Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8821548821548821"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest=RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(x_train, y_train)\n",
    "y_pred_RF=random_forest.predict(x_test)\n",
    "acc_random_forest=random_forest.score(x_train, y_train)\n",
    "acc_random_forest\n",
    "#socre : 0.77511"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6) AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8496071829405163"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_boost=AdaBoostClassifier(n_estimators=100)\n",
    "ada_boost.fit(x_train , y_train)\n",
    "y_pred_ab=ada_boost.predict(x_test)\n",
    "acc_ada_boost=ada_boost.score(x_train, y_train)\n",
    "acc_ada_boost\n",
    "#score : 0.78947"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 7) Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8821548821548821"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging=BaggingClassifier(n_estimators=100)\n",
    "bagging.fit(x_train, y_train)\n",
    "y_pred_bag=bagging.predict(x_test)\n",
    "acc_bagging=bagging.score(x_train, y_train)\n",
    "acc_bagging\n",
    "#score : 0.77033"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         0\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission=pd.DataFrame({\"PassengerId\": sub, \"Survived\":y_pred_ab})\n",
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('Titanic_Submission.csv', index=False)"
   ]
  }
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