exTitanic2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68a3f11d",
   "metadata": {},
   "source": [
    "### 타이타닉 생존자 예측\n",
    "데이터를 읽고 전체적인 상황 이해하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ffb0c89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f0756dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 읽어오기\n",
    "raw_data = pd.read_csv('./titanic/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95c7f2ed",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "876934dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
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       "      <td>S</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f017d2fd",
   "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": [
    "# 데이터 결측치 존재 여부, 데이터 타입, 컬럼의 수\n",
    "# 데이터 프레임의 메모리 사용량\n",
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b94975b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>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",
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      ],
      "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터프레임의 통계정보\n",
    "raw_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b96a789f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\izimi\\.conda\\envs\\ds\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 왼쪽: 원형차트, 오른쪽: 바차트\n",
    "f, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "\n",
    "# raw_data중 'Survived' 열 데이터만 (0과 1의 개수를 세고) 원형 그래프로 그린다. (거리는 0.1띄우고, 소수점 둘째자리까지)\n",
    "raw_data['Survived'].value_counts().plot.pie(explode=[0, 0.1], autopct='%1.2f%%', ax=ax[0])\n",
    "ax[0].set_title('Survived pie chart')\n",
    "ax[0].set_ylabel('')\n",
    "\n",
    "sns.countplot('Survived', data=raw_data)\n",
    "ax[1].set_title('Survived bar chart')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d68ccd13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 승객의 나이에 대한 분포도\n",
    "# bins : 최소~최대값을 20개 구간으로 나눈다\n",
    "raw_data['Age'].hist(bins=20, figsize=(18, 8), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a1a7286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>461.597222</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>38.233441</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.356481</td>\n",
       "      <td>84.154687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>445.956522</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>29.877630</td>\n",
       "      <td>0.402174</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>20.662183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>439.154786</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>25.140620</td>\n",
       "      <td>0.615071</td>\n",
       "      <td>0.393075</td>\n",
       "      <td>13.675550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId  Survived        Age     SibSp     Parch       Fare\n",
       "Pclass                                                                 \n",
       "1        461.597222  0.629630  38.233441  0.416667  0.356481  84.154687\n",
       "2        445.956522  0.472826  29.877630  0.402174  0.380435  20.662183\n",
       "3        439.154786  0.242363  25.140620  0.615071  0.393075  13.675550"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pclass를 기준으로 모든 열데이터 평균값 구하기\n",
    "raw_data.groupby('Pclass').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e103bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상관계수 구하기\n",
    "# Pclass와 Fare 상관도가 크다\n",
    "raw_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "24d4ac6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "sns.heatmap(raw_data.corr(), linewidth=0.01, square=True, annot=True, cmap=plt.cm.Reds, linecolor='white')\n",
    "plt.title('Correlation between features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d59a07d6",
   "metadata": {},
   "source": [
    "### 나이별 구분 및 성별 생존율 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e5f3035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</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 Age_cat  \n",
       "0      0         A/5 21171   7.2500   NaN        S   young  \n",
       "1      0          PC 17599  71.2833   C85        C   adult  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S   young  \n",
       "3      0            113803  53.1000  C123        S   adult  \n",
       "4      0            373450   8.0500   NaN        S   adult  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# age_cat 새로운열 생성\n",
    "raw_data['Age_cat'] = pd.cut(raw_data['Age'], \n",
    "                            bins=[0, 3, 7, 15, 30, 60, 100], \n",
    "                            include_lowest=True,\n",
    "                            labels=['baby', 'children', 'teenage', 'young', 'adult', 'old'])\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0aae9508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>baby</th>\n",
       "      <td>396.700000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>1.672333</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>1.366667</td>\n",
       "      <td>33.364307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>466.700000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>4.950000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1.350000</td>\n",
       "      <td>27.905830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teenage</th>\n",
       "      <td>442.060606</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>2.696970</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>1.121212</td>\n",
       "      <td>35.217430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>young</th>\n",
       "      <td>435.058282</td>\n",
       "      <td>0.358896</td>\n",
       "      <td>2.426380</td>\n",
       "      <td>23.262270</td>\n",
       "      <td>0.355828</td>\n",
       "      <td>0.263804</td>\n",
       "      <td>28.015413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adult</th>\n",
       "      <td>470.388693</td>\n",
       "      <td>0.420495</td>\n",
       "      <td>1.954064</td>\n",
       "      <td>41.132509</td>\n",
       "      <td>0.363958</td>\n",
       "      <td>0.392226</td>\n",
       "      <td>42.429225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>old</th>\n",
       "      <td>432.545455</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>1.590909</td>\n",
       "      <td>66.022727</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>41.371214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
       "Age_cat                                                                    \n",
       "baby       396.700000  0.666667  2.533333   1.672333  1.733333  1.366667   \n",
       "children   466.700000  0.700000  2.650000   4.950000  1.750000  1.350000   \n",
       "teenage    442.060606  0.454545  2.696970  11.500000  1.727273  1.121212   \n",
       "young      435.058282  0.358896  2.426380  23.262270  0.355828  0.263804   \n",
       "adult      470.388693  0.420495  1.954064  41.132509  0.363958  0.392226   \n",
       "old        432.545455  0.227273  1.590909  66.022727  0.136364  0.272727   \n",
       "\n",
       "               Fare  \n",
       "Age_cat              \n",
       "baby      33.364307  \n",
       "children  27.905830  \n",
       "teenage   35.217430  \n",
       "young     28.015413  \n",
       "adult     42.429225  \n",
       "old       41.371214  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.groupby('Age_cat').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "43db3497",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\izimi\\.conda\\envs\\ds\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\izimi\\.conda\\envs\\ds\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\izimi\\.conda\\envs\\ds\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15, 5])\n",
    "plt.subplot(131)\n",
    "sns.barplot('Pclass', 'Survived', data=raw_data)\n",
    "plt.subplot(132)\n",
    "sns.barplot('Age_cat', 'Survived', data=raw_data)\n",
    "plt.subplot(133)\n",
    "sns.barplot('Sex', 'Survived', data=raw_data)\n",
    "plt.subplots_adjust(top=1, bottom=0.1, left=0.10, right=1, hspace=0.5, wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "33759db1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\izimi\\.conda\\envs\\ds\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\izimi\\.conda\\envs\\ds\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(12,6))\n",
    "sns.countplot('Sex',data=raw_data, ax=ax[0])\n",
    "ax[0].set_title('Count of Passengers by Sex')\n",
    "\n",
    "sns.countplot('Sex',hue='Survived',data=raw_data, ax=ax[1])\n",
    "ax[1].set_title('Sex:Survived vs Dead')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42643ddf",
   "metadata": {},
   "source": [
    "### 귀족들의 생존율만 조사를 해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b5cac025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund, Mr. Owen Harris\n",
       "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                                 Heikkinen, Miss. Laina\n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "886                                Montvila, Rev. Juozas\n",
       "887                         Graham, Miss. Margaret Edith\n",
       "888             Johnston, Miss. Catherine Helen \"Carrie\"\n",
       "889                                Behr, Mr. Karl Howell\n",
       "890                                  Dooley, Mr. Patrick\n",
       "Name: Name, Length: 891, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3e156b14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Braund, Mr. Owen Harris\n",
      " Mr. Owen Harris\n",
      " Mr\n",
      "Mr\n"
     ]
    }
   ],
   "source": [
    "print(raw_data['Name'][0])\n",
    "print(raw_data['Name'][0].split(',')[1])\n",
    "print(raw_data['Name'][0].split(',')[1].split('.')[0])\n",
    "print(raw_data['Name'][0].split(',')[1].split('.')[0].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "15a0cb0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Mr\n",
       "1       Mrs\n",
       "2      Miss\n",
       "3       Mrs\n",
       "4        Mr\n",
       "       ... \n",
       "886     Rev\n",
       "887    Miss\n",
       "888    Miss\n",
       "889      Mr\n",
       "890      Mr\n",
       "Name: Title, Length: 891, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이름에서 그 사람의 신분을 확인하는 문자열을 추출 -> lambda\n",
    "conversion_rare = lambda x: x.split(',')[1].split('.')[0].strip()\n",
    "\n",
    "# Name열을 입력해서 신분만 추출 -> 'Title' 이라는 열을 생성\n",
    "raw_data['Title'] = raw_data['Name'].map(conversion_rare)\n",
    "raw_data['Title']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "363b7b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
       "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
       "       'Jonkheer'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'Title' 열중에 고유한 이름만 확인\n",
    "raw_data['Title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "173973ff",
   "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>female</th>\n",
       "      <th>male</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>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",
       "    <tr>\n",
       "      <th>the Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex           female  male\n",
       "Title                     \n",
       "Capt               0     1\n",
       "Col                0     2\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\n",
       "the Countess       1     0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(raw_data['Title'], raw_data['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a820a60a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Major', 'Lady',\n",
       "       'Sir', 'Col', 'Capt', 'the Countess', 'Jonkheer'], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['Title'] = raw_data['Title'].replace('Mlle', 'Miss')\n",
    "raw_data['Title'] = raw_data['Title'].replace('Ms', 'Miss')\n",
    "raw_data['Title'] = raw_data['Title'].replace('Mme', 'Mrs')\n",
    "\n",
    "raw_data['Title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ae02023d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Capt</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Col</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Don</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dr</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jonkheer</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lady</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Major</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rev</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sir</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>the Countess</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Title  Survived\n",
       "0           Capt  0.000000\n",
       "1            Col  0.500000\n",
       "2            Don  0.000000\n",
       "3             Dr  0.428571\n",
       "4       Jonkheer  0.000000\n",
       "5           Lady  1.000000\n",
       "6          Major  0.500000\n",
       "7         Master  0.575000\n",
       "8           Miss  0.702703\n",
       "9             Mr  0.156673\n",
       "10           Mrs  0.793651\n",
       "11           Rev  0.000000\n",
       "12           Sir  1.000000\n",
       "13  the Countess  1.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[['Title', 'Survived']].groupby('Title', as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9e15fa65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 귀족의 신분을 리스트로 생성\n",
    "Rare = ['Lady','the Countess','Countess','Capt', 'Master',\n",
    "        'Col','Don','Dr','Major','Rev','Sir','Jonkheer', 'Dona']\n",
    "\n",
    "for each in Rare:\n",
    "    raw_data['Title'] = raw_data['Title'].replace(each, 'Rare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "90cf8373",
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.492063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Title  Survived\n",
       "0  Miss  0.702703\n",
       "1    Mr  0.156673\n",
       "2   Mrs  0.793651\n",
       "3  Rare  0.492063"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[['Title', 'Survived']].groupby('Title', as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1e6f37",
   "metadata": {},
   "source": [
    "### 생존율 예측을 위해 데이터를 학습\n",
    "학습에 사용할 특징데이터를 선택해야한다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3925febe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 보다 좋은 학습을 위해 성별 'female', 'male' -> '0', '1' 로 변환해준다\n",
    "tmp = []\n",
    "\n",
    "for each in raw_data['Sex']:\n",
    "    if each=='female':\n",
    "        tmp.append(0)\n",
    "    elif each=='male':\n",
    "        tmp.append(1)\n",
    "    else:\n",
    "        # 예외처리코드\n",
    "        tmp.append(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "44581d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age_cat</th>\n",
       "      <th>title</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>adult</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</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>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0     3.0   \n",
       "1            2       1.0     1.0   \n",
       "2            3       1.0     3.0   \n",
       "3            4       1.0     1.0   \n",
       "4            5       0.0     3.0   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked Age_cat title Title  \n",
       "0         A/5 21171   7.2500   NaN        S   young    Mr    Mr  \n",
       "1          PC 17599  71.2833   C85        C   adult   Mrs   Mrs  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S   young  Miss  Miss  \n",
       "3            113803  53.1000  C123        S   adult   Mrs   Mrs  \n",
       "4            373450   8.0500   NaN        S   adult    Mr    Mr  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['Sex'] = tmp\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "539dbeda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 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",
      " 12  Age_cat      714 non-null    category\n",
      " 13  title        891 non-null    object  \n",
      " 14  Title        891 non-null    object  \n",
      "dtypes: category(1), float64(2), int64(5), object(7)\n",
      "memory usage: 98.7+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2272a849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Age_cat</th>\n",
       "      <th>title</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>adult</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>young</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>adult</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0     3.0   \n",
       "1            2       1.0     1.0   \n",
       "2            3       1.0     3.0   \n",
       "3            4       1.0     1.0   \n",
       "4            5       0.0     3.0   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  1.0  22.0    1.0    0.0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  0.0  38.0    1.0    0.0   \n",
       "2                             Heikkinen, Miss. Laina  0.0  26.0    0.0    0.0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  0.0  35.0    1.0    0.0   \n",
       "4                           Allen, Mr. William Henry  1.0  35.0    0.0    0.0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked Age_cat title Title  \n",
       "0         A/5 21171   7.2500   NaN        S   young    Mr    Mr  \n",
       "1          PC 17599  71.2833   C85        C   adult   Mrs   Mrs  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S   young  Miss  Miss  \n",
       "3            113803  53.1000  C123        S   adult   Mrs   Mrs  \n",
       "4            373450   8.0500   NaN        S   adult    Mr    Mr  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data['Survived'] = raw_data['Survived'].astype('float')\n",
    "raw_data['Pclass'] = raw_data['Pclass'].astype('float')\n",
    "raw_data['Sex'] = raw_data['Sex'].astype('float')\n",
    "raw_data['SibSp'] = raw_data['SibSp'].astype('float')\n",
    "raw_data['Parch'] = raw_data['Parch'].astype('float')\n",
    "raw_data['Fare'] = raw_data['Fare'].astype('float')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d783dd6",
   "metadata": {},
   "source": [
    "### 생존자 예측 모델 수립"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "17e74de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('./titanic/test.csv')\n",
    "x_test = test_data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0ffcfe39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7a6df084",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex   Age  SibSp  Parch     Fare\n",
       "0     3.0  1.0  22.0    1.0    0.0   7.2500\n",
       "1     1.0  0.0  38.0    1.0    0.0  71.2833\n",
       "2     3.0  0.0  26.0    0.0    0.0   7.9250\n",
       "3     1.0  0.0  35.0    1.0    0.0  53.1000\n",
       "4     3.0  1.0  35.0    0.0    0.0   8.0500"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pre = raw_data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare']]\n",
    "train_pre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "28fea12d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Pclass  891 non-null    float64\n",
      " 1   Sex     891 non-null    float64\n",
      " 2   Age     714 non-null    float64\n",
      " 3   SibSp   891 non-null    float64\n",
      " 4   Parch   891 non-null    float64\n",
      " 5   Fare    891 non-null    float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 41.9 KB\n"
     ]
    }
   ],
   "source": [
    "train_pre.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5d84b6b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Pclass  891 non-null    float64\n",
      " 1   Sex     891 non-null    float64\n",
      " 2   Age     891 non-null    float64\n",
      " 3   SibSp   891 non-null    float64\n",
      " 4   Parch   891 non-null    float64\n",
      " 5   Fare    891 non-null    float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 41.9 KB\n"
     ]
    }
   ],
   "source": [
    "train_pre.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "d8398f2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-121-d2f58d75802b>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train_pre['Age'] = train_pre['Age'].fillna(meanAge)\n"
     ]
    }
   ],
   "source": [
    "# Age 결측치 채우기\n",
    "meanAge = train_pre['Age'].mean()\n",
    "train_pre['Age'] = train_pre['Age'].fillna(meanAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e35b929d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train_pre\n",
    "y_train = raw_data[['Survived']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "8f1bc6e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 6), (418, 6))"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e85dcaa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reset_index(drop=True)\n",
    "x_test = x_train.reset_index(drop=True)\n",
    "y_train = x_train.reset_index(drop=True)\n",
    "y_test = x_train.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "c078fa69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Pclass</th>\n",
       "      <th>Sex</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>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>31.3875</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>796</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>799</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.7500</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>801 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pclass  Sex        Age  SibSp  Parch     Fare\n",
       "0       3.0  0.0  18.000000    0.0    0.0   9.8417\n",
       "1       2.0  1.0  29.699118    0.0    0.0  13.8625\n",
       "2       2.0  1.0  54.000000    0.0    0.0  14.0000\n",
       "3       3.0  1.0   3.000000    4.0    2.0  31.3875\n",
       "4       1.0  1.0  37.000000    0.0    1.0  29.7000\n",
       "..      ...  ...        ...    ...    ...      ...\n",
       "796     2.0  0.0  34.000000    0.0    1.0  23.0000\n",
       "797     2.0  0.0  30.000000    0.0    0.0  12.3500\n",
       "798     3.0  1.0  32.000000    0.0    0.0   7.9250\n",
       "799     3.0  1.0  30.000000    0.0    0.0   7.2500\n",
       "800     3.0  1.0  29.000000    0.0    0.0   7.7500\n",
       "\n",
       "[801 rows x 6 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "024e707f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 801 entries, 0 to 800\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Pclass  801 non-null    float64\n",
      " 1   Sex     801 non-null    float64\n",
      " 2   Age     801 non-null    float64\n",
      " 3   SibSp   801 non-null    float64\n",
      " 4   Parch   801 non-null    float64\n",
      " 5   Fare    801 non-null    float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 37.7 KB\n"
     ]
    }
   ],
   "source": [
    "x_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "e68456fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 생존의 문제는 Classification\n",
    "# DecisionTree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# 1. 모델 정의\n",
    "model = DecisionTreeClassifier(max_depth=3, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f4dbedd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, random_state=123)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. 모델 학습\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "123ba4de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 84.44444444444444\n"
     ]
    }
   ],
   "source": [
    "# Accuracy, Recall, precision, F1 : 성능(정확도) 평가기준\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 3. predict\n",
    "prediction = model.predict(x_test)\n",
    "print(\"Accuracy : {}\".format(accuracy_score(y_test, prediction)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "934466fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 생존자 예측\n",
    "# Pclass, Sex, Age, SibSp, Parch, Fare\n",
    "dicaprio = [3., 1., 19., 0., 0., 5.]\n",
    "winslet = [1., 0., 17., 1., 1., 100.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "8bd18c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.87692308, 0.12307692]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba([dicaprio])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "3ba9048e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04026846, 0.95973154]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba([winslet])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "0595cb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isSurvived(name, person):\n",
    "    isSurvive = 'not survived' if model.predict([person])[0] == 0 else 'Survived'\n",
    "    print(name, ' is ', isSurvive, \n",
    "          ' --> ', max(model.predict_proba([person])[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "52bce3cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dicaprio  is  not survived  -->  0.8769230769230769\n",
      "Winslet  is  Survived  -->  0.959731543624161\n"
     ]
    }
   ],
   "source": [
    "isSurvived('Dicaprio', dicaprio)\n",
    "isSurvived('Winslet', winslet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ec30c39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f86123d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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