Titanic_pt.2_Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11887911",
   "metadata": {},
   "source": [
    "# <center><span style=\"color:DarkBlue\">타이타닉 생존 예측 (2): 2차 전처리 및 모델링</span></center> <a class=\"tocSkip\">\n",
    "- 수많은 유명인사와 부호들의 죽음으로 사람들에게 각인되었던 타이타닉호 침몰 사건.\n",
    "- 타이타닉 승객들에 관한 데이터를 토대로 이들의 생존 여부를 예측해보기.\n",
    "- 이를 통해, 기초적인 데이터 분석 과정과 Classification(분류) 문제를 경험해볼 수 있음."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46181ab",
   "metadata": {},
   "source": [
    "**변수 설명**\n",
    "\n",
    "\\**일차적으로 다음의 변수들은 모델링에 사용하지 않기로 함.*\n",
    "<br>\\**사용X 변수: PassengerId, Name, Surname, Firstname, SibSp, Parch, Ticket, Cabin*\n",
    "\n",
    "\n",
    "- ~(1) PassengerId: 승객의 고유 식별자 (데이터셋 내에서 각 승객마다 고유한 ID가 부여됨.)~\n",
    "- (2) Survived: 생존 여부 (0은 사망, 1은 생존을 의미)\n",
    "- (3) Pclass: 승객의 객실 등급 (1은 일등석, 2는 이등석, 3은 삼등석을 나타냄.)\n",
    "- ~(4) Name: 승객의 이름 (형식: 성, 호칭. 이름)~\n",
    "- ~(5) Surname: Name에서 '성'에 해당하는 파생변수~\n",
    "- (6) Title: Name에서 '호칭'에 해당하는 파생변수\n",
    "- ~(7) Firstname: Name에서 '이름'에 해당하는 파생변수~\n",
    "- (8) Sex: 승객의 성별 (\"male\"은 남성, \"female\"은 여성을 의미함.)\n",
    "- (9) Age: 승객의 나이\n",
    "- ~(10) SibSp: 함께 탑승한 형제자매 또는 배우자의 수~\n",
    "- ~(11) Parch: 함께 탑승한 부모 또는 자녀의 수~\n",
    "- ~(12) Ticket: 티켓 번호~\n",
    "- (13) Fare: 지불한 운임 요금\n",
    "- ~(14) Cabin: 객실 번호~\n",
    "- (15) Embarked: 탑승한 항구 (\"C\"는 Cherbourg, \"Q\"는 Queenstown, \"S\"는 Southampton을 의미함.)\n",
    "- (16) FamilySize: SibSp와 Parch를 더한 '가족수'를 의미하는 파생변수"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7313d95b",
   "metadata": {},
   "source": [
    "## 라이브러리 설치 및 환경 설정 <a class=\"tocSkip\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d8b123c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#기본 라이브러리 설치\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%config InlineBackend.figure_format = 'retina'  #plot내 글씨를 선명하게 해주는 옵션\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4d01cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\lys17\\\\Desktop'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#working directory 확인\n",
    "os.getcwd() #현재 작업 위치 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0286c620",
   "metadata": {},
   "source": [
    "# 데이터 불러오기\n",
    "1차 전처리가 완료된 Titanic_clean.csv 파일 불러옴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00d86ed7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Cabin</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>Braund</td>\n",
       "      <td>Mr</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>John Bradley (Florence Briggs Thayer)</td>\n",
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       "      <td>38.000000</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>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Mrs</td>\n",
       "      <td>Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Mr</td>\n",
       "      <td>William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>Montvila</td>\n",
       "      <td>Others</td>\n",
       "      <td>Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "      <th>887</th>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>Graham</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>Johnston</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.845638</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
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       "      <td>S</td>\n",
       "      <td>3</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>Behr</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>0</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>Dooley</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>891 rows × 16 columns</p>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name    Surname   Title  \\\n",
       "0                              Braund, Mr. Owen Harris     Braund      Mr   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    Cumings     Mrs   \n",
       "2                               Heikkinen, Miss. Laina  Heikkinen    Miss   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)   Futrelle     Mrs   \n",
       "4                             Allen, Mr. William Henry      Allen      Mr   \n",
       "..                                                 ...        ...     ...   \n",
       "886                              Montvila, Rev. Juozas   Montvila  Others   \n",
       "887                       Graham, Miss. Margaret Edith     Graham    Miss   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"   Johnston    Miss   \n",
       "889                              Behr, Mr. Karl Howell       Behr      Mr   \n",
       "890                                Dooley, Mr. Patrick     Dooley      Mr   \n",
       "\n",
       "                                 Firstname     Sex        Age  SibSp  Parch  \\\n",
       "0                              Owen Harris    male  22.000000      1      0   \n",
       "1    John Bradley (Florence Briggs Thayer)  female  38.000000      1      0   \n",
       "2                                    Laina  female  26.000000      0      0   \n",
       "3            Jacques Heath (Lily May Peel)  female  35.000000      1      0   \n",
       "4                            William Henry    male  35.000000      0      0   \n",
       "..                                     ...     ...        ...    ...    ...   \n",
       "886                                 Juozas    male  27.000000      0      0   \n",
       "887                         Margaret Edith  female  19.000000      0      0   \n",
       "888               Catherine Helen \"Carrie\"  female  21.845638      1      2   \n",
       "889                            Karl Howell    male  26.000000      0      0   \n",
       "890                                Patrick    male  32.000000      0      0   \n",
       "\n",
       "               Ticket     Fare Cabin Embarked  FamilySize  \n",
       "0           A/5 21171   7.2500   NaN        S           1  \n",
       "1            PC 17599  71.2833   C85        C           1  \n",
       "2    STON/O2. 3101282   7.9250   NaN        S           0  \n",
       "3              113803  53.1000  C123        S           1  \n",
       "4              373450   8.0500   NaN        S           0  \n",
       "..                ...      ...   ...      ...         ...  \n",
       "886            211536  13.0000   NaN        S           0  \n",
       "887            112053  30.0000   B42        S           0  \n",
       "888        W./C. 6607  23.4500   NaN        S           3  \n",
       "889            111369  30.0000  C148        C           0  \n",
       "890            370376   7.7500   NaN        Q           0  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Titanic_clean.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39aacae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 16 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   Surname      891 non-null    object \n",
      " 5   Title        891 non-null    object \n",
      " 6   Firstname    891 non-null    object \n",
      " 7   Sex          891 non-null    object \n",
      " 8   Age          891 non-null    float64\n",
      " 9   SibSp        891 non-null    int64  \n",
      " 10  Parch        891 non-null    int64  \n",
      " 11  Ticket       891 non-null    object \n",
      " 12  Fare         891 non-null    float64\n",
      " 13  Cabin        204 non-null    object \n",
      " 14  Embarked     891 non-null    object \n",
      " 15  FamilySize   891 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(8)\n",
      "memory usage: 111.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44c6db9",
   "metadata": {},
   "source": [
    "# 범주형 데이터 수치화\n",
    "- 명목형 변수: Title, Sex, Embarked\n",
    "- 순서형 변수: Pclass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8a01de",
   "metadata": {},
   "source": [
    "## One-Hot Encoding\n",
    "명목형 변수를 수치화할 때 사용"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b780de57",
   "metadata": {},
   "source": [
    "### pd.get_dummies() 이용\n",
    "- 문제점: train 데이터의 특성을 학습하지 않기 때문에, train에만 있고 test에는 없는 카테고리를 test에서 원핫인코딩으로 표현할 수 없음.\n",
    "<br>=> 따라서 sklearn의 OneHotEncoder 사용하는게 좋음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "467e2351",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Others</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Others  Sex_female  \\\n",
       "0               0           0         1          0             0           0   \n",
       "1               0           0         0          1             0           1   \n",
       "2               0           1         0          0             0           1   \n",
       "3               0           0         0          1             0           1   \n",
       "4               0           0         1          0             0           0   \n",
       "..            ...         ...       ...        ...           ...         ...   \n",
       "886             0           0         0          0             1           0   \n",
       "887             0           1         0          0             0           1   \n",
       "888             0           1         0          0             0           1   \n",
       "889             0           0         1          0             0           0   \n",
       "890             0           0         1          0             0           0   \n",
       "\n",
       "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0           1           0           0           1  \n",
       "1           0           1           0           0  \n",
       "2           0           0           0           1  \n",
       "3           0           0           0           1  \n",
       "4           1           0           0           1  \n",
       "..        ...         ...         ...         ...  \n",
       "886         1           0           0           1  \n",
       "887         0           0           0           1  \n",
       "888         0           0           0           1  \n",
       "889         1           1           0           0  \n",
       "890         1           0           1           0  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Title, Sex, Embarked 변수를 더미변수화\n",
    "df_dummy = pd.get_dummies(data[[\"Title\",\"Sex\",\"Embarked\"]])\n",
    "df_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d0e2e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<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>Surname</th>\n",
       "      <th>Title</th>\n",
       "      <th>Firstname</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>...</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Others</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>Braund</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>Cumings</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>Allen</td>\n",
       "      <td>Mr</td>\n",
       "      <td>William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>Montvila</td>\n",
       "      <td>Others</td>\n",
       "      <td>Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>Graham</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>Johnston</td>\n",
       "      <td>Miss</td>\n",
       "      <td>Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.845638</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>Behr</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>Dooley</td>\n",
       "      <td>Mr</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name    Surname   Title  \\\n",
       "0                              Braund, Mr. Owen Harris     Braund      Mr   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    Cumings     Mrs   \n",
       "2                               Heikkinen, Miss. Laina  Heikkinen    Miss   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)   Futrelle     Mrs   \n",
       "4                             Allen, Mr. William Henry      Allen      Mr   \n",
       "..                                                 ...        ...     ...   \n",
       "886                              Montvila, Rev. Juozas   Montvila  Others   \n",
       "887                       Graham, Miss. Margaret Edith     Graham    Miss   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"   Johnston    Miss   \n",
       "889                              Behr, Mr. Karl Howell       Behr      Mr   \n",
       "890                                Dooley, Mr. Patrick     Dooley      Mr   \n",
       "\n",
       "                                 Firstname     Sex        Age  SibSp  ...  \\\n",
       "0                              Owen Harris    male  22.000000      1  ...   \n",
       "1    John Bradley (Florence Briggs Thayer)  female  38.000000      1  ...   \n",
       "2                                    Laina  female  26.000000      0  ...   \n",
       "3            Jacques Heath (Lily May Peel)  female  35.000000      1  ...   \n",
       "4                            William Henry    male  35.000000      0  ...   \n",
       "..                                     ...     ...        ...    ...  ...   \n",
       "886                                 Juozas    male  27.000000      0  ...   \n",
       "887                         Margaret Edith  female  19.000000      0  ...   \n",
       "888               Catherine Helen \"Carrie\"  female  21.845638      1  ...   \n",
       "889                            Karl Howell    male  26.000000      0  ...   \n",
       "890                                Patrick    male  32.000000      0  ...   \n",
       "\n",
       "     Title_Master Title_Miss  Title_Mr Title_Mrs Title_Others  Sex_female  \\\n",
       "0               0          0         1         0            0           0   \n",
       "1               0          0         0         1            0           1   \n",
       "2               0          1         0         0            0           1   \n",
       "3               0          0         0         1            0           1   \n",
       "4               0          0         1         0            0           0   \n",
       "..            ...        ...       ...       ...          ...         ...   \n",
       "886             0          0         0         0            1           0   \n",
       "887             0          1         0         0            0           1   \n",
       "888             0          1         0         0            0           1   \n",
       "889             0          0         1         0            0           0   \n",
       "890             0          0         1         0            0           0   \n",
       "\n",
       "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0           1           0           0           1  \n",
       "1           0           1           0           0  \n",
       "2           0           0           0           1  \n",
       "3           0           0           0           1  \n",
       "4           1           0           0           1  \n",
       "..        ...         ...         ...         ...  \n",
       "886         1           0           0           1  \n",
       "887         0           0           0           1  \n",
       "888         0           0           0           1  \n",
       "889         1           1           0           0  \n",
       "890         1           0           1           0  \n",
       "\n",
       "[891 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#더미변수들을 데이터프레임에 합침\n",
    "data_v1 = data.copy() #얉은 복사\n",
    "\n",
    "data_v1 = pd.concat([data_v1, df_dummy], axis=1)\n",
    "data_v1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d630aaa7",
   "metadata": {},
   "source": [
    "### sklearn의 OneHotEncoder 이용\n",
    "- 변수 스케일링과 유사한 주의사항 있음.\n",
    "<br>train 셋에 fit된 encoder()를 사용하여 valid와 test 셋을 스케일링해야함."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bb51a00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 0., 0., 1.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 1., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 1., ..., 1., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = OneHotEncoder(sparse_output=False) #sparse_output=False: 희소행렬로 반환 X\n",
    "\n",
    "result_ohe = ohe.fit_transform(data_v1[[\"Title\",\"Sex\",\"Embarked\"]])\n",
    "result_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1c3c861b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['Master', 'Miss', 'Mr', 'Mrs', 'Others'], dtype=object),\n",
       " array(['female', 'male'], dtype=object),\n",
       " array(['C', 'Q', 'S'], dtype=object)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#각 변수들의 카테고리가 학습된 것을 확인\n",
    "ohe.categories_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73f495bd",
   "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>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Others</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Title_Master  Title_Miss  Title_Mr  Title_Mrs  Title_Others  Sex_female  \\\n",
       "0             0.0         0.0       1.0        0.0           0.0         0.0   \n",
       "1             0.0         0.0       0.0        1.0           0.0         1.0   \n",
       "2             0.0         1.0       0.0        0.0           0.0         1.0   \n",
       "3             0.0         0.0       0.0        1.0           0.0         1.0   \n",
       "4             0.0         0.0       1.0        0.0           0.0         0.0   \n",
       "..            ...         ...       ...        ...           ...         ...   \n",
       "886           0.0         0.0       0.0        0.0           1.0         0.0   \n",
       "887           0.0         1.0       0.0        0.0           0.0         1.0   \n",
       "888           0.0         1.0       0.0        0.0           0.0         1.0   \n",
       "889           0.0         0.0       1.0        0.0           0.0         0.0   \n",
       "890           0.0         0.0       1.0        0.0           0.0         0.0   \n",
       "\n",
       "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0         1.0         0.0         0.0         1.0  \n",
       "1         0.0         1.0         0.0         0.0  \n",
       "2         0.0         0.0         0.0         1.0  \n",
       "3         0.0         0.0         0.0         1.0  \n",
       "4         1.0         0.0         0.0         1.0  \n",
       "..        ...         ...         ...         ...  \n",
       "886       1.0         0.0         0.0         1.0  \n",
       "887       0.0         0.0         0.0         1.0  \n",
       "888       0.0         0.0         0.0         1.0  \n",
       "889       1.0         1.0         0.0         0.0  \n",
       "890       1.0         0.0         1.0         0.0  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#result_ohe는 numpy.array이므로 데이터프레임으로 변환\n",
    "df_ohe = pd.DataFrame(result_ohe, columns=ohe.get_feature_names_out([\"Title\",\"Sex\",\"Embarked\"]))\n",
    "df_ohe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81fbf786",
   "metadata": {},
   "source": [
    "## Label Encoding\n",
    "순서형 변수를 수치화할 때 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b580a481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      2\n",
       "2      0\n",
       "3      2\n",
       "4      0\n",
       "      ..\n",
       "886    1\n",
       "887    2\n",
       "888    0\n",
       "889    2\n",
       "890    0\n",
       "Name: Pclass, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#수치 맵핑(Mapping)\n",
    "#1등석 -> 2\n",
    "#2등석 -> 1\n",
    "#3등석 -> 0\n",
    "\n",
    "data_v1[\"Pclass\"] = data_v1[\"Pclass\"].map({1:2, 2:1, 3:0})\n",
    "data_v1[\"Pclass\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f5120c8",
   "metadata": {},
   "source": [
    "# 변수 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bfe959c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 26 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   Surname       891 non-null    object \n",
      " 5   Title         891 non-null    object \n",
      " 6   Firstname     891 non-null    object \n",
      " 7   Sex           891 non-null    object \n",
      " 8   Age           891 non-null    float64\n",
      " 9   SibSp         891 non-null    int64  \n",
      " 10  Parch         891 non-null    int64  \n",
      " 11  Ticket        891 non-null    object \n",
      " 12  Fare          891 non-null    float64\n",
      " 13  Cabin         204 non-null    object \n",
      " 14  Embarked      891 non-null    object \n",
      " 15  FamilySize    891 non-null    int64  \n",
      " 16  Title_Master  891 non-null    uint8  \n",
      " 17  Title_Miss    891 non-null    uint8  \n",
      " 18  Title_Mr      891 non-null    uint8  \n",
      " 19  Title_Mrs     891 non-null    uint8  \n",
      " 20  Title_Others  891 non-null    uint8  \n",
      " 21  Sex_female    891 non-null    uint8  \n",
      " 22  Sex_male      891 non-null    uint8  \n",
      " 23  Embarked_C    891 non-null    uint8  \n",
      " 24  Embarked_Q    891 non-null    uint8  \n",
      " 25  Embarked_S    891 non-null    uint8  \n",
      "dtypes: float64(2), int64(6), object(8), uint8(10)\n",
      "memory usage: 120.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data_v1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6f8ab144",
   "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   Survived      891 non-null    int64  \n",
      " 1   Pclass        891 non-null    int64  \n",
      " 2   Age           891 non-null    float64\n",
      " 3   Fare          891 non-null    float64\n",
      " 4   FamilySize    891 non-null    int64  \n",
      " 5   Title_Master  891 non-null    uint8  \n",
      " 6   Title_Miss    891 non-null    uint8  \n",
      " 7   Title_Mr      891 non-null    uint8  \n",
      " 8   Title_Mrs     891 non-null    uint8  \n",
      " 9   Title_Others  891 non-null    uint8  \n",
      " 10  Sex_female    891 non-null    uint8  \n",
      " 11  Sex_male      891 non-null    uint8  \n",
      " 12  Embarked_C    891 non-null    uint8  \n",
      " 13  Embarked_Q    891 non-null    uint8  \n",
      " 14  Embarked_S    891 non-null    uint8  \n",
      "dtypes: float64(2), int64(3), uint8(10)\n",
      "memory usage: 43.6 KB\n"
     ]
    }
   ],
   "source": [
    "#필요없는 열은 제거\n",
    "data_final = data_v1.drop([\"PassengerId\",\"Name\",\"Surname\",\"Title\",\"Firstname\",\"Sex\",\"SibSp\",\"Parch\",\"Ticket\",\"Cabin\",\"Embarked\"], axis=1)\n",
    "data_final.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f55564",
   "metadata": {},
   "source": [
    "# train, valid, test 셋 분리\n",
    "- 타이타닉 데이터는 test셋 파일이 따로 있으므로 train_test_split을 한 번만 해주면 됨.\n",
    "- 실제 데이터의 경우, train, valid, test 셋 분리를 위해 train_test_split을 두 번 해줘야 함.\n",
    "- stratify=타겟변수: 타겟값의 class 비율을 유지한 채로 데이터셋을 분리해줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c88320ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X(독립변수), y(종속변수) 분리\n",
    "X = data_final.drop(\"Survived\", axis=1)\n",
    "y = data_final[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3f615344",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(712, 14) (179, 14) (712,) (179,)\n"
     ]
    }
   ],
   "source": [
    "#train과 valid 셋 분리\n",
    "#분류 문제에서 'stratify=타겟변수' 옵션은 중요함\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, test_size=0.2, stratify=y, random_state=100)\n",
    "print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c105d4f",
   "metadata": {},
   "source": [
    "=> 데이터셋 분리 후, 차원 확인하기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb1fc36",
   "metadata": {},
   "source": [
    "# 변수 스케일링"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e31ab5b",
   "metadata": {},
   "source": [
    "## Standard Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7f0662d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<train 셋>\n",
      "평균: 3.599760047741278e-17\n",
      "분산: 0.9999999999999999\n",
      "<valid 셋>\n",
      "평균: -0.0029392635343672546\n",
      "분산: 0.9792864271985184\n"
     ]
    }
   ],
   "source": [
    "#객체 생성\n",
    "ss = StandardScaler()\n",
    "\n",
    "#학습 및 변환\n",
    "X_train_ss = ss.fit_transform(X_train)\n",
    "X_val_ss = ss.transform(X_val)\n",
    "#X_test_ss = ss.transform(X_test) #test셋이 있는 경우\n",
    "\n",
    "print(\"<train 셋>\")\n",
    "print(f'평균: {X_train_ss.mean()}\\n분산: {X_train_ss.std()}')\n",
    "print(\"<valid 셋>\")\n",
    "print(f'평균: {X_val_ss.mean()}\\n분산: {X_val_ss.std()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020f714b",
   "metadata": {},
   "source": [
    "## MinMax Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "baa84b7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<train 셋>\n",
      "최대: 1.0\n",
      "최소: 0.0\n",
      "<valid 셋>\n",
      "최대: 1.0\n",
      "최소: 0.0\n"
     ]
    }
   ],
   "source": [
    "#객체 생성\n",
    "mms = MinMaxScaler()\n",
    "\n",
    "#학습 및 변환\n",
    "X_train_mms = mms.fit_transform(X_train)\n",
    "X_val_mms = mms.transform(X_val)\n",
    "#X_test_mms = mms.transform(X_test) #test셋이 있는 경우\n",
    "\n",
    "print(\"<train 셋>\")\n",
    "print(f'최대: {X_train_mms.max()}\\n최소: {X_train_mms.min()}')\n",
    "print(\"<valid 셋>\")\n",
    "print(f'최대: {X_val_mms.max()}\\n최소: {X_val_mms.min()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb7e1bb",
   "metadata": {},
   "source": [
    "## MaxAbs Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "43fb1d01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<train 셋>\n",
      "최대: 1.0\n",
      "최소: 0.0\n",
      "<valid 셋>\n",
      "최대: 1.0\n",
      "최소: 0.0\n"
     ]
    }
   ],
   "source": [
    "#객체 생성\n",
    "mas = MaxAbsScaler()\n",
    "\n",
    "#학습 및 변환\n",
    "X_train_mas = mas.fit_transform(X_train)\n",
    "X_val_mas = mas.transform(X_val)\n",
    "#X_test_mas = mas.transform(X_test) #test셋이 있는 경우\n",
    "\n",
    "print(\"<train 셋>\")\n",
    "print(f'최대: {X_train_mas.max()}\\n최소: {X_train_mas.min()}')\n",
    "print(\"<valid 셋>\")\n",
    "print(f'최대: {X_val_mas.max()}\\n최소: {X_val_mas.min()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af852d76",
   "metadata": {},
   "source": [
    "## Robust Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "44629c9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<train 셋>\n",
      "평균: 0.16630486268773823\n",
      "분산: 0.9550796180307523\n",
      "<valid 셋>\n",
      "평균: 0.16763370385787865\n",
      "분산: 0.885141523125194\n"
     ]
    }
   ],
   "source": [
    "#객체 생성\n",
    "rs = RobustScaler()\n",
    "\n",
    "#학습 및 변환\n",
    "X_train_rs = rs.fit_transform(X_train)\n",
    "X_val_rs = rs.transform(X_val)\n",
    "#X_test_rs = rs.transform(X_test) #test셋이 있는 경우\n",
    "\n",
    "print(\"<train 셋>\")\n",
    "print(f'평균: {X_train_rs.mean()}\\n분산: {X_train_rs.std()}')\n",
    "print(\"<valid 셋>\")\n",
    "print(f'평균: {X_val_rs.mean()}\\n분산: {X_val_rs.std()}')"
   ]
  }
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