HyeongWookKim/Kaggle_Titanic_Advanced_Part_2.ipynb

## Kaggle_Titanic_Advanced_Part_2.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle - Titanic_Part 2\n",
    "**<분석 목적>**\n",
    "- 타이타닉에 탑승한 사람들의 신상정보를 활용하여, 승선한 사람들의 **생존 여부**를 **예측**하는 모델을 생성하는 것이다.\n",
    "- 지난 튜토리얼에서는 단순히 랜덤 포레스트 모델을 사용해서 타이타닉 생존자 예측 여부를 예측해보았다.\n",
    "- 이번에는 처음부터 끝까지 모든 과정을 스스로 분석해보며 최적의 모델을 만들어내 볼 것이다.\n",
    "    - **목표는 80% 이상의 정확도(accuracy)를 갖는 모델**을 생성해내는 것이다!!\n",
    "\n",
    "**<분석 과정>**\n",
    "1. 데이터 셋 확인\n",
    "2. 탐색적 데이터 분석(EDA)\n",
    "3. Feature Engineering(특성 공학)\n",
    "4. 모델 생성 - ***Logistic Regression, Decision Tree, Random Forest*** 이렇게 총 3개의 모델을 사용할 것이다. \n",
    "5. 모델 학습 및 예측\n",
    "6. 모델 평가"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**본격적으로 <Part 2>를 시작하기에 앞서, <Part 1>에서 수행해주었던 작업들을 다시 수행해주겠다.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:47.713939Z",
     "start_time": "2020-06-10T13:59:44.159416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# 이 두 줄의 코드는 matplotlib의 기본 scheme말고, seaborn scheme을 세팅해준다\n",
    "# 일일이 graph의 font size를 지정할 필요 없이, seaborn의 font_scale을 사용하면 편리하다\n",
    "plt.style.use('seaborn')\n",
    "sns.set(font_scale = 2.5)\n",
    "\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected = True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:47.809008Z",
     "start_time": "2020-06-10T13:59:47.724947Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('../titanic/train.csv')\n",
    "df_test = pd.read_csv('../titanic/test.csv')\n",
    "\n",
    "# SibSp, Parch 변수를 하나의 변수(FamilySize)로 합쳐준다\n",
    "# 자신을 포함해야하므로, 1 을 더해준다\n",
    "df_train['FamilySize'] = df_train['SibSp'] + df_train['Parch'] + 1\n",
    "df_test['FamilySize'] = df_test['SibSp'] + df_test['Parch'] + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:47.868049Z",
     "start_time": "2020-06-10T13:59:47.816013Z"
    }
   },
   "outputs": [],
   "source": [
    "# 정규표현식을 사용하여 title을 추출\n",
    "df_train['Initial'] = df_train.Name.str.extract('([A-Za-z]+)\\.')\n",
    "df_test['Initial'] = df_test.Name.str.extract('([A-Za-z]+)\\.')\n",
    "\n",
    "# 위에서 생성된 테이블을 참고하여 남자, 여자가 쓰는 Initial을 구분\n",
    "df_train['Initial'].replace(['Mlle', 'Mme', 'Ms', 'Dr', 'Major',\n",
    "                             'Lady', 'Countess', 'Jonkheer', 'Col',\n",
    "                             'Rev', 'Capt', 'Sir', 'Don', 'Dona'],\n",
    "                            ['Miss', 'Miss', 'Miss', 'Mr', 'Mr',\n",
    "                             'Mrs', 'Mrs', 'Other', 'Other',\n",
    "                             'Other', 'Mr', 'Mr', 'Mr', 'Mr'], inplace = True)\n",
    "\n",
    "df_test['Initial'].replace(['Mlle', 'Mme', 'Ms', 'Dr', 'Major',\n",
    "                            'Lady', 'Countess', 'Jonkheer', 'Col',\n",
    "                            'Rev', 'Capt', 'Sir', 'Don', 'Dona'],\n",
    "                           ['Miss', 'Miss', 'Miss', 'Mr', 'Mr',\n",
    "                            'Mrs', 'Mrs', 'Other', 'Other',\n",
    "                            'Other', 'Mr', 'Mr', 'Mr', 'Mr'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering(특성 공학)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare 변수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:47.897069Z",
     "start_time": "2020-06-10T13:59:47.876054Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1. Null 값 대체\n",
    "# 테스트 데이터 셋의 Fare 변수에 존재하는 Null 값을 평균값으로 대체\n",
    "df_test.loc[df_test.Fare.isnull(), 'Fare'] = df_test['Fare'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **테스트 데이터 셋**에 존재하는 **Fare 변수의 Null 값들을 평균값으로 대체**해주겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:48.660611Z",
     "start_time": "2020-06-10T13:59:47.902073Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. 로그 변환\n",
    "# apply 대신 map을 사용해도 결과는 동일\n",
    "df_train['Fare'] = df_train['Fare'].apply(lambda x: np.log(x) if x > 0 else 0)\n",
    "df_test['Fare'] = df_test['Fare'].apply(lambda x: np.log(x) if x > 0 else 0)\n",
    "\n",
    "fare_hist = sns.distplot(df_train['Fare'], label = 'Skewness: {:.2f}'.format(df_train['Fare'].skew()))\n",
    "fare_hist = fare_hist.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <Part 1>에서 Fare 변수의 분포를 확인해 본 결과, 굉장히 치우친 분포를 보이는 것을 알 수 있었다.\n",
    "- 따라서 Fare 변수에 대해서 **로그 변환**을 수행해주었다.\n",
    "    - 확실히 로그 변환을 해주니, 치우침의 정도가 어느 정도 개선되었음을 알 수 있다.\n",
    "- 이렇게 **로그 변환된 Fare 변수도 모델의 feature**로 넣어줄 것이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age 변수\n",
    "- Age 변수는 Null 값이 177개나 존재한다.\n",
    "- 이를 채워주기 위해, <Part 1>에서 만들어준 **Initial** 변수를 사용해보겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:48.699640Z",
     "start_time": "2020-06-10T13:59:48.664615Z"
    }
   },
   "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",
       "      <th>FamilySize</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Initial</th>\n",
       "      <th></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>Master</th>\n",
       "      <td>414.975000</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>4.574167</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>3.340710</td>\n",
       "      <td>4.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>411.741935</td>\n",
       "      <td>0.704301</td>\n",
       "      <td>2.284946</td>\n",
       "      <td>21.860000</td>\n",
       "      <td>0.698925</td>\n",
       "      <td>0.537634</td>\n",
       "      <td>3.123713</td>\n",
       "      <td>2.236559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>455.880907</td>\n",
       "      <td>0.162571</td>\n",
       "      <td>2.381853</td>\n",
       "      <td>32.739609</td>\n",
       "      <td>0.293006</td>\n",
       "      <td>0.151229</td>\n",
       "      <td>2.651507</td>\n",
       "      <td>1.444234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>456.393701</td>\n",
       "      <td>0.795276</td>\n",
       "      <td>1.984252</td>\n",
       "      <td>35.981818</td>\n",
       "      <td>0.692913</td>\n",
       "      <td>0.818898</td>\n",
       "      <td>3.443751</td>\n",
       "      <td>2.511811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>564.444444</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>45.888889</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>2.641605</td>\n",
       "      <td>1.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
       "Initial                                                                   \n",
       "Master    414.975000  0.575000  2.625000   4.574167  2.300000  1.375000   \n",
       "Miss      411.741935  0.704301  2.284946  21.860000  0.698925  0.537634   \n",
       "Mr        455.880907  0.162571  2.381853  32.739609  0.293006  0.151229   \n",
       "Mrs       456.393701  0.795276  1.984252  35.981818  0.692913  0.818898   \n",
       "Other     564.444444  0.111111  1.666667  45.888889  0.111111  0.111111   \n",
       "\n",
       "             Fare  FamilySize  \n",
       "Initial                        \n",
       "Master   3.340710    4.675000  \n",
       "Miss     3.123713    2.236559  \n",
       "Mr       2.651507    1.444234  \n",
       "Mrs      3.443751    2.511811  \n",
       "Other    2.641605    1.222222  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Null 값 대체\n",
    "# Age 변수에 존재하는 Null 값들을 Age 변수의 평균값으로 대체하기 위해, 평균값들을 확인\n",
    "df_train.groupby('Initial').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:48.773692Z",
     "start_time": "2020-06-10T13:59:48.702643Z"
    }
   },
   "outputs": [],
   "source": [
    "# 학습 데이터 셋과 테스트 데이터 셋에 존재하는 Age 변수의 Null 값들을 평균 값들로 대체\n",
    "df_train.loc[(df_train.Age.isnull()) & (df_train['Initial'] == 'Master'), 'Age'] = 5\n",
    "df_train.loc[(df_train.Age.isnull()) & (df_train['Initial'] == 'Miss'), 'Age'] = 22\n",
    "df_train.loc[(df_train.Age.isnull()) & (df_train['Initial'] == 'Mr'), 'Age'] = 33\n",
    "df_train.loc[(df_train.Age.isnull()) & (df_train['Initial'] == 'Mrs'), 'Age'] = 36\n",
    "df_train.loc[(df_train.Age.isnull()) & (df_train['Initial'] == 'Other'), 'Age'] = 46\n",
    "\n",
    "df_test.loc[(df_test.Age.isnull()) & (df_test['Initial'] == 'Master'), 'Age'] = 5\n",
    "df_test.loc[(df_test.Age.isnull()) & (df_test['Initial'] == 'Miss'), 'Age'] = 22\n",
    "df_test.loc[(df_test.Age.isnull()) & (df_test['Initial'] == 'Mr'), 'Age'] = 33\n",
    "df_test.loc[(df_test.Age.isnull()) & (df_test['Initial'] == 'Mrs'), 'Age'] = 36\n",
    "df_test.loc[(df_test.Age.isnull()) & (df_test['Initial'] == 'Other'), 'Age'] = 46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.366114Z",
     "start_time": "2020-06-10T13:59:48.780698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "age_full_hist = sns.distplot(df_train['Age'], label = 'Skewness: {:.2f}'.format(df_train['Age'].skew()))\n",
    "age_full_hist = age_full_hist.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Age 변수**에 존재하는 **Null 값들을 모두 평균 값으로 대체**해서 채워주었더니, **치우침의 정도가 조금(?) 좋아졌다**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**연속형 변수인 Age를 범주화**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.388128Z",
     "start_time": "2020-06-10T13:59:49.371118Z"
    }
   },
   "outputs": [],
   "source": [
    "def category_age(x):\n",
    "    if x <= 16:\n",
    "        return 0\n",
    "    elif x <= 32:\n",
    "        return 1\n",
    "    elif x <= 48:\n",
    "        return 2\n",
    "    elif x <= 64:\n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "df_train['Age_cat'] = df_train['Age'].apply(category_age)\n",
    "df_test['Age_cat'] = df_test['Age'].apply(category_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.420152Z",
     "start_time": "2020-06-10T13:59:49.393133Z"
    }
   },
   "outputs": [],
   "source": [
    "# 이제 원래 컬럼인 Age를 제거해주겠다\n",
    "df_train.drop(['Age'], axis = 1, inplace = True)\n",
    "df_test.drop(['Age'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FamilySize 변수\n",
    "- 가족 수를 기준으로 3개의 범주로 범주화를시켜주겠다\n",
    "    - FamilySize = 1, 2, 3 : \"0\"\n",
    "    - FamilySize = 4, 5, 6 : \"1\"\n",
    "    - FamilySize = 7, 8, 11 : \"2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.455178Z",
     "start_time": "2020-06-10T13:59:49.429160Z"
    }
   },
   "outputs": [],
   "source": [
    "def category_FamilySize(x):\n",
    "    if x <= 3:\n",
    "        return 0\n",
    "    elif x <= 6:\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "    \n",
    "df_train['FamilySize_cat'] = df_train['FamilySize'].apply(category_FamilySize)\n",
    "df_test['FamilySize_cat'] = df_test['FamilySize'].apply(category_FamilySize)\n",
    "\n",
    "del df_train['FamilySize']\n",
    "del df_test['FamilySize']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.477193Z",
     "start_time": "2020-06-10T13:59:49.459183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    800\n",
       "1     66\n",
       "2     25\n",
       "Name: FamilySize_cat, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['FamilySize_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embarked 변수\n",
    "- Embarked 변수에서 \"S\" 값이 가장 많으므로, 모든 Null 값들을 \"S\"로 대체해주겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.498208Z",
     "start_time": "2020-06-10T13:59:49.482197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['Embarked'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.518222Z",
     "start_time": "2020-06-10T13:59:49.502211Z"
    }
   },
   "outputs": [],
   "source": [
    "# Null 값 대체\n",
    "df_train['Embarked'].fillna('S', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 문자열 변수인 Initial, Embarked, Sex를 수치형 변수로 변환"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Initial 변수를 수치화**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.545240Z",
     "start_time": "2020-06-10T13:59:49.525227Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train['Initial'] = df_train['Initial'].map({'Master': 0, 'Miss': 1, 'Mr': 2, 'Mrs': 3, 'Other': 4})\n",
    "df_test['Initial'] = df_test['Initial'].map({'Master': 0, 'Miss': 1, 'Mr': 2, 'Mrs': 3, 'Other': 4})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Embarked 변수를 수치화**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.568257Z",
     "start_time": "2020-06-10T13:59:49.549243Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train['Embarked'] = df_train['Embarked'].map({'C': 0, 'Q': 1, 'S': 2})\n",
    "df_test['Embarked'] = df_test['Embarked'].map({'C': 0, 'Q': 1, 'S': 2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sex 변수를 수치화**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:49.605284Z",
     "start_time": "2020-06-10T13:59:49.582267Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train['Sex'] = df_train['Sex'].map({'female': 0, 'male': 1})\n",
    "df_test['Sex'] = df_test['Sex'].map({'female': 0, 'male': 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 각 feature 간의 상관관계를 시각화\n",
    "- **피어슨 상관계수**가 **1 에 가까우면 양의 상관관계**, **-1 에 가까우면 음의 상관관계**이다.\n",
    "- **피어슨 상관계수가 0** 이라는 것은 상관관계가 없다는 의미가 아니라, **선형적인 상관관계가 없다는 의미**이다.\n",
    "    - 즉, **비선형적 상관관계는 존재할 수 있다!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:50.751097Z",
     "start_time": "2020-06-10T13:59:49.609287Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap_data = df_train[['Survived', 'Pclass', 'Sex', \n",
    "                         'Fare', 'Embarked', 'FamilySize_cat', \n",
    "                         'Initial', 'Age_cat']]\n",
    "\n",
    "colormap = plt.cm.RdBu\n",
    "\n",
    "plt.figure(figsize = (14, 12))\n",
    "plt.title('Pearson Correlation of Features', size = 20)\n",
    "sns.heatmap(heatmap_data.astype(float).corr(), cmap = colormap, square = True,\n",
    "            linecolor = 'white', linewidth = 0.1, annot = True, annot_kws = {'size': 15})\n",
    "\n",
    "del heatmap_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 앞선 EDA 과정에서 살펴봤듯이, **Sex와 Pclass 변수가 Survived 변수와 어느 정도 상관관계가 존재함**을 알 수 있다.\n",
    "- 하지만 **서로 강한 상관관계를 갖는 feature들은 존재하지 않는 것을 확인**할 수 있다.\n",
    "    - 즉, **다중 공선성을 보이는 변수들이 존재하지 않는다**는 의미이다.\n",
    "    - 다시 말해, 모델 생성에 있어서 **불필요한 feature가 없다**는 말이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding(원-핫 인코딩)\n",
    "- 수치화를 시켜준 범주형 데이터를 그대로 모델에 넣어줘도 되지만, 모델의 성능을 높여주기 위해서 **one-hot encoding** 작업을 수행해주겠다.\n",
    "    - 원-핫 인코딩이라는게 쉽게 말하면 **가변수(더미변수)를 만들어주겠다**는 말이랑 동일하다.\n",
    "    - **Pandas**의 ```get_dummies()```를 사용하면 쉽게 수행해 줄 수 있다.\n",
    "        - ```prefix``` 옵션을 사용하면, **가변수에 공통으로 접두사를 추가**할 수 있다.\n",
    "        - ```drop_first = True``` 옵션을 설정하면, **가변수의 첫 번째 변수를 자동으로 삭제**해준다.\n",
    "            - 즉, **가변수 함정(dummy_trap)을 피할 수 있게 해준다 --> 총 (k-1)개의 가변수 생성!!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Initial 변수에 대해서 One-Hot Encoding 처리**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:50.781118Z",
     "start_time": "2020-06-10T13:59:50.755100Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train = pd.get_dummies(df_train, columns = ['Initial'], prefix = 'Initial')\n",
    "df_test = pd.get_dummies(df_test, columns = ['Initial'], prefix = 'Initial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:50.828152Z",
     "start_time": "2020-06-10T13:59:50.786123Z"
    }
   },
   "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>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>FamilySize_cat</th>\n",
       "      <th>Initial_0</th>\n",
       "      <th>Initial_1</th>\n",
       "      <th>Initial_2</th>\n",
       "      <th>Initial_3</th>\n",
       "      <th>Initial_4</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "   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  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0      1      0   \n",
       "4                           Allen, Mr. William Henry    1      0      0   \n",
       "\n",
       "             Ticket      Fare Cabin  Embarked  Age_cat  FamilySize_cat  \\\n",
       "0         A/5 21171  1.981001   NaN         2        1               0   \n",
       "1          PC 17599  4.266662   C85         0        2               0   \n",
       "2  STON/O2. 3101282  2.070022   NaN         2        1               0   \n",
       "3            113803  3.972177  C123         2        2               0   \n",
       "4            373450  2.085672   NaN         2        2               0   \n",
       "\n",
       "   Initial_0  Initial_1  Initial_2  Initial_3  Initial_4  \n",
       "0          0          0          1          0          0  \n",
       "1          0          0          0          1          0  \n",
       "2          0          1          0          0          0  \n",
       "3          0          0          0          1          0  \n",
       "4          0          0          1          0          0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Embarked 변수에 대해서 One-Hot Encoding 처리**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:50.869180Z",
     "start_time": "2020-06-10T13:59:50.831154Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train = pd.get_dummies(df_train, columns = ['Embarked'], prefix = 'Embarked')\n",
    "df_test = pd.get_dummies(df_test, columns = ['Embarked'], prefix = 'Embarked')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:50.915214Z",
     "start_time": "2020-06-10T13:59:50.873185Z"
    },
    "scrolled": true
   },
   "outputs": [
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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",
       "                                                Name  Sex  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0      1      0   \n",
       "4                           Allen, Mr. William Henry    1      0      0   \n",
       "\n",
       "             Ticket      Fare Cabin  Age_cat  FamilySize_cat  Initial_0  \\\n",
       "0         A/5 21171  1.981001   NaN        1               0          0   \n",
       "1          PC 17599  4.266662   C85        2               0          0   \n",
       "2  STON/O2. 3101282  2.070022   NaN        1               0          0   \n",
       "3            113803  3.972177  C123        2               0          0   \n",
       "4            373450  2.085672   NaN        2               0          0   \n",
       "\n",
       "   Initial_1  Initial_2  Initial_3  Initial_4  Embarked_0  Embarked_1  \\\n",
       "0          0          1          0          0           0           0   \n",
       "1          0          0          1          0           1           0   \n",
       "2          1          0          0          0           0           0   \n",
       "3          0          0          1          0           0           0   \n",
       "4          0          1          0          0           0           0   \n",
       "\n",
       "   Embarked_2  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 불필요한 컬럼 제거\n",
    "- 실질적으로 분석에 사용할 feature들만 남기고 나머지는 제거해주겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:50.943233Z",
     "start_time": "2020-06-10T13:59:50.925220Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train.drop(['PassengerId', 'Name', 'SibSp', 'Parch', 'Ticket', 'Cabin'], axis = 1, inplace = True)\n",
    "df_test.drop(['PassengerId', 'Name', 'SibSp', 'Parch', 'Ticket', 'Cabin'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:50.972255Z",
     "start_time": "2020-06-10T13:59:50.947235Z"
    }
   },
   "outputs": [
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       "   Survived  Pclass  Sex      Fare  Age_cat  FamilySize_cat  Initial_0  \\\n",
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       "0          0          1          0          0           0           0   \n",
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_train.head()"
   ]
  },
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   "cell_type": "code",
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     "end_time": "2020-06-10T13:59:51.010281Z",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_cat</th>\n",
       "      <th>FamilySize_cat</th>\n",
       "      <th>Initial_0</th>\n",
       "      <th>Initial_1</th>\n",
       "      <th>Initial_2</th>\n",
       "      <th>Initial_3</th>\n",
       "      <th>Initial_4</th>\n",
       "      <th>Embarked_0</th>\n",
       "      <th>Embarked_1</th>\n",
       "      <th>Embarked_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.057860</td>\n",
       "      <td>2</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.270836</td>\n",
       "      <td>3</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.159003</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2.508582</td>\n",
       "      <td>1</td>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex      Fare  Age_cat  FamilySize_cat  Initial_0  Initial_1  \\\n",
       "0       3    1  2.057860        2               0          0          0   \n",
       "1       3    0  1.945910        2               0          0          0   \n",
       "2       2    1  2.270836        3               0          0          0   \n",
       "3       3    1  2.159003        1               0          0          0   \n",
       "4       3    0  2.508582        1               0          0          0   \n",
       "\n",
       "   Initial_2  Initial_3  Initial_4  Embarked_0  Embarked_1  Embarked_2  \n",
       "0          1          0          0           0           1           0  \n",
       "1          0          1          0           0           0           1  \n",
       "2          1          0          0           0           1           0  \n",
       "3          1          0          0           0           0           1  \n",
       "4          0          1          0           0           0           1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 머신러닝 모델 생성 및 예측\n",
    "- 본 튜토리얼은 **0 과 1 로 이루어진 target 변수에 대한 예측**을 수행하는 모델을 만드는 것이다.\n",
    "    - 즉, **이진 분류 문제**라고 할 수 있다.\n",
    "- 우선 **학습 데이터 셋**에서 **Survived를 제외한 input**을 가지고, **모델 최적화**를 수행해 줄 것이다.\n",
    "- 그 후에 모델이 학습하지 않았던 **테스트 데이터 셋을 input**으로 주어서, **테스트 데이터 셋의 각 탑승객의 생존 여부를 예측**해보겠다.\n",
    "- 이번 분석에서는 ***Logistic Regression, Decision Tree, Random Forest*** 이렇게 총 3개의 모델을 사용할 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:51.449610Z",
     "start_time": "2020-06-10T13:59:51.013285Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 먼저 **학습 데이터 셋(train dataset)**과 **target label(Survived)**을 **분리**하겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:51.460602Z",
     "start_time": "2020-06-10T13:59:51.451594Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = df_train.drop(['Survived'], axis = 1).values\n",
    "y_target = df_train['Survived'].values\n",
    "X_test = df_test.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 다음으로 ```train_test_split()```을 사용하여, **학습 데이터 셋과 테스트 데이터 셋을 분할**해준다.\n",
    "    - **엄밀하게 말하면, 학습 데이터 셋(train dataset)과 검증 데이터 셋(valid dataset)으로 분할**하는 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:51.481616Z",
     "start_time": "2020-06-10T13:59:51.463604Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_target, test_size = 0.3, random_state = 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 모델 학습 및 예측\n",
    "- **본고사를 치르기 전에 모의고사를 여러 번 보는 것**이라고 생각하면 이해하기 쉽다.\n",
    "- 즉, **본고사**가 **테스트 데이터 셋에 대해 평가**하는 것이라면, **모의고사**는 **교차 검중에서 많은 학습과 검증 데이트 셋에서 알고리즘 학습과 평가**를 수행하는 것이다.\n",
    "- 3가지 모델에 대한 하이퍼 파라미터 튜닝은 따로 진행해주지 않겠다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:51.526647Z",
     "start_time": "2020-06-10T13:59:51.484619Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8284\n"
     ]
    }
   ],
   "source": [
    "# 로지스틱 회귀의 경우, 정규성 가정이 필요하므로 \"표준화\" 작업을 수행\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_std = scaler.fit_transform(X_train)\n",
    "X_valid_std = scaler.transform(X_valid)\n",
    "\n",
    "# 모델 학습 및 예측\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train_std, y_train)\n",
    "pred = lr.predict(X_valid_std)\n",
    "\n",
    "print('Accuracy Score: {:.4f}'.format(accuracy_score(y_valid, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 하이퍼 파라미터 튜닝을 따로 해주지 않은 **로지스틱 회귀 모델**의 경우, 예측 정확도가 **약 83%**로 나왔다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Decision Tree**\n",
    "- 결정 트리 모델은 \"표준화\" 또는 \"정규화\" 작업을 수행해주지 않아도 된다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:51.545662Z",
     "start_time": "2020-06-10T13:59:51.530653Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8172\n"
     ]
    }
   ],
   "source": [
    "dt_clf = DecisionTreeClassifier(random_state = 2020)\n",
    "dt_clf.fit(X_train, y_train)\n",
    "pred = dt_clf.predict(X_valid)\n",
    "\n",
    "print('Accuracy Score: {:.4f}'.format(accuracy_score(y_valid, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 하이퍼 파라미터 튜닝을 따로 해주지 않은 **결정 트리 모델**의 경우, 예측 정확도가 **약 82%**로 나왔다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:59:51.932936Z",
     "start_time": "2020-06-10T13:59:51.549664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score: 0.8470\n"
     ]
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(random_state = 2020)\n",
    "rf_clf.fit(X_train, y_train)\n",
    "pred = rf_clf.predict(X_valid)\n",
    "\n",
    "print('Accuracy Score: {:.4f}'.format(accuracy_score(y_valid, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 하이퍼 파라미터 튜닝을 따로 해주지 않은 **랜덤 포레스트 모델**의 경우, 예측 정확도가 **약 85%**로 나왔다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<3개의 모델 성능 비교 결과>**\n",
    "- **랜덤 포레스트 모델**의 예측 정확도가 가장 높게 나왔으므로, 해당 모델을 사용하도록 하겠다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature 중요도 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T14:02:13.983334Z",
     "start_time": "2020-06-10T14:02:13.952312Z"
    }
   },
   "outputs": [],
   "source": [
    "from pandas import Series\n",
    "\n",
    "feature_importance = rf_clf.feature_importances_\n",
    "Series_feat_imp = Series(feature_importance, index = df_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T14:02:15.267247Z",
     "start_time": "2020-06-10T14:02:15.258241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'Sex', 'Fare', 'Age_cat', 'FamilySize_cat', 'Initial_0',\n",
       "       'Initial_1', 'Initial_2', 'Initial_3', 'Initial_4', 'Embarked_0',\n",
       "       'Embarked_1', 'Embarked_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T14:02:21.121407Z",
     "start_time": "2020-06-10T14:02:20.551000Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "Series_feat_imp.sort_values(ascending = True).plot.barh()\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Feature 중요도**를 살펴보면, **Fare 변수**의 **중요도가 엄청나게 높은 것을 확인**할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 테스트 데이터 셋에 대해서 예측을 수행\n",
    "- 이제 모델이 학습하지 않았던 테스트 데이터 셋을 input으로 넣어주고, 각 탑승객의 생존 여부를 예측해보겠다.\n",
    "- Kaggle에서 준 파일인 **gender_submission.csv** 파일을 읽어와서 제출할 준비를 하겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T14:04:38.557138Z",
     "start_time": "2020-06-10T14:04:38.537107Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission = pd.read_csv('../titanic/gender_submission.csv')\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T14:05:28.558637Z",
     "start_time": "2020-06-10T14:05:28.523612Z"
    }
   },
   "outputs": [],
   "source": [
    "prediction = rf_clf.predict(X_test)\n",
    "submission['Survived'] = prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T14:06:05.650983Z",
     "start_time": "2020-06-10T14:06:05.632970Z"
    }
   },
   "outputs": [],
   "source": [
    "submission.to_csv('./my_second_submission.csv', index = False)"
   ]
  },
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   "source": [
    "**여기까지 해서 타이타닉 Advanced <Part 2>를 마무리하도록 하겠다!**\n",
    "\n",
    "**비록 튜토리얼에서 수행해 준 전처리 방법을 거의 참고했지만, 튜토리얼을 할 때보다는 다양한 모델들 사용해서 예측 모델을 만들어보았다.**\n",
    "- 이번 타아타닉 생존자 예측을 수행하면서 데이터 전처리의 중요성을 다시 한 번 깨달았다. 명심하자. ***Garbage in, Garbage out!!***"
   ]
  }
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