Kaggle - Predicting Survival on the Titanic - Mini - Trainee.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle Titanic 생존자 예측하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "캐글의 튜토리얼 중 하나인 타이타닉호 생존자 예측하기입니다.\n",
    "\n",
    "https://www.kaggle.com/c/titanic/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Graphlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "graphlab 라이브러리를 import합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import graphlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 화일 압축 풀기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 kaggle_titanic.zip 화일을 data 폴더 아래에 업로드합니다. 그런 다음 아래 명령을 실행해서 압축을 풉니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ../data/kaggle_titanic.zip\n",
      "  inflating: ../data/kaggle_titanic/test.csv  \n",
      "  inflating: ../data/kaggle_titanic/gender_submission.csv  \n",
      "  inflating: ../data/kaggle_titanic/train.csv  \n"
     ]
    }
   ],
   "source": [
    "!unzip -o ../data/kaggle_titanic.zip -d ../data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "압축이 잘 풀렸는지 꼭 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender_submission.csv  test.csv\r\n",
      "submission.csv         train.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls ../data/kaggle_titanic/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 가져오기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 train.csv 화일을 가져 옵시다. 별도의 검증 데이터가 존재하지 않기 때문에 학습 데이터를 가져와서 train_data와 validation_data로 나눠서 사용하고자 합니다. 따라서 전체 학습 데이터는 **full_train_data**라는 변수에 저장합니다.\n",
    "\n",
    "    graphlab.SFrame(csv화일이름)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /home/nbuser/library/data/kaggle_titanic/train.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /home/nbuser/library/data/kaggle_titanic/train.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.584998 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100 lines in 0.584998 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,int,int,str,str,float,int,int,str,float,str,str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /home/nbuser/library/data/kaggle_titanic/train.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /home/nbuser/library/data/kaggle_titanic/train.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 891 lines in 0.265967 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 891 lines in 0.265967 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data = graphlab.SFrame('../data/kaggle_titanic/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "head메소드로 데이터를 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.1333</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.0708</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">C</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 12 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tPassengerId\tint\n",
       "\tSurvived\tint\n",
       "\tPclass\tint\n",
       "\tName\tstr\n",
       "\tSex\tstr\n",
       "\tAge\tfloat\n",
       "\tSibSp\tint\n",
       "\tParch\tint\n",
       "\tTicket\tstr\n",
       "\tFare\tfloat\n",
       "\tCabin\tstr\n",
       "\tEmbarked\tstr\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------------+----------+--------+--------------------------------+--------+\n",
       "| PassengerId | Survived | Pclass |              Name              |  Sex   |\n",
       "+-------------+----------+--------+--------------------------------+--------+\n",
       "|      1      |    0     |   3    |    Braund, Mr. Owen Harris     |  male  |\n",
       "|      2      |    1     |   1    | Cumings, Mrs. John Bradley...  | female |\n",
       "|      3      |    1     |   3    |     Heikkinen, Miss. Laina     | female |\n",
       "|      4      |    1     |   1    | Futrelle, Mrs. Jacques Hea...  | female |\n",
       "|      5      |    0     |   3    |    Allen, Mr. William Henry    |  male  |\n",
       "|      6      |    0     |   3    |        Moran, Mr. James        |  male  |\n",
       "|      7      |    0     |   1    |    McCarthy, Mr. Timothy J     |  male  |\n",
       "|      8      |    0     |   3    | Palsson, Master. Gosta Leonard |  male  |\n",
       "|      9      |    1     |   3    | Johnson, Mrs. Oscar W (Eli...  | female |\n",
       "|      10     |    1     |   2    | Nasser, Mrs. Nicholas (Ade...  | female |\n",
       "+-------------+----------+--------+--------------------------------+--------+\n",
       "+------+-------+-------+------------------+---------+-------+----------+\n",
       "| Age  | SibSp | Parch |      Ticket      |   Fare  | Cabin | Embarked |\n",
       "+------+-------+-------+------------------+---------+-------+----------+\n",
       "| 22.0 |   1   |   0   |    A/5 21171     |   7.25  |       |    S     |\n",
       "| 38.0 |   1   |   0   |     PC 17599     | 71.2833 |  C85  |    C     |\n",
       "| 26.0 |   0   |   0   | STON/O2. 3101282 |  7.925  |       |    S     |\n",
       "| 35.0 |   1   |   0   |      113803      |   53.1  |  C123 |    S     |\n",
       "| 35.0 |   0   |   0   |      373450      |   8.05  |       |    S     |\n",
       "| None |   0   |   0   |      330877      |  8.4583 |       |    Q     |\n",
       "| 54.0 |   0   |   0   |      17463       | 51.8625 |  E46  |    S     |\n",
       "| 2.0  |   3   |   1   |      349909      |  21.075 |       |    S     |\n",
       "| 27.0 |   0   |   2   |      347742      | 11.1333 |       |    S     |\n",
       "| 14.0 |   1   |   0   |      237736      | 30.0708 |       |    C     |\n",
       "+------+-------+-------+------------------+---------+-------+----------+\n",
       "[10 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "test.csv에 들어 있는 테스트 데이터를 **test_data**에 저장합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Read 100 lines. Lines per second: 246.979</pre>"
      ],
      "text/plain": [
       "Read 100 lines. Lines per second: 246.979"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /home/nbuser/library/data/kaggle_titanic/test.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /home/nbuser/library/data/kaggle_titanic/test.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 100 lines in 0.405742 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 100 lines in 0.405742 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------\n",
      "Inferred types from first 100 line(s) of file as \n",
      "column_type_hints=[int,int,str,str,float,int,int,str,float,str,str]\n",
      "If parsing fails due to incorrect types, you can correct\n",
      "the inferred type list above and pass it to read_csv in\n",
      "the column_type_hints argument\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Finished parsing file /home/nbuser/library/data/kaggle_titanic/test.csv</pre>"
      ],
      "text/plain": [
       "Finished parsing file /home/nbuser/library/data/kaggle_titanic/test.csv"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Parsing completed. Parsed 418 lines in 0.259688 secs.</pre>"
      ],
      "text/plain": [
       "Parsing completed. Parsed 418 lines in 0.259688 secs."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data = graphlab.SFrame('../data/kaggle_titanic/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마찬가지로 head 메소드로 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PassengerId</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Pclass</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sex</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Age</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SibSp</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Parch</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Ticket</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Fare</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Cabin</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">892</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Kelly, Mr. James</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">34.5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">330911</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.8292</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">893</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Wilkes, Mrs. James (Ellen<br>Needs) ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">363272</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">894</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Myles, Mr. Thomas Francis</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">62.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">240276</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.6875</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">895</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Wirz, Mr. Albert</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">27.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">315154</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.6625</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">896</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Hirvonen, Mrs. Alexander<br>(Helga E Lindqvist) ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3101298</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">12.2875</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">897</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Svensson, Mr. Johan<br>Cervin ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7538</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9.225</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">898</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Connolly, Miss. Kate</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">330972</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.6292</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">899</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Caldwell, Mr. Albert<br>Francis ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">248738</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">29.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">900</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Abrahim, Mrs. Joseph<br>(Sophie Halaut Easu) ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">18.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2657</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.2292</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">901</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Davies, Mr. John Samuel</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">21.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A/4 48871</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24.15</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 11 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tPassengerId\tint\n",
       "\tPclass\tint\n",
       "\tName\tstr\n",
       "\tSex\tstr\n",
       "\tAge\tfloat\n",
       "\tSibSp\tint\n",
       "\tParch\tint\n",
       "\tTicket\tstr\n",
       "\tFare\tfloat\n",
       "\tCabin\tstr\n",
       "\tEmbarked\tstr\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------------+--------+-------------------------------+--------+------+-------+\n",
       "| PassengerId | Pclass |              Name             |  Sex   | Age  | SibSp |\n",
       "+-------------+--------+-------------------------------+--------+------+-------+\n",
       "|     892     |   3    |        Kelly, Mr. James       |  male  | 34.5 |   0   |\n",
       "|     893     |   3    | Wilkes, Mrs. James (Ellen ... | female | 47.0 |   1   |\n",
       "|     894     |   2    |   Myles, Mr. Thomas Francis   |  male  | 62.0 |   0   |\n",
       "|     895     |   3    |        Wirz, Mr. Albert       |  male  | 27.0 |   0   |\n",
       "|     896     |   3    | Hirvonen, Mrs. Alexander (... | female | 22.0 |   1   |\n",
       "|     897     |   3    |   Svensson, Mr. Johan Cervin  |  male  | 14.0 |   0   |\n",
       "|     898     |   3    |      Connolly, Miss. Kate     | female | 30.0 |   0   |\n",
       "|     899     |   2    |  Caldwell, Mr. Albert Francis |  male  | 26.0 |   1   |\n",
       "|     900     |   3    | Abrahim, Mrs. Joseph (Soph... | female | 18.0 |   0   |\n",
       "|     901     |   3    |    Davies, Mr. John Samuel    |  male  | 21.0 |   2   |\n",
       "+-------------+--------+-------------------------------+--------+------+-------+\n",
       "+-------+-----------+---------+-------+----------+\n",
       "| Parch |   Ticket  |   Fare  | Cabin | Embarked |\n",
       "+-------+-----------+---------+-------+----------+\n",
       "|   0   |   330911  |  7.8292 |       |    Q     |\n",
       "|   0   |   363272  |   7.0   |       |    S     |\n",
       "|   0   |   240276  |  9.6875 |       |    Q     |\n",
       "|   0   |   315154  |  8.6625 |       |    S     |\n",
       "|   1   |  3101298  | 12.2875 |       |    S     |\n",
       "|   0   |    7538   |  9.225  |       |    S     |\n",
       "|   0   |   330972  |  7.6292 |       |    Q     |\n",
       "|   1   |   248738  |   29.0  |       |    S     |\n",
       "|   0   |    2657   |  7.2292 |       |    C     |\n",
       "|   0   | A/4 48871 |  24.15  |       |    S     |\n",
       "+-------+-----------+---------+-------+----------+\n",
       "[10 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그래프 시각화를 위해서 아래 명령을 실행합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "graphlab.canvas.set_target('ipynb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 정의\n",
    "\n",
    "원본은 다음 링크에서 확인하실 수 있습니다.\n",
    "https://www.kaggle.com/c/titanic/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<tbody>\n",
    "<tr><th><b>Variable</b></th><th><b>Definition</b></th><th><b>Key</b></th></tr>\n",
    "<tr>\n",
    "<td>survival</td>\n",
    "<td>Survival</td>\n",
    "<td>0 = No, 1 = Yes</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pclass</td>\n",
    "<td>Ticket class</td>\n",
    "<td>1 = 1st, 2 = 2nd, 3 = 3rd</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>sex</td>\n",
    "<td>Sex</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Age</td>\n",
    "<td>Age in years</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>sibsp</td>\n",
    "<td># of siblings / spouses aboard the Titanic</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>parch</td>\n",
    "<td># of parents / children aboard the Titanic</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>ticket</td>\n",
    "<td>Ticket number</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>fare</td>\n",
    "<td>Passenger fare</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>cabin</td>\n",
    "<td>Cabin number</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>embarked</td>\n",
    "<td>Port of Embarkation</td>\n",
    "<td>C = Cherbourg, Q = Queenstown, S = Southampton</td>\n",
    "</tr>\n",
    "</tbody>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 변수 노트\n",
    "\n",
    "**pclass**: A proxy for socio-economic status (SES)<br>\n",
    "1st = Upper<br>\n",
    "2nd = Middle<br>\n",
    "3rd = Lower\n",
    "\n",
    "**age**: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "**sibsp**: The dataset defines family relations in this way...<br>\n",
    "Sibling = brother, sister, stepbrother, stepsister<br>\n",
    "Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "**parch**: The dataset defines family relations in this way...<br>\n",
    "Parent = mother, father<br>\n",
    "Child = daughter, son, stepdaughter, stepson<br>\n",
    "Some children travelled only with a nanny, therefore parch=0 for them.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 살펴보기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show 메소드로 학습 데이터를 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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Basle 541\": {\"frequency\": 1, \"value\": \"SC/AH Basle 541\"}, \"250649\": {\"frequency\": 2, \"value\": \"250649\"}, \"250648\": {\"frequency\": 1, \"value\": \"250648\"}, \"250647\": {\"frequency\": 2, \"value\": \"250647\"}, \"250646\": {\"frequency\": 1, \"value\": \"250646\"}, \"250644\": {\"frequency\": 2, \"value\": \"250644\"}, \"347082\": {\"frequency\": 7, \"value\": \"347082\"}, \"347083\": {\"frequency\": 1, \"value\": \"347083\"}, \"347080\": {\"frequency\": 2, \"value\": \"347080\"}, \"347081\": {\"frequency\": 1, \"value\": \"347081\"}, \"A/5. 10482\": {\"frequency\": 1, \"value\": \"A/5. 10482\"}, \"2649\": {\"frequency\": 1, \"value\": \"2649\"}, \"234360\": {\"frequency\": 1, \"value\": \"234360\"}, \"36963\": {\"frequency\": 1, \"value\": \"36963\"}, \"2641\": {\"frequency\": 1, \"value\": \"2641\"}, \"345763\": {\"frequency\": 1, \"value\": \"345763\"}, \"349256\": {\"frequency\": 1, \"value\": \"349256\"}, \"36967\": {\"frequency\": 1, \"value\": \"36967\"}, \"345764\": {\"frequency\": 2, \"value\": \"345764\"}, \"345767\": {\"frequency\": 1, \"value\": \"345767\"}, \"2647\": {\"frequency\": 1, \"value\": \"2647\"}, \"237668\": {\"frequency\": 1, \"value\": \"237668\"}, \"113794\": {\"frequency\": 1, \"value\": \"113794\"}, \"35852\": {\"frequency\": 1, \"value\": \"35852\"}, \"350417\": {\"frequency\": 1, \"value\": \"350417\"}, \"350026\": {\"frequency\": 1, \"value\": \"350026\"}, \"349251\": {\"frequency\": 1, \"value\": \"349251\"}, \"2003\": {\"frequency\": 1, \"value\": \"2003\"}, \"349253\": {\"frequency\": 1, \"value\": \"349253\"}, \"349252\": {\"frequency\": 1, \"value\": \"349252\"}, \"S.C./A.4. 23567\": {\"frequency\": 1, \"value\": \"S.C./A.4. 23567\"}, \"349254\": {\"frequency\": 1, \"value\": \"349254\"}, \"349257\": {\"frequency\": 1, \"value\": \"349257\"}, \"371060\": {\"frequency\": 1, \"value\": \"371060\"}, \"28206\": {\"frequency\": 1, \"value\": \"28206\"}}, \"size\": 891}, \"Cabin\": {\"complete\": true, \"numeric\": false, \"num_unique\": 148, \"num_undefined\": 0, \"progress\": 1.0, \"frequent_items\": {\"\": {\"frequency\": 687, \"value\": \"\"}, \"E36\": {\"frequency\": 1, \"value\": \"E36\"}, \"D17\": {\"frequency\": 2, \"value\": \"D17\"}, \"D50\": {\"frequency\": 1, \"value\": \"D50\"}, \"E77\": {\"frequency\": 1, \"value\": \"E77\"}, \"C30\": {\"frequency\": 1, \"value\": \"C30\"}, \"D56\": {\"frequency\": 1, \"value\": \"D56\"}, \"C32\": {\"frequency\": 1, \"value\": \"C32\"}, \"G6\": {\"frequency\": 4, \"value\": \"G6\"}, \"B50\": {\"frequency\": 1, \"value\": \"B50\"}, \"C62 C64\": {\"frequency\": 1, \"value\": \"C62 C64\"}, \"D\": {\"frequency\": 3, \"value\": \"D\"}, \"B102\": {\"frequency\": 1, \"value\": \"B102\"}, \"B101\": {\"frequency\": 1, \"value\": \"B101\"}, \"E68\": {\"frequency\": 1, \"value\": \"E68\"}, \"F33\": {\"frequency\": 3, \"value\": \"F33\"}, \"T\": {\"frequency\": 1, \"value\": \"T\"}, \"F38\": {\"frequency\": 1, \"value\": \"F38\"}, \"A24\": {\"frequency\": 1, \"value\": \"A24\"}, \"E63\": {\"frequency\": 1, \"value\": \"E63\"}, \"E67\": {\"frequency\": 2, \"value\": \"E67\"}, \"B28\": {\"frequency\": 2, \"value\": \"B28\"}, \"B22\": {\"frequency\": 2, \"value\": \"B22\"}, \"B20\": {\"frequency\": 2, \"value\": \"B20\"}, \"B4\": {\"frequency\": 1, \"value\": \"B4\"}, \"B5\": {\"frequency\": 2, \"value\": \"B5\"}, \"B58 B60\": {\"frequency\": 2, \"value\": \"B58 B60\"}, \"B3\": {\"frequency\": 1, \"value\": \"B3\"}, \"F G73\": {\"frequency\": 2, \"value\": \"F G73\"}, \"C54\": {\"frequency\": 1, \"value\": \"C54\"}, \"C52\": {\"frequency\": 2, \"value\": \"C52\"}, \"C50\": {\"frequency\": 1, \"value\": \"C50\"}, \"C46\": {\"frequency\": 1, \"value\": \"C46\"}, \"F E69\": {\"frequency\": 1, \"value\": \"F E69\"}, \"E58\": {\"frequency\": 1, \"value\": \"E58\"}, \"E50\": {\"frequency\": 1, \"value\": \"E50\"}, \"A14\": {\"frequency\": 1, \"value\": \"A14\"}, \"B38\": {\"frequency\": 1, \"value\": \"B38\"}, \"B39\": {\"frequency\": 1, \"value\": \"B39\"}, \"B35\": {\"frequency\": 2, \"value\": \"B35\"}, \"B37\": {\"frequency\": 1, \"value\": \"B37\"}, \"B30\": {\"frequency\": 1, \"value\": \"B30\"}, \"E121\": {\"frequency\": 2, \"value\": \"E121\"}, \"E25\": {\"frequency\": 2, \"value\": \"E25\"}, \"E8\": {\"frequency\": 2, \"value\": \"E8\"}, \"F G63\": {\"frequency\": 1, \"value\": \"F G63\"}, \"C45\": {\"frequency\": 1, \"value\": \"C45\"}, \"E40\": {\"frequency\": 1, \"value\": \"E40\"}, \"C47\": {\"frequency\": 1, \"value\": \"C47\"}, \"E46\": {\"frequency\": 1, \"value\": \"E46\"}, \"E44\": {\"frequency\": 2, \"value\": \"E44\"}, \"B80\": {\"frequency\": 1, \"value\": \"B80\"}, \"E49\": {\"frequency\": 1, \"value\": \"E49\"}, \"C49\": {\"frequency\": 1, \"value\": \"C49\"}, \"B86\": {\"frequency\": 1, \"value\": \"B86\"}, \"B82 B84\": {\"frequency\": 1, \"value\": \"B82 B84\"}, \"C23 C25 C27\": {\"frequency\": 4, \"value\": \"C23 C25 C27\"}, \"D48\": {\"frequency\": 1, \"value\": \"D48\"}, \"C104\": {\"frequency\": 1, \"value\": \"C104\"}, \"C106\": {\"frequency\": 1, \"value\": \"C106\"}, \"C78\": {\"frequency\": 2, \"value\": \"C78\"}, \"C101\": {\"frequency\": 1, \"value\": \"C101\"}, \"C103\": {\"frequency\": 1, \"value\": \"C103\"}, \"D15\": {\"frequency\": 1, \"value\": \"D15\"}, \"E34\": {\"frequency\": 1, \"value\": \"E34\"}, \"E33\": {\"frequency\": 2, \"value\": \"E33\"}, \"D11\": {\"frequency\": 1, \"value\": \"D11\"}, \"E31\": {\"frequency\": 1, \"value\": \"E31\"}, \"C70\": {\"frequency\": 1, \"value\": \"C70\"}, \"B94\": {\"frequency\": 1, \"value\": \"B94\"}, \"D19\": {\"frequency\": 1, \"value\": \"D19\"}, \"E38\": {\"frequency\": 1, \"value\": \"E38\"}, \"B18\": {\"frequency\": 2, \"value\": \"B18\"}, \"B19\": {\"frequency\": 1, \"value\": \"B19\"}, \"D37\": {\"frequency\": 1, \"value\": \"D37\"}, \"A32\": {\"frequency\": 1, \"value\": \"A32\"}, \"A31\": {\"frequency\": 1, \"value\": \"A31\"}, \"A36\": {\"frequency\": 1, \"value\": \"A36\"}, \"A34\": {\"frequency\": 1, \"value\": \"A34\"}, \"E101\": {\"frequency\": 3, \"value\": \"E101\"}, \"C118\": {\"frequency\": 1, \"value\": \"C118\"}, \"C2\": {\"frequency\": 2, \"value\": \"C2\"}, \"C7\": {\"frequency\": 1, \"value\": \"C7\"}, \"C111\": {\"frequency\": 1, \"value\": \"C111\"}, \"C110\": {\"frequency\": 1, \"value\": \"C110\"}, \"E24\": {\"frequency\": 2, \"value\": \"E24\"}, \"B51 B53 B55\": {\"frequency\": 2, \"value\": \"B51 B53 B55\"}, \"E12\": {\"frequency\": 1, \"value\": \"E12\"}, \"B57 B59 B63 B66\": {\"frequency\": 2, \"value\": \"B57 B59 B63 B66\"}, \"C68\": {\"frequency\": 2, \"value\": \"C68\"}, \"D21\": {\"frequency\": 1, \"value\": \"D21\"}, \"D20\": {\"frequency\": 2, \"value\": \"D20\"}, \"C65\": {\"frequency\": 2, \"value\": \"C65\"}, \"D26\": {\"frequency\": 2, \"value\": \"D26\"}, \"E17\": {\"frequency\": 1, \"value\": \"E17\"}, \"A20\": {\"frequency\": 1, \"value\": \"A20\"}, \"F2\": {\"frequency\": 3, \"value\": \"F2\"}, \"A23\": {\"frequency\": 1, \"value\": \"A23\"}, \"F4\": {\"frequency\": 2, \"value\": \"F4\"}, \"A26\": {\"frequency\": 1, \"value\": \"A26\"}, \"B69\": {\"frequency\": 1, \"value\": \"B69\"}, \"B42\": {\"frequency\": 1, \"value\": \"B42\"}, \"C128\": {\"frequency\": 1, \"value\": \"C128\"}, \"C123\": {\"frequency\": 2, \"value\": \"C123\"}, \"C126\": {\"frequency\": 2, \"value\": \"C126\"}, \"C124\": {\"frequency\": 2, \"value\": \"C124\"}, \"C125\": {\"frequency\": 2, \"value\": \"C125\"}, \"D28\": {\"frequency\": 1, \"value\": \"D28\"}, \"D36\": {\"frequency\": 2, \"value\": \"D36\"}, \"E10\": {\"frequency\": 1, \"value\": \"E10\"}, \"D35\": {\"frequency\": 2, \"value\": \"D35\"}, \"D33\": {\"frequency\": 2, \"value\": \"D33\"}, \"D30\": {\"frequency\": 1, \"value\": \"D30\"}, \"B96 B98\": {\"frequency\": 4, \"value\": \"B96 B98\"}, \"D10 D12\": {\"frequency\": 1, \"value\": \"D10 D12\"}, \"C93\": {\"frequency\": 2, \"value\": \"C93\"}, \"C92\": {\"frequency\": 2, \"value\": \"C92\"}, \"C91\": {\"frequency\": 1, \"value\": \"C91\"}, \"A16\": {\"frequency\": 1, \"value\": \"A16\"}, \"A10\": {\"frequency\": 1, \"value\": \"A10\"}, \"C95\": {\"frequency\": 1, \"value\": \"C95\"}, \"B77\": {\"frequency\": 2, \"value\": \"B77\"}, \"B78\": {\"frequency\": 1, \"value\": \"B78\"}, \"B79\": {\"frequency\": 1, \"value\": \"B79\"}, \"C99\": {\"frequency\": 1, \"value\": \"C99\"}, \"A19\": {\"frequency\": 1, \"value\": \"A19\"}, \"C90\": {\"frequency\": 1, \"value\": \"C90\"}, \"A5\": {\"frequency\": 1, \"value\": \"A5\"}, \"A7\": {\"frequency\": 1, \"value\": \"A7\"}, \"A6\": {\"frequency\": 1, \"value\": \"A6\"}, \"C22 C26\": {\"frequency\": 3, \"value\": \"C22 C26\"}, \"B71\": {\"frequency\": 1, \"value\": \"B71\"}, \"D49\": {\"frequency\": 1, \"value\": \"D49\"}, \"B73\": {\"frequency\": 1, \"value\": \"B73\"}, \"D47\": {\"frequency\": 1, \"value\": \"D47\"}, \"D46\": {\"frequency\": 1, \"value\": \"D46\"}, \"D45\": {\"frequency\": 1, \"value\": \"D45\"}, \"C82\": {\"frequency\": 1, \"value\": \"C82\"}, \"C83\": {\"frequency\": 2, \"value\": \"C83\"}, \"B41\": {\"frequency\": 1, \"value\": \"B41\"}, \"C85\": {\"frequency\": 1, \"value\": \"C85\"}, \"C86\": {\"frequency\": 1, \"value\": \"C86\"}, \"C87\": {\"frequency\": 1, \"value\": \"C87\"}, \"C148\": {\"frequency\": 1, \"value\": \"C148\"}, \"B49\": {\"frequency\": 2, \"value\": \"B49\"}, \"D9\": {\"frequency\": 1, \"value\": \"D9\"}, \"D6\": {\"frequency\": 1, \"value\": \"D6\"}, \"D7\": {\"frequency\": 1, \"value\": \"D7\"}}, \"size\": 891}}, \"selected_variable\": {\"name\": [\"full_train_data\"], \"descriptives\": {\"rows\": 891, \"columns\": 12}, \"view_component\": \"Summary\", \"view_file\": \"sframe\", \"view_params\": {\"y\": null, \"x\": null, \"columns\": [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Fare\", \"Cabin\", \"Embarked\"], \"view\": null}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}], \"column_identifiers\": [\"Fare\", \"Name\", \"Embarked\", \"Age\", \"Parch\", \"Pclass\", \"Sex\", \"Survived\", \"SibSp\", \"PassengerId\", \"Ticket\", \"Cabin\"]}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "shape 메소드로 학습데이터의 전체 길이와 컬럼 개수를 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "총 891명, PassengerId와 Survived 제외하면 총 10개의 컬럼 존재\n",
    "\n",
    "Pclass \tName \tSex \tAge \tSibSp \tParch \tTicket \tFare \tCabin \tEmbarked\n",
    "\n",
    "- Cabin은 비어 있는 데이터가 많음\n",
    "- Ticket 값은 알 수 없는 값이 많음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 성별만으로 예측하기\n",
    "\n",
    "\n",
    "### 성별 데이터 살펴보기\n",
    "\n",
    "#### 데이터 전처리\n",
    "- 비어 있는 값 (없는 값)이 있는지 확인하기. 있다면 값 채우기\n",
    "- 생존율과의 상관관계 확인하기\n",
    "\n",
    "\n",
    "먼저 성별의 분포를 알아 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"complete\": true, \"numeric\": false, \"num_unique\": 2, \"num_undefined\": 0, \"progress\": 1.0, \"frequent_items\": {\"male\": {\"frequency\": 577, \"value\": \"male\"}, \"female\": {\"frequency\": 314, \"value\": \"female\"}}, \"size\": 891}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"str\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 891}, \"type\": \"SArray\", \"view_components\": [\"Categorical\"]}, \"histogram\": null}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#full_train_data['Sex'].show()\n",
    "full_train_data['Sex'].show(view='Categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다행히 비어 있는 값이 없군요.\n",
    "\n",
    "생존율의 분포도 확인해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 0.48631931786709987, \"complete\": true, \"min\": 0.0, \"max\": 1.0, \"quantile\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"median\": 0.0, \"numeric\": true, \"num_unique\": 2, \"num_undefined\": 0, \"var\": 0.23650647893072133, \"progress\": 1.0, \"size\": 891, \"frequent_items\": {\"0\": {\"frequency\": 549, \"value\": 0}, \"1\": {\"frequency\": 342, \"value\": 1}}, \"mean\": 0.3838383838383839}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"int\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 891}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 1.0, \"bins\": [549, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 342], \"min\": 0.0}, \"min\": 0, \"complete\": 1, \"max\": 1}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data['Survived'].show(view='Categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "성별과 생존율의 상관 관계를 확인해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "\n",
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       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"<SFrame>\"], \"descriptives\": {\"rows\": 549, \"columns\": 12}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"PassengerId\", \"x\": \"Sex\", \"columns\": [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Fare\", \"Cabin\", \"Embarked\"], \"view\": \"Bar Chart\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}], \"column_identifiers\": [\"Fare\", \"Name\", \"Embarked\", \"Age\", \"Parch\", \"Pclass\", \"Sex\", \"Survived\", \"SibSp\", \"PassengerId\", \"Ticket\", \"Cabin\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": {\"grouped\": [[\"female\", {\"count\": 81, \"std\": 261.8903319203684, \"min\": 15, \"max\": 889, \"sum\": 35223, \"var\": 68586.54595336074, \"mean\": 434.85185185185185}], [\"male\", {\"count\": 468, \"std\": 260.0869432939534, \"min\": 1, \"max\": 891, \"sum\": 210189, \"var\": 67645.21807199213, \"mean\": 449.1217948717949}]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 0].show(view=\"Bar Chart\", x=\"Sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
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       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"<SFrame>\"], \"descriptives\": {\"rows\": 342, \"columns\": 12}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"PassengerId\", \"x\": \"Sex\", \"columns\": [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Fare\", \"Cabin\", \"Embarked\"], \"view\": \"Bar Chart\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}], \"column_identifiers\": [\"Fare\", \"Name\", \"Embarked\", \"Age\", \"Parch\", \"Pclass\", \"Sex\", \"Survived\", \"SibSp\", \"PassengerId\", \"Ticket\", \"Cabin\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": {\"grouped\": [[\"female\", {\"count\": 233, \"std\": 254.50039393847155, \"min\": 2, \"max\": 888, \"sum\": 100120, \"var\": 64770.45051483721, \"mean\": 429.69957081545067}], [\"male\", {\"count\": 109, \"std\": 243.59233773067282, \"min\": 18, \"max\": 890, \"sum\": 51854, \"var\": 59337.227001094165, \"mean\": 475.72477064220175}]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 1].show(view=\"Bar Chart\", x=\"Sex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터에는 문제가 없어 보이고 생존율과의 상관관계도 어느 정도 있어 보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 전체 학습 데이터를 학습 데이터와 검증 데이터로 분리합시다. **train_data, validation_data**라는 변수에 저장합시다.\n",
    "\n",
    "        SFrame.random_split(비율, seed=시드값)\n",
    "        \n",
    "시드값은 0을 사용합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data,validation_data =  full_train_data.random_split(.8, seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 성별 데이터만으로 모델 학습시키기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 아래 classifier를 이용해서 모델을 학습시키고 **sex_only_model**에 저장합시다.\n",
    "\n",
    "    graphlab.logistic_classifier.create(학습데이터, target=타겟컬럼이름, features=피쳐컬럼이름의리스트, validation_set=검증데이터)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Logistic regression:</pre>"
      ],
      "text/plain": [
       "Logistic regression:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 703</pre>"
      ],
      "text/plain": [
       "Number of examples          : 703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 1</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 1</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 2</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting Newton Method</pre>"
      ],
      "text/plain": [
       "Starting Newton Method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Elapsed Time | Training-accuracy | Validation-accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 2        | 1.002831     | 0.800853          | 0.734043            |</pre>"
      ],
      "text/plain": [
       "| 1         | 2        | 1.002831     | 0.800853          | 0.734043            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 3        | 1.005063     | 0.800853          | 0.734043            |</pre>"
      ],
      "text/plain": [
       "| 2         | 3        | 1.005063     | 0.800853          | 0.734043            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 4        | 1.007791     | 0.800853          | 0.734043            |</pre>"
      ],
      "text/plain": [
       "| 3         | 4        | 1.007791     | 0.800853          | 0.734043            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>SUCCESS: Optimal solution found.</pre>"
      ],
      "text/plain": [
       "SUCCESS: Optimal solution found."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre></pre>"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_only_model = graphlab.logistic_classifier.create(train_data, target='Survived', features=['Sex'], validation_set=validation_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 검증데이터를 이용해서 모델 검증하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show 메소드로 모델의 결과를 살펴 봅시다.\n",
    "    \n",
    "    model.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
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       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('model','Summary', {\"comparison\": null, \"selected_variable\": {\"comparison\": null, \"name\": [\"sex_only_model\"], \"view_file\": \"model\", \"view_component\": \"Summary\", \"view_params\": {\"model_type\": \"regression\", \"view\": \"Summary\"}, \"view_components\": [\"Summary\", \"Evaluation\", \"Comparison\"], \"model_type\": \"regression\", \"attributes\": {\"section_titles\": [\"Schema\", \"Hyperparameters\", \"Training Summary\", \"Settings\", \"Highest Positive Coefficients\", \"Lowest Negative Coefficients\"], \"sections\": [[[\"Number of coefficients\", 2], [\"Number of examples\", 703], [\"Number of classes\", 2], [\"Number of feature columns\", 1], [\"Number of unpacked features\", 1]], [[\"L1 penalty\", 0.0], [\"L2 penalty\", 0.01]], [[\"Solver\", \"newton\"], [\"Solver iterations\", 3], [\"Solver status\", \"SUCCESS: Optimal solution found.\"], [\"Training time (sec)\", 1.0089]], [[\"Log-likelihood\", 349.375]], [[\"Sex[female]\", 2.6898]], [[\"(intercept)\", -1.5183]]]}, \"evaluations\": [], \"type\": \"Model\"}, \"ipython\": true, \"view_params\": {\"model_type\": \"regression\", \"view\": \"Summary\"}, \"model_type\": \"regression\", \"attributes\": {\"section_titles\": [\"Schema\", \"Hyperparameters\", \"Training Summary\", \"Settings\", \"Highest Positive Coefficients\", \"Lowest Negative Coefficients\"], \"sections\": [[[\"Number of coefficients\", 2], [\"Number of examples\", 703], [\"Number of classes\", 2], [\"Number of feature columns\", 1], [\"Number of unpacked features\", 1]], [[\"L1 penalty\", 0.0], [\"L2 penalty\", 0.01]], [[\"Solver\", \"newton\"], [\"Solver iterations\", 3], [\"Solver status\", \"SUCCESS: Optimal solution found.\"], [\"Training time (sec)\", 1.0089]], [[\"Log-likelihood\", 349.375]], [[\"Sex[female]\", 2.6898]], [[\"(intercept)\", -1.5183]]]}, \"evaluations\": []}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_only_model.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "검증 데이터를 이용해서 검증합시다.\n",
    "\n",
    "    model.evaluate(검증데이터)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7340425531914894,\n",
       " 'auc': 0.7204719764011799,\n",
       " 'confusion_matrix': Columns:\n",
       " \ttarget_label\tint\n",
       " \tpredicted_label\tint\n",
       " \tcount\tint\n",
       " \n",
       " Rows: 4\n",
       " \n",
       " Data:\n",
       " +--------------+-----------------+-------+\n",
       " | target_label | predicted_label | count |\n",
       " +--------------+-----------------+-------+\n",
       " |      0       |        1        |   24  |\n",
       " |      1       |        0        |   26  |\n",
       " |      1       |        1        |   49  |\n",
       " |      0       |        0        |   89  |\n",
       " +--------------+-----------------+-------+\n",
       " [4 rows x 3 columns],\n",
       " 'f1_score': 0.6621621621621622,\n",
       " 'log_loss': 0.5855304729422249,\n",
       " 'precision': 0.6712328767123288,\n",
       " 'recall': 0.6533333333333333,\n",
       " 'roc_curve': Columns:\n",
       " \tthreshold\tfloat\n",
       " \tfpr\tfloat\n",
       " \ttpr\tfloat\n",
       " \tp\tint\n",
       " \tn\tint\n",
       " \n",
       " Rows: 100001\n",
       " \n",
       " Data:\n",
       " +-----------+-----+-----+----+-----+\n",
       " | threshold | fpr | tpr | p  |  n  |\n",
       " +-----------+-----+-----+----+-----+\n",
       " |    0.0    | 1.0 | 1.0 | 75 | 113 |\n",
       " |   1e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   2e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   3e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   4e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   5e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   6e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   7e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   8e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " |   9e-05   | 1.0 | 1.0 | 75 | 113 |\n",
       " +-----------+-----+-----+----+-----+\n",
       " [100001 rows x 5 columns]\n",
       " Note: Only the head of the SFrame is printed.\n",
       " You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_only_model.evaluate(validation_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "accuracy가 얼마 나왔나요?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위의 코드를 수정해서 accuracy만 출력해 보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7340425531914894"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_only_model.evaluate(validation_data)['accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (고급) 직접 accuracy 구해보기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 학습된 모델을 이용해서 검증 데이터에 대한 예측값을 구합시다. 생존 확률이 0.5 이상인 경우 생존으로 판별하기로 합니다.\n",
    "\n",
    "참고: output_type을 지정하지 않으면 0과 1로 판별되어 나옵니다. 아래 코드를 이용하면 생존확률의 기준 (0.5)을 높이거나 낮출 수 있습니다. (예. 0.7)\n",
    "\n",
    "    model.predict(검증데이터, output_type='probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: float\n",
       "Rows: 188\n",
       "[0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.7634083386399846, 0.7634083386399846, 0.7634083386399846, 0.7634083386399846, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, 0.17970543072834824, ... ]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_only_model.predict(validation_data, output_type='probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: int\n",
       "Rows: 188\n",
       "[0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, ... ]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_only_model.predict(validation_data, output_type='probability') >= 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 결과값을 **predictions**에 저장합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = sex_only_model.predict(validation_data) >= 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 예측값을 검증데이터의 'Survived' 컬럼과 비교합시다. 즉 모델이 예측한 값이 검증데이터의 관측값과 얼마나 일치하는지 개수를 세는 겁니다. \n",
    "\n",
    "참고로 동일한 값을 확인하는 연산자 (operator)는 **==**입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: int\n",
       "Rows: 188\n",
       "[1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, ... ]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions == validation_data['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 결과값을 **corrects**에 저장합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corrects = predictions == validation_data['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "정확도는 아래와 같은 식으로 계산할 수 있습니다. \n",
    "\n",
    "    accuracy = 맞은 개수 / 전체 개수\n",
    "    \n",
    "여기서 맞은 개수는 어떻게 구할 수 있을까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(corrects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "138개가 나와야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 정확도(accuracy)를 구해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.734042553191\n"
     ]
    }
   ],
   "source": [
    "print(sum(corrects)/float(len(validation_data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "0.734042553191가 나와야 합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다음 방법으로 정확도를 간단히 구할 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7340425531914894"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_only_model.evaluate(validation_data)['accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline과 비교하기\n",
    "\n",
    "이제 baseline을 구해서 우리가 만든 모델의 정확도가 의미 있는 것인지 확인해 봅시다.\n",
    "\n",
    "random_guessing인 경우 얼마일까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "majority class를 구해 봅시다.\n",
    "\n",
    "    SFrame.show(view=뷰유형)\n",
    "    \n",
    "뷰 유형으로는 'Categorical'을 사용하세요.\n",
    "\n",
    "어떤 데이터의 어떤 컬럼을 이용해서 majority class를 구해야 할까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
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       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 0.48631931786709987, \"complete\": true, \"min\": 0.0, \"max\": 1.0, \"quantile\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"median\": 0.0, \"numeric\": true, \"num_unique\": 2, \"num_undefined\": 0, \"var\": 0.23650647893072133, \"progress\": 1.0, \"size\": 891, \"frequent_items\": {\"0\": {\"frequency\": 549, \"value\": 0}, \"1\": {\"frequency\": 342, \"value\": 1}}, \"mean\": 0.3838383838383839}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"int\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 891}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 1.0, \"bins\": [549, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 342], \"min\": 0.0}, \"min\": 0, \"complete\": 1, \"max\": 1}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"complete\": true, \"numeric\": false, \"num_unique\": 2, \"num_undefined\": 0, \"progress\": 1.0, \"frequent_items\": {\"male\": {\"frequency\": 577, \"value\": \"male\"}, \"female\": {\"frequency\": 314, \"value\": \"female\"}}, \"size\": 891}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"str\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 891}, \"type\": \"SArray\", \"view_components\": [\"Categorical\"]}, \"histogram\": null}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data['Survived'].show(view='Categorical')\n",
    "full_train_data['Sex'].show(view='Categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "우리가 만든 모델의 예측값이 baseline인 남자의 생존율 60.106%보다 월등히 높다는 것을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 더 많은 데이터를 이용한 모델 만들기\n",
    "\n",
    "이제 추가적인 데이터를 이용해서 모델을 만들어 봅시다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 전처리를 위한 변수 만들기\n",
    "\n",
    "데이터 처리를 하기 전에 데이터를 새로운 변수에 저장합시다. 이제 수정한 결과값은 아래 두 변수에 저장할 겁니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_full_train_data = full_train_data\n",
    "new_test_data = test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pclass 살펴 보기\n",
    "\n",
    "먼저 Pclass에 대해서 살펴 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: int\n",
       "Rows: 891\n",
       "[3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 1, 3, 3, 3, 1, 3, 3, 1, 1, 3, 2, 1, 1, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 2, 1, 1, 2, 3, 2, 3, 3, 1, 1, 3, 1, 3, 2, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 1, 2, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 1, 2, 2, ... ]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_full_train_data['Pclass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: int\n",
       "Rows: 418\n",
       "[3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 1, 1, 2, 1, 2, 2, 3, 3, 3, 1, 3, 1, 1, 1, 3, 1, 3, 1, 3, 2, 2, 3, 3, 1, 3, 3, 3, 3, 3, 3, 1, 3, 2, 1, 3, 1, 3, 1, 3, 1, 2, 2, 1, 2, 3, 3, 3, 3, 1, 3, 2, 3, 3, 1, 2, 3, 1, 1, 1, 3, 3, 3, 1, 1, 1, 3, 1, 2, 3, 3, 1, 1, 3, 2, 3, 3, 3, 3, 2, 3, 3, 1, 3, 1, 3, 1, 3, 3, 3, ... ]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test_data['Pclass']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show 메소드를 이용해서 데이터의 분포를 확인합시다.\n",
    "\n",
    "    SArray.show(view=뷰유형)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 0.8356019334795168, \"complete\": true, \"min\": 1.0, \"max\": 3.0, \"quantile\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0], \"median\": 3.0, \"numeric\": true, \"num_unique\": 3, \"num_undefined\": 0, \"var\": 0.6982305912347069, \"progress\": 1.0, \"size\": 891, \"frequent_items\": {\"1\": {\"frequency\": 216, \"value\": 1}, \"2\": {\"frequency\": 184, \"value\": 2}, \"3\": {\"frequency\": 491, \"value\": 3}}, \"mean\": 2.3086419753086425}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"int\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 891}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 3.0, \"bins\": [216, 0, 0, 0, 0, 0, 184, 0, 0, 0, 0, 491], \"min\": 1.0}, \"min\": 1, \"complete\": 1, \"max\": 3}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 0.8408299662625103, \"complete\": true, \"min\": 1.0, \"max\": 3.0, \"quantile\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0], \"median\": 3.0, \"numeric\": true, \"num_unique\": 3, \"num_undefined\": 0, \"var\": 0.7069950321650142, \"progress\": 1.0, \"size\": 418, \"frequent_items\": {\"1\": {\"frequency\": 107, \"value\": 1}, \"2\": {\"frequency\": 93, \"value\": 2}, \"3\": {\"frequency\": 218, \"value\": 3}}, \"mean\": 2.2655502392344498}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"int\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 418}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 3.0150000000000006, \"bins\": [107, 0, 0, 0, 0, 93, 0, 0, 0, 0, 0, 218], \"min\": 0.9874799999999833}, \"min\": 1, \"complete\": 1, \"max\": 3}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_full_train_data['Pclass'].show(view='Categorical')\n",
    "new_test_data['Pclass'].show(view='Categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "값이 없는 데이터가 있는지 확인합시다.\n",
    "\n",
    "힌트: None을 이용하면 됩니다.\n",
    "\n",
    "항상 full_train_data와 test_data 모두를 확인해야 합니다. 왜일까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PassengerId</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Survived</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Pclass</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sex</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Age</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SibSp</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Parch</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Ticket</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Fare</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Cabin</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Embarked</th>\n",
       "    </tr>\n",
       "</table>\n",
       "[? rows x 12 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tPassengerId\tint\n",
       "\tSurvived\tint\n",
       "\tPclass\tint\n",
       "\tName\tstr\n",
       "\tSex\tstr\n",
       "\tAge\tfloat\n",
       "\tSibSp\tint\n",
       "\tParch\tint\n",
       "\tTicket\tstr\n",
       "\tFare\tfloat\n",
       "\tCabin\tstr\n",
       "\tEmbarked\tstr\n",
       "\n",
       "Rows: Unknown\n",
       "\n",
       "Data:\n",
       "\t[]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Slib'] == None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PassengerId</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Pclass</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sex</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Age</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SibSp</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Parch</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Ticket</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Fare</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Cabin</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Embarked</th>\n",
       "    </tr>\n",
       "</table>\n",
       "[? rows x 11 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tPassengerId\tint\n",
       "\tPclass\tint\n",
       "\tName\tstr\n",
       "\tSex\tstr\n",
       "\tAge\tfloat\n",
       "\tSibSp\tint\n",
       "\tParch\tint\n",
       "\tTicket\tstr\n",
       "\tFare\tfloat\n",
       "\tCabin\tstr\n",
       "\tEmbarked\tstr\n",
       "\n",
       "Rows: Unknown\n",
       "\n",
       "Data:\n",
       "\t[]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[test_data['Pclass'] == None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다행히 없군요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "생존율과의 상관 관계를 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"<SFrame>\"], \"descriptives\": {\"rows\": 549, \"columns\": 12}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"PassengerId\", \"x\": \"Pclass\", \"columns\": [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Fare\", \"Cabin\", \"Embarked\"], \"view\": \"Bar Chart\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}], \"column_identifiers\": [\"Fare\", \"Name\", \"Embarked\", \"Age\", \"Parch\", \"Pclass\", \"Sex\", \"Survived\", \"SibSp\", \"PassengerId\", \"Ticket\", \"Cabin\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": {\"grouped\": [[1, {\"count\": 80, \"std\": 251.071523674032, \"min\": 7, \"max\": 873, \"sum\": 32824, \"var\": 63036.91000000001, \"mean\": 410.3}], [2, {\"count\": 97, \"std\": 256.43890603914974, \"min\": 21, \"max\": 887, \"sum\": 43856, \"var\": 65760.91253055587, \"mean\": 452.1237113402063}], [3, {\"count\": 372, \"std\": 262.7366551282308, \"min\": 1, \"max\": 891, \"sum\": 168732, \"var\": 69030.54994797087, \"mean\": 453.58064516129025}]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 0].show(view=\"Bar Chart\", x=\"Pclass\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"<SFrame>\"], \"descriptives\": {\"rows\": 342, \"columns\": 12}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"PassengerId\", \"x\": \"Pclass\", \"columns\": [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Fare\", \"Cabin\", \"Embarked\"], \"view\": \"Bar Chart\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}], \"column_identifiers\": [\"Fare\", \"Name\", \"Embarked\", \"Age\", \"Parch\", \"Pclass\", \"Sex\", \"Survived\", \"SibSp\", \"PassengerId\", \"Ticket\", \"Cabin\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": {\"grouped\": [[1, {\"count\": 136, \"std\": 238.12666448119103, \"min\": 2, \"max\": 890, \"sum\": 66881, \"var\": 56704.30833693773, \"mean\": 491.7720588235295}], [2, {\"count\": 87, \"std\": 242.80436372410355, \"min\": 10, \"max\": 881, \"sum\": 38200, \"var\": 58953.95904346678, \"mean\": 439.08045977011494}], [3, {\"count\": 119, \"std\": 263.5657969329511, \"min\": 3, \"max\": 876, \"sum\": 46893, \"var\": 69466.92931290163, \"mean\": 394.05882352941177}]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 1].show(view=\"Bar Chart\", x=\"Pclass\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare 살펴보기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show 메소드로 살펴 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Numeric', {\"ipython\": true, \"sketch\": {\"std\": 49.66553444477412, \"complete\": true, \"min\": 0.0, \"max\": 512.3292, \"quantile\": [0.0, 0.0, 6.2375, 6.975, 7.05, 7.225, 7.225, 7.2292, 7.25, 7.25, 7.55, 7.7292, 7.75, 7.75, 7.75, 7.75, 7.775, 7.775, 7.7958, 7.8542, 7.8542, 7.8958, 7.8958, 7.8958, 7.8958, 7.8958, 7.925, 7.925, 8.05, 8.05, 8.05, 8.05, 8.1125, 8.6542, 8.6625, 9.0, 9.5, 9.5875, 10.4625, 10.5, 10.5, 11.1333, 12.275, 12.525, 13.0, 13.0, 13.0, 13.0, 13.0, 14.0, 14.4542, 14.5, 15.2458, 15.5, 15.85, 16.1, 17.8, 19.2583, 20.2125, 21.0, 21.6792, 23.25, 24.15, 25.5875, 26.0, 26.0, 26.0, 26.25, 26.2875, 26.55, 27.0, 27.75, 29.0, 29.7, 30.5, 31.0, 31.3875, 34.0208, 35.5, 39.0, 39.6875, 46.9, 50.0, 52.0, 53.1, 56.4958, 57.9792, 66.6, 69.55, 73.5, 77.9583, 79.2, 82.1708, 89.1042, 93.5, 113.275, 134.5, 151.55, 211.3375, 262.375, 512.3292], \"median\": 14.4542, \"numeric\": true, \"num_unique\": 248, \"num_undefined\": 0, 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{\"frequency\": 1, \"value\": 8.3}, \"23.25\": {\"frequency\": 2, \"value\": 23.25}, \"6.45\": {\"frequency\": 1, \"value\": 6.45}, \"21.075\": {\"frequency\": 4, \"value\": 21.075}, \"6.975\": {\"frequency\": 2, \"value\": 6.975}, \"16.7\": {\"frequency\": 2, \"value\": 16.7}, \"30.0708\": {\"frequency\": 2, \"value\": 30.0708}, \"7.3125\": {\"frequency\": 1, \"value\": 7.3125}, \"38.5\": {\"frequency\": 1, \"value\": 38.5}, \"12.2875\": {\"frequency\": 1, \"value\": 12.2875}, \"22.525\": {\"frequency\": 1, \"value\": 22.525}, \"61.3792\": {\"frequency\": 1, \"value\": 61.3792}, \"211.3375\": {\"frequency\": 3, \"value\": 211.3375}, \"28.7125\": {\"frequency\": 1, \"value\": 28.7125}, \"12.65\": {\"frequency\": 1, \"value\": 12.65}, \"8.1375\": {\"frequency\": 1, \"value\": 8.1375}, \"27.75\": {\"frequency\": 4, \"value\": 27.75}, \"26.55\": {\"frequency\": 15, \"value\": 26.55}, \"34.375\": {\"frequency\": 4, \"value\": 34.375}, \"6.4958\": {\"frequency\": 2, \"value\": 6.4958}, \"32.3208\": {\"frequency\": 1, \"value\": 32.3208}, \"28.5\": {\"frequency\": 1, \"value\": 28.5}, \"89.1042\": {\"frequency\": 2, \"value\": 89.1042}, \"8.4333\": {\"frequency\": 1, \"value\": 8.4333}, \"110.8833\": {\"frequency\": 4, \"value\": 110.8833}, \"106.425\": {\"frequency\": 2, \"value\": 106.425}, \"153.4625\": {\"frequency\": 3, \"value\": 153.4625}, \"135.6333\": {\"frequency\": 3, \"value\": 135.6333}, \"51.8625\": {\"frequency\": 2, \"value\": 51.8625}, \"32.5\": {\"frequency\": 1, \"value\": 32.5}, \"15.2458\": {\"frequency\": 5, \"value\": 15.2458}}, \"mean\": 32.204207968574664}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"float\", \"view_component\": \"Numeric\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 891}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 514.6498692, \"bins\": [716, 109, 28, 18, 4, 7, 6, 0, 0, 0, 0, 3], \"min\": -1.7146619999999757}, \"min\": 0.0, \"complete\": 1, \"max\": 512.3292}}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data['Fare'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### None 찾아서 값 채우기\n",
    "\n",
    "값이 없는 데이터가 있는지 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PassengerId</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Survived</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Pclass</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sex</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Age</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SibSp</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Parch</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Ticket</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Fare</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Cabin</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Embarked</th>\n",
       "    </tr>\n",
       "</table>\n",
       "[? rows x 12 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tPassengerId\tint\n",
       "\tSurvived\tint\n",
       "\tPclass\tint\n",
       "\tName\tstr\n",
       "\tSex\tstr\n",
       "\tAge\tfloat\n",
       "\tSibSp\tint\n",
       "\tParch\tint\n",
       "\tTicket\tstr\n",
       "\tFare\tfloat\n",
       "\tCabin\tstr\n",
       "\tEmbarked\tstr\n",
       "\n",
       "Rows: Unknown\n",
       "\n",
       "Data:\n",
       "\t[]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Fare'] == None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PassengerId</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Pclass</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sex</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Age</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SibSp</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Parch</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Ticket</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Fare</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Cabin</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1044</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Storey, Mr. Thomas</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">60.5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3701</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[? rows x 11 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tPassengerId\tint\n",
       "\tPclass\tint\n",
       "\tName\tstr\n",
       "\tSex\tstr\n",
       "\tAge\tfloat\n",
       "\tSibSp\tint\n",
       "\tParch\tint\n",
       "\tTicket\tstr\n",
       "\tFare\tfloat\n",
       "\tCabin\tstr\n",
       "\tEmbarked\tstr\n",
       "\n",
       "Rows: Unknown\n",
       "\n",
       "Data:\n",
       "+-------------+--------+--------------------+------+------+-------+-------+--------+\n",
       "| PassengerId | Pclass |        Name        | Sex  | Age  | SibSp | Parch | Ticket |\n",
       "+-------------+--------+--------------------+------+------+-------+-------+--------+\n",
       "|     1044    |   3    | Storey, Mr. Thomas | male | 60.5 |   0   |   0   |  3701  |\n",
       "+-------------+--------+--------------------+------+------+-------+-------+--------+\n",
       "+------+-------+----------+\n",
       "| Fare | Cabin | Embarked |\n",
       "+------+-------+----------+\n",
       "| None |       |    S     |\n",
       "+------+-------+----------+\n",
       "[? rows x 11 columns]\n",
       "Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
       "You can use sf.materialize() to force materialization."
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[test_data['Fare'] == None]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "test_data에 한명의 데이터가 없다는 것을 알 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 비어 있는 값 채우기\n",
    "\n",
    "비어 있는 값을 어떻게 채우면 좋을까요? 여기에서는 test_data의 평균값을 사용합시다.\n",
    "\n",
    "먼저 'Fare'의 평균값을 구하세요.\n",
    "\n",
    "    SArray.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.62718848920863"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data['Fare'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "35.627188489208635가 나와야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method fillna in module graphlab.data_structures.sframe:\n",
      "\n",
      "fillna(self, column, value) unbound graphlab.data_structures.sframe.SFrame method\n",
      "    Fill all missing values with a given value in a given column. If the\n",
      "    ``value`` is not the same type as the values in ``column``, this method\n",
      "    attempts to convert the value to the original column's type. If this\n",
      "    fails, an error is raised.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    column : str\n",
      "        The name of the column to modify.\n",
      "    \n",
      "    value : type convertible to SArray's type\n",
      "        The value used to replace all missing values.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : SFrame\n",
      "        A new SFrame with the specified value in place of missing values.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    dropna\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> sf = graphlab.SFrame({'a':[1, None, None],\n",
      "    ...                       'b':['13.1', '17.2', None]})\n",
      "    >>> sf = sf.fillna('a', 0)\n",
      "    >>> sf\n",
      "    +---+------+\n",
      "    | a |  b   |\n",
      "    +---+------+\n",
      "    | 1 | 13.1 |\n",
      "    | 0 | 17.2 |\n",
      "    | 0 | None |\n",
      "    +---+------+\n",
      "    [3 rows x 2 columns]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(graphlab.SFrame.fillna)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fillna 메소드를 이용해서 None으로 비어 있는 값을 지정할 수 있습니다.\n",
    "\n",
    "    SFrame.fillna(column=컬럼이름, value=지정하고자하는값)\n",
    "    \n",
    "**주의: 반드시 다시 SFrame에 저장해야 합니다.** 여기서는 **new_test_data**라는 새로운 변수명을 사용합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_data = full_train_data.fillna(column='Fare', value='35.62718848920863')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PassengerId가 1044인 승객을 다시 출력해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">PassengerId</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Survived</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Pclass</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sex</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Age</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">SibSp</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Parch</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Ticket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Braund, Mr. Owen Harris</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cumings, Mrs. John<br>Bradley (Florence Briggs ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">38.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Heikkinen, Miss. Laina</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Futrelle, Mrs. Jacques<br>Heath (Lily May Peel) ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Allen, Mr. William Henry</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">35.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Moran, Mr. James</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">330877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">McCarthy, Mr. Timothy J</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">54.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">17463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Palsson, Master. Gosta<br>Leonard ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">male</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">349909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Johnson, Mrs. Oscar W<br>(Elisabeth Vilhelmina ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">27.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">347742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">10</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Nasser, Mrs. Nicholas<br>(Adele Achem) ...</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">female</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">237736</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Fare</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Cabin</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.25</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">71.2833</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">C85</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7.925</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">53.1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">C123</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.05</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8.4583</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">51.8625</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">E46</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">21.075</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11.1333</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">30.0708</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\"></td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">C</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[891 rows x 12 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tPassengerId\tint\n",
       "\tSurvived\tint\n",
       "\tPclass\tint\n",
       "\tName\tstr\n",
       "\tSex\tstr\n",
       "\tAge\tfloat\n",
       "\tSibSp\tint\n",
       "\tParch\tint\n",
       "\tTicket\tstr\n",
       "\tFare\tfloat\n",
       "\tCabin\tstr\n",
       "\tEmbarked\tstr\n",
       "\n",
       "Rows: 891\n",
       "\n",
       "Data:\n",
       "+-------------+----------+--------+--------------------------------+--------+\n",
       "| PassengerId | Survived | Pclass |              Name              |  Sex   |\n",
       "+-------------+----------+--------+--------------------------------+--------+\n",
       "|      1      |    0     |   3    |    Braund, Mr. Owen Harris     |  male  |\n",
       "|      2      |    1     |   1    | Cumings, Mrs. John Bradley...  | female |\n",
       "|      3      |    1     |   3    |     Heikkinen, Miss. Laina     | female |\n",
       "|      4      |    1     |   1    | Futrelle, Mrs. Jacques Hea...  | female |\n",
       "|      5      |    0     |   3    |    Allen, Mr. William Henry    |  male  |\n",
       "|      6      |    0     |   3    |        Moran, Mr. James        |  male  |\n",
       "|      7      |    0     |   1    |    McCarthy, Mr. Timothy J     |  male  |\n",
       "|      8      |    0     |   3    | Palsson, Master. Gosta Leonard |  male  |\n",
       "|      9      |    1     |   3    | Johnson, Mrs. Oscar W (Eli...  | female |\n",
       "|      10     |    1     |   2    | Nasser, Mrs. Nicholas (Ade...  | female |\n",
       "+-------------+----------+--------+--------------------------------+--------+\n",
       "+------+-------+-------+------------------+---------+-------+----------+\n",
       "| Age  | SibSp | Parch |      Ticket      |   Fare  | Cabin | Embarked |\n",
       "+------+-------+-------+------------------+---------+-------+----------+\n",
       "| 22.0 |   1   |   0   |    A/5 21171     |   7.25  |       |    S     |\n",
       "| 38.0 |   1   |   0   |     PC 17599     | 71.2833 |  C85  |    C     |\n",
       "| 26.0 |   0   |   0   | STON/O2. 3101282 |  7.925  |       |    S     |\n",
       "| 35.0 |   1   |   0   |      113803      |   53.1  |  C123 |    S     |\n",
       "| 35.0 |   0   |   0   |      373450      |   8.05  |       |    S     |\n",
       "| None |   0   |   0   |      330877      |  8.4583 |       |    Q     |\n",
       "| 54.0 |   0   |   0   |      17463       | 51.8625 |  E46  |    S     |\n",
       "| 2.0  |   3   |   1   |      349909      |  21.075 |       |    S     |\n",
       "| 27.0 |   0   |   2   |      347742      | 11.1333 |       |    S     |\n",
       "| 14.0 |   1   |   0   |      237736      | 30.0708 |       |    C     |\n",
       "+------+-------+-------+------------------+---------+-------+----------+\n",
       "[891 rows x 12 columns]\n",
       "Note: Only the head of the SFrame is printed.\n",
       "You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(full_train_data[full_train_data['Embarked']=='C'])\n",
    "full_train_data\n",
    "#full_train_data[full_train_data['Fare'] < 80].print_rows(num_rows=10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare에 35.627...로 값이 잘 지정된 것을 확인할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "생존율과의 상관관계를 확인합시다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "                });\n",
       "            })();\n",
       "        "
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 0].show(view=\"Bar Chart\", x=\"Fare\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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431, \"var\": 16002.25, \"mean\": 215.5}], [512.3292, {\"count\": 3, \"std\": 213.4494475670215, \"min\": 259, \"max\": 738, \"sum\": 1677, \"var\": 45560.666666666664, \"mean\": 559.0}]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 1].show(view=\"Bar Chart\", x=\"Fare\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "상관 관계가 있어 보이나요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embarked 살펴보기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show 메소드로 살펴봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
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       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"complete\": true, \"numeric\": false, \"num_unique\": 4, \"num_undefined\": 0, \"progress\": 1.0, \"frequent_items\": {\"Q\": {\"frequency\": 77, \"value\": \"Q\"}, \"\": {\"frequency\": 2, \"value\": \"\"}, \"S\": {\"frequency\": 644, \"value\": \"S\"}, \"C\": {\"frequency\": 168, \"value\": \"C\"}}, \"size\": 891}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"str\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 891}, \"type\": \"SArray\", \"view_components\": [\"Categorical\"]}, \"histogram\": null}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data['Embarked'].show(view='Categorical')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "테스트 데이터도 마찬가지로 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"complete\": true, \"numeric\": false, \"num_unique\": 3, \"num_undefined\": 0, \"progress\": 1.0, \"frequent_items\": {\"Q\": {\"frequency\": 46, \"value\": \"Q\"}, \"S\": {\"frequency\": 270, \"value\": \"S\"}, \"C\": {\"frequency\": 102, \"value\": \"C\"}}, \"size\": 418}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"str\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 418}, \"type\": \"SArray\", \"view_components\": [\"Categorical\"]}, \"histogram\": null}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### None 찾아서 값 채우기\n",
    "\n",
    "값이 없는 데이터가 있는지 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def countNone(filed):\n",
    "    for "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "graphlab.data_structures.sframe.SFrame"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(full_train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "full_train_data에 2명의 데이터가 Embarked 값이 없다는 것을 확인할 수 있습니다.\n",
    "가장 많은 값인 S로 채워봅시다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 공백을 S로 변환하고 다른 값은 그대로 두는 함수를 만듭니다. 이름은 **fillSforEmbarked**로 합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "apply 메소드를 이용해서 Embarked 컬럼에 적용합시다. 이 결과값을 new_full_train_data의 'Embarekd' 컬럼에 저장합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PassengerId 62번 승객의 데이터를 찾아서 Embarked가 잘 지정되었는지 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "생존율과의 관계를 확인합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
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       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"<SFrame>\"], \"descriptives\": {\"rows\": 549, \"columns\": 12}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"PassengerId\", \"x\": \"Embarked\", \"columns\": [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Fare\", \"Cabin\", \"Embarked\"], \"view\": \"Bar Chart\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}], \"column_identifiers\": [\"Fare\", \"Name\", \"Embarked\", \"Age\", \"Parch\", \"Pclass\", \"Sex\", \"Survived\", \"SibSp\", \"PassengerId\", \"Ticket\", \"Cabin\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": {\"grouped\": [[\"C\", {\"count\": 75, \"std\": 262.45516154616246, \"min\": 27, \"max\": 860, \"sum\": 30739, \"var\": 68882.71182222222, \"mean\": 409.85333333333335}], [\"Q\", {\"count\": 47, \"std\": 250.56461005433204, \"min\": 6, \"max\": 891, \"sum\": 22579, \"var\": 62782.62381167948, \"mean\": 480.40425531914894}], [\"S\", {\"count\": 427, \"std\": 260.3865647997057, \"min\": 1, \"max\": 889, \"sum\": 192094, \"var\": 67801.16312819133, \"mean\": 449.86885245901647}]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 0].show(view=\"Bar Chart\", x=\"Embarked\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
       "                    var thisScriptTag = scripts[scripts.length-1];\n",
       "                    var parentDiv = thisScriptTag.parentNode;\n",
       "                    e = document.createElement('div');\n",
       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
       "                    app.attachView('sframe','Plots', {\"selected_variable\": {\"name\": [\"<SFrame>\"], \"descriptives\": {\"rows\": 342, \"columns\": 12}, \"view_component\": \"Plots\", \"view_file\": \"sframe\", \"view_params\": {\"y\": \"PassengerId\", \"x\": \"Embarked\", \"columns\": [\"PassengerId\", \"Survived\", \"Pclass\", \"Name\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Ticket\", \"Fare\", \"Cabin\", \"Embarked\"], \"view\": \"Bar Chart\"}, \"view_components\": [\"Summary\", \"Table\", \"Bar Chart\", \"BoxWhisker Plot\", \"Line Chart\", \"Scatter Plot\", \"Heat Map\", \"Plots\"], \"type\": \"SFrame\", \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}], \"column_identifiers\": [\"Fare\", \"Name\", \"Embarked\", \"Age\", \"Parch\", \"Pclass\", \"Sex\", \"Survived\", \"SibSp\", \"PassengerId\", \"Ticket\", \"Cabin\"]}, \"complete\": 1, \"ipython\": true, \"progress\": 1.0, \"data\": {\"grouped\": [[\"\", {\"count\": 2, \"std\": 384.0, \"min\": 62, \"max\": 830, \"sum\": 892, \"var\": 147456.0, \"mean\": 446.0}], [\"C\", {\"count\": 93, \"std\": 251.97860015910703, \"min\": 2, \"max\": 890, \"sum\": 44081, \"var\": 63493.21493814313, \"mean\": 473.98924731182797}], [\"Q\", {\"count\": 30, \"std\": 221.16652599844812, \"min\": 23, \"max\": 829, \"sum\": 9599, \"var\": 48914.63222222223, \"mean\": 319.96666666666664}], [\"S\", {\"count\": 217, \"std\": 249.41508137569218, \"min\": 3, \"max\": 888, \"sum\": 97402, \"var\": 62207.88281764315, \"mean\": 448.8571428571427}]], \"omitted\": 0}, \"columns\": [{\"dtype\": \"int\", \"name\": \"PassengerId\"}, {\"dtype\": \"int\", \"name\": \"Survived\"}, {\"dtype\": \"int\", \"name\": \"Pclass\"}, {\"dtype\": \"str\", \"name\": \"Name\"}, {\"dtype\": \"str\", \"name\": \"Sex\"}, {\"dtype\": \"float\", \"name\": \"Age\"}, {\"dtype\": \"int\", \"name\": \"SibSp\"}, {\"dtype\": \"int\", \"name\": \"Parch\"}, {\"dtype\": \"str\", \"name\": \"Ticket\"}, {\"dtype\": \"float\", \"name\": \"Fare\"}, {\"dtype\": \"str\", \"name\": \"Cabin\"}, {\"dtype\": \"str\", \"name\": \"Embarked\"}]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_train_data[full_train_data['Survived'] == 1].show(view=\"Bar Chart\", x=\"Embarked\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4개의 피쳐를 이용해서 모델 학습시키기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "위에서 살펴본 4가지 데이터를 이용해서 모델을 학습해 봅시다. 먼저 4가지 데이터 컬럼 이름을 이용해서 **my_features**라는 변수를 지정합시다.\n",
    "\n",
    "    'Sex', 'Pclass', 'Fare', 'Embarked'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_features = ['Sex', 'Pclass', 'Fare', 'Embared']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 수정한 데이터셋을 나누기\n",
    "\n",
    "**new_full_train_data**를 **new_train_data**와 **new_validation_data**로 나눕시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 가장 간단한 모델을 이용해서 학습시킵시다.\n",
    "\n",
    "    graphlab.logistic_classifier.create(학습데이터, target=타겟컬럼이름, features=피쳐컬럼이름의리스트, validation_set=검증데이터)\n",
    "    \n",
    "**my_features_model**에 저장합시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
       "}));\n",
       "$(\"head\").append($(\"<link/>\").attr({\n",
       "  rel:  \"stylesheet\",\n",
       "  type: \"text/css\",\n",
       "  href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
       "}));\n",
       "\n",
       "            (function(){\n",
       "\n",
       "                var e = null;\n",
       "                if (typeof element == 'undefined') {\n",
       "                    var scripts = document.getElementsByTagName('script');\n",
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       "                    parentDiv.appendChild(e);\n",
       "                } else {\n",
       "                    e = element[0];\n",
       "                }\n",
       "\n",
       "                if (typeof requirejs !== 'undefined') {\n",
       "                    // disable load timeout; ipython_app.js is large and can take a while to load.\n",
       "                    requirejs.config({waitSeconds: 0});\n",
       "                }\n",
       "\n",
       "                require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
       "                    var app = new IPythonApp();\n",
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\"p\": 75, \"fpr\": 0.0, \"tpr\": 0.0, \"n\": 113}, {\"threshold\": 1.0, \"p\": 75, \"fpr\": 0.0, \"tpr\": 0.0, \"n\": 113}], \"confusion_matrix\": [{\"count\": 30, \"target_label\": 0, \"predicted_label\": 1}, {\"count\": 23, \"target_label\": 1, \"predicted_label\": 0}, {\"count\": 52, \"target_label\": 1, \"predicted_label\": 1}, {\"count\": 83, \"target_label\": 0, \"predicted_label\": 0}], \"accuracy\": 0.7180851063829787}]]}, e);\n",
       "                });\n",
       "            })();\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**my_features_model**의 검증데이터에 대한 정확도를 출력해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**sex_only_model**의 검증데이터에 대한 정확도를 출력해 봅시다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 망했어요 vs 아니다\n",
    "\n",
    "어느 값이 더 큰가요? 모델에 피쳐를 추가하면서 정확도가 나아졌나요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 다른 모델들도 학습시키기\n",
    "\n",
    "graphlab에서 제공하는 다른 classifier들을 이용해서 학습시켜 봅시다.\n",
    "\n",
    "- Random Forest\n",
    "- Decision Tree\n",
    "- SVM\n",
    "- Nearest Neighbor\n",
    "\n",
    "https://turi.com/products/create/docs/graphlab.toolkits.classifier.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Random forest classifier:</pre>"
      ],
      "text/plain": [
       "Random forest classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 703</pre>"
      ],
      "text/plain": [
       "Number of examples          : 703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 4</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 4</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.005806     | 0.812233          | 0.808511            | 0.443208          | 0.497624            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.005806     | 0.812233          | 0.808511            | 0.443208          | 0.497624            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 0.014374     | 0.836415          | 0.771277            | 0.417967          | 0.504589            |</pre>"
      ],
      "text/plain": [
       "| 2         | 0.014374     | 0.836415          | 0.771277            | 0.417967          | 0.504589            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 0.019353     | 0.860597          | 0.776596            | 0.404951          | 0.513251            |</pre>"
      ],
      "text/plain": [
       "| 3         | 0.019353     | 0.860597          | 0.776596            | 0.404951          | 0.513251            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 0.024214     | 0.847795          | 0.750000            | 0.406192          | 0.524992            |</pre>"
      ],
      "text/plain": [
       "| 4         | 0.024214     | 0.847795          | 0.750000            | 0.406192          | 0.524992            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 0.029828     | 0.847795          | 0.750000            | 0.402975          | 0.525359            |</pre>"
      ],
      "text/plain": [
       "| 5         | 0.029828     | 0.847795          | 0.750000            | 0.402975          | 0.525359            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 0.033709     | 0.847795          | 0.750000            | 0.401080          | 0.523546            |</pre>"
      ],
      "text/plain": [
       "| 6         | 0.033709     | 0.847795          | 0.750000            | 0.401080          | 0.523546            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_features_model_rf = graphlab.random_forest_classifier.create(new_train_data,\n",
    "                                                        target='Survived',\n",
    "                                                        features=my_features,\n",
    "                                                        validation_set=new_validation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7446808510638298"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_features_model_rf.evaluate(validation_data)['accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Decision tree classifier:</pre>"
      ],
      "text/plain": [
       "Decision tree classifier:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 703</pre>"
      ],
      "text/plain": [
       "Number of examples          : 703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 4</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 4</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |</pre>"
      ],
      "text/plain": [
       "| Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 0.007587     | 0.853485          | 0.750000            | 0.557577          | 0.598062            |</pre>"
      ],
      "text/plain": [
       "| 1         | 0.007587     | 0.853485          | 0.750000            | 0.557577          | 0.598062            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+--------------+-------------------+---------------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+--------------+-------------------+---------------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_features_model_dt = graphlab.decision_tree_classifier.create(new_train_data,\n",
    "                                                        target='Survived',\n",
    "                                                        features=my_features,\n",
    "                                                        validation_set=new_validation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_features_model_dt.evaluate(validation_data)['accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>SVM:</pre>"
      ],
      "text/plain": [
       "SVM:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 703</pre>"
      ],
      "text/plain": [
       "Number of examples          : 703"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 4</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 4</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 6</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 4        | 0.007112  | 0.001495     | 0.778094          | 0.792553            |</pre>"
      ],
      "text/plain": [
       "| 1         | 4        | 0.007112  | 0.001495     | 0.778094          | 0.792553            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 6        | 1.000000  | 0.003231     | 0.792319          | 0.734043            |</pre>"
      ],
      "text/plain": [
       "| 2         | 6        | 1.000000  | 0.003231     | 0.792319          | 0.734043            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 7        | 1.000000  | 0.004652     | 0.792319          | 0.734043            |</pre>"
      ],
      "text/plain": [
       "| 3         | 7        | 1.000000  | 0.004652     | 0.792319          | 0.734043            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 8        | 1.000000  | 0.005762     | 0.790896          | 0.728723            |</pre>"
      ],
      "text/plain": [
       "| 4         | 8        | 1.000000  | 0.005762     | 0.790896          | 0.728723            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 5         | 9        | 1.000000  | 0.007908     | 0.233286          | 0.303191            |</pre>"
      ],
      "text/plain": [
       "| 5         | 9        | 1.000000  | 0.007908     | 0.233286          | 0.303191            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 6         | 11       | 1.000000  | 0.010607     | 0.752489          | 0.680851            |</pre>"
      ],
      "text/plain": [
       "| 6         | 11       | 1.000000  | 0.010607     | 0.752489          | 0.680851            |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+---------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>TERMINATED: Iteration limit reached.</pre>"
      ],
      "text/plain": [
       "TERMINATED: Iteration limit reached."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
      ],
      "text/plain": [
       "This model may not be optimal. To improve it, consider increasing `max_iterations`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_features_model_svm = graphlab.svm_classifier.create(new_train_data,\n",
    "                                                        target='Survived',\n",
    "                                                        features=my_features,\n",
    "                                                        validation_set=new_validation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_features_model_dt.evaluate(validation_data)['accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nearest Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to brute force instead of ball tree because there are multiple distance components.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting brute force nearest neighbors model training.</pre>"
      ],
      "text/plain": [
       "Starting brute force nearest neighbors model training."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_features_model_nn = graphlab.nearest_neighbor_classifier.create(new_train_data,\n",
    "                                                                   target='Survived',\n",
    "                                                                   features=my_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 188     | 0.142248    | 1.185ms      |</pre>"
      ],
      "text/plain": [
       "| 0            | 188     | 0.142248    | 1.185ms      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 43.162ms     |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 43.162ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting pairwise querying.</pre>"
      ],
      "text/plain": [
       "Starting pairwise querying."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Query points | # Pairs | % Complete. | Elapsed Time |</pre>"
      ],
      "text/plain": [
       "| Query points | # Pairs | % Complete. | Elapsed Time |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0            | 188     | 0.142248    | 1.061ms      |</pre>"
      ],
      "text/plain": [
       "| 0            | 188     | 0.142248    | 1.061ms      |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Done         |         | 100         | 73.432ms     |</pre>"
      ],
      "text/plain": [
       "| Done         |         | 100         | 73.432ms     |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+--------------+---------+-------------+--------------+</pre>"
      ],
      "text/plain": [
       "+--------------+---------+-------------+--------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.6968085106382979"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_features_model_nn.evaluate(validation_data)['accuracy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age 데이터 처리하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_for_submission = my_features_model_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model_for_submission.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = graphlab.SFrame({\"PassengerId\": test_data['PassengerId'], \n",
    "                              \"Survived\": predictions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission.export_csv('../data/kaggle_titanic/submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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