AI_Class/006/svm_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7hZ-HgwAgF5g"
   },
   "source": [
    "# SVM\n",
    "\n",
    "Support Vector Machine의 약자\n",
    "\n",
    "이름대로 Vector 개념을 사용\n",
    "\n",
    "SVM은 서포트 벡터(support vectors)를 사용해서 결정 경계(Decision Boundary)를 정의하고, 분류되지 않은 점을 해당 결정 경계와 비교해서 분류하는 모델\n",
    "\n",
    "![006-6](https://user-images.githubusercontent.com/63298243/125198484-06fe3d00-e29d-11eb-86b6-760108a860f1.PNG)\n",
    "\n",
    "여기서 서포트 벡터(support vectors)는 결정 경계에 가장 가까운 각 클래스의 점들을 말함\n",
    "\n",
    "SVM의 주 목적은 두 Margin의 거리를 최대화 시킬 수 있는 즉, GAP을 최대화 시킬 수 있는 지점을 찾는 것\n",
    "\n",
    "그래서 분류되지 않은 새로운 점이 나타나면 경계의 어느 쪽에 속하는지 확인해서 분류 과제를 수행할 수 있음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gbSaJvEIgF5j"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data\n",
    "\n",
    "- survived : 생존 여부 (0, 1)  \n",
    "- pclass : 선실 등급 (1, 2, 3)  \n",
    "- sex : 성별  \n",
    "- age : 나이  \n",
    "- sibsp : 함께 탑승한 형제 또는 배우자 수  \n",
    "- parch : 함께 탑승한 부모 또는 자녀 수  \n",
    "- fare : 요금  \n",
    "- embarked : 탑승장소 S = Southhampton, C = Cherbourg, Q = Queenstown  \n",
    "- class : 선실 등급  \n",
    "- who : 성별  \n",
    "- adult_male : 성인 남성 여부  \n",
    "- alone : 1인 탑승 여부"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "2LOBjeixgF5j"
   },
   "outputs": [],
   "source": [
    "df = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eDw9oh_igF5k"
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "id": "ambuUT9nlMAF",
    "outputId": "61c4dfac-6028-446d-f726-77970cd93b52"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  ...  deck  embark_town  alive  alone\n",
       "0         0       3    male  22.0  ...   NaN  Southampton     no  False\n",
       "1         1       1  female  38.0  ...     C    Cherbourg    yes  False\n",
       "2         1       3  female  26.0  ...   NaN  Southampton    yes   True\n",
       "3         1       1  female  35.0  ...     C  Southampton    yes  False\n",
       "4         0       3    male  35.0  ...   NaN  Southampton     no   True\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BkzGj_OjlPrh",
    "outputId": "4c444e4a-f368-44b1-d3f9-c474b42539be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "4evAP3_JlPuL",
    "outputId": "a6948d77-6aae-4cfa-a8fe-df9b36410e15"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "RtD81fodlPw0"
   },
   "outputs": [],
   "source": [
    "df = df.dropna(subset=[\"age\"], how = 'any', axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "U8diMdfmlPzT"
   },
   "outputs": [],
   "source": [
    "most_freq = df['embarked'].value_counts(dropna=True).idxmax()\n",
    "df['embarked'].fillna(most_freq, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wERAAElDlP1w",
    "outputId": "9ea2ca5d-9919-4d96-ea7a-6725ef727327"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age              0\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         0\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           530\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "fOvQaVBdlP4v"
   },
   "outputs": [],
   "source": [
    "df = df.drop(['deck', 'class',\t'who',\t'adult_male',\t'embark_town',\t'alive',\t'alone'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "id": "BIW_ummPlP7U",
    "outputId": "1ce9d272-68d5-42ea-b6d3-84581df0e360"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
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       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>714 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked\n",
       "0           0       3    male  22.0      1      0   7.2500        S\n",
       "1           1       1  female  38.0      1      0  71.2833        C\n",
       "2           1       3  female  26.0      0      0   7.9250        S\n",
       "3           1       1  female  35.0      1      0  53.1000        S\n",
       "4           0       3    male  35.0      0      0   8.0500        S\n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...\n",
       "885         0       3  female  39.0      0      5  29.1250        Q\n",
       "886         0       2    male  27.0      0      0  13.0000        S\n",
       "887         1       1  female  19.0      0      0  30.0000        S\n",
       "889         1       1    male  26.0      0      0  30.0000        C\n",
       "890         0       3    male  32.0      0      0   7.7500        Q\n",
       "\n",
       "[714 rows x 8 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "0SVQ_QNFlP-Z"
   },
   "outputs": [],
   "source": [
    "onehot_sex = pd.get_dummies(df['sex'])\n",
    "df = pd.concat([df, onehot_sex], axis = 1)\n",
    "\n",
    "onehot_em = pd.get_dummies(df['embarked'])\n",
    "df = pd.concat([df, onehot_em], axis = 1)\n",
    "\n",
    "df.drop(['sex','embarked'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "id": "Sj02kumYm2QA",
    "outputId": "d18a97e4-fa20-4d34-db5f-a09d2cc8cce4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age  sibsp  parch     fare  female  male  C  Q  S\n",
       "0         0       3  22.0      1      0   7.2500       0     1  0  0  1\n",
       "1         1       1  38.0      1      0  71.2833       1     0  1  0  0\n",
       "2         1       3  26.0      0      0   7.9250       1     0  0  0  1\n",
       "3         1       1  35.0      1      0  53.1000       1     0  0  0  1\n",
       "4         0       3  35.0      0      0   8.0500       0     1  0  0  1"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HpwTWDx2pCnA"
   },
   "source": [
    "### 상관관계 히트맵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 884
    },
    "id": "o7qSH2Nom2TB",
    "outputId": "2bf69772-3cb2-4215-b3c4-bd4162355cbe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbbb2d31f90>"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(data = df.corr(), annot=True, fmt = '.2f', linewidths = .5, cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jBRC9y7kgF5k"
   },
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "uWD0O7U_gF5k"
   },
   "outputs": [],
   "source": [
    "X=df[['pclass', 'age', 'sibsp', 'parch', 'female', 'male', 'C', 'Q', 'S']]  #독립 변수 X\n",
    "y=df['survived']                      #종속 변수 Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N4bEYWCCpR9Y"
   },
   "source": [
    "## 정규화 (스케일링)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "zV4_tZbNgF5k"
   },
   "outputs": [],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OeRp7II_pkAP",
    "outputId": "3b7bdd9f-5ad4-433c-814d-3f741c4325bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.91123237, -0.53037664,  0.52457013, ..., -0.47180795,\n",
       "        -0.20203051,  0.53307848],\n",
       "       [-1.47636364,  0.57183099,  0.52457013, ...,  2.11950647,\n",
       "        -0.20203051, -1.87589641],\n",
       "       [ 0.91123237, -0.25482473, -0.55170307, ..., -0.47180795,\n",
       "        -0.20203051,  0.53307848],\n",
       "       ...,\n",
       "       [-1.47636364, -0.73704057, -0.55170307, ..., -0.47180795,\n",
       "        -0.20203051,  0.53307848],\n",
       "       [-1.47636364, -0.25482473, -0.55170307, ...,  2.11950647,\n",
       "        -0.20203051, -1.87589641],\n",
       "       [ 0.91123237,  0.15850313, -0.55170307, ..., -0.47180795,\n",
       "         4.94974747, -1.87589641]])"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uo1dsjIygF5k",
    "outputId": "43a318a7-6a24-4dfd-9fe3-ec5f508e2201",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data 개수:  (499, 9)\n",
      "test data 개수:  (215, 9)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10) \n",
    "print('train data 개수: ', X_train.shape)\n",
    "print('test data 개수: ', X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ApucCni1gF5l"
   },
   "source": [
    "## SVM Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YyLhoy_hgF5l"
   },
   "source": [
    "#### 모델 객체 생성 (kernel='rbf' 적용)\n",
    "\n",
    "sklearn에서 가져온 svm 모듈의 svc() 함수를 사용하여 모델 객체를 생성\n",
    "\n",
    "이때 데이터를 벡터 공간으로 매핑하는 함수를 커널이라 하는데\n",
    "\n",
    "일단은 'rbf' 옵션으로 함수를 적용한다.\n",
    "\n",
    "rbf : radical basis function (방사형 기저 함수)\n",
    "\n",
    "이외에도 Linear, Polynimial 등의 커널 옵션을 줄 수 있다.\n",
    "![006-2](https://user-images.githubusercontent.com/63298243/125198208-ed102a80-e29b-11eb-85de-edd8284f3c41.png)\n",
    "![006-3](https://user-images.githubusercontent.com/63298243/125198209-ee415780-e29b-11eb-984c-11f712b52dbb.png)\n",
    "![006-12](https://user-images.githubusercontent.com/63298243/125198214-f1d4de80-e29b-11eb-9806-79cf931f0d7f.png)\n",
    "![006-13](https://user-images.githubusercontent.com/63298243/125198218-f3060b80-e29b-11eb-82d0-3c810cc622fc.png)\n",
    "\n",
    "\n",
    "\n",
    "#### C\n",
    "데이터들을 선형적으로 완벽하게 분리해내는 것은 불가능하다. \n",
    "\n",
    "이를 해결하기 위해서 약간의 오류를 허용하는 전략이 만들어졌다. \n",
    "\n",
    "이것과 관련된 파라미터가 바로 cost(C)이다. \n",
    "\n",
    "C는 얼마나 많은 데이터 샘플이 다른 클래스에 놓이는 것을 허용하는지를 결정한다. \n",
    "\n",
    "작을 수록 많이 허용하고, 클 수록 적게 허용한다. \n",
    "\n",
    "![006-7](https://user-images.githubusercontent.com/63298243/125198319-6b6ccc80-e29c-11eb-82a5-125881f3afc4.png)\n",
    "\n",
    "\n",
    "#### gamma (rbf커널에서만 적용)\n",
    "gamma 파라미터는 결정 경계의 곡률을 조정한다.\n",
    "\n",
    "![006-8](https://user-images.githubusercontent.com/63298243/125198331-758ecb00-e29c-11eb-8728-e185341405b0.png)\n",
    "![006-9](https://user-images.githubusercontent.com/63298243/125198332-76bff800-e29c-11eb-9120-d70576e596b5.png)\n",
    "![006-10](https://user-images.githubusercontent.com/63298243/125198333-76bff800-e29c-11eb-9354-da119bfe0575.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "yDtIPC7cgF5l"
   },
   "outputs": [],
   "source": [
    "svm_model = svm.SVC(kernel='rbf', C = 0.5, gamma=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5G2r-aNEgF5l",
    "outputId": "4fe2d248-88e6-4b76-b995-20e2f3f9619f",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.5, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf', max_iter=-1,\n",
       "    probability=False, random_state=None, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_model.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QbMScwMRgF5l",
    "outputId": "ae2ba9e3-3c80-4568-e98f-7d8db6ff622b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.86046511627907\n"
     ]
    }
   ],
   "source": [
    "r_square = svm_model.score(X_test, y_test)\n",
    "print(r_square*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5qrL31iXqHWE"
   },
   "outputs": [],
   "source": []
  }
 ],
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