data_Nth_preprocessing.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#전체-데이터\" data-toc-modified-id=\"전체-데이터-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>전체 데이터</a></span><ul class=\"toc-item\"><li><span><a href=\"#데이터-가져오기\" data-toc-modified-id=\"데이터-가져오기-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>데이터 가져오기</a></span><ul class=\"toc-item\"><li><span><a href=\"#원본-데이터\" data-toc-modified-id=\"원본-데이터-1.1.1\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>원본 데이터</a></span></li><li><span><a href=\"#전처리-된-데이터\" data-toc-modified-id=\"전처리-된-데이터-1.1.2\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>전처리 된 데이터</a></span></li></ul></li><li><span><a href=\"#데이터-그대로-모델-돌려보기-(Baseline)\" data-toc-modified-id=\"데이터-그대로-모델-돌려보기-(Baseline)-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>데이터 그대로 모델 돌려보기 (Baseline)</a></span><ul class=\"toc-item\"><li><span><a href=\"#완전-날-것-데이터-모델-돌려기\" data-toc-modified-id=\"완전-날-것-데이터-모델-돌려기-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>완전 날 것 데이터 모델 돌려기</a></span></li><li><span><a href=\"#컬럼명-바꾼-데이터-모델-돌려보기\" data-toc-modified-id=\"컬럼명-바꾼-데이터-모델-돌려보기-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>컬럼명 바꾼 데이터 모델 돌려보기</a></span></li></ul></li></ul></li><li><span><a href=\"#Feature-selection\" data-toc-modified-id=\"Feature-selection-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Feature selection</a></span><ul class=\"toc-item\"><li><span><a href=\"#Decision-Tree\" data-toc-modified-id=\"Decision-Tree-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Decision Tree</a></span><ul class=\"toc-item\"><li><span><a href=\"#원본데이터\" data-toc-modified-id=\"원본데이터-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>원본데이터</a></span></li><li><span><a href=\"#전처리-된-데이터\" data-toc-modified-id=\"전처리-된-데이터-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>전처리 된 데이터</a></span></li><li><span><a href=\"#score-vs-voted\" data-toc-modified-id=\"score-vs-voted-2.1.3\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span>score vs voted</a></span></li></ul></li><li><span><a href=\"#feature_importance-DF\" data-toc-modified-id=\"feature_importance-DF-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>feature_importance DF</a></span></li><li><span><a href=\"#feature_importance-0인-컬럼들-빼고-돌린거\" data-toc-modified-id=\"feature_importance-0인-컬럼들-빼고-돌린거-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>feature_importance 0인 컬럼들 빼고 돌린거</a></span><ul class=\"toc-item\"><li><span><a href=\"#feature-importance-가-0으로-나온-컬럼들-분석\" data-toc-modified-id=\"feature-importance-가-0으로-나온-컬럼들-분석-2.3.1\"><span class=\"toc-item-num\">2.3.1&nbsp;&nbsp;</span>feature importance 가 0으로 나온 컬럼들 분석</a></span></li></ul></li><li><span><a href=\"#feature_importance-상위-45개-교집합\" data-toc-modified-id=\"feature_importance-상위-45개-교집합-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>feature_importance 상위 45개 교집합</a></span></li><li><span><a href=\"#feature_importance-상위-45개-합집합\" data-toc-modified-id=\"feature_importance-상위-45개-합집합-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>feature_importance 상위 45개 합집합</a></span></li></ul></li><li><span><a href=\"#RobustScaler\" data-toc-modified-id=\"RobustScaler-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>RobustScaler</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#robust-함수-적용-후-비교\" data-toc-modified-id=\"robust-함수-적용-후-비교-3.0.1\"><span class=\"toc-item-num\">3.0.1&nbsp;&nbsp;</span>robust 함수 적용 후 비교</a></span></li><li><span><a href=\"#robust-scaling-전-/-후--시각화\" data-toc-modified-id=\"robust-scaling-전-/-후--시각화-3.0.2\"><span class=\"toc-item-num\">3.0.2&nbsp;&nbsp;</span>robust scaling 전 / 후  시각화</a></span><ul class=\"toc-item\"><li><span><a href=\"#age\" data-toc-modified-id=\"age-3.0.2.1\"><span class=\"toc-item-num\">3.0.2.1&nbsp;&nbsp;</span><code>age</code></a></span></li><li><span><a href=\"#family-size\" data-toc-modified-id=\"family-size-3.0.2.2\"><span class=\"toc-item-num\">3.0.2.2&nbsp;&nbsp;</span><code>family size</code></a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#feature_addition\" data-toc-modified-id=\"feature_addition-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>feature_addition</a></span><ul class=\"toc-item\"><li><span><a href=\"#추가-컬럼-생성-클래스\" data-toc-modified-id=\"추가-컬럼-생성-클래스-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>추가 컬럼 생성 클래스</a></span></li><li><span><a href=\"#컬럼-빼기전-추가-컬럼-생성-후-결과\" data-toc-modified-id=\"컬럼-빼기전-추가-컬럼-생성-후-결과-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>컬럼 빼기전 추가 컬럼 생성 후 결과</a></span></li><li><span><a href=\"#컬럼-뺀-후-추가-컬럼-생성-결과\" data-toc-modified-id=\"컬럼-뺀-후-추가-컬럼-생성-결과-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>컬럼 뺀 후 추가 컬럼 생성 결과</a></span></li></ul></li><li><span><a href=\"#모듈-만들기\" data-toc-modified-id=\"모듈-만들기-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>모듈 만들기</a></span><ul class=\"toc-item\"><li><span><a href=\"#N차-전처리-모듈\" data-toc-modified-id=\"N차-전처리-모듈-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>N차 전처리 모듈</a></span></li><li><span><a href=\"#모델링-모듈\" data-toc-modified-id=\"모델링-모듈-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>모델링 모듈</a></span></li><li><span><a href=\"#그리드서치/하이퍼파라미터-모듈\" data-toc-modified-id=\"그리드서치/하이퍼파라미터-모듈-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>그리드서치/하이퍼파라미터 모듈</a></span></li><li><span><a href=\"#machi_vote_ml.py\" data-toc-modified-id=\"machi_vote_ml.py-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>machi_vote_ml.py</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 전체 데이터 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import (LabelEncoder, LabelBinarizer, RobustScaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import (train_test_split, cross_val_score, StratifiedKFold ,GridSearchCV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (accuracy_score, roc_auc_score, classification_report, confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from graphviz import Source\n",
    "from sklearn.tree import export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import time\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hangul font is set!\n"
     ]
    }
   ],
   "source": [
    "import hangul_font"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 가져오기 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 원본 데이터 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7901.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8647.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>414.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Business</td>\n",
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       "    <tr>\n",
       "      <th>73488</th>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7567.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5766.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73489 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1A   Q1I      Q1E  Q2A   Q2I      Q2E  Q3A   Q3I      Q3E  Q4A  ...  \\\n",
       "0      3.0   6.0  21017.0  3.0   7.0  18600.0  5.0  20.0  14957.0  2.0  ...   \n",
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       "2      5.0  16.0   4186.0  5.0  12.0   2900.0  1.0   2.0   7160.0  1.0  ...   \n",
       "3      2.0  12.0   9373.0  4.0   1.0  10171.0  2.0   7.0  10117.0  1.0  ...   \n",
       "4      5.0  13.0   9465.0  5.0   7.0   5284.0  2.0  19.0   8872.0  1.0  ...   \n",
       "...    ...   ...      ...  ...   ...      ...  ...   ...      ...  ...  ...   \n",
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       "73485  5.0   9.0   5816.0  5.0   4.0   4368.0  1.0  15.0   7246.0  1.0  ...   \n",
       "73486  2.0  17.0   5353.0  1.0  18.0   6846.0  1.0   9.0   4920.0  1.0  ...   \n",
       "73487  4.0  15.0   7974.0  3.0   9.0   7901.0  5.0  17.0   8647.0  3.0  ...   \n",
       "73488  3.0  12.0   7567.0  1.0  10.0   5766.0  5.0  13.0  37103.0  1.0  ...   \n",
       "\n",
       "       screenw  screenh  hand  religion  orientation  race  voted  married  \\\n",
       "0       1440.0    900.0     1         7            1    30      1        2   \n",
       "1       1536.0    864.0     1         1            1    60      2        1   \n",
       "2        375.0    667.0     1         2            2    10      2        1   \n",
       "3       1280.0    720.0     1         6            1    60      1        3   \n",
       "4        360.0    640.0     1         4            3    60      1        1   \n",
       "...        ...      ...   ...       ...          ...   ...    ...      ...   \n",
       "73484   2048.0   1080.0     1        12            1    10      2        2   \n",
       "73485   1440.0    900.0     1         2            3    60      2        1   \n",
       "73486   1280.0   1024.0     1         1            1    60      2        1   \n",
       "73487    414.0    736.0     1         3            1    10      1        1   \n",
       "73488   1366.0    768.0     1         4            1    70      2        1   \n",
       "\n",
       "       familysize                    major  \n",
       "0               5               Marketing   \n",
       "1               2              mathematics  \n",
       "2               2                Chemistry  \n",
       "3               2  international relations  \n",
       "4               2               Management  \n",
       "...           ...                      ...  \n",
       "73484           1                 business  \n",
       "73485           2                sociology  \n",
       "73486           3                      NaN  \n",
       "73487           4                 Business  \n",
       "73488           4                      NaN  \n",
       "\n",
       "[73489 rows x 105 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_origin = pd.read_csv('./MACH_data/data.csv',delimiter='\\t')\n",
    "df_origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# major 컬럼 삭제\n",
    "df_origin.drop('major', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 투표 가능한 나이대 18세 이상\n",
    "df_origin = df_origin[df_origin['age'] > 17] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>61968 rows × 288 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1A   Q1I      Q1E  Q2A   Q2I      Q2E  Q3A   Q3I      Q3E  Q4A  ...  \\\n",
       "0      3.0   6.0  21017.0  3.0   7.0  18600.0  5.0  20.0  14957.0  2.0  ...   \n",
       "1      5.0  17.0   3818.0  5.0   9.0   7850.0  1.0  16.0   5902.0  3.0  ...   \n",
       "3      2.0  12.0   9373.0  4.0   1.0  10171.0  2.0   7.0  10117.0  1.0  ...   \n",
       "4      5.0  13.0   9465.0  5.0   7.0   5284.0  2.0  19.0   8872.0  1.0  ...   \n",
       "6      3.0  11.0   3933.0  3.0  10.0   3760.0  2.0  17.0   4684.0  4.0  ...   \n",
       "...    ...   ...      ...  ...   ...      ...  ...   ...      ...  ...  ...   \n",
       "73484  2.0  17.0   9925.0  1.0   6.0  14218.0  2.0  16.0  25499.0  4.0  ...   \n",
       "73485  5.0   9.0   5816.0  5.0   4.0   4368.0  1.0  15.0   7246.0  1.0  ...   \n",
       "73486  2.0  17.0   5353.0  1.0  18.0   6846.0  1.0   9.0   4920.0  1.0  ...   \n",
       "73487  4.0  15.0   7974.0  3.0   9.0   7901.0  5.0  17.0   8647.0  3.0  ...   \n",
       "73488  3.0  12.0   7567.0  1.0  10.0   5766.0  5.0  13.0  37103.0  1.0  ...   \n",
       "\n",
       "       country_UY  country_UZ  country_VC  country_VE  country_VI  country_VN  \\\n",
       "0               0           0           0           0           0           0   \n",
       "1               0           0           0           0           0           0   \n",
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       "4               0           0           0           0           0           0   \n",
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       "...           ...         ...         ...         ...         ...         ...   \n",
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       "73487           0           0           0           0           0           0   \n",
       "73488           0           0           0           0           0           0   \n",
       "\n",
       "       country_VU  country_ZA  country_ZM  country_ZW  \n",
       "0               0           0           0           0  \n",
       "1               0           0           0           0  \n",
       "3               0           0           0           0  \n",
       "4               0           0           0           0  \n",
       "6               0           0           0           0  \n",
       "...           ...         ...         ...         ...  \n",
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       "\n",
       "[61968 rows x 288 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country 컬럼을 위해서 get_dummies 실행\n",
    "df_origin= pd.get_dummies(df_origin)\n",
    "df_origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# target columns 1=yes, 2=no -> 0=no, 1=yes 변환\n",
    "df_origin['voted'] = df_origin['voted'].apply(lambda x: x if (x == 1) else int(str(x).replace(\"2\", \"0\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "3        1\n",
       "4        1\n",
       "6        1\n",
       "        ..\n",
       "73484    0\n",
       "73485    0\n",
       "73486    0\n",
       "73487    1\n",
       "73488    0\n",
       "Name: voted, Length: 61968, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_origin['voted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_origin = df_origin.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_origin.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61965, 288)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_origin.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_origin = df_origin.drop('voted', axis=1)\n",
    "y_origin = df_origin['voted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_o, X_test_o, y_train_o, y_test_o = train_test_split(X_origin, y_origin, test_size=0.2,\n",
    "                                                   random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((49572, 287), (12393, 287), (49572,), (12393,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_o.shape, X_test_o.shape, y_train_o.shape, y_test_o.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 전처리 된 데이터 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Buddhist</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never married</td>\n",
       "      <td>4</td>\n",
       "      <td>53.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54717</th>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Christian(Catholic)</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>Never married</td>\n",
       "      <td>4</td>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54718 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "0                  3.0   6.0           21.0                  3.0   7.0   \n",
       "1                  5.0  17.0            4.0                  5.0   9.0   \n",
       "2                  2.0  12.0            9.0                  4.0   1.0   \n",
       "3                  5.0  13.0            9.0                  5.0   7.0   \n",
       "4                  3.0  11.0            4.0                  3.0  10.0   \n",
       "...                ...   ...            ...                  ...   ...   \n",
       "54713              2.0  17.0           10.0                  1.0   6.0   \n",
       "54714              5.0   9.0            6.0                  5.0   4.0   \n",
       "54715              2.0  17.0            5.0                  1.0  18.0   \n",
       "54716              4.0  15.0            8.0                  3.0   9.0   \n",
       "54717              3.0  12.0            8.0                  1.0  10.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "0                   19.0             5.0  20.0          15.0             2.0   \n",
       "1                    8.0             1.0  16.0           6.0             3.0   \n",
       "2                   10.0             2.0   7.0          10.0             1.0   \n",
       "3                    5.0             2.0  19.0           9.0             1.0   \n",
       "4                    4.0             2.0  17.0           5.0             4.0   \n",
       "...                  ...             ...   ...           ...             ...   \n",
       "54713               14.0             2.0  16.0          25.0             4.0   \n",
       "54714                4.0             1.0  15.0           7.0             1.0   \n",
       "54715                7.0             1.0   9.0           5.0             1.0   \n",
       "54716                8.0             5.0  17.0           9.0             3.0   \n",
       "54717                6.0             5.0  13.0          37.0             1.0   \n",
       "\n",
       "       ...               religion   orientation   race  voted  \\\n",
       "0      ...      Christian(othrer)  Heterosexual  Black    Yes   \n",
       "1      ...               Agnostic  Heterosexual  White     No   \n",
       "2      ...  Christian(Protestant)  Heterosexual  White    Yes   \n",
       "3      ...    Christian(Catholic)    Homosexual  White    Yes   \n",
       "4      ...                Atheist  Heterosexual  White    Yes   \n",
       "...    ...                    ...           ...    ...    ...   \n",
       "54713  ...                  Other  Heterosexual  Asian     No   \n",
       "54714  ...                Atheist    Homosexual  White     No   \n",
       "54715  ...               Agnostic  Heterosexual  White     No   \n",
       "54716  ...               Buddhist  Heterosexual  Asian    Yes   \n",
       "54717  ...    Christian(Catholic)  Heterosexual  Other     No   \n",
       "\n",
       "                  married  familysize  score  v_score  t_score  m_score  \n",
       "0       Currently married           5   75.0     35.0     33.0      7.0  \n",
       "1           Never married           2   91.0     37.0     45.0      9.0  \n",
       "2      Previously married           2   78.0     37.0     36.0      5.0  \n",
       "3           Never married           2   85.0     35.0     41.0      9.0  \n",
       "4           Never married           2   66.0     28.0     31.0      7.0  \n",
       "...                   ...         ...    ...      ...      ...      ...  \n",
       "54713   Currently married           1   59.0     28.0     26.0      5.0  \n",
       "54714       Never married           2   90.0     37.0     43.0     10.0  \n",
       "54715       Never married           3   69.0     34.0     27.0      8.0  \n",
       "54716       Never married           4   53.0     25.0     22.0      6.0  \n",
       "54717       Never married           4   64.0     36.0     23.0      5.0  \n",
       "\n",
       "[54718 rows x 108 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18 = pd.read_csv('./MACH_data/data_cutoff_age18.csv' )\n",
    "df_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Q1_TP_notell_2u', 'Q1I', 'Q1E_notell_2u', 'Q2_TP_ppl_nd_dangun', 'Q2I',\n",
       "       'Q2E_ppl_nd_dangun', 'Q3_TN_do_moral', 'Q3I', 'Q3E_do_moral',\n",
       "       'Q4_VN_ppl_good',\n",
       "       ...\n",
       "       'religion', 'orientation', 'race', 'voted', 'married', 'familysize',\n",
       "       'score', 'v_score', 't_score', 'm_score'],\n",
       "      dtype='object', length=108)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lb = LabelBinarizer()\n",
    "df_18['voted'] = lb.fit_transform(df_18['voted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "54713    0\n",
       "54714    0\n",
       "54715    0\n",
       "54716    1\n",
       "54717    0\n",
       "Name: voted, Length: 54718, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18['voted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_18_dumi = pd.get_dummies(df_18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1_TP_notell_2u</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E_notell_2u</th>\n",
       "      <th>Q2_TP_ppl_nd_dangun</th>\n",
       "      <th>Q2I</th>\n",
       "      <th>Q2E_ppl_nd_dangun</th>\n",
       "      <th>Q3_TN_do_moral</th>\n",
       "      <th>Q3I</th>\n",
       "      <th>Q3E_do_moral</th>\n",
       "      <th>Q4_VN_ppl_good</th>\n",
       "      <th>...</th>\n",
       "      <th>race_Arab</th>\n",
       "      <th>race_Asian</th>\n",
       "      <th>race_Black</th>\n",
       "      <th>race_Indigenous Australian</th>\n",
       "      <th>race_Native American</th>\n",
       "      <th>race_Other</th>\n",
       "      <th>race_White</th>\n",
       "      <th>married_Currently married</th>\n",
       "      <th>married_Never married</th>\n",
       "      <th>married_Previously married</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54713</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54714</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54715</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54716</th>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54717</th>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 340 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "54713              2.0  17.0           10.0                  1.0   6.0   \n",
       "54714              5.0   9.0            6.0                  5.0   4.0   \n",
       "54715              2.0  17.0            5.0                  1.0  18.0   \n",
       "54716              4.0  15.0            8.0                  3.0   9.0   \n",
       "54717              3.0  12.0            8.0                  1.0  10.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "54713               14.0             2.0  16.0          25.0             4.0   \n",
       "54714                4.0             1.0  15.0           7.0             1.0   \n",
       "54715                7.0             1.0   9.0           5.0             1.0   \n",
       "54716                8.0             5.0  17.0           9.0             3.0   \n",
       "54717                6.0             5.0  13.0          37.0             1.0   \n",
       "\n",
       "       ...  race_Arab  race_Asian  race_Black  race_Indigenous Australian  \\\n",
       "54713  ...          0           1           0                           0   \n",
       "54714  ...          0           0           0                           0   \n",
       "54715  ...          0           0           0                           0   \n",
       "54716  ...          0           1           0                           0   \n",
       "54717  ...          0           0           0                           0   \n",
       "\n",
       "       race_Native American  race_Other  race_White  \\\n",
       "54713                     0           0           0   \n",
       "54714                     0           0           1   \n",
       "54715                     0           0           1   \n",
       "54716                     0           0           0   \n",
       "54717                     0           1           0   \n",
       "\n",
       "       married_Currently married  married_Never married  \\\n",
       "54713                          1                      0   \n",
       "54714                          0                      1   \n",
       "54715                          0                      1   \n",
       "54716                          0                      1   \n",
       "54717                          0                      1   \n",
       "\n",
       "       married_Previously married  \n",
       "54713                           0  \n",
       "54714                           0  \n",
       "54715                           0  \n",
       "54716                           0  \n",
       "54717                           0  \n",
       "\n",
       "[5 rows x 340 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18_dumi.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54718, 340)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18_dumi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_18_dumi = df_18_dumi.drop('voted', axis=1)\n",
    "y_18_dumi = df_18_dumi['voted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_d, X_test_d, y_train_d, y_test_d = train_test_split(X_18_dumi, y_18_dumi, test_size=0.2,\n",
    "                                                   random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((43774, 339), (10944, 339), (43774,), (10944,))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_d.shape, X_test_d.shape, y_train_d.shape, y_test_d.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 그대로 모델 돌려보기 (Baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 완전 날 것 데이터 모델 돌려기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train_o, X_test_o, y_train_o, y_test_o "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ada, gbc, xgb, lgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada = AdaBoostClassifier()\n",
    "gbc = GradientBoostingClassifier()\n",
    "xgb = XGBClassifier()\n",
    "lgbm = LGBMClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [ada, gbc, xgb, lgbm]\n",
    "model_names = ['Ada', 'GBC', 'XGB', 'LGBM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (accuracy_score, precision_score, recall_score, \n",
    "                             f1_score, roc_auc_score, confusion_matrix)\n",
    "\n",
    "def get_score(y_test, pred):\n",
    "    acc = accuracy_score(y_test, pred)\n",
    "    pre = precision_score(y_test, pred)\n",
    "    rec = recall_score(y_test, pred)\n",
    "    f1 = f1_score(y_test, pred)\n",
    "    auc = roc_auc_score(y_test, pred)\n",
    "   \n",
    "    return acc, auc, pre, rec, f1\n",
    "\n",
    "def print_score(y_test, pred, confusion=False, score=False):\n",
    "    acc, auc, pre, rec, f1 = get_score(y_test, pred)\n",
    "    con = confusion_matrix(y_test, pred)\n",
    "    if confusion==True:\n",
    "        print('confusion matrix')\n",
    "        print(con)\n",
    "        print('='*20)\n",
    "    \n",
    "    if score==True:\n",
    "        print('Accuracy: {0:.4f}, AUC: {1:.4f}'.format(acc, auc))\n",
    "        print('Recall: {0:.4f}, f1_score: {1:.4f}, precision: {2:.4f}'.format(rec, f1, pre))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_model(model, X_train, X_test, y_train, y_test):\n",
    "#     skfold = StratifiedKFold(n_splits=5)\n",
    "#     cv_scores = []\n",
    "#     for score in ['accuracy', 'roc_auc']:    \n",
    "#         score_cv = cross_val_score(model, X_train_d, y_train_d, scoring=score, cv=5)\n",
    "#         cv_scores.append(round(score_cv.mean(), 4))\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pre_tr = model.predict(X_train)\n",
    "    y_pre_test = model.predict(X_test)\n",
    "    \n",
    "    return get_score(y_test, y_pre_test)\n",
    "\n",
    "def models_score_df(models, model_names, X_train, X_test, y_train, y_test):\n",
    "    cols_names = ['accuracy', 'AUC', 'precision', 'recall', 'f1']\n",
    "    datas = []\n",
    "    for model in models:\n",
    "        datas.append(fit_model(model, X_train, X_test, y_train, y_test))\n",
    "     \n",
    "    return pd.DataFrame(datas, columns=cols_names, index=model_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm.fit(X_train_o, y_train_o)\n",
    "y_pre_test_o = lgbm.predict(X_test_o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      "[[3372 2557]\n",
      " [1716 4748]]\n",
      "====================\n",
      "Accuracy: 0.6552, AUC: 0.6516\n",
      "Recall: 0.7345, f1_score: 0.6897, precision: 0.6500\n"
     ]
    }
   ],
   "source": [
    "print_score(y_test_o, y_pre_test_o, confusion=True, score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit time : 88.17821216583252\n"
     ]
    },
    {
     "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>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.648592</td>\n",
       "      <td>0.644708</td>\n",
       "      <td>0.642712</td>\n",
       "      <td>0.734684</td>\n",
       "      <td>0.685628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.647220</td>\n",
       "      <td>0.643093</td>\n",
       "      <td>0.640252</td>\n",
       "      <td>0.738707</td>\n",
       "      <td>0.685965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.632212</td>\n",
       "      <td>0.629347</td>\n",
       "      <td>0.634453</td>\n",
       "      <td>0.695699</td>\n",
       "      <td>0.663666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.655209</td>\n",
       "      <td>0.651630</td>\n",
       "      <td>0.649966</td>\n",
       "      <td>0.734530</td>\n",
       "      <td>0.689665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.648592  0.644708   0.642712  0.734684  0.685628\n",
       "GBC   0.647220  0.643093   0.640252  0.738707  0.685965\n",
       "XGB   0.632212  0.629347   0.634453  0.695699  0.663666\n",
       "LGBM  0.655209  0.651630   0.649966  0.734530  0.689665"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "result_o = models_score_df(models, model_names, X_train_o, X_test_o, y_train_o, y_test_o )\n",
    "\n",
    "print(\"Fit time : {}\".format(time.time()-start_time))\n",
    "result_o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 컬럼명 바꾼 데이터 모델 돌려보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train_d, X_test_d, y_train_d, y_test_d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm.fit(X_train_d, y_train_d)\n",
    "y_pre_test_d = lgbm.predict(X_test_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix\n",
      "[[2834 2349]\n",
      " [1402 4359]]\n",
      "====================\n",
      "Accuracy: 0.6573, AUC: 0.6517\n",
      "Recall: 0.7566, f1_score: 0.6992, precision: 0.6498\n"
     ]
    }
   ],
   "source": [
    "print_score(y_test_d, y_pre_test_d, confusion=True, score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit time : 62.2814679145813\n"
     ]
    },
    {
     "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>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.650402</td>\n",
       "      <td>0.644314</td>\n",
       "      <td>0.641925</td>\n",
       "      <td>0.759590</td>\n",
       "      <td>0.695818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.656159</td>\n",
       "      <td>0.649482</td>\n",
       "      <td>0.643907</td>\n",
       "      <td>0.775907</td>\n",
       "      <td>0.703771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.648209</td>\n",
       "      <td>0.644031</td>\n",
       "      <td>0.648809</td>\n",
       "      <td>0.723138</td>\n",
       "      <td>0.683960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.657255</td>\n",
       "      <td>0.651714</td>\n",
       "      <td>0.649821</td>\n",
       "      <td>0.756639</td>\n",
       "      <td>0.699174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.650402  0.644314   0.641925  0.759590  0.695818\n",
       "GBC   0.656159  0.649482   0.643907  0.775907  0.703771\n",
       "XGB   0.648209  0.644031   0.648809  0.723138  0.683960\n",
       "LGBM  0.657255  0.651714   0.649821  0.756639  0.699174"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "result_d = models_score_df(models, model_names, X_train_d, X_test_d, y_train_d, y_test_d )\n",
    "\n",
    "print(\"Fit time : {}\".format(time.time()-start_time))\n",
    "result_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 원본데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ACC : 1.0, Train AUC : 1.0\n",
      "Test ACC : 0.5773123087785361, Test AUC : 0.5761699250092238\n"
     ]
    }
   ],
   "source": [
    "dt_o = DecisionTreeClassifier()\n",
    "dt_o.fit(X_train_o, y_train_o)\n",
    "\n",
    "y_pre_tr_o=dt_o.predict(X_train_o)\n",
    "y_pre_test_o=dt_o.predict(X_test_o)\n",
    "\n",
    "print('Train ACC : {}, Train AUC : {}'.format(accuracy_score(y_train_o, y_pre_tr_o), \n",
    "                                              roc_auc_score(y_train_o, y_pre_tr_o)))\n",
    "print('Test ACC : {}, Test AUC : {}'.format(accuracy_score(y_test_o, y_pre_test_o), \n",
    "                                             roc_auc_score(y_test_o, y_pre_test_o)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q1A</td>\n",
       "      <td>0.004626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q1I</td>\n",
       "      <td>0.014202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q1E</td>\n",
       "      <td>0.015096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q2A</td>\n",
       "      <td>0.004543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q2I</td>\n",
       "      <td>0.011313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>religion</td>\n",
       "      <td>0.007941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>orientation</td>\n",
       "      <td>0.002609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>race</td>\n",
       "      <td>0.031460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>married</td>\n",
       "      <td>0.010885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>familysize</td>\n",
       "      <td>0.006393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         columns  importance\n",
       "0            Q1A    0.004626\n",
       "1            Q1I    0.014202\n",
       "2            Q1E    0.015096\n",
       "3            Q2A    0.004543\n",
       "4            Q2I    0.011313\n",
       "..           ...         ...\n",
       "98      religion    0.007941\n",
       "99   orientation    0.002609\n",
       "100         race    0.031460\n",
       "101      married    0.010885\n",
       "102   familysize    0.006393\n",
       "\n",
       "[103 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ori_fi = pd.DataFrame(zip(X_origin.columns, dt_o.feature_importances_), columns=['columns', 'importance'])\n",
    "ori_fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>age</td>\n",
       "      <td>0.091795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>country</td>\n",
       "      <td>0.036289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>race</td>\n",
       "      <td>0.031460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Q7E</td>\n",
       "      <td>0.024006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Q15E</td>\n",
       "      <td>0.020590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Q13E</td>\n",
       "      <td>0.020324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Q14E</td>\n",
       "      <td>0.019669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Q18E</td>\n",
       "      <td>0.019179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Q6E</td>\n",
       "      <td>0.018962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Q19E</td>\n",
       "      <td>0.018863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     columns  importance\n",
       "94       age    0.091795\n",
       "60   country    0.036289\n",
       "100     race    0.031460\n",
       "20       Q7E    0.024006\n",
       "44      Q15E    0.020590\n",
       "38      Q13E    0.020324\n",
       "41      Q14E    0.019669\n",
       "53      Q18E    0.019179\n",
       "17       Q6E    0.018962\n",
       "56      Q19E    0.018863"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ori_fi.sort_values('importance', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 전처리 된 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ACC : 1.0, Train AUC : 1.0\n",
      "Test ACC : 0.5696271929824561, Test AUC : 0.5683072284804886\n"
     ]
    }
   ],
   "source": [
    "dt_d = DecisionTreeClassifier()\n",
    "dt_d.fit(X_train_d, y_train_d)\n",
    "\n",
    "y_pre_tr_d=dt_d.predict(X_train_d)\n",
    "y_pre_test_d=dt_d.predict(X_test_d)\n",
    "\n",
    "print('Train ACC : {}, Train AUC : {}'.format(accuracy_score(y_train_d, y_pre_tr_d), \n",
    "                                              roc_auc_score(y_train_d, y_pre_tr_d)))\n",
    "print('Test ACC : {}, Test AUC : {}'.format(accuracy_score(y_test_d, y_pre_test_d), \n",
    "                                             roc_auc_score(y_test_d, y_pre_test_d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q1_TP_notell_2u</td>\n",
       "      <td>0.003956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q1I</td>\n",
       "      <td>0.012073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q1E_notell_2u</td>\n",
       "      <td>0.010923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q2_TP_ppl_nd_dangun</td>\n",
       "      <td>0.003718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q2I</td>\n",
       "      <td>0.011664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>race_Other</td>\n",
       "      <td>0.000589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>race_White</td>\n",
       "      <td>0.019007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>married_Currently married</td>\n",
       "      <td>0.007057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>married_Never married</td>\n",
       "      <td>0.001360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>married_Previously married</td>\n",
       "      <td>0.000663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>339 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        columns  importance\n",
       "0               Q1_TP_notell_2u    0.003956\n",
       "1                           Q1I    0.012073\n",
       "2                 Q1E_notell_2u    0.010923\n",
       "3           Q2_TP_ppl_nd_dangun    0.003718\n",
       "4                           Q2I    0.011664\n",
       "..                          ...         ...\n",
       "334                  race_Other    0.000589\n",
       "335                  race_White    0.019007\n",
       "336   married_Currently married    0.007057\n",
       "337       married_Never married    0.001360\n",
       "338  married_Previously married    0.000663\n",
       "\n",
       "[339 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dumi_fi = pd.DataFrame(zip(X_18_dumi.columns, dt_d.feature_importances_), columns=['columns', 'importance'])\n",
    "dumi_fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>age</td>\n",
       "      <td>0.072226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>surveyelapse</td>\n",
       "      <td>0.020482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>race_White</td>\n",
       "      <td>0.019007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>introelapse</td>\n",
       "      <td>0.016232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Q5I</td>\n",
       "      <td>0.015926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Q19I</td>\n",
       "      <td>0.015761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Q18I</td>\n",
       "      <td>0.014939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Q13I</td>\n",
       "      <td>0.014892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Q8I</td>\n",
       "      <td>0.014798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Q20I</td>\n",
       "      <td>0.014688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          columns  importance\n",
       "73            age    0.072226\n",
       "62   surveyelapse    0.020482\n",
       "335    race_White    0.019007\n",
       "60    introelapse    0.016232\n",
       "13            Q5I    0.015926\n",
       "55           Q19I    0.015761\n",
       "52           Q18I    0.014939\n",
       "37           Q13I    0.014892\n",
       "22            Q8I    0.014798\n",
       "58           Q20I    0.014688"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dumi_fi.sort_values('importance', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### score vs voted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> score는 중요컬럼이라고 생각되서 가중치를 주기로 결정.  \n",
    "그 가중치 기준을 정하기 위해 score와 voted의 관계를 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>v_score</th>\n",
       "      <th>t_score</th>\n",
       "      <th>m_score</th>\n",
       "      <th>voted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54713</th>\n",
       "      <td>59.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54714</th>\n",
       "      <td>90.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54715</th>\n",
       "      <td>69.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54716</th>\n",
       "      <td>53.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54717</th>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       score  v_score  t_score  m_score  voted\n",
       "54713   59.0     28.0     26.0      5.0      0\n",
       "54714   90.0     37.0     43.0     10.0      0\n",
       "54715   69.0     34.0     27.0      8.0      0\n",
       "54716   53.0     25.0     22.0      6.0      1\n",
       "54717   64.0     36.0     23.0      5.0      0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_score = df_18[['score', 'v_score', 't_score', 'm_score', 'voted']]\n",
    "df_score.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_s = df_score.drop('voted', axis=1)\n",
    "y_s = df_score['voted']\n",
    "\n",
    "X_train_s, X_test_s, y_train_s, y_test_s = train_test_split(X_s, y_s, test_size=0.2,\n",
    "                                                   random_state=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train ACC : 0.6327500342669165, Train AUC : 0.6319700720732493\n",
      "Test ACC : 0.5138888888888888, Test AUC : 0.5127130565814703\n"
     ]
    }
   ],
   "source": [
    "dt_s = DecisionTreeClassifier()\n",
    "dt_s.fit(X_train_s, y_train_s)\n",
    "\n",
    "y_pre_tr_s=dt_s.predict(X_train_s)\n",
    "y_pre_test_s=dt_s.predict(X_test_s)\n",
    "\n",
    "print('Train ACC : {}, Train AUC : {}'.format(accuracy_score(y_train_s, y_pre_tr_s), \n",
    "                                              roc_auc_score(y_train_s, y_pre_tr_s)))\n",
    "print('Test ACC : {}, Test AUC : {}'.format(accuracy_score(y_test_s, y_pre_test_s), \n",
    "                                             roc_auc_score(y_test_s, y_pre_test_s)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>score</td>\n",
       "      <td>0.278289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t_score</td>\n",
       "      <td>0.271786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>v_score</td>\n",
       "      <td>0.256770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m_score</td>\n",
       "      <td>0.193156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   columns  importance\n",
       "0    score    0.278289\n",
       "2  t_score    0.271786\n",
       "1  v_score    0.256770\n",
       "3  m_score    0.193156"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_fm = pd.DataFrame(zip(X_s.columns, dt_s.feature_importances_), columns=['columns', 'importance'])\n",
    "score_fm.sort_values('importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb6184a3910>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 391
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=df_score['score'], hue=df_score['voted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb62c3fbaf0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 391
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=df_score['t_score'], hue=df_score['voted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb659b4a1c0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 391
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=df_score['v_score'], hue=df_score['voted'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb62cc40670>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XV+x/8sknGTZsGFtvvXUPj+ztDBuSJElSkxk/fjxLly7l4Ycffkv7n/70J5544gn222+/Oo3srQwbkiRJUpM5/vjjATjvvPPe0l76/QknnNDTQ6poi3oPQJIkSVLXfOQjH+FTn/oU11xzDePHj2e//fbjgQce4Ne//jVHHHEEH/vYx+o9RMCwIUmSpBZ02kG713sIhbv66qt5z3vew+zZs7nwwgvZYYcdmDFjBmeffXa9h7aeYUOSJElqQn369GHGjBnMmDGj3kOpyrAhSZKklpFSqvcQVMYbxCVJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCsCFJkiSpEIYNSZIkSYUwbEiSJEkqhGFDkiRJLSMimuJTa6+++iojRoxg2rRpNd93dxg2JEmSpCb2xhtvcPzxx/Pss8/Weyhvs0W9ByBJkiTVWrrj/HoPoaI44Oya7u+ll17i2GOP5ZZbbqnpfmvFmQ1JkiSpCf3oRz9i9OjR/PKXv+TQQw+t93AqcmZDkiRJakLf//73GThwIP/5n//JVltt1ZCzG85sSJIkSU1o+vTpPPbYY3z84x+v91CqcmZDkrRZefyZpex+4ner9j//l+U9OBpJ2nQTJ06s9xA2yrAhSdqsvN4OXzjrvKr9Z592ag+ORpJam8uoJEmSJBXCsCFJkiSpEIYNSZIkSYVo6bAREQMj4t8i4umIWBcRz0fEv0fEOyrUnhQRD0XE6oh4NiIujYjBVfZ7eETcHxGrImJZRMyOiHdWqZ0QEXdGxKsR0RYRN0TEqFqfqyRJktRoWjZsRMQWwFzgy8BzwEXAQuBkYEF5kIiI6cBlwDrgYmARMBW4PSL6ddjvCcD1wLbAJcBtwLHA3R1DTEQcCNwBjAK+B1wHHAzcFxG71vaMJUmSpMbSyk+jOhLYB/hRSumYUmNEnA18A/gS8PWIGAmcC/wGmJhSei2vmw7MAD4PzMrbBpOFkSeAcSml5Xn7rcAVwNnA6Xlbb7KAsRzYI6X0XN4+B7gduCAfoyRJktSSWjlsvC/f/qhD+2yysLFP/vspZNfhG6Wgkfsm2azI8eRhAzgc2AY4uxQ0AFJKs/NwMiUipqWUEjAe2A34t1LQyGvnRcRdwCcjYpuU0ss1OFdJkiSViQPOrvcQRAsvoyJbOgUwpkN76X6JV/LtBKAdmF9elFJaBdwHjI2IgWW1APMqHG8eMAwY3YnaO4DewN4bPgVJkiRp4/bff39SSlx44YX1HspbtHLYmAM8CXwlIv5PRLwvIj5NNrORgMvzulHAkpTSygr7eBwIYJey2pTvt1ItZLMZpVrIllxtrFaSJEk1kFJqis/momWXUaWUXo6IicAtwLfLutqAv08pzc1/P4TK4QFgRVlNabsypbSuk7Wl422stqqIWFila3SVdkmSJKkhtOzMRkTsDCwAdge+A3yW7Kbs/sCXI2J4Xtqf7ClUlazOtwM2sZYq9R1rJUmSpJbTsjMbZE+NGgFMSildV2qMiGuBe4BryW7iXsubwaCjvvm2FA66Wktev2IjtVWllMZVas9nPMZu7PuSJElSvbTkzEZE9AE+DvxPedAASCn9FrgR2DcidiNb5rR1lV11XArVBgzI99+ZWqrse0NLrCRJkqSW0JJhAxhK9rSnP1Xpfyrf7gQsBnaIiC0r1I0kuyG8dJP3Yt56w3jH2lJN+bbSy/s61kqSJEktp1XDxotkS5TeExGVzrF0c/Uysvs6AphYXpCHj72AhWVPqlqQbw+ssM+J+f4e7UTtAcDrwN0bPRNJkiSpxnrqiVgtGTbyl/NdC7wbOLO8LyI+AnwMeAR4CPgh2Xs2zomI8ntYpgFbkb0ZvOR64FXgtIgYVLbP48hmK65MKbXnzXcBfwROjIgdy2r3A/YHfuYL/SRJkromIgBob2/fSKU2pBQ2StezKK18g/g0sreEfyMi/g64nywQHEEWGI7P3/T9WETMzOsXRsQtZO+/OBK4F7istMOUUltEnEn2KN0HI+I6YHvgGLKlVueX1b4WEVPJ7g9ZGBFXkz196jiyezXOKPLkJUmSWlG/fv1Ys2YNK1euZNCgQRv/gipauTJbuNOvX79Cj9OSMxsAKaVlZMugLiELGacB+wE/BsallB4oKz8D+BLZU6JOJXuz9yXAQR3fqZFS+g4wmewN5FOBQ4GrgH1TSi91qL0ZOJjs3oyTgaOBW4EPpZSeQpIkSV1SChhLlixhxYoVtLe3b1YvyeuOlBLt7e2sWLGCJUuWABQe2Fp5ZoOU0l+Af8w/G6pLwKz805n9ziF7Q3lnaucCczdaKEmSpI0aMmQIK1euZNWqVTz77LP1Hk5T23LLLRkyZKPvmO6Wlg4bkiRJai29evVixIgRtLW1sWLFCtauXevMRhdEBP369WPQoEEMGTKEXr2KXehk2JAkSVJT6dWrF0OHDmXo0KH1Hoo2omXv2ZAkSZJUX4YNSZIkSYUwbEiSJEkqhGFDkiRJUiEMG5IkSZIKYdiQJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSIQwbkiRJkgph2JAkSZJUCMOGJEmSpEIYNiRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCsCFJkiSpEIYNSZIkSYUwbEiSJEkqhGFDkiRJUiEMG5IkSZIKYdiQJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSIQwbkiRJkgph2JAkSZJUCMOGJEmSpEIYNiRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCsCFJkiSpEIYNSZIkSYUwbEiSJEkqhGFDkiRJUiEMG5IkSZIKYdiQJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSIQwbkiRJkgph2JAkSZJUCMOGJEmSpEIYNiRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKkTLh42IODQi7oqI5RHxYkTcGhHjK9SdFBEPRcTqiHg2Ii6NiMFV9nl4RNwfEasiYllEzI6Id1apnRARd0bEqxHRFhE3RMSoWp+nJEmS1GhaOmxExMnAL4CRwH8APwH2Au6IiIllddOBy4B1wMXAImAqcHtE9OuwzxOA64FtgUuA24Bjgbsj4h0dag8E7gBGAd8DrgMOBu6LiF1rfLqSJElSQ9mi3gMoSkTsSBYGHgT2Tym9krf/B7AQ+Dqwb0SMBM4FfgNMTCm9ltdNB2YAnwdm5W2DycLIE8C4lNLyvP1W4ArgbOD0vK03WcBYDuyRUnoub58D3A5cABxZ5DWQJEmS6qmVZzY+D/QDTikFDYCU0u+BacDPIyKAKWSh6xuloJH7JrASOL6s7XBgG+CiUtDI9zkbeAqYku8TYDywG3BZKWjktfOAu4BPRsQ2tTpZSZIkqdG0ctg4GPhzSukegIjYtnRfRUrpWymlf0kpJWAC0A7ML/9ySmkVcB8wNiIG5s0T8u28CsebBwwDRnei9g6gN7D3ppyYJEmS1AxaMmzkswvvAR6JiA9GxG+BNuD5iHg8Ij5aVj4KWJJSWllhV48DAexSVpuAJ6vUQjabUaqFbMnVxmolSZKkltOq92xsDQwEtiO7P+IB4GTgncCpZEuoDk4p3Q4MoXJ4AFiRb4eUbVemlNZ1shaykLOx2qoiYmGVrtFV2iVJkqSG0Kpho7Ts6b1kT4D6VL5kioi4hWx51Cyy2Y/+ZE+hqmR1vh2Qb7taS5X6jrWSJElSy2nVsNFe9ut/LgUNgJTSAxExF/ho/r6LtbwZDDrqm29L4aCrteT1KzZSW1VKaVyl9nzGY+zGvi9JkiTVS0ves0H2uFmAN4DHKvQ/nG93IlvmtHWV/XRcCtUGDIiIPp2spcq+N7TESpIkSWoJLRk28pu9l5A98WmrCiVb5ts1wGJgh4jYskLdSLIbwks3eS/mrTeMd6wt1ZRvK728r2OtJEmS1HJaMmzk7s63B1To+xDZUqv/BhaQBYiJ5QV5+NgLWFj2pKoF+fbACvucCCwDHu1E7QHA62VjlCRJklpOK4eNy/Lt1yJi/exGRHwK+FvghpTSS8APyYLHORFRfg/LNLJZkSvK2q4HXgVOi4hBZfs8jmy24sqUUul+kbuAPwIn5m8zL9XuB+wP/Cyl9HINzlOSJElqSK16gzgppV9FxLeBLwK/j4ifkj0KdwrZEqvT8rrHImImWbhYmD+tajfgSOBe3gwtpJTaIuJM4NvAgxFxHbA9cAzZUqvzy2pfi4ipwI35fq8me/rUcWT3apxR5PlLkiRJ9dbKMxuklKby5g/3/wgcTjY7sXdK6U9lpWcAXyJ7StSpZG/2vgQ4qOM7NVJK3wEmA68AU4FDgauAffOZkvLam8neZL6Y7D0fRwO3Ah9KKT1V05OVJEmSGkzLzmyUpJR+SLZUakM1iey9G7M6uc85wJxO1s4F5namVpIkSWolLT2zIUmSJKl+DBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBWiZmEjIiZExIiN1IzJX4AnSZIkqcXVcmZjHnDsRmqOAb5bw2NKkiRJalCb/J6NiJgE7FneBBwSEdtW+coA4Ahg5aYeU5IkSVLz6M5L/R4ie3N23/z3CRiffzbkK904piRJkqQmsclhI6X0WETsCWxLNqtxB/CfZAHkbeXAOuCZlNJzm3pMSZIkSc2jOzMbpJT+UPp1RJwH3J5SWtDtUUmSJElqet0KG+VSSufVal+SJEmSml/NwgZAROwFnADsBPQjW17VUUopHVjL40qSJElqPDULGxHxUeDmTuwz1eqYkiRJkhpXLWc2ziWbyfgiWeh4CYOFJEmStNmqZdgYB1ydUvKlfZIkSZJq+gbxVcDzNdyfJEmSpCZWy7DxK+BjEVHppnBJkiRJm5laho0zgIHAjyPigxExJCK2rPSp4TElSZIkNaha3rOxgCxsfAqYtIG6VOPjSpIkSWpAtfyh/0/49ClJkiRJuVq+QXz/Wu1LkiRJUvOr5T0bkiRJkrReLd8g/m+dLE0ppX+q1XElSZIkNaZa3rMxbSP9iewN4wkwbEiSJEktrpZhY2KV9i2BMcDnyW4in1zDY0qSJElqULW8QfyuDXT/MiIuBx4BvghMr9VxJUmSJDWmHrtBPKX0EvBT4ISeOqYkSZKk+qnH06iG1uGYkiRJknpYj4WNiJgAHEe2lEqSJElSi6vlo2+XVenqBWwF9CF7GtW/1OqYkiRJkhpXLZ9GtYrssbYdJeDPwJPAD1JKN9bwmJIkSZIaVC2fRvVXtdqXJEmSpOZXjxvEJUmSJG0Gah42ImJKRMyLiL9ExJqIWBoRcyNiSq2PJUmSJKlx1TRsRMQc4ApgP+AN4PH8GH8HXJH3S5IkSdoM1CxsRMQXgKOB24HRKaXtUkp/k1IaBrwfuAU4KiKOr9UxJUmSJDWuWs5snEQ2k/GJlNL/lneklB4CPgk8BPxDDY8pSZIkqUHVMmy8B/hFSmlNpc6UUjvwC+BvanhMSZIkSQ2qlmFjHdB7IzXb5XWSJEmSWlwtw8YDZPdkDK3UGREjgCOAe2t4TEmSJEkNqpZh45tkMxcPRMSJETEmIrbNt/8fsADYGrighseUJEmS1KBq+QbxuRFxOlno+I8O3QG0A6enlO6s1TElSZIkNa6ahQ2AlNLFEfFzYDLZjeCDgeVkT6Ga0/EpVZIkSZJaV03DBkBK6XFgRq33K0mSJKm51OSejYjYNSKGVOmbHhF71eI4kiRJkppHt8JGRPSLiKuBR4DDKvQPB6YDd0fENRExuDvHkyRJktQ8NjlsRERv4GbgM8CfgZcqlK0GzgEWA0cCN0VEbOoxJUmSJDWP7sxsnAj8HfATYNeU0k0dC1JKr6SU/gV4f173YeCz3TimJEmSpCbRnbBxHPAn4PiU0gbfCp5SWkMWTv4MHN+NY0qSJAo60aIAACAASURBVElqEt0JG38D/HJjQaMkpbQKmAu8rxvHlCRJktQkuhM2+gIvdPE7zwL9u3FMSZIkSU2iO2FjCTCyi995N/BiN44pSZIkqUl0J2zMBz5W7f0aHeV1nwDu78YxJUmSJDWJ7oSNbwNbAz+JiK02VBgRA4Gf5vWzu3FMSZIkSU1ik8NGSukB4ELgQODRiPiniHh/RAyMzKCI+EBEnAH8D3AAcG2lR+RKkiRJaj1bdPP7/wS8lm/Pzz8dBfAGMDOvkyRJkrQZ6FbYSCkl4JyIuBL4B2A/4K+AgcAK4DHgDuAHKaUnuzdUSZIkSc2kuzMbAKSUHgP+sRb7kiRJktQaahI2JElSazho4gRefGFJ1f6hw4Yzd978HhyRpGZm2JAkSeu9+MISFs36bNX+sVMv78HRSGp23Xn0rSRJkiRVZdiQJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSITarsBERF0VEioj9K/SdFBEPRcTqiHg2Ii6NiMFV9nN4RNwfEasiYllEzI6Id1apnRARd0bEqxHRFhE3RMSoGp+aJEmS1HA2m7AREXsD/1ilbzpwGbAOuBhYBEwFbo+Ifh1qTwCuB7YFLgFuA44F7o6Id3SoPRC4AxgFfA+4DjgYuC8idq3VuUmSJEmNaIt6D6AnRER/4AdUCFcRMRI4F/gNMDGl9FrePh2YAXwemJW3DSYLI08A41JKy/P2W4ErgLOB0/O23mQBYzmwR0rpubx9DnA7cAFwZBHnK0mSJDWCzWVm46vACLIZiY6mkIWub5SCRu6bwErg+LK2w4FtgItKQQMgpTQbeAqYEhGRN48HdgMuKwWNvHYecBfwyYjYprsnJkmSJDWqlg8bEbEncBrZ7MVTFUomAO3A/PLGlNIq4D5gbEQMLKsFmFdhP/OAYcDoTtTeAfQG9u7cWUiSJEnNp6XDRn6/xeXAA8ClVcpGAUtSSisr9D0OBLBLWW0CnqxSC9lsRqkWsiVXG6uVJEmSWk6r37MxA9gVGJtSan9zhdNbDKFyeABYUVZT2q5MKa3rZC1AWydqq4qIhVW6RldplyRJkhpCy85sRMQ44MvA+SmlhzdQ2p/sKVSVrM63Azaxlir1HWslSZKkltOSMxsR0Zfs6VCPAOdvpHwtbwaDjvrm29WbWEtev2IjtVWllMZVas9nPMZu7PuSJElSvbRk2ADOAd4LTAZ2Kls+VXr607vy91w8TbbMaesq++m4FKoNGB0RfTo8uapaLfm+X9hIrSRJktRyWjVs7Jdvr67Sf1W+3RlYDEyIiC3zJ1CVG0l2Q3jpJu/FwL5kN4w/WqG2VFO+3bXs19VqJUmSpJbTqmFjOtljaDs6DjiM7Mbxh4FlwAKycDIR+EWpMCK2BPYCFpY9qWoB2Xs3DuTtYWNivr9Hy2rJa3/VofYA4HXg7q6dliRJxXr8maXsfuJ3q/Y//5flVfskqaOWDBsppbsqtUfEh/Jf3pVSujNv+yFwFnBORNyaUno9r5kGbEV270fJ9WRvED8tIq5MKa3I93Ec2WzFhSml9tIxgD8CJ0bEJSmlZ/Pa/YD9getSSi/X4HQlSaqZ19vhC2edV7X/7NNO7cHRSGp2LRk2uiKl9FhEzCQLFwsj4hay918cCdwLXFZW2xYRZwLfBh6MiOuA7YFjyJZanV9W+1pETAVuzPd7NdnTp44ju1fjjJ44P0mSJKleWvbRt110BvAlsqdEnUr2Zu9LgIM6vlMjpfQdshvPXwGmAoeS3QOyb0rppQ61NwMHk92bcTJwNHAr8KGUUqW3mUuSJEktY7Oa2UgpTSObwejYnoBZ+acz+5kDzOlk7VxgbheGKUmSJLUEZzYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSIQwbkiRJkgph2JAkSZJUCMOGJEmSpEIYNiRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCsCFJkiSpEIYNSZIkSYUwbEiSJEkqhGFDkiRJUiEMG5IkSZIKYdiQJEmSVIgt6j0ASZKkVjFh4oEsXfZC1f7ttxvG/Hm39+CIpPoybEiSJNXIokW/Y8cDJ1fvv/3qHhyNVH+GDUlqMgdNnMCLLyyp2j902HDmzpvfgyOSVPJG+xucfMgHqvaf/esre3A0Uv0ZNiSpybz4whIWzfps1f6xUy/vwdFIklSdN4hLkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCsCFJkiSpEIYNSZIkSYUwbEiSJEkqhGFDkiRJUiEMG5IkSZIKYdiQJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSIQwbkiRJkgph2JAkSZJUCMOGJEmSpEIYNiRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKsQW9R6AJKlrHn9mKbuf+N2q/c//ZXkPjkaSpOoMG5LUZF5vhy+cdV7V/rNPO7UHRyNJUnUuo5IkSZJUCGc2JEmS1COGDB3G6rXrqvYP6NeXthdf6MERqWiGDUmSJPWI1WvXcf7Mi6v2uwy09biMSpIkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCp1FJkiRJDWDCxANZuqz6o3+3324Y8+fd3oMj6j7DhiRJktQAli57gS9869qq/f/+j5N6cDS14TIqSZIkSYUwbEiSJEkqhGFDkiRJUiEMG5IkSZIKYdiQJEmSVAjDhiRJkqRCtHTYiIhtImJmRDwVEesiYmlEXBURIyvUnhQRD0XE6oh4NiIujYjBVfZ7eETcHxGrImJZRMyOiHdWqZ0QEXdGxKsR0RYRN0TEqFqfqyRJktRoWjZsRMQQ4F7gVOB/gYuAu4FjgPvKA0dETAcuA9YBFwOLgKnA7RHRr8N+TwCuB7YFLgFuA44F7o6Id3SoPRC4AxgFfA+4Djg4P/6utT1jSZIkqbG08kv9zgV2B6allC4qNUbEZOAq4N+ASXnoOBf4DTAxpfRaXjcdmAF8HpiVtw0mCyNPAONSSsvz9luBK4CzgdPztt5kAWM5sEdK6bm8fQ5wO3ABcGRhZy9JkiTVWcvObACTgDaycLBeSulqsrBwaN40hSx0faMUNHLfBFYCx5e1HQ5sA1xUChr5PmcDTwFTIiLy5vHAbsBlpaCR184D7gI+GRHbdPckJUmSpEbVymFjJlmAaK/Q9wrQJ599mAC0A/PLC1JKq4D7gLERMTBvnpBv51XY5zxgGDC6E7V3AL2BvTt3KpIkSVLzadllVCmlb1Vqj4gdgfcDD6eU3shv1l6SUlpZofxx4ABgF+APZPdeJODJKrWQzWY8ktdCNouyodpfbug8ImJhla7RVdolSZKkhtCyYaOSiOgF/DvZrMJ38+YhVA4PACvKakrblSmldZ2shWwp18ZqJUmStJl77tln+f73Zm2wv9lsVmGDLGB8jOxpUz/I2/qTPYWqktX5dsAm1lKlvmNtVSmlcZXa8xmPsRv7viRJkprDG+1vcPIhH6jaf/avr+zB0dRGK9+zsV5E9IqI7wMnA88Cf192M/ha3gwGHfXNt6s3sZYq9R1rJUmSpJbT8mEjIvoD15A9wvYZssfbPlNW0gZsXeXrHZdCtQEDIqJPJ2upsu8NLbGSJEmSWkJLh42IGATMBY4A/gcYn1Ja3KFsMbBDRGxZYRcjyW4If6KsNshuGK9UW6op31Z6eV/HWkmSJKnltGzYiIi+wE3Ah4H/Igsaf6pQuoAsQEzs8P0tgb2AhWVPqlqQbw+ssJ+JwDLg0U7UHgC8TvZGc0mSJKkltWzYAM4D9gfuAQ5OKb1Upe6HZO/ZOCciym+YnwZsRfZm8JLrgVeB0/JZEwAi4jiy2Yory97rcRfwR+DE/HG7pdr98nH9LKX08qaenCRJktToWvJpVBHxDuBL+W8fIgsHlUpnpZQei4iZZOFiYUTcQvb+iyOBe4HLSsUppbaIOBP4NvBgRFwHbA8cQ7bU6vyy2tciYipwY77fq8mePnUc2b0aZ9TwlCVJkqSG05JhA/gQULoH4/MbqLuK7G3iZwB/Ak4BTgX+AlwCfKXjOzVSSt+JiJfIwslUspmOq4AzO86epJRujoiDgelkT8JaB9wKnJFSeqpbZyhJkiQ1uJYMGymlX5Ddh9HZ+gTMyj+dqZ8DzOlk7Vyym9QlSZKkzUor37MhSZIkqY4MG5IkSZIKYdiQJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSIQwbkiRJkgph2JAkSZJUiC3qPQBJ6uigiRN48YUlVfuHDhvO3Hnze3BEkiRpUxg2JDWcF19YwqJZn63aP3bq5T04GkmStKlcRiVJkiSpEIYNSZIkSYVwGZW6zPX0kiRJ6gzDhrrM9fSSJEnqDJdRSZIkSSqEMxuSGs7jzyxl9xO/W7X/+b8s78HRSJKkTWXYkNRwXm+HL5x1XtX+s087tQdHI0mSNpXLqCRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCN4iryx5/Zim7n/jdqv3P/2V5D45GkiRJjcqwoS57vR2+cNZ5VfvPPu3UHhyNJEmSGpXLqCRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw4YkSZKkQhg2JEmSJBXCN4hLBTho4gRefGFJ1f6hw4Yzd978HhyRJElSzzNsSAV48YUlLJr12ar9Y6de3oOjkSRJqg+XUUmSJEkqhGFDkiRJUiEMG5IkSZIKYdiQJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhfClflIBHn9mKbuf+N2q/c//ZXkPjkaSJKk+DBtSAV5vhy+cdV7V/rNPO7UHRyNJklQfLqOSJEmSVAjDhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOSJElSIQwbkiRJkgph2JAkSZJUCMOGJEmSpEIYNiRJkiQVwrAhSZIkqRCGDUmSJEmFMGxIkiRJKoRhQ5IkSVIhDBuSJEmSCmHYkCRJklQIw0YPiIjeEfFPEfF4RKyJiCcj4qsR0bfeY5MkSZKKYtjoGf8X+FfgWWAm8EfgK8C1ERH1HJgkSZJUlC3qPYBWFxETgBOAnwJHp5RS3v4D4LPAx4Gb6zZASZIkqSDObBTvs/l2Rilo5M4DEnB8zw9JkiRJKp5ho3gTgGUppUfKG1NKfwSeAj5cl1FJkiRJBTNsFCi/AfyvgMVVSh4HtouIwT02KEmSJKmHxFtX9qiWImIYsAz4eUrpsAr91wCTgJ1TSk9X2cfCKrt//4ABA3qPGTOmVsPttN89+CDv2nFE1f7nnn2GD/zt3/bgiBqP16h7vH4b5vXpHq/fhnl9usfrt2Fenw1r1OvzyCOPsHr16raU0ju6+l3DRoEiYgTwJ+D6lNKRFfqvBKYA7+m4zKqsplrYeC/wKvB0bUbbaaPz7aM9fNxm5jXrGq9X13i9usbr1TVer67xenWN16tr6nm9/gpYnlLauatf9GlUxVqbb/tX6S+9Z2N1tR2klMbVdETdVAo/jTauRuY16xqvV9d4vbrG69U1Xq+u8Xp1jdera5r1ennPRrFeBtqBrav0D8m3L/XMcCRJkqSeY9goUEppHfAMsGuVkpHACymlV3puVJIkSVLPMGwUbwGwfUT8dXljRLwb2AW4qy6jkiRJkgpm2Cje7Hw7vUN76fdX9NxQJEmSpJ7jDeIFSyn9On/E7aciYgHZTMYewEfInlL1i7oOUJIkSSqIj77tARHRBzgHOB7YAXiebEbj/JTSa3UcmiRJklQYw4YkSZKkQnjPhiRJkqRCGDYkSZIkFcKwIUmSJKkQhg1JkiRJhTBsSJIkSSqEYUOdFhHbRMTMiHgqItZFxNKIuCoiRtZ7bI0oIraPiFkR8WRErImIxyLinyKid73H1ugi4qKISBGxf73H0qgi4v/m16jS59v1Hl8jiohDI+KuiFgeES9GxK0RMb7e42okG/h/qvyzf73H2WgiYmBE/FtEPJ3//fh8RPx7RLyj3mNrRPnfj9/Pr9OaiHg4Iv5//358U0RsFRHPRMSFVfpPioiHImJ1RDwbEZdGxOCeHmdn+OhbdUpEDAHuBnYH5gKLgNHAJ4G/AHullJ6s3wgbS3697gV2BX4GPAaMzz/XpZQm1XF4DS0i9gYWkP1jyMSU0p31HVFjiogHgHcDsyp0P5BSurWHh9TQIuJk4N+BZ4GfAAOAycCWwEdTSvPqOLyGERHnVul6F/AFYCnwvpTSsp4bVWOLiC3IXti7D9nfk/OB9wIfBx4l+/txef1G2FgiYnvgPmAn4Ddk1+x9wEeBG4BJKaX2+o2w/vLQ9VPgCOCilNK0Dv3TgRlkP4vdSvb/22HAb4HxKaW1PTrgjUkp+fGz0Q8wE0jA6R3aJ+ft19Z7jI30AS7Ir8spHdqvyNsPrvcYG/ED9Aceya9RAvav95ga8QP0BlYDN9Z7LM3wAXYE1gC/A7Yua38/8Drwm3qPsZE/ZMF/HtAOHFjv8TTaBzgq//NqTof2s/P2c+s9xkb6AFfm12Vmh/Zz8/Z/qPcY63x9tgV+Ufb34IUd+kcCr5H9o1yfsvbpef3Uep9Dx4/LqNRZk4A24OLyxpTS1cATwKH1GFQD2xp4GPiPDu3X5tt9enY4TeOrwAjg+noPpMHtThbM/lDvgTSJzwP9yML/K6XGlNLvgWnAzyMi6jW4JvAPwP7AZSml2+s8lkb0vnz7ow7ts/Otf97n8lmgI4EXycJYuX8F/gyc0tPjahQR8Rmy2bBDgFuqlE0BtgC+kVJ6raz9m8BK4PhCB7kJtqj3ANQ0ZgK9UuWpzVeAnSKid0rpjR4eV0NKKX2hStf78+3LPTWWZhERewKnkf3wt2Odh9Po/jbfPgQQEcOA11JK/n9V2cHAn1NK9wBExLZA/5TSn1NK36rv0BpbviT0a2R/Zp1V5+E0qufy7Rjg5rL2Ufn2FVQyjGzp4t0ppTXlHSml1yPiYeDvImKbzfTPs5PJAsOJwKtU/ofcCWSzjPPLG1NKqyLiPmBiRAxMKa0serCd5cyGOiWl9K2U0syO7RGxI9kP0P9j0KgsIraNiHER8a9k/3L/CtmaceUioh9wOfAAcGmdh9MMSqF1n4h4BlgGvBQRCyLiA3UcV8PJZyzeAzwSER+MiN+SzdI+HxGPR8RH6zvChncq2bKOC1JKbfUeTIOa8//au/NguYoqjuPfn4JAAoKEXQQDYVEQLUBCFUsCkhJFBEXAKkqQVUVASkE2WQKWIkIJBlBL0VgBRJaIgIgssoVFUHZJsYUYRIIYkISEBEiOf5weMgwzeY/gfXOH9/tUveqXezszZ24lM3Nud58GpgDHS/q6pE0k7UGObAT53mbp5dIu2+F8Y0H9egMQSx2NBTaIiKsW0Wd9YHqHZOIxQMC6VQS3uDyyYYtN0rvIBZfvBs7tcjh1dlT5AfgXsHNEPL2I/oPRSeRi+k0jYoFntPSpkWzsBpwDTAe2AvYDJkkaFRF/7VZwNbM8MBRYBbiBTGi/AqxOfpG+StKOnh70ZpKGAIcAM/F7fEcR8V9J25HTXporwT0P7BoR13Ynsvop1+ohYDNJH4mIBxvnJH2Mhe9ttayqVLXoX6GKFcnktp1ZTX1qw8mGvR3nAjuR1RB+2eVY6mwSueh5Q+Aw4BeSnHAUkjYDjgROiYi/dzueHvFIafeJiGfL7+Ml3UbeRf0J8PGuRFY/Q0u7MXAZsHuU1ZSSriar4owjRz/sjfYEVgDODFdT6kjScLIa1epk8v9X8v3+UOBISXdFxPQuhlg3J5L/F/8k6RjgfmAj4DTyxska5E1Ma29p4JUO5xojR8sMUCz94mTD3rIyovETctHlP8k7N68u+m8NXs3DoZL+QM6znABs37WgakLSe8gKXZOB73U3mt4REd/ocHy8pKOBzSWtGRH/HODQ6qh5ndkJjUQDICLulnQt8ElJ60fEowMfXq3tXdrx3QyiB/yILGzxhYi4rHFQ0qXAHWRhEO/nUkTEREkHkGtBx5fDr5DJxtLkur3arDeooXnkdWrnPaV9ucP5rvCaDXtLJC0NXEImGk+R+yA81d2oekdETCJrim9X1rsMdseRd5y/TxYZGCFpBHk3FeD95ZhvjPRfY3ToA12Noj4ad+Tns3BEqFnjeq09MOH0BkkrAdsAk0vVLmtD0pLkfhoPNycaAGUq4++BrSQN1jUIbUXEeeTeLWPIAg5rRMTx5KgGLFx0b2/2PDk9tJ0Vm/rUhj/Ard8kLUfWft4GeBj4VERM625U9VM+fD4PLIiIS9p0aQynr0mODA1mo0p7QYfz55d2ODC18mh6gKShZCnNWRFxZ5suy5X2pYGLqr4iYrak6cBq5KLU1spAQ0o7F2u2AzmV5ZpuB1JzK5HXqdNn4ZOlXZtcvGvk52REvARc33JqU/K9y98tOnsc2FbSkIiY03JuHbIowRMDH1ZnTjasX8p0lyvIRONWYJeIeKG7UdXWAuA8YI6kic1VusoUtEa1oMGeaEDO3V25zfG9yd1QTyLvPHu34oWWA64FHiX323idpGWBzci7+ZMHPrTaup28AbA9uUNxsy3J/7MPDXRQNdeY9nNzV6Oov/+QU1Y+LKldefgNS+v3sELSDeRUzxVbPh83Ia/XxA5l9i1NIm/UbUfeAAZeL+gwEvhbncregqdRWf+NJTd1uoPc/dqJRgflzfNi8kv04S2njyXvPNzo+fQQETdHxKWtP+QXaYDG+da7N4NWWWh6B7C+pL1bTp9MDqP/NCJeG/Dg6uvnpT2lJGQASNqd3LPkd35Pe5ONS3tPV6OoubJe8VJgLeDo5nOSxpBFVCZT9sQxAO4kq029vvlcKX/eKHs+rhtB9ZAJ5A2S41qmGB9Bjt6O70ZQi6KmtXJmbUkaRg5pDiF3xO60RmNc8+68g5mk1cgqN2uRmzw9TN5xGE1ev20jYmq34qs7SacD3yLXBN3U5XBqp5SIvIn8wJ5I7ji7Ffnv6y/kdavVAsFukzSOLOM6hbwZsAq5E+8MYKSnhL6RpGnAsIgY2mfnQU7SKuTo2brAjcBd5E2lzwNzgE9ExN3di7BeyvV6gNxT4wJyavFnyU0Rz46IQ7sYXm1IGk3+ezojIo5oOfdDMrl4gCy5vB5ZCv1OYFREdKpW1RVONqxPknYCFrXBTMNwf4FeSNKq5J3mnclRjqfJKRzfjYgZ3Yyt7pxs9E3SOuQ0s0+Sm65NIzcXO9UjQe1J+hJZfnpjcurLtcDRft96M0kvkhuHbdBnZ2vclDse2JVc5PwCcB0wNiK8VqOFpA+QFQi3J9+/JgPnloXjRp/JhsibJweTaxpnkMV7jo+IWdSMkw0zMzMzM6uE12yYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmVllJN0kaaqk4ZIulzRT0ouSJkpaTdIwSb+SNEPS85KulPTBxXieEZIukPSUpJclPS7pR5JWbtN3tKRrJL0g6b+SbpW0S5t+W5SYZ0iaK+nvko6RtFRLv6mSJknaR9Iz5XWc0HR+R0l/Lq97tqS7JB0oSW/1dZqZ9RpFRLdjMDOzdyhJNwEbA7OBZ4EbgS2BbYFbgOWBdwNXAxsCnwXuATaPfn5ASVodeAhYBrgYeKo85y7AfcDIiHi19N0LmADMAC4CXgb2ANYGvhoRPyv9dgcuBOYCvwFmAjsAHwVuBcZExLzSdyowBFiuPLaA6yPit5IOAn4GTAOuLNdhFDASGB8R+/bzUpqZ9SQnG2ZmVpmSbIwCLgP2jIj5kt4FPAasA9wObBcRr5T+NwDbAxtGxCP9fI7DgLOAfSNifNPxM4BvkonB9ZLeCzwNzAM+GhFPl37DgAeBpYFVgaFkciBgs4h4tPRbAjgP2Bs4KSLGluNTyWTlqIg4ren53w88DtwGfCYi5jadOwk4EdglIq7oz+s0M+tFnkZlZmYD4ayImA8QEQvI0QuAMxuJRnFvadd6C4/9Wmk3bZniNBZYIyKuL3/+HLAscG4j0SjxzAAOB04lE41dyVGKcY1Eo/R7jUxeZgP7tYljQsuf9yITmJ8Da5apXiMkjQD+2BSTmdk71hLdDsDMzAaF1lGK2aWd0nL8pdIuRf9dQiYWhwL7SLqO/DJ/VUQ809TvQ6W9vfUBIuLixu+SNim/3tam3wxJk4HNJS0fES+WU/Nangtg89JetIjY11nEOTOznudkw8zMBsKcDsfnvd0HjojnJG0BfAfYrelnvqSLgIMiYg4wrPyV5/p4yPeWdnqH88+WdijQSDZeatPvfaU9oKlfq+f7iMXMrKc52TAzs54XEU8C+0v6CrAFuZj7IHIq07PAt4BZpXu7ClVLAfPLVKmZ5fBqHZ6ucbxTAtHQeL57IuLeRfY0M3uH8poNMzPraZJ2k3S2pPdFxGsRcXtEnEwmHTS195V2ZJuHORKYK+nT5GJxgK3bPNfKwEbAExExu/V8i/tLO6bN44wspXeP6+MxzMx6mpMNMzPrdZsAXwday8g21kM0FoNfTo5aHCxplUanUqVqf3Kq16TSbxZwSNP6jYax5KLv8/sR1wRy8fq3JQ1ver4lgdOA2kpzxAAAAYlJREFUFcgyumZm71ieRmVmZr3uHODLwOmSRgMPkCVsv0guRP8+QETMlPQ1MlG4v6znWECu71gbODAiZgJI2o/cZ+NOSZcB/yBL+G5NJgg/6CuoiJgi6QjgTOBeSReSazt2JvcU+XFE3PL/uABmZnXlZMPMzHpaRPxb0jbAseSUpTHkIvCrgBNbytdeKOk54BhyNGM+8Ddyj44bm/pdKmkKcBzwabJk7qPAEWSS8Go/YztL0mPkmpG9yBkFj5Clc3/9tl64mVkP8KZ+ZmZWO5JWAlbqR9c5ETGt6njMzGzxeGTDzMzq6BByh+2+3AyMrjYUMzNbXE42zMysji4GHupHv772zDAzsy7yNCozMzMzM6uES9+amZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVklnGyYmZmZmVkl/gfZXxSdOPXxnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 397
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(x=df_score['m_score'], hue=df_score['voted'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature_importance DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada_fm=pd.DataFrame(zip(X_train_d.columns, ada.feature_importances_))\n",
    "ada_list1 = list(ada_fm[ada_fm[1]==0][0])\n",
    "ada_list2 = ada_fm.sort_values(1, ascending=False)[:45][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbc_fm=pd.DataFrame(zip(X_train_d.columns, gbc.feature_importances_))\n",
    "gbc_list1 = list(gbc_fm[gbc_fm[1]==0][0])\n",
    "gbc_list2 = gbc_fm.sort_values(1, ascending=False)[:45][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_fm=pd.DataFrame(zip(X_train_d.columns, xgb.feature_importances_))\n",
    "xgb_list1 = list(xgb_fm[xgb_fm[1]==0][0])\n",
    "xgb_list2 = xgb_fm.sort_values(1, ascending=False)[:45][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_fm=pd.DataFrame(zip(X_train_d.columns, lgbm.feature_importances_))\n",
    "lgbm_list1 = list(lgbm_fm[lgbm_fm[1]==0][0])\n",
    "lgbm_list2 = lgbm_fm.sort_values(1, ascending=False)[:45][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_importance 가 0인 애들 교집합\n",
    "ada_gbc1 = list(set(ada_list1).intersection(gbc_list1))\n",
    "ada_gbc_xgb1 = list(set(ada_gbc1).intersection(xgb_list1))\n",
    "ada_gbc_xgb_lgbm1 = list(set(ada_gbc_xgb1).intersection(lgbm_list1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ada_gbc_xgb_lgbm1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 116 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_importance 상위 45인 애들 교집합\n",
    "ada_gbc2 = list(set(ada_list2).intersection(gbc_list2))\n",
    "ada_gbc_xgb2 = list(set(ada_gbc2).intersection(xgb_list2))\n",
    "ada_gbc_xgb_lgbm2 = list(set(ada_gbc_xgb2).intersection(lgbm_list2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ada_gbc_xgb_lgbm2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_importance 상위 45인 애들 합집합\n",
    "ada_gbc_xgb_lgbm3 = list(ada_list2) + list(gbc_list2) + list(xgb_list2) + list(lgbm_list2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ada_gbc_xgb_lgbm3 = list(set(ada_gbc_xgb_lgbm3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ada_gbc_xgb_lgbm3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 94"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature_importance 0인 컬럼들 빼고 돌린거"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 118개 컬럼 drop. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_d1 = X_train_d.drop(ada_gbc_xgb_lgbm1, axis=1)\n",
    "X_test_d1 = X_test_d.drop(ada_gbc_xgb_lgbm1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit time : 47.64043092727661\n"
     ]
    },
    {
     "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>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.650402</td>\n",
       "      <td>0.644314</td>\n",
       "      <td>0.641925</td>\n",
       "      <td>0.759590</td>\n",
       "      <td>0.695818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.656250</td>\n",
       "      <td>0.649578</td>\n",
       "      <td>0.643999</td>\n",
       "      <td>0.775907</td>\n",
       "      <td>0.703826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.648209</td>\n",
       "      <td>0.644031</td>\n",
       "      <td>0.648809</td>\n",
       "      <td>0.723138</td>\n",
       "      <td>0.683960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.657255</td>\n",
       "      <td>0.651714</td>\n",
       "      <td>0.649821</td>\n",
       "      <td>0.756639</td>\n",
       "      <td>0.699174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.650402  0.644314   0.641925  0.759590  0.695818\n",
       "GBC   0.656250  0.649578   0.643999  0.775907  0.703826\n",
       "XGB   0.648209  0.644031   0.648809  0.723138  0.683960\n",
       "LGBM  0.657255  0.651714   0.649821  0.756639  0.699174"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "result1 = models_score_df(models, model_names, X_train_d1, X_test_d1, y_train_d, y_test_d)\n",
    "\n",
    "print(\"Fit time : {}\".format(time.time()-start_time))\n",
    "result1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_d1.to_csv('../MACH_data/X_train_data_without_0feature.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_d1.to_csv('../MACH_data/X_test_data_without_0feature.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_d.to_csv('../MACH_data/Y_train_data_without_0feature.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_d.to_csv('../MACH_data/Y_test_data_without_0feature.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature importance 가 0으로 나온 컬럼들 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ada_gbc_xgb_lgbm1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['country_MP',\n",
       " 'country_VE',\n",
       " 'country_NONE',\n",
       " 'country_FJ',\n",
       " 'country_JP',\n",
       " 'country_LS',\n",
       " 'country_NR',\n",
       " 'country_LU',\n",
       " 'country_EE',\n",
       " 'country_PA',\n",
       " 'country_TZ',\n",
       " 'country_RE',\n",
       " 'country_BB',\n",
       " 'country_RW',\n",
       " 'country_AZ',\n",
       " 'country_MC',\n",
       " 'country_ME',\n",
       " 'country_GM',\n",
       " 'country_SO',\n",
       " 'country_MN',\n",
       " 'country_MV',\n",
       " 'country_KZ',\n",
       " 'country_BA',\n",
       " 'country_IQ',\n",
       " 'country_UG',\n",
       " 'country_GD',\n",
       " 'country_PY',\n",
       " 'country_GE',\n",
       " 'country_GY',\n",
       " 'country_GI',\n",
       " 'country_VU',\n",
       " 'country_MO',\n",
       " 'country_PE',\n",
       " 'country_CM',\n",
       " 'country_NP',\n",
       " 'country_SY',\n",
       " 'country_HT',\n",
       " 'country_LY',\n",
       " 'country_BW',\n",
       " 'country_DZ',\n",
       " 'country_CY',\n",
       " 'country_FO',\n",
       " 'country_OM',\n",
       " 'country_KN',\n",
       " 'country_MW',\n",
       " 'country_DO',\n",
       " 'country_AM',\n",
       " 'country_MM',\n",
       " 'country_VI',\n",
       " 'country_CL',\n",
       " 'country_CI',\n",
       " 'country_LC',\n",
       " 'country_GF',\n",
       " 'country_LT',\n",
       " 'country_MU',\n",
       " 'country_UY',\n",
       " 'country_ZW',\n",
       " 'country_BM',\n",
       " 'country_DJ',\n",
       " 'country_IM',\n",
       " 'country_SR',\n",
       " 'country_KH',\n",
       " 'country_AG',\n",
       " 'country_NI',\n",
       " 'country_MG',\n",
       " 'country_SI',\n",
       " 'country_CW',\n",
       " 'country_BT',\n",
       " 'country_CD',\n",
       " 'country_SN',\n",
       " 'country_VC',\n",
       " 'country_PF',\n",
       " 'country_LR',\n",
       " 'country_GL',\n",
       " 'country_TN',\n",
       " 'country_AF',\n",
       " 'country_SD',\n",
       " 'country_PS',\n",
       " 'country_PL',\n",
       " 'country_BQ',\n",
       " 'country_TL',\n",
       " 'country_GP',\n",
       " 'country_GU',\n",
       " 'country_LA',\n",
       " 'country_JE',\n",
       " 'country_KG',\n",
       " 'country_MD',\n",
       " 'country_GG',\n",
       " 'country_SC',\n",
       " 'country_GA',\n",
       " 'country_MZ',\n",
       " 'country_LI',\n",
       " 'country_TJ',\n",
       " 'country_CR',\n",
       " 'country_BJ',\n",
       " 'country_BS',\n",
       " 'country_AO',\n",
       " 'country_PG',\n",
       " 'country_TO',\n",
       " 'country_BO',\n",
       " 'country_BY',\n",
       " 'country_CU',\n",
       " 'country_UZ',\n",
       " 'country_ML',\n",
       " 'country_PK']"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi_0_country = []\n",
    "for x in ada_gbc_xgb_lgbm1:\n",
    "    if 'country' in x:\n",
    "        fi_0_country.append(x)\n",
    "fi_0_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(fi_0_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['VCL3_n_know',\n",
       " 'VCL2_n_know',\n",
       " 'VCL4_n_know',\n",
       " 'VCL16_n_know',\n",
       " 'VCL5_n_know',\n",
       " 'VCL14_n_know',\n",
       " 'VCL15_n_know',\n",
       " 'VCL13_n_know',\n",
       " 'VCL1_n_know',\n",
       " 'VCL10_n_know']"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi_0_vcl = []\n",
    "for x in ada_gbc_xgb_lgbm1:\n",
    "    if 'VCL' in x:\n",
    "        fi_0_vcl.append(x)\n",
    "fi_0_vcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['race_Indigenous Australian', 'religion_Sikh', 'hand_Both']"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi_0_etc = []\n",
    "for x in ada_gbc_xgb_lgbm1:\n",
    "    if ('VCL' not in x) & ('country' not in x):\n",
    "        fi_0_etc.append(x)\n",
    "fi_0_etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature_importance 상위 45개 교집합"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 9개 컬럼만 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q4_VN_ppl_good',\n",
       " 'race_White',\n",
       " 'age',\n",
       " 'TYP_quiet',\n",
       " 'education_Less than high school',\n",
       " 'education_High school',\n",
       " 'country_US',\n",
       " 'TYP_depnd',\n",
       " 'country_HU']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada_gbc_xgb_lgbm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_d2 = X_train_d.drop(ada_gbc_xgb_lgbm2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_d2 = X_test_d.drop(ada_gbc_xgb_lgbm2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit time : 58.48924708366394\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.635874</td>\n",
       "      <td>0.632508</td>\n",
       "      <td>0.642171</td>\n",
       "      <td>0.696233</td>\n",
       "      <td>0.668110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.633955</td>\n",
       "      <td>0.630047</td>\n",
       "      <td>0.638037</td>\n",
       "      <td>0.704044</td>\n",
       "      <td>0.669417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.626736</td>\n",
       "      <td>0.623142</td>\n",
       "      <td>0.633270</td>\n",
       "      <td>0.691199</td>\n",
       "      <td>0.660968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.636696</td>\n",
       "      <td>0.632834</td>\n",
       "      <td>0.640573</td>\n",
       "      <td>0.705954</td>\n",
       "      <td>0.671676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.635874  0.632508   0.642171  0.696233  0.668110\n",
       "GBC   0.633955  0.630047   0.638037  0.704044  0.669417\n",
       "XGB   0.626736  0.623142   0.633270  0.691199  0.660968\n",
       "LGBM  0.636696  0.632834   0.640573  0.705954  0.671676"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "result2 = models_score_df(models, model_names, X_train_d2, X_test_d2, y_train_d, y_test_d)\n",
    "\n",
    "print(\"Fit time : {}\".format(time.time()-start_time))\n",
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature_importance 상위 45개 합집합"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 94개의 컬럼만 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ada_gbc_xgb_lgbm3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_d3 = X_train_d.drop(ada_gbc_xgb_lgbm3, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_d3 = X_test_d.drop(ada_gbc_xgb_lgbm3, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit time : 36.968353033065796\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.581506</td>\n",
       "      <td>0.576212</td>\n",
       "      <td>0.589294</td>\n",
       "      <td>0.676445</td>\n",
       "      <td>0.629869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.582420</td>\n",
       "      <td>0.576606</td>\n",
       "      <td>0.588603</td>\n",
       "      <td>0.686686</td>\n",
       "      <td>0.633873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.581049</td>\n",
       "      <td>0.576098</td>\n",
       "      <td>0.589881</td>\n",
       "      <td>0.669849</td>\n",
       "      <td>0.627327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.584887</td>\n",
       "      <td>0.579365</td>\n",
       "      <td>0.591414</td>\n",
       "      <td>0.683909</td>\n",
       "      <td>0.634307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.581506  0.576212   0.589294  0.676445  0.629869\n",
       "GBC   0.582420  0.576606   0.588603  0.686686  0.633873\n",
       "XGB   0.581049  0.576098   0.589881  0.669849  0.627327\n",
       "LGBM  0.584887  0.579365   0.591414  0.683909  0.634307"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "result3 = models_score_df(models, model_names, X_train_d3, X_test_d3, y_train_d, y_test_d)\n",
    "\n",
    "print(\"Fit time : {}\".format(time.time()-start_time))\n",
    "result3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RobustScaler "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### robust 함수 적용 후 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.650402</td>\n",
       "      <td>0.644314</td>\n",
       "      <td>0.641925</td>\n",
       "      <td>0.759590</td>\n",
       "      <td>0.695818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.656159</td>\n",
       "      <td>0.649482</td>\n",
       "      <td>0.643907</td>\n",
       "      <td>0.775907</td>\n",
       "      <td>0.703771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.648209</td>\n",
       "      <td>0.644031</td>\n",
       "      <td>0.648809</td>\n",
       "      <td>0.723138</td>\n",
       "      <td>0.683960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.657255</td>\n",
       "      <td>0.651714</td>\n",
       "      <td>0.649821</td>\n",
       "      <td>0.756639</td>\n",
       "      <td>0.699174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.650402  0.644314   0.641925  0.759590  0.695818\n",
       "GBC   0.656159  0.649482   0.643907  0.775907  0.703771\n",
       "XGB   0.648209  0.644031   0.648809  0.723138  0.683960\n",
       "LGBM  0.657255  0.651714   0.649821  0.756639  0.699174"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q1E_notell_2u',\n",
       " 'Q2E_ppl_nd_dangun',\n",
       " 'Q3E_do_moral',\n",
       " 'Q4E_ppl_good',\n",
       " 'Q5E_ppl_bad',\n",
       " 'Q6E_hnsty_best',\n",
       " 'Q7E_lying_bad',\n",
       " 'Q8E_ppl_lazy',\n",
       " 'Q9E_humble_hnst',\n",
       " 'Q10E_hnstly_ask',\n",
       " 'Q11E_leader_clean',\n",
       " 'Q12E_trust_trouble',\n",
       " 'Q13E_ppl_criminal',\n",
       " 'Q14E_ppl_brave',\n",
       " 'Q15E_abu_good',\n",
       " 'Q16E_ppl_good',\n",
       " 'Q17E_ppl_notbad',\n",
       " 'Q18E_komsu_better',\n",
       " 'Q19E_anrocksa_ok',\n",
       " 'Q20E_money_good',\n",
       " 'introelapse',\n",
       " 'testelapse',\n",
       " 'surveyelapse',\n",
       " 'age',\n",
       " 'familysize']"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = []\n",
    "for col in df_18.columns:\n",
    "    if (\"E\" in col) | (\"age\" in col) |(\"family\" in col) |(\"elapse\" in col):\n",
    "        num_cols.append(col)\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_d_ro = X_train_d.copy()\n",
    "X_test_d_ro = X_test_d.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1E_notell_2u</th>\n",
       "      <th>Q2E_ppl_nd_dangun</th>\n",
       "      <th>Q3E_do_moral</th>\n",
       "      <th>Q4E_ppl_good</th>\n",
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       "      <th>Q9E_humble_hnst</th>\n",
       "      <th>Q10E_hnstly_ask</th>\n",
       "      <th>...</th>\n",
       "      <th>Q16E_ppl_good</th>\n",
       "      <th>Q17E_ppl_notbad</th>\n",
       "      <th>Q18E_komsu_better</th>\n",
       "      <th>Q19E_anrocksa_ok</th>\n",
       "      <th>Q20E_money_good</th>\n",
       "      <th>introelapse</th>\n",
       "      <th>testelapse</th>\n",
       "      <th>surveyelapse</th>\n",
       "      <th>age</th>\n",
       "      <th>familysize</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>54695</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>21</td>\n",
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       "      <th>46053</th>\n",
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       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>217</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>32791</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>138</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>33975</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>148</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>22260</th>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>341.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>120</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33634</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>176</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>32842</th>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>214</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>47280</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>184</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33106</th>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>171</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43774 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1E_notell_2u  Q2E_ppl_nd_dangun  Q3E_do_moral  Q4E_ppl_good  \\\n",
       "54695            3.0                4.0           4.0           2.0   \n",
       "46053            8.0                6.0           5.0           4.0   \n",
       "28461            5.0                3.0           1.0          14.0   \n",
       "32791            7.0                4.0           4.0           4.0   \n",
       "33975            6.0                5.0           6.0           3.0   \n",
       "...              ...                ...           ...           ...   \n",
       "22260            6.0               19.0          10.0           6.0   \n",
       "33634           11.0                7.0           7.0           6.0   \n",
       "32842           11.0                6.0          82.0          60.0   \n",
       "47280            5.0                3.0          13.0           4.0   \n",
       "33106           11.0                5.0           5.0           4.0   \n",
       "\n",
       "       Q5E_ppl_bad  Q6E_hnsty_best  Q7E_lying_bad  Q8E_ppl_lazy  \\\n",
       "54695          7.0             2.0            2.0          10.0   \n",
       "46053          6.0            13.0           46.0           6.0   \n",
       "28461          6.0             3.0           10.0          12.0   \n",
       "32791          6.0             3.0            3.0           3.0   \n",
       "33975          9.0             4.0            8.0           6.0   \n",
       "...            ...             ...            ...           ...   \n",
       "22260         14.0            11.0           12.0          90.0   \n",
       "33634         16.0             5.0           15.0           7.0   \n",
       "32842         15.0             4.0           11.0           7.0   \n",
       "47280          5.0             9.0            3.0           4.0   \n",
       "33106          9.0             4.0            4.0           7.0   \n",
       "\n",
       "       Q9E_humble_hnst  Q10E_hnstly_ask  ...  Q16E_ppl_good  Q17E_ppl_notbad  \\\n",
       "54695              5.0             11.0  ...            3.0              6.0   \n",
       "46053              7.0             12.0  ...            3.0              7.0   \n",
       "28461              7.0              9.0  ...            5.0              6.0   \n",
       "32791             12.0              5.0  ...            3.0              5.0   \n",
       "33975             18.0             20.0  ...            7.0             10.0   \n",
       "...                ...              ...  ...            ...              ...   \n",
       "22260             20.0             20.0  ...            4.0             41.0   \n",
       "33634              7.0             14.0  ...            6.0             12.0   \n",
       "32842             17.0             32.0  ...            6.0              7.0   \n",
       "47280              7.0             12.0  ...            5.0              4.0   \n",
       "33106             11.0             19.0  ...            6.0              9.0   \n",
       "\n",
       "       Q18E_komsu_better  Q19E_anrocksa_ok  Q20E_money_good  introelapse  \\\n",
       "54695                7.0               4.0              4.0         22.0   \n",
       "46053               13.0              14.0              8.0          4.0   \n",
       "28461                4.0               6.0              9.0         13.0   \n",
       "32791                6.0               7.0             11.0         13.0   \n",
       "33975                6.0               5.0             21.0          4.0   \n",
       "...                  ...               ...              ...          ...   \n",
       "22260              341.0               5.0              5.0         13.0   \n",
       "33634                9.0               8.0             20.0         30.0   \n",
       "32842                7.0               7.0             11.0         24.0   \n",
       "47280                6.0               8.0             18.0          6.0   \n",
       "33106                6.0               6.0             15.0          2.0   \n",
       "\n",
       "       testelapse  surveyelapse  age  familysize  \n",
       "54695        91.0           225   21           2  \n",
       "46053       184.0           236   28           3  \n",
       "28461       163.0           217   35           2  \n",
       "32791       106.0           138   18           1  \n",
       "33975       172.0           148   23           3  \n",
       "...           ...           ...  ...         ...  \n",
       "22260       634.0           120   22           1  \n",
       "33634       184.0           176   21           1  \n",
       "32842       324.0           214   20           4  \n",
       "47280       133.0           184   18           2  \n",
       "33106       150.0           171   52           2  \n",
       "\n",
       "[43774 rows x 25 columns]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_d[num_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "rbscale = RobustScaler().fit(X_train_d_ro[num_cols])\n",
    "X_train_d_ro[num_cols] = rbscale.transform(X_train_d_ro[num_cols])\n",
    "X_test_d_ro[num_cols] = rbscale.transform(X_test_d_ro[num_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Q1_TP_notell_2u</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E_notell_2u</th>\n",
       "      <th>Q2_TP_ppl_nd_dangun</th>\n",
       "      <th>Q2I</th>\n",
       "      <th>Q2E_ppl_nd_dangun</th>\n",
       "      <th>Q3_TN_do_moral</th>\n",
       "      <th>Q3I</th>\n",
       "      <th>Q3E_do_moral</th>\n",
       "      <th>Q4_VN_ppl_good</th>\n",
       "      <th>...</th>\n",
       "      <th>race_Arab</th>\n",
       "      <th>race_Asian</th>\n",
       "      <th>race_Black</th>\n",
       "      <th>race_Indigenous Australian</th>\n",
       "      <th>race_Native American</th>\n",
       "      <th>race_Other</th>\n",
       "      <th>race_White</th>\n",
       "      <th>married_Currently married</th>\n",
       "      <th>married_Never married</th>\n",
       "      <th>married_Previously married</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54695</th>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46053</th>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28461</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>33975</th>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22260</th>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33634</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32842</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>47280</th>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>33106</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
       "<p>43774 rows × 339 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "54695              5.0  11.0            3.0                  5.0  18.0   \n",
       "46053              3.0  19.0            8.0                  2.0   8.0   \n",
       "28461              5.0   7.0            5.0                  4.0  18.0   \n",
       "32791              2.0  17.0            7.0                  1.0  12.0   \n",
       "33975              5.0  13.0            6.0                  5.0  20.0   \n",
       "...                ...   ...            ...                  ...   ...   \n",
       "22260              4.0  19.0            6.0                  5.0   3.0   \n",
       "33634              5.0   3.0           11.0                  5.0  11.0   \n",
       "32842              4.0   7.0           11.0                  3.0  12.0   \n",
       "47280              4.0  17.0            5.0                  5.0  19.0   \n",
       "33106              1.0  10.0           11.0                  1.0   3.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "54695                4.0             1.0  14.0           4.0             1.0   \n",
       "46053                6.0             5.0   7.0           5.0             3.0   \n",
       "28461                3.0             5.0  11.0           1.0             4.0   \n",
       "32791                4.0             4.0  14.0           4.0             4.0   \n",
       "33975                5.0             2.0   4.0           6.0             1.0   \n",
       "...                  ...             ...   ...           ...             ...   \n",
       "22260               19.0             3.0   9.0          10.0             3.0   \n",
       "33634                7.0             5.0  16.0           7.0             3.0   \n",
       "32842                6.0             2.0  13.0          82.0             2.0   \n",
       "47280                3.0             5.0  13.0          13.0             4.0   \n",
       "33106                5.0             3.0  14.0           5.0             5.0   \n",
       "\n",
       "       ...  race_Arab  race_Asian  race_Black  race_Indigenous Australian  \\\n",
       "54695  ...          0           0           0                           0   \n",
       "46053  ...          0           0           0                           0   \n",
       "28461  ...          0           0           0                           0   \n",
       "32791  ...          0           1           0                           0   \n",
       "33975  ...          0           0           0                           0   \n",
       "...    ...        ...         ...         ...                         ...   \n",
       "22260  ...          0           1           0                           0   \n",
       "33634  ...          0           0           1                           0   \n",
       "32842  ...          0           0           0                           0   \n",
       "47280  ...          0           0           0                           0   \n",
       "33106  ...          0           0           0                           0   \n",
       "\n",
       "       race_Native American  race_Other  race_White  \\\n",
       "54695                     0           0           1   \n",
       "46053                     0           0           1   \n",
       "28461                     0           0           1   \n",
       "32791                     0           0           0   \n",
       "33975                     0           0           1   \n",
       "...                     ...         ...         ...   \n",
       "22260                     0           0           0   \n",
       "33634                     0           0           0   \n",
       "32842                     0           0           1   \n",
       "47280                     0           0           1   \n",
       "33106                     0           0           1   \n",
       "\n",
       "       married_Currently married  married_Never married  \\\n",
       "54695                          0                      1   \n",
       "46053                          0                      1   \n",
       "28461                          0                      1   \n",
       "32791                          0                      1   \n",
       "33975                          0                      1   \n",
       "...                          ...                    ...   \n",
       "22260                          0                      1   \n",
       "33634                          0                      1   \n",
       "32842                          0                      1   \n",
       "47280                          0                      1   \n",
       "33106                          0                      0   \n",
       "\n",
       "       married_Previously married  \n",
       "54695                           0  \n",
       "46053                           0  \n",
       "28461                           0  \n",
       "32791                           0  \n",
       "33975                           0  \n",
       "...                           ...  \n",
       "22260                           0  \n",
       "33634                           0  \n",
       "32842                           0  \n",
       "47280                           0  \n",
       "33106                           1  \n",
       "\n",
       "[43774 rows x 339 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1_TP_notell_2u</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E_notell_2u</th>\n",
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       "      <td>-0.25</td>\n",
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       "    <tr>\n",
       "      <th>22260</th>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>33634</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.666667</td>\n",
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       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>32842</th>\n",
       "      <td>4.0</td>\n",
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       "      <td>0.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>13.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>47280</th>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>33106</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.666667</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43774 rows × 339 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "54695              5.0  11.0      -0.666667                  5.0  18.0   \n",
       "46053              3.0  19.0       0.166667                  2.0   8.0   \n",
       "28461              5.0   7.0      -0.333333                  4.0  18.0   \n",
       "32791              2.0  17.0       0.000000                  1.0  12.0   \n",
       "33975              5.0  13.0      -0.166667                  5.0  20.0   \n",
       "...                ...   ...            ...                  ...   ...   \n",
       "22260              4.0  19.0      -0.166667                  5.0   3.0   \n",
       "33634              5.0   3.0       0.666667                  5.0  11.0   \n",
       "32842              4.0   7.0       0.666667                  3.0  12.0   \n",
       "47280              4.0  17.0      -0.333333                  5.0  19.0   \n",
       "33106              1.0  10.0       0.666667                  1.0   3.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "54695              -0.50             1.0  14.0     -0.666667             1.0   \n",
       "46053               0.00             5.0   7.0     -0.500000             3.0   \n",
       "28461              -0.75             5.0  11.0     -1.166667             4.0   \n",
       "32791              -0.50             4.0  14.0     -0.666667             4.0   \n",
       "33975              -0.25             2.0   4.0     -0.333333             1.0   \n",
       "...                  ...             ...   ...           ...             ...   \n",
       "22260               3.25             3.0   9.0      0.333333             3.0   \n",
       "33634               0.25             5.0  16.0     -0.166667             3.0   \n",
       "32842               0.00             2.0  13.0     12.333333             2.0   \n",
       "47280              -0.75             5.0  13.0      0.833333             4.0   \n",
       "33106              -0.25             3.0  14.0     -0.500000             5.0   \n",
       "\n",
       "       ...  race_Arab  race_Asian  race_Black  race_Indigenous Australian  \\\n",
       "54695  ...          0           0           0                           0   \n",
       "46053  ...          0           0           0                           0   \n",
       "28461  ...          0           0           0                           0   \n",
       "32791  ...          0           1           0                           0   \n",
       "33975  ...          0           0           0                           0   \n",
       "...    ...        ...         ...         ...                         ...   \n",
       "22260  ...          0           1           0                           0   \n",
       "33634  ...          0           0           1                           0   \n",
       "32842  ...          0           0           0                           0   \n",
       "47280  ...          0           0           0                           0   \n",
       "33106  ...          0           0           0                           0   \n",
       "\n",
       "       race_Native American  race_Other  race_White  \\\n",
       "54695                     0           0           1   \n",
       "46053                     0           0           1   \n",
       "28461                     0           0           1   \n",
       "32791                     0           0           0   \n",
       "33975                     0           0           1   \n",
       "...                     ...         ...         ...   \n",
       "22260                     0           0           0   \n",
       "33634                     0           0           0   \n",
       "32842                     0           0           1   \n",
       "47280                     0           0           1   \n",
       "33106                     0           0           1   \n",
       "\n",
       "       married_Currently married  married_Never married  \\\n",
       "54695                          0                      1   \n",
       "46053                          0                      1   \n",
       "28461                          0                      1   \n",
       "32791                          0                      1   \n",
       "33975                          0                      1   \n",
       "...                          ...                    ...   \n",
       "22260                          0                      1   \n",
       "33634                          0                      1   \n",
       "32842                          0                      1   \n",
       "47280                          0                      1   \n",
       "33106                          0                      0   \n",
       "\n",
       "       married_Previously married  \n",
       "54695                           0  \n",
       "46053                           0  \n",
       "28461                           0  \n",
       "32791                           0  \n",
       "33975                           0  \n",
       "...                           ...  \n",
       "22260                           0  \n",
       "33634                           0  \n",
       "32842                           0  \n",
       "47280                           0  \n",
       "33106                           1  \n",
       "\n",
       "[43774 rows x 339 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_d_ro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Q1_TP_notell_2u</th>\n",
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       "      <th>966</th>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10944 rows × 339 columns</p>\n",
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      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "33229              2.0  17.0      -0.333333                  2.0  20.0   \n",
       "9494               1.0  15.0      -0.333333                  2.0   4.0   \n",
       "19146              1.0   9.0       6.333333                  5.0  18.0   \n",
       "41249              5.0  19.0       0.000000                  2.0   6.0   \n",
       "46296              5.0  20.0      -0.833333                  4.0   8.0   \n",
       "...                ...   ...            ...                  ...   ...   \n",
       "23269              5.0   3.0       0.333333                  4.0  13.0   \n",
       "34878              1.0   3.0       0.666667                  1.0   4.0   \n",
       "11125              4.0   9.0       0.166667                  5.0  16.0   \n",
       "21557              4.0   1.0      -0.666667                  4.0  18.0   \n",
       "966                5.0  11.0       0.333333                  5.0  17.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "33229                0.8             5.0   1.0      3.666667             3.0   \n",
       "9494                -0.2             2.0   9.0     -0.500000             5.0   \n",
       "19146                1.2             1.0   5.0      3.666667             5.0   \n",
       "41249                0.8             2.0   1.0      0.333333             1.0   \n",
       "46296               -1.0             2.0   1.0      0.000000             2.0   \n",
       "...                  ...             ...   ...           ...             ...   \n",
       "23269               -0.2             3.0   6.0      1.166667             4.0   \n",
       "34878                0.0             3.0  15.0     -0.666667             5.0   \n",
       "11125               -0.4             1.0   1.0     -0.500000             5.0   \n",
       "21557               -0.4             3.0   4.0      0.166667             2.0   \n",
       "966                  0.8             2.0   8.0      0.000000             1.0   \n",
       "\n",
       "       ...  race_Arab  race_Asian  race_Black  race_Indigenous Australian  \\\n",
       "33229  ...          0           0           0                           0   \n",
       "9494   ...          0           0           0                           0   \n",
       "19146  ...          0           0           0                           0   \n",
       "41249  ...          0           0           0                           0   \n",
       "46296  ...          0           1           0                           0   \n",
       "...    ...        ...         ...         ...                         ...   \n",
       "23269  ...          0           0           0                           0   \n",
       "34878  ...          0           0           0                           0   \n",
       "11125  ...          0           0           0                           0   \n",
       "21557  ...          0           0           0                           0   \n",
       "966    ...          1           0           0                           0   \n",
       "\n",
       "       race_Native American  race_Other  race_White  \\\n",
       "33229                     0           0           1   \n",
       "9494                      0           0           1   \n",
       "19146                     0           0           1   \n",
       "41249                     0           0           1   \n",
       "46296                     0           0           0   \n",
       "...                     ...         ...         ...   \n",
       "23269                     0           0           1   \n",
       "34878                     0           0           1   \n",
       "11125                     0           0           1   \n",
       "21557                     0           0           1   \n",
       "966                       0           0           0   \n",
       "\n",
       "       married_Currently married  married_Never married  \\\n",
       "33229                          0                      1   \n",
       "9494                           0                      1   \n",
       "19146                          0                      1   \n",
       "41249                          0                      1   \n",
       "46296                          0                      1   \n",
       "...                          ...                    ...   \n",
       "23269                          0                      1   \n",
       "34878                          0                      0   \n",
       "11125                          0                      1   \n",
       "21557                          1                      0   \n",
       "966                            1                      0   \n",
       "\n",
       "       married_Previously married  \n",
       "33229                           0  \n",
       "9494                            0  \n",
       "19146                           0  \n",
       "41249                           0  \n",
       "46296                           0  \n",
       "...                           ...  \n",
       "23269                           0  \n",
       "34878                           1  \n",
       "11125                           0  \n",
       "21557                           0  \n",
       "966                             0  \n",
       "\n",
       "[10944 rows x 339 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1_TP_notell_2u</th>\n",
       "      <th>Q1I</th>\n",
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       "</table>\n",
       "<p>10944 rows × 339 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "33229              2.0  17.0      -1.222222                  2.0  20.0   \n",
       "9494               1.0  15.0      -1.222222                  2.0   4.0   \n",
       "19146              1.0   9.0      -0.111111                  5.0  18.0   \n",
       "41249              5.0  19.0      -1.166667                  2.0   6.0   \n",
       "46296              5.0  20.0      -1.305556                  4.0   8.0   \n",
       "...                ...   ...            ...                  ...   ...   \n",
       "23269              5.0   3.0      -1.111111                  4.0  13.0   \n",
       "34878              1.0   3.0      -1.055556                  1.0   4.0   \n",
       "11125              4.0   9.0      -1.138889                  5.0  16.0   \n",
       "21557              4.0   1.0      -1.277778                  4.0  18.0   \n",
       "966                5.0  11.0      -1.111111                  5.0  17.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "33229              -1.30             5.0   1.0     -0.722222             3.0   \n",
       "9494               -1.55             2.0   9.0     -1.416667             5.0   \n",
       "19146              -1.20             1.0   5.0     -0.722222             5.0   \n",
       "41249              -1.30             2.0   1.0     -1.277778             1.0   \n",
       "46296              -1.75             2.0   1.0     -1.333333             2.0   \n",
       "...                  ...             ...   ...           ...             ...   \n",
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       "966                -1.30             2.0   8.0     -1.333333             1.0   \n",
       "\n",
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       "\n",
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       "41249                     0           0           1   \n",
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       "...                     ...         ...         ...   \n",
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       "966                       0           0           0   \n",
       "\n",
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       "33229                          0                      1   \n",
       "9494                           0                      1   \n",
       "19146                          0                      1   \n",
       "41249                          0                      1   \n",
       "46296                          0                      1   \n",
       "...                          ...                    ...   \n",
       "23269                          0                      1   \n",
       "34878                          0                      0   \n",
       "11125                          0                      1   \n",
       "21557                          1                      0   \n",
       "966                            1                      0   \n",
       "\n",
       "       married_Previously married  \n",
       "33229                           0  \n",
       "9494                            0  \n",
       "19146                           0  \n",
       "41249                           0  \n",
       "46296                           0  \n",
       "...                           ...  \n",
       "23269                           0  \n",
       "34878                           1  \n",
       "11125                           0  \n",
       "21557                           0  \n",
       "966                             0  \n",
       "\n",
       "[10944 rows x 339 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_d_ro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit time : 55.79932713508606\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.648940</td>\n",
       "      <td>0.642983</td>\n",
       "      <td>0.641332</td>\n",
       "      <td>0.755772</td>\n",
       "      <td>0.693865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.653417</td>\n",
       "      <td>0.647129</td>\n",
       "      <td>0.643440</td>\n",
       "      <td>0.766186</td>\n",
       "      <td>0.699469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.645285</td>\n",
       "      <td>0.641486</td>\n",
       "      <td>0.648163</td>\n",
       "      <td>0.713418</td>\n",
       "      <td>0.679227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.652778</td>\n",
       "      <td>0.648680</td>\n",
       "      <td>0.653036</td>\n",
       "      <td>0.726263</td>\n",
       "      <td>0.687705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.648940  0.642983   0.641332  0.755772  0.693865\n",
       "GBC   0.653417  0.647129   0.643440  0.766186  0.699469\n",
       "XGB   0.645285  0.641486   0.648163  0.713418  0.679227\n",
       "LGBM  0.652778  0.648680   0.653036  0.726263  0.687705"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "result_ro = models_score_df(models, model_names, X_train_d_ro, X_test_d_ro, y_train_d, y_test_d)\n",
    "\n",
    "print(\"Fit time : {}\".format(time.time()-start_time))\n",
    "result_ro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### robust scaling 전 / 후  시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `age`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_age = df_18['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_18_cp = df_18.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1_TP_notell_2u</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E_notell_2u</th>\n",
       "      <th>Q2_TP_ppl_nd_dangun</th>\n",
       "      <th>Q2I</th>\n",
       "      <th>Q2E_ppl_nd_dangun</th>\n",
       "      <th>Q3_TN_do_moral</th>\n",
       "      <th>Q3I</th>\n",
       "      <th>Q3E_do_moral</th>\n",
       "      <th>Q4_VN_ppl_good</th>\n",
       "      <th>...</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>race</th>\n",
       "      <th>voted</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>score</th>\n",
       "      <th>v_score</th>\n",
       "      <th>t_score</th>\n",
       "      <th>m_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Christian(othrer)</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>Black</td>\n",
       "      <td>1</td>\n",
       "      <td>Currently married</td>\n",
       "      <td>3.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Agnostic</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>Never married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Christian(Protestant)</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>Previously married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Christian(Catholic)</td>\n",
       "      <td>Homosexual</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>Never married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Atheist</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>Never married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54713</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Other</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>Asian</td>\n",
       "      <td>0</td>\n",
       "      <td>Currently married</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54714</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Atheist</td>\n",
       "      <td>Homosexual</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>Never married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54715</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Agnostic</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>Never married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54716</th>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Buddhist</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>Asian</td>\n",
       "      <td>1</td>\n",
       "      <td>Never married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54717</th>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Christian(Catholic)</td>\n",
       "      <td>Heterosexual</td>\n",
       "      <td>Other</td>\n",
       "      <td>0</td>\n",
       "      <td>Never married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54718 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "0                  3.0   6.0       2.333333                  3.0   7.0   \n",
       "1                  5.0  17.0      -0.500000                  5.0   9.0   \n",
       "2                  2.0  12.0       0.333333                  4.0   1.0   \n",
       "3                  5.0  13.0       0.333333                  5.0   7.0   \n",
       "4                  3.0  11.0      -0.500000                  3.0  10.0   \n",
       "...                ...   ...            ...                  ...   ...   \n",
       "54713              2.0  17.0       0.500000                  1.0   6.0   \n",
       "54714              5.0   9.0      -0.166667                  5.0   4.0   \n",
       "54715              2.0  17.0      -0.333333                  1.0  18.0   \n",
       "54716              4.0  15.0       0.166667                  3.0   9.0   \n",
       "54717              3.0  12.0       0.166667                  1.0  10.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "0                    2.6             5.0  20.0      1.166667             2.0   \n",
       "1                    0.4             1.0  16.0     -0.333333             3.0   \n",
       "2                    0.8             2.0   7.0      0.333333             1.0   \n",
       "3                   -0.2             2.0  19.0      0.166667             1.0   \n",
       "4                   -0.4             2.0  17.0     -0.500000             4.0   \n",
       "...                  ...             ...   ...           ...             ...   \n",
       "54713                1.6             2.0  16.0      2.833333             4.0   \n",
       "54714               -0.4             1.0  15.0     -0.166667             1.0   \n",
       "54715                0.2             1.0   9.0     -0.500000             1.0   \n",
       "54716                0.4             5.0  17.0      0.166667             3.0   \n",
       "54717                0.0             5.0  13.0      4.833333             1.0   \n",
       "\n",
       "       ...               religion   orientation   race  voted  \\\n",
       "0      ...      Christian(othrer)  Heterosexual  Black      1   \n",
       "1      ...               Agnostic  Heterosexual  White      0   \n",
       "2      ...  Christian(Protestant)  Heterosexual  White      1   \n",
       "3      ...    Christian(Catholic)    Homosexual  White      1   \n",
       "4      ...                Atheist  Heterosexual  White      1   \n",
       "...    ...                    ...           ...    ...    ...   \n",
       "54713  ...                  Other  Heterosexual  Asian      0   \n",
       "54714  ...                Atheist    Homosexual  White      0   \n",
       "54715  ...               Agnostic  Heterosexual  White      0   \n",
       "54716  ...               Buddhist  Heterosexual  Asian      1   \n",
       "54717  ...    Christian(Catholic)  Heterosexual  Other      0   \n",
       "\n",
       "                  married  familysize  score  v_score  t_score  m_score  \n",
       "0       Currently married         3.0   75.0     35.0     33.0      7.0  \n",
       "1           Never married         0.0   91.0     37.0     45.0      9.0  \n",
       "2      Previously married         0.0   78.0     37.0     36.0      5.0  \n",
       "3           Never married         0.0   85.0     35.0     41.0      9.0  \n",
       "4           Never married         0.0   66.0     28.0     31.0      7.0  \n",
       "...                   ...         ...    ...      ...      ...      ...  \n",
       "54713   Currently married        -1.0   59.0     28.0     26.0      5.0  \n",
       "54714       Never married         0.0   90.0     37.0     43.0     10.0  \n",
       "54715       Never married         1.0   69.0     34.0     27.0      8.0  \n",
       "54716       Never married         2.0   53.0     25.0     22.0      6.0  \n",
       "54717       Never married         2.0   64.0     36.0     23.0      5.0  \n",
       "\n",
       "[54718 rows x 108 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tran_robust(df_18_cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb62e8cb730>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 369,
       "width": 1159
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18['age'].value_counts().plot(kind='bar', figsize=(20, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb62b6eba60>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 459,
       "width": 1159
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18_cp['age'].value_counts().plot(kind='bar', figsize=(20, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(count    54718.000000\n",
       " mean        31.739537\n",
       " std         19.666469\n",
       " min         18.000000\n",
       " 25%         22.000000\n",
       " 50%         28.000000\n",
       " 75%         39.000000\n",
       " max       1997.000000\n",
       " Name: age, dtype: float64,\n",
       " count    54718.000000\n",
       " mean         0.219973\n",
       " std          1.156851\n",
       " min         -0.588235\n",
       " 25%         -0.352941\n",
       " 50%          0.000000\n",
       " 75%          0.647059\n",
       " max        115.823529\n",
       " Name: age, dtype: float64)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18['age'].describe(), df_18_cp['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb659534a90>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 713
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(121)\n",
    "df_18['age'].plot(kind='box')\n",
    "plt.subplot(122)\n",
    "df_18_cp['age'].plot(kind='box');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb65beb0f40>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 723
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(121)\n",
    "df_18['age'].plot(kind='kde')\n",
    "plt.subplot(122)\n",
    "df_18_cp['age'].plot(kind='kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `family size`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 366,
       "width": 1165
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(211)\n",
    "df_18['familysize'].value_counts().plot(kind='bar', figsize=(20, 6))\n",
    "plt.subplot(212)\n",
    "df_18_cp['familysize'].value_counts().plot(kind='bar', figsize=(20, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(count    54718.000000\n",
       " mean         2.650316\n",
       " std          1.547000\n",
       " min          1.000000\n",
       " 25%          2.000000\n",
       " 50%          2.000000\n",
       " 75%          3.000000\n",
       " max         96.000000\n",
       " Name: familysize, dtype: float64,\n",
       " count    54718.000000\n",
       " mean         0.650316\n",
       " std          1.547000\n",
       " min         -1.000000\n",
       " 25%          0.000000\n",
       " 50%          0.000000\n",
       " 75%          1.000000\n",
       " max         94.000000\n",
       " Name: familysize, dtype: float64)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_18['familysize'].describe(), df_18_cp['familysize'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb65b4c6850>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 358,
       "width": 707
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(121)\n",
    "df_18['familysize'].plot(kind='box')\n",
    "plt.subplot(122)\n",
    "df_18_cp['familysize'].plot(kind='box');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 357,
       "width": 725
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(121)\n",
    "df_18['familysize'].plot(kind='kde')\n",
    "plt.subplot(122)\n",
    "df_18_cp['familysize'].plot(kind='kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb55bed19a0>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 268,
       "width": 377
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_18['familysize'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# feature_addition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 추가 컬럼 생성 클래스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Add_columns:\n",
    "    def __init__(self, X_train, X_test, y_train, y_test):\n",
    "        self.X_train = X_train\n",
    "        self.X_test = X_test\n",
    "        self.y_train = y_train\n",
    "        self.y_test = y_test\n",
    "        \n",
    "    def score(self, column=\"score\", voted=\"voted\",col_name=\"rate\"):\n",
    "        df_tr = pd.concat([self.X_train, self.y_train], axis=1)\n",
    "        df_te = self.X_test\n",
    "        add_all_tr = df_tr[[voted, column]].groupby(column).count()\n",
    "        add_yes_tr = df_tr[[voted, column]].groupby(column).sum()\n",
    "        add_no_tr = add_all_tr - add_yes_tr\n",
    "        df_add_tr = round((add_yes_tr - add_no_tr)/ add_all_tr, 4)\n",
    "        df_add_tr = df_add_tr.rename(columns={voted:col_name})\n",
    "        \n",
    "        df1_tr = pd.merge(left=df_tr, right=df_add_tr, how=\"left\", right_index=True, left_on=column)\n",
    "        df1_te = pd.merge(left=df_te, right=df_add_tr, how=\"left\", right_index=True, left_on=column)\n",
    "        self.train_X=df1_tr.drop(voted, axis=1)\n",
    "        self.train_Y=pd.DataFrame(df1_tr[voted])\n",
    "        self.test_X=df1_te.fillna(0)\n",
    "        self.test_Y=self.y_test\n",
    "        return self.train_X, self.test_X, self.train_Y, self.test_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (accuracy_score, precision_score, recall_score, \n",
    "                             f1_score, roc_auc_score, confusion_matrix, classification_report)\n",
    "\n",
    "from sklearn.model_selection import (train_test_split, cross_val_score, StratifiedKFold ,GridSearchCV)\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "class Train_models:    \n",
    "    def __init__(self,X_train, X_test, y_train, y_test):\n",
    "        ada = AdaBoostClassifier()\n",
    "        gbc = GradientBoostingClassifier()\n",
    "        xgb = XGBClassifier()\n",
    "        lgbm = LGBMClassifier()\n",
    "        self.datas = []\n",
    "        self.models = [ada, gbc, xgb, lgbm]\n",
    "        self.model_names = ['Ada', 'GBC', 'XGB', 'LGBM']\n",
    "        \n",
    "        self.X_train = X_train\n",
    "        self.X_test = X_test\n",
    "        self.y_train = y_train\n",
    "        self.y_test = y_test\n",
    "\n",
    "    def get_score(self, pred):\n",
    "        acc = accuracy_score(self.y_test, pred)\n",
    "        pre = precision_score(self.y_test, pred)\n",
    "        rec = recall_score(self.y_test, pred)\n",
    "        f1 = f1_score(self.y_test, pred)\n",
    "        auc = roc_auc_score(self.y_test, pred)\n",
    "       \n",
    "        return acc, auc, pre, rec, f1\n",
    "    \n",
    "    def fit_model(self, model):\n",
    "\n",
    "        model.fit(self.X_train, self.y_train)\n",
    "        y_pre_tr = model.predict(self.X_train)\n",
    "        self.y_pre_test = model.predict(self.X_test)\n",
    "        total_score = self.get_score(self.y_pre_test)\n",
    "        \n",
    "        return total_score\n",
    "\n",
    "    def models_score_df(self):\n",
    "        cols_names = ['accuracy', 'AUC', 'precision', 'recall', 'f1']\n",
    "\n",
    "        for model in self.models:\n",
    "            self.datas.append(self.fit_model(model))\n",
    "\n",
    "        return pd.DataFrame(self.datas, columns=cols_names, index=self.model_names)\n",
    "    \n",
    "\n",
    "    def print_score(self):\n",
    "        for model in self.models:\n",
    "        \n",
    "            acc, auc, pre, rec, f1 = self.fit_model(model)\n",
    "            con = confusion_matrix(self.y_test, self.y_pre_test)\n",
    "            print('='*20)\n",
    "            print(model)\n",
    "            print('confusion matrix')\n",
    "            print(con)\n",
    "            print('='*20)\n",
    "\n",
    "            print('Accuracy: {0:.4f}, AUC: {1:.4f}'.format(acc, auc))\n",
    "            print('Recall: {0:.4f}, f1_score: {1:.4f}, precision: {2:.4f}'.format(rec, f1, pre))\n",
    "            print('='*20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 컬럼 빼기전 추가 컬럼 생성 후 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_add = Add_columns(X_train_d, X_test_d, y_train_d, y_test_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = col_add.score(column=\"score\", voted=\"voted\", col_name=\"score_voted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_add_1 = Add_columns(train_x, test_x, train_y, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = col_add_1.score(column=\"age\", voted=\"voted\", col_name=\"age_voted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_models = Train_models(train_x, test_x, train_y, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.648666</td>\n",
       "      <td>0.642191</td>\n",
       "      <td>0.638921</td>\n",
       "      <td>0.764798</td>\n",
       "      <td>0.696216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.652412</td>\n",
       "      <td>0.645352</td>\n",
       "      <td>0.639407</td>\n",
       "      <td>0.779031</td>\n",
       "      <td>0.702347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.652961</td>\n",
       "      <td>0.648496</td>\n",
       "      <td>0.651396</td>\n",
       "      <td>0.733032</td>\n",
       "      <td>0.689807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.658077</td>\n",
       "      <td>0.652630</td>\n",
       "      <td>0.650919</td>\n",
       "      <td>0.755772</td>\n",
       "      <td>0.699438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.648666  0.642191   0.638921  0.764798  0.696216\n",
       "GBC   0.652412  0.645352   0.639407  0.779031  0.702347\n",
       "XGB   0.652961  0.648496   0.651396  0.733032  0.689807\n",
       "LGBM  0.658077  0.652630   0.650919  0.755772  0.699438"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_models.models_score_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================\n",
      "AdaBoostClassifier()\n",
      "confusion matrix\n",
      "[[2693 2490]\n",
      " [1355 4406]]\n",
      "====================\n",
      "Accuracy: 0.6487, AUC: 0.6422\n",
      "Recall: 0.7648, f1_score: 0.6962, precision: 0.6389\n",
      "====================\n",
      "====================\n",
      "GradientBoostingClassifier()\n",
      "confusion matrix\n",
      "[[2651 2532]\n",
      " [1274 4487]]\n",
      "====================\n",
      "Accuracy: 0.6522, AUC: 0.6452\n",
      "Recall: 0.7789, f1_score: 0.7022, precision: 0.6393\n",
      "====================\n",
      "====================\n",
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
      "              importance_type='gain', interaction_constraints='',\n",
      "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
      "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
      "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
      "              tree_method='exact', validate_parameters=1, verbosity=None)\n",
      "confusion matrix\n",
      "[[2923 2260]\n",
      " [1538 4223]]\n",
      "====================\n",
      "Accuracy: 0.6530, AUC: 0.6485\n",
      "Recall: 0.7330, f1_score: 0.6898, precision: 0.6514\n",
      "====================\n",
      "====================\n",
      "LGBMClassifier()\n",
      "confusion matrix\n",
      "[[2848 2335]\n",
      " [1407 4354]]\n",
      "====================\n",
      "Accuracy: 0.6581, AUC: 0.6526\n",
      "Recall: 0.7558, f1_score: 0.6994, precision: 0.6509\n",
      "====================\n"
     ]
    }
   ],
   "source": [
    "t_models.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 컬럼 뺀 후 추가 컬럼 생성 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.read_csv('./MACH_data/X_test_data_without_0feature.csv' )\n",
    "X_train = pd.read_csv('./MACH_data/X_train_data_without_0feature.csv' )\n",
    "y_test = pd.read_csv('./MACH_data/Y_test_data_without_0feature.csv' )\n",
    "y_train = pd.read_csv('./MACH_data/Y_train_data_without_0feature.csv' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_add = Add_columns(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = col_add.score(column=\"score\", voted=\"voted\", col_name=\"score_voted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_add_1 = Add_columns(train_x, test_x, train_y, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = col_add_1.score(column=\"age\", voted=\"voted\", col_name=\"age_voted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_models = Train_models(train_x, test_x, train_y, test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ada</th>\n",
       "      <td>0.648666</td>\n",
       "      <td>0.642191</td>\n",
       "      <td>0.638921</td>\n",
       "      <td>0.764798</td>\n",
       "      <td>0.696216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBC</th>\n",
       "      <td>0.652321</td>\n",
       "      <td>0.645256</td>\n",
       "      <td>0.639316</td>\n",
       "      <td>0.779031</td>\n",
       "      <td>0.702292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>0.648300</td>\n",
       "      <td>0.643876</td>\n",
       "      <td>0.647713</td>\n",
       "      <td>0.727651</td>\n",
       "      <td>0.685359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LGBM</th>\n",
       "      <td>0.658626</td>\n",
       "      <td>0.653257</td>\n",
       "      <td>0.651731</td>\n",
       "      <td>0.754904</td>\n",
       "      <td>0.699534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy       AUC  precision    recall        f1\n",
       "Ada   0.648666  0.642191   0.638921  0.764798  0.696216\n",
       "GBC   0.652321  0.645256   0.639316  0.779031  0.702292\n",
       "XGB   0.648300  0.643876   0.647713  0.727651  0.685359\n",
       "LGBM  0.658626  0.653257   0.651731  0.754904  0.699534"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_models.models_score_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================\n",
      "AdaBoostClassifier()\n",
      "confusion matrix\n",
      "[[2693 2490]\n",
      " [1355 4406]]\n",
      "====================\n",
      "Accuracy: 0.6487, AUC: 0.6422\n",
      "Recall: 0.7648, f1_score: 0.6962, precision: 0.6389\n",
      "====================\n",
      "====================\n",
      "GradientBoostingClassifier()\n",
      "confusion matrix\n",
      "[[2652 2531]\n",
      " [1274 4487]]\n",
      "====================\n",
      "Accuracy: 0.6523, AUC: 0.6453\n",
      "Recall: 0.7789, f1_score: 0.7022, precision: 0.6394\n",
      "====================\n",
      "====================\n",
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
      "              importance_type='gain', interaction_constraints='',\n",
      "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
      "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
      "              n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
      "              tree_method='exact', validate_parameters=1, verbosity=None)\n",
      "confusion matrix\n",
      "[[2903 2280]\n",
      " [1569 4192]]\n",
      "====================\n",
      "Accuracy: 0.6483, AUC: 0.6439\n",
      "Recall: 0.7277, f1_score: 0.6854, precision: 0.6477\n",
      "====================\n",
      "====================\n",
      "LGBMClassifier()\n",
      "confusion matrix\n",
      "[[2859 2324]\n",
      " [1412 4349]]\n",
      "====================\n",
      "Accuracy: 0.6586, AUC: 0.6533\n",
      "Recall: 0.7549, f1_score: 0.6995, precision: 0.6517\n",
      "====================\n"
     ]
    }
   ],
   "source": [
    "t_models.print_score()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 모듈 만들기 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N차 전처리 모듈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile nthpreprocessing.py\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "            \n",
    "# N차 전처리 클래스(feature 선별, robust scaling, weight 컬럼 추가)\n",
    "class NthPreprocessing():\n",
    "    def __init__(self):\n",
    "        ada = AdaBoostClassifier()\n",
    "        gbc = GradientBoostingClassifier()\n",
    "        xgb = XGBClassifier()\n",
    "        lgbm = LGBMClassifier()\n",
    "        self.datas = []\n",
    "        self.models = [ada, gbc, xgb, lgbm]\n",
    "        self.model_names = ['Ada', 'GBC', 'XGB', 'LGBM']            \n",
    "    \n",
    "    def feature_selection(self, X_train, X_test, y_train, y_test):\n",
    "        datas = []\n",
    "        for model in self.models:\n",
    "            model.fit(self.X_train, self.y_train)\n",
    "        \n",
    "        # 모델 fit 이후 feature_importances_ 0인 컬럼을 선별하기 위함.\n",
    "        ada_fi = self.models[0].feature_importances_\n",
    "        gbc_fi = self.models[1].feature_importances_\n",
    "        xgb_fi = self.models[2].feature_importances_\n",
    "        lgbm_fi = self.models[3].feature_importances_\n",
    "        \n",
    "        ada_fm = pd.DataFrame(zip(self.X_train.columns, ada_fi))\n",
    "        ada_list1 = list(ada_fm[ada_fm[1]==0][0])\n",
    "        gbc_fm = pd.DataFrame(zip(self.X_train.columns, gbc_fi))\n",
    "        gbc_list1 = list(gbc_fm[gbc_fm[1]==0][0])\n",
    "        xgb_fm = pd.DataFrame(zip(self.X_train.columns, xgb_fi))\n",
    "        xgb_list1 = list(xgb_fm[xgb_fm[1]==0][0])\n",
    "        lgbm_fm = pd.DataFrame(zip(self.X_train.columns, lgbm_fi))\n",
    "        lgbm_list1 = list(lgbm_fm[lgbm_fm[1]==0][0])\n",
    "        \n",
    "        # 4가지 모델에서의 feature_importances_가 모두 0인 컬럼들의 교집합 확인. \n",
    "        ada_gbc1 = list(set(ada_list1).intersection(gbc_list1))\n",
    "        ada_gbc_xgb1 = list(set(ada_gbc1).intersection(xgb_list1))\n",
    "        ada_gbc_xgb_lgbm1 = list(set(ada_gbc_xgb1).intersection(lgbm_list1))\n",
    "        \n",
    "        # 해당 컬럼들을 제외한 X데이터를 생성\n",
    "        self.X_train = self.X_train.drop(ada_gbc_xgb_lgbm1, axis=1)\n",
    "        self.X_test = self.X_test.drop(ada_gbc_xgb_lgbm1, axis=1)\n",
    "        \n",
    "        return self.X_train, self.X_test, self.y_train, self.y_test\n",
    "\n",
    "    def scale_robust(self, X_train, X_test, y_train, y_test):\n",
    "        num_cols = []\n",
    "        for col in self.X_train.columns:\n",
    "            if (\"E\" in col) | (\"age\" in col) |(\"family\" in col) |(\"elapse\" in col):\n",
    "                num_cols.append(col)\n",
    "                \n",
    "        rbscale = RobustScaler().fit(self.X_train[num_cols])\n",
    "        self.X_train_ro[num_cols] = rbscale.transform(self.X_train[num_cols])\n",
    "        self.X_test[num_cols] = rbscale.transform(self.X_test[num_cols])\n",
    "\n",
    "        return self.X_train, self.X_test, self.y_train, self.y_test\n",
    "\n",
    "       \n",
    "    def feature_addition(self, column=\"score\", voted=\"voted\",col_name=\"rate\"):\n",
    "        df_tr = pd.concat([self.X_train, self.y_train], axis=1)\n",
    "        df_te = self.X_test\n",
    "        add_all_tr = df_tr[[voted, column]].groupby(column).count()\n",
    "        add_yes_tr = df_tr[[voted, column]].groupby(column).sum()\n",
    "        add_no_tr = add_all_tr - add_yes_tr\n",
    "        df_add_tr = round((add_yes_tr - add_no_tr)/ add_all_tr, 4)\n",
    "        df_add_tr = df_add_tr.rename(columns={voted:col_name})\n",
    "        \n",
    "        df1_tr = pd.merge(left=df_tr, right=df_add_tr, how=\"left\", right_index=True, left_on=column)\n",
    "        df1_te = pd.merge(left=df_te, right=df_add_tr, how=\"left\", right_index=True, left_on=column)\n",
    "        self.train_X=df1_tr.drop(voted, axis=1)\n",
    "        self.train_Y=pd.DataFrame(df1_tr[voted])\n",
    "        self.test_X=df1_te.fillna(0)\n",
    "        self.test_Y=self.y_test\n",
    "        \n",
    "        return self.train_X, self.test_X, self.train_Y, self.test_Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모델링 모듈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile modeling.py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import (accuracy_score, precision_score, recall_score, \n",
    "                             f1_score, roc_auc_score, confusion_matrix, classification_report)\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.model_selection import (train_test_split, cross_val_score, StratifiedKFold ,GridSearchCV)\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "    \n",
    "# ada, gbc, xgb, lgbm 모델링 클래스 \n",
    "class modeling:    \n",
    "    def __init__(self,X_train, X_test, y_train, y_test):\n",
    "        ada = AdaBoostClassifier()\n",
    "        gbc = GradientBoostingClassifier()\n",
    "        xgb = XGBClassifier()\n",
    "        lgbm = LGBMClassifier()\n",
    "        self.datas = []\n",
    "        self.models = [ada, gbc, xgb, lgbm]\n",
    "        self.model_names = ['Ada', 'GBC', 'XGB', 'LGBM']\n",
    "        \n",
    "        # train, test 분리작업까지 마친 상태로 받아오기\n",
    "        self.X_train = X_train\n",
    "        self.X_test = X_test\n",
    "        self.y_train = y_train\n",
    "        self.y_test = y_test\n",
    "        \n",
    "        # 분류모델 평가지표 계산함수 (AUC, ACC를 우선순위로 사용함)\n",
    "    def get_score(self, pred):\n",
    "        acc = accuracy_score(self.y_test, pred)\n",
    "        pre = precision_score(self.y_test, pred)\n",
    "        rec = recall_score(self.y_test, pred)\n",
    "        f1 = f1_score(self.y_test, pred)\n",
    "        auc = roc_auc_score(self.y_test, pred)\n",
    "       \n",
    "        return acc, auc, pre, rec, f1\n",
    "    \n",
    "    def fit_model(self, model):\n",
    "\n",
    "        model.fit(self.X_train, self.y_train)\n",
    "        y_pre_tr = model.predict(self.X_train)\n",
    "        self.y_pre_test = model.predict(self.X_test)\n",
    "        total_score = self.get_score(self.y_pre_test)\n",
    "        \n",
    "        return total_score\n",
    "\n",
    "    def models_score_df(self):\n",
    "        cols_names = ['accuracy', 'AUC', 'precision', 'recall', 'f1']\n",
    "\n",
    "        for model in self.models:\n",
    "            self.datas.append(self.fit_model(model))\n",
    "\n",
    "        return pd.DataFrame(self.datas, columns=cols_names, index=self.model_names)\n",
    "    \n",
    "    # 평가지표와 confusion matrix 출력 함수 \n",
    "    def print_score(self):\n",
    "        datas = []\n",
    "        for model in self.models:\n",
    "            datas.append(self.fit_model(model))\n",
    "        \n",
    "            acc, auc, pre, rec, f1 = datas[0]\n",
    "            con = confusion_matrix(self.y_test, self.y_pre_test)\n",
    "            print('='*20)\n",
    "            print(model)\n",
    "            print('confusion matrix')\n",
    "            print(con)\n",
    "            print('='*20)\n",
    "\n",
    "            print('Accuracy: {0:.4f}, AUC: {1:.4f}'.format(acc, auc))\n",
    "            print('Recall: {0:.4f}, f1_score: {1:.4f}, precision: {2:.4f}'.format(rec, f1, pre))\n",
    "            print('='*20)\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 그리드서치/하이퍼파라미터 모듈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## machi_vote_ml.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing MACH.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile machi_vote_ml.py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import (accuracy_score, precision_score, recall_score, \n",
    "                             f1_score, roc_auc_score, confusion_matrix, classification_report)\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn.model_selection import (train_test_split, cross_val_score, StratifiedKFold ,GridSearchCV)\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "cates = {\n",
    "    'education' : ['Less than high school','High school','University degree','Graduate degree'],\n",
    "    'urban' : ['Rural','Suburban','Urban'],\n",
    "    'gender' : ['Male','Female','Other'],\n",
    "    'engnat' : ['Yes','No'],\n",
    "    'hand' : ['Right','Left','Both'],\n",
    "    'religion' : ['Agnostic','Atheist','Buddhist','Christian(Catholic)','Christian(Mormon)','Christian(Protestant)','Christian(othrer)','Hindu','Jewish','Muslim','Sikh','Other'],\n",
    "    'orientation' : ['Heterosexual','Bisexual','Homosexual','Asexual','Other'],\n",
    "    'race' : ['Asian','Arab','Black','Indigenous Australian','Native American','White','Other'],\n",
    "    'voted' : ['1', '0'],\n",
    "    'married' : ['Never married','Currently married','Previously married'],\n",
    "}\n",
    "\n",
    "\n",
    "class Preprocessing():\n",
    "    \n",
    "    def __init__(self, df):\n",
    "        self.df = df\n",
    "        \n",
    "    def tran_cate(self, df, cate, x, y):\n",
    "        self.df[cate] = self.df[cate].astype('str').replace(x, y)\n",
    "        return self.df[cate]\n",
    "\n",
    "    def preprocessing_1th(self):\n",
    "        # 직관적 EDA를 위해 컬럼명 수정\n",
    "        self.df.rename(columns = {\"Q1A\" : \"Q1_TP_notell_2u\", \"Q2A\" : \"Q2_TP_ppl_nd_dangun\", \"Q3A\" : \"Q3_TN_do_moral\", \"Q4A\" : \"Q4_VN_ppl_good\", \"Q5A\" : \"Q5_VP_ppl_bad\", \"Q6A\" : \"Q6_TN_hnsty_best\", \"Q7A\" : \"Q7_TN_lying_bad\", \"Q8A\" : \"Q8_VP_ppl_lazy\", \"Q9A\" : \"Q9_MN_humble_hnst\", \"Q10A\" : \"Q10_TN_hnstly_ask\", \"Q11A\" : \"Q11_VN_leader_clean\", \"Q12A\" : \"Q12_TP_trust_trouble\", \"Q13A\" : \"Q13_VP_ppl_criminal\", \"Q14A\" : \"Q14_VN_ppl_brave\", \"Q15A\" : \"Q15_TP_abu_good\", \"Q16A\" : \"Q16_TN_ppl_good\", \"Q17A\" : \"Q17_VN_ppl_notbad\", \"Q18A\" : \"Q18_VP_komsu_better\", \"Q19A\" : \"Q19_MP_anrocksa_ok\", \"Q20A\" : \"Q20_VP_money_good\",\n",
    "                     \"Q1E\" : \"Q1E_notell_2u\", \"Q2E\" : \"Q2E_ppl_nd_dangun\", \"Q3E\" : \"Q3E_do_moral\", \"Q4E\" : \"Q4E_ppl_good\", \"Q5E\" : \"Q5E_ppl_bad\", \"Q6E\" : \"Q6E_hnsty_best\", \"Q7E\" : \"Q7E_lying_bad\", \"Q8E\" : \"Q8E_ppl_lazy\", \"Q9E\" : \"Q9E_humble_hnst\", \"Q10E\" : \"Q10E_hnstly_ask\", \"Q11E\" : \"Q11E_leader_clean\", \"Q12E\" : \"Q12E_trust_trouble\", \"Q13E\" : \"Q13E_ppl_criminal\", \"Q14E\" : \"Q14E_ppl_brave\", \"Q15E\" : \"Q15E_abu_good\", \"Q16E\" : \"Q16E_ppl_good\", \"Q17E\" : \"Q17E_ppl_notbad\", \"Q18E\" : \"Q18E_komsu_better\", \"Q19E\" : \"Q19E_anrocksa_ok\", \"Q20E\" : \"Q20E_money_good\",\n",
    "                     \"TIPI1\":\"TYP_out\", \"TIPI2\":\"TYP_fight\", \"TIPI3\":\"TYP_depnd\", \"TIPI4\":\"TYP_anx\", \"TIPI5\":\"TYP_try\", \"TIPI6\":\"TYP_quiet\", \"TIPI7\":\"TYP_warm\", \"TIPI8\":\"TYP_disorg\", \"TIPI9\":\"TYP_calm\", \"TIPI10\":\"TYP_stable\",\n",
    "                     \"VCL6\" : \"VCL6_F\", \"VCL9\" : \"VCL9_F\", \"VCL12\" : \"VCL12_F\"\n",
    "                    }, inplace=True)        \n",
    "        \n",
    "        # score 컬럼 추가\n",
    "        col_list = list(self.df.columns)\n",
    "        pos_col = []\n",
    "        neg_col = []\n",
    "\n",
    "        for col in col_list:\n",
    "            if \"P\" in col and \"Y\" not in col:\n",
    "                pos_col.append(col)\n",
    "            if \"N\" in col:\n",
    "                neg_col.append(col)\n",
    "\n",
    "        self.df[\"score\"] = self.df[pos_col].sum(axis=1) + self.df[neg_col].apply(lambda x: 6 -x).sum(axis=1)\n",
    "        \n",
    "        # V, T, M score 컬럼 추가 \n",
    "        v_score = []\n",
    "        t_score = []\n",
    "        m_score = []\n",
    "\n",
    "        for col in col_list:\n",
    "            if \"T\" in col:\n",
    "                t_score.append(col)\n",
    "            if \"M\" in col:\n",
    "                m_score.append(col)\n",
    "            if \"V\" in col:\n",
    "                v_score.append(col)\n",
    "        self.df[\"v_score\"] = self.df[v_score].sum(axis=1)\n",
    "        self.df[\"t_score\"] = self.df[t_score].sum(axis=1)\n",
    "        self.df[\"m_score\"] = self.df[m_score].sum(axis=1)\n",
    "        \n",
    "                \n",
    "        # 텍스트 데이터로 변환, 시간 데이터 초단위로 환산\n",
    "        vcl_col = []\n",
    "        sec_col = []\n",
    "\n",
    "        for col in col_list:\n",
    "            if \"VCL\" in col:\n",
    "                vcl_col.append(col)\n",
    "            if \"E\" in col:\n",
    "                sec_col.append(col)\n",
    "        \n",
    "        self.df[vcl_col] = self.df[vcl_col].applymap(lambda x: str(x).replace(\"1\", \"know\") if x==1 \n",
    "                                           else str(x).replace(\"0\", \"n_know\"))\n",
    "        self.df[sec_col] = self.df[sec_col].apply(lambda x: round(x*0.001))\n",
    "        \n",
    "        for x in list(cates.keys()):\n",
    "            for idx, y in enumerate(cates[x]):\n",
    "                if x == 'race':\n",
    "                    self.tran_cate(self.df, x, \"{}\".format((idx+1)*10) ,y)\n",
    "                else:    \n",
    "                    self.tran_cate(self.df, x, \"{}\".format(idx+1) ,y)\n",
    "\n",
    "        # major 컬럼 drop\n",
    "        self.df.drop(columns = \"major\", inplace = True)\n",
    "        col_list2 = list(self.df.columns)\n",
    "\n",
    "        \n",
    "        # null, 0 데이터 제거\n",
    "        self.df.dropna(inplace=True)\n",
    "        zero_idx = []\n",
    "        for col in col_list2:\n",
    "            zero_idx += list((self.df[(self.df[col] == 0)].index))\n",
    "        zero_idx = list(set(zero_idx))\n",
    "        self.df.drop(zero_idx, inplace=True)\n",
    "        \n",
    "        # 나이 18세 이하 데이터 drop\n",
    "        self.df = self.df[self.df[\"age\"]>17]\n",
    "        \n",
    "        # train, test로 나누기 \n",
    "        df_X = self.df.drop('voted', axis=1)\n",
    "        df_X = pd.get_dummies(df_X)\n",
    "        \n",
    "        # 0 데이터 추가 확인되어 drop \n",
    "        col_in_0 = [col for col in df_X.columns if '_0' in col]\n",
    "        df_X.drop(col_in_0, axis=1, inplace=True)\n",
    "        \n",
    "        df_y = self.df['voted'].astype('int')\n",
    "        \n",
    "                \n",
    "        X_train, X_test, y_train, y_test=\\\n",
    "        train_test_split(df_X, df_y, test_size=0.2,\n",
    "                         random_state=13, stratify=df_y)    \n",
    "\n",
    "        \n",
    "        return X_train, X_test, y_train, y_test\n",
    "\n",
    "            \n",
    "# N차 전처리 클래스(feature 선별, robust scaling, weight 컬럼 추가)\n",
    "class NthPreprocessing():\n",
    "    def __init__(self):\n",
    "        ada = AdaBoostClassifier()\n",
    "        gbc = GradientBoostingClassifier()\n",
    "        xgb = XGBClassifier()\n",
    "        lgbm = LGBMClassifier()\n",
    "        self.datas = []\n",
    "        self.models = [ada, gbc, xgb, lgbm]\n",
    "        self.model_names = ['Ada', 'GBC', 'XGB', 'LGBM']            \n",
    "    \n",
    "    def feature_selection(self, X_train, X_test, y_train, y_test):\n",
    "        datas = []\n",
    "        for model in self.models:\n",
    "            model.fit(self.X_train, self.y_train)\n",
    "        \n",
    "        # 모델 fit 이후 feature_importances_ 0인 컬럼을 선별하기 위함.\n",
    "        ada_fi = self.models[0].feature_importances_\n",
    "        gbc_fi = self.models[1].feature_importances_\n",
    "        xgb_fi = self.models[2].feature_importances_\n",
    "        lgbm_fi = self.models[3].feature_importances_\n",
    "        \n",
    "        ada_fm = pd.DataFrame(zip(self.X_train.columns, ada_fi))\n",
    "        ada_list1 = list(ada_fm[ada_fm[1]==0][0])\n",
    "        gbc_fm = pd.DataFrame(zip(self.X_train.columns, gbc_fi))\n",
    "        gbc_list1 = list(gbc_fm[gbc_fm[1]==0][0])\n",
    "        xgb_fm = pd.DataFrame(zip(self.X_train.columns, xgb_fi))\n",
    "        xgb_list1 = list(xgb_fm[xgb_fm[1]==0][0])\n",
    "        lgbm_fm = pd.DataFrame(zip(self.X_train.columns, lgbm_fi))\n",
    "        lgbm_list1 = list(lgbm_fm[lgbm_fm[1]==0][0])\n",
    "        \n",
    "        # 4가지 모델에서의 feature_importances_가 모두 0인 컬럼들의 교집합 확인. \n",
    "        ada_gbc1 = list(set(ada_list1).intersection(gbc_list1))\n",
    "        ada_gbc_xgb1 = list(set(ada_gbc1).intersection(xgb_list1))\n",
    "        ada_gbc_xgb_lgbm1 = list(set(ada_gbc_xgb1).intersection(lgbm_list1))\n",
    "        \n",
    "        # 해당 컬럼들을 제외한 X데이터를 생성\n",
    "        self.X_train = self.X_train.drop(ada_gbc_xgb_lgbm1, axis=1)\n",
    "        self.X_test = self.X_test.drop(ada_gbc_xgb_lgbm1, axis=1)\n",
    "        \n",
    "        return self.X_train, self.X_test, self.y_train, self.y_test\n",
    "\n",
    "    def scale_robust(self, X_train, X_test, y_train, y_test):\n",
    "        num_cols = []\n",
    "        for col in self.X_train.columns:\n",
    "            if (\"E\" in col) | (\"age\" in col) |(\"family\" in col) |(\"elapse\" in col):\n",
    "                num_cols.append(col)\n",
    "                \n",
    "        rbscale = RobustScaler().fit(self.X_train[num_cols])\n",
    "        self.X_train_ro[num_cols] = rbscale.transform(self.X_train[num_cols])\n",
    "        self.X_test[num_cols] = rbscale.transform(self.X_test[num_cols])\n",
    "\n",
    "        return self.X_train, self.X_test, self.y_train, self.y_test\n",
    "\n",
    "       \n",
    "    def feature_addition(self, column=\"score\", voted=\"voted\",col_name=\"rate\"):\n",
    "        df_tr = pd.concat([self.X_train, self.y_train], axis=1)\n",
    "        df_te = self.X_test\n",
    "        add_all_tr = df_tr[[voted, column]].groupby(column).count()\n",
    "        add_yes_tr = df_tr[[voted, column]].groupby(column).sum()\n",
    "        add_no_tr = add_all_tr - add_yes_tr\n",
    "        df_add_tr = round((add_yes_tr - add_no_tr)/ add_all_tr, 4)\n",
    "        df_add_tr = df_add_tr.rename(columns={voted:col_name})\n",
    "        \n",
    "        df1_tr = pd.merge(left=df_tr, right=df_add_tr, how=\"left\", right_index=True, left_on=column)\n",
    "        df1_te = pd.merge(left=df_te, right=df_add_tr, how=\"left\", right_index=True, left_on=column)\n",
    "        self.train_X=df1_tr.drop(voted, axis=1)\n",
    "        self.train_Y=pd.DataFrame(df1_tr[voted])\n",
    "        self.test_X=df1_te.fillna(0)\n",
    "        self.test_Y=self.y_test\n",
    "        \n",
    "        return self.train_X, self.test_X, self.train_Y, self.test_Y\n",
    "\n",
    "    \n",
    "# ada, gbc, xgb, lgbm 모델링 클래스 \n",
    "class modeling:    \n",
    "    def __init__(self,X_train, X_test, y_train, y_test):\n",
    "        ada = AdaBoostClassifier()\n",
    "        gbc = GradientBoostingClassifier()\n",
    "        xgb = XGBClassifier()\n",
    "        lgbm = LGBMClassifier()\n",
    "        self.datas = []\n",
    "        self.models = [ada, gbc, xgb, lgbm]\n",
    "        self.model_names = ['Ada', 'GBC', 'XGB', 'LGBM']\n",
    "        \n",
    "        # train, test 분리작업까지 마친 상태로 받아오기\n",
    "        self.X_train = X_train\n",
    "        self.X_test = X_test\n",
    "        self.y_train = y_train\n",
    "        self.y_test = y_test\n",
    "        \n",
    "        # 분류모델 평가지표 계산함수 (AUC, ACC를 우선순위로 사용함)\n",
    "    def get_score(self, pred):\n",
    "        acc = accuracy_score(self.y_test, pred)\n",
    "        pre = precision_score(self.y_test, pred)\n",
    "        rec = recall_score(self.y_test, pred)\n",
    "        f1 = f1_score(self.y_test, pred)\n",
    "        auc = roc_auc_score(self.y_test, pred)\n",
    "       \n",
    "        return acc, auc, pre, rec, f1\n",
    "    \n",
    "    def fit_model(self, model):\n",
    "\n",
    "        model.fit(self.X_train, self.y_train)\n",
    "        y_pre_tr = model.predict(self.X_train)\n",
    "        self.y_pre_test = model.predict(self.X_test)\n",
    "        total_score = self.get_score(self.y_pre_test)\n",
    "        \n",
    "        return total_score\n",
    "\n",
    "    def models_score_df(self):\n",
    "        cols_names = ['accuracy', 'AUC', 'precision', 'recall', 'f1']\n",
    "\n",
    "        for model in self.models:\n",
    "            self.datas.append(self.fit_model(model))\n",
    "\n",
    "        return pd.DataFrame(self.datas, columns=cols_names, index=self.model_names)\n",
    "    \n",
    "    # 평가지표와 confusion matrix 출력 함수 \n",
    "    def print_score(self):\n",
    "        datas = []\n",
    "        for model in self.models:\n",
    "            datas.append(self.fit_model(model))\n",
    "        \n",
    "            acc, auc, pre, rec, f1 = datas[0]\n",
    "            con = confusion_matrix(self.y_test, self.y_pre_test)\n",
    "            print('='*20)\n",
    "            print(model)\n",
    "            print('confusion matrix')\n",
    "            print(con)\n",
    "            print('='*20)\n",
    "\n",
    "            print('Accuracy: {0:.4f}, AUC: {1:.4f}'.format(acc, auc))\n",
    "            print('Recall: {0:.4f}, f1_score: {1:.4f}, precision: {2:.4f}'.format(rec, f1, pre))\n",
    "            print('='*20)\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1A</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E</th>\n",
       "      <th>Q2A</th>\n",
       "      <th>Q2I</th>\n",
       "      <th>Q2E</th>\n",
       "      <th>Q3A</th>\n",
       "      <th>Q3I</th>\n",
       "      <th>Q3E</th>\n",
       "      <th>Q4A</th>\n",
       "      <th>...</th>\n",
       "      <th>screenw</th>\n",
       "      <th>screenh</th>\n",
       "      <th>hand</th>\n",
       "      <th>religion</th>\n",
       "      <th>orientation</th>\n",
       "      <th>race</th>\n",
       "      <th>voted</th>\n",
       "      <th>married</th>\n",
       "      <th>familysize</th>\n",
       "      <th>major</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21017.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18600.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14957.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>3818.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7850.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5902.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>mathematics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4186.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7160.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>375.0</td>\n",
       "      <td>667.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9373.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10171.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10117.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>international relations</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9465.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5284.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8872.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>360.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73484</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9925.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14218.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>25499.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2048.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73485</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5816.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4368.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7246.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>sociology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73486</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5353.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6846.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4920.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73487</th>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7974.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7901.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8647.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>414.0</td>\n",
       "      <td>736.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73488</th>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7567.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5766.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>768.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73489 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1A   Q1I      Q1E  Q2A   Q2I      Q2E  Q3A   Q3I      Q3E  Q4A  ...  \\\n",
       "0      3.0   6.0  21017.0  3.0   7.0  18600.0  5.0  20.0  14957.0  2.0  ...   \n",
       "1      5.0  17.0   3818.0  5.0   9.0   7850.0  1.0  16.0   5902.0  3.0  ...   \n",
       "2      5.0  16.0   4186.0  5.0  12.0   2900.0  1.0   2.0   7160.0  1.0  ...   \n",
       "3      2.0  12.0   9373.0  4.0   1.0  10171.0  2.0   7.0  10117.0  1.0  ...   \n",
       "4      5.0  13.0   9465.0  5.0   7.0   5284.0  2.0  19.0   8872.0  1.0  ...   \n",
       "...    ...   ...      ...  ...   ...      ...  ...   ...      ...  ...  ...   \n",
       "73484  2.0  17.0   9925.0  1.0   6.0  14218.0  2.0  16.0  25499.0  4.0  ...   \n",
       "73485  5.0   9.0   5816.0  5.0   4.0   4368.0  1.0  15.0   7246.0  1.0  ...   \n",
       "73486  2.0  17.0   5353.0  1.0  18.0   6846.0  1.0   9.0   4920.0  1.0  ...   \n",
       "73487  4.0  15.0   7974.0  3.0   9.0   7901.0  5.0  17.0   8647.0  3.0  ...   \n",
       "73488  3.0  12.0   7567.0  1.0  10.0   5766.0  5.0  13.0  37103.0  1.0  ...   \n",
       "\n",
       "       screenw  screenh  hand  religion  orientation  race  voted  married  \\\n",
       "0       1440.0    900.0     1         7            1    30      1        2   \n",
       "1       1536.0    864.0     1         1            1    60      2        1   \n",
       "2        375.0    667.0     1         2            2    10      2        1   \n",
       "3       1280.0    720.0     1         6            1    60      1        3   \n",
       "4        360.0    640.0     1         4            3    60      1        1   \n",
       "...        ...      ...   ...       ...          ...   ...    ...      ...   \n",
       "73484   2048.0   1080.0     1        12            1    10      2        2   \n",
       "73485   1440.0    900.0     1         2            3    60      2        1   \n",
       "73486   1280.0   1024.0     1         1            1    60      2        1   \n",
       "73487    414.0    736.0     1         3            1    10      1        1   \n",
       "73488   1366.0    768.0     1         4            1    70      2        1   \n",
       "\n",
       "       familysize                    major  \n",
       "0               5               Marketing   \n",
       "1               2              mathematics  \n",
       "2               2                Chemistry  \n",
       "3               2  international relations  \n",
       "4               2               Management  \n",
       "...           ...                      ...  \n",
       "73484           1                 business  \n",
       "73485           2                sociology  \n",
       "73486           3                      NaN  \n",
       "73487           4                 Business  \n",
       "73488           4                      NaN  \n",
       "\n",
       "[73489 rows x 105 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_origin = pd.read_csv('./MACH_data/data.csv', delimiter='\\t')\n",
    "df_origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# .py 모듈 import \n",
    "from preprocessing import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 클래스 객체화\n",
    "data = Preprocessing(df_origin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = data.preprocessing_1th()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46661, 339)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nthpreprocessing import NthPreprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 원본데이터로 클래스 객체화\n",
    "data_origin = NthPreprocessing(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[AdaBoostClassifier(),\n",
       " GradientBoostingClassifier(),\n",
       " XGBClassifier(base_score=None, booster=None, colsample_bylevel=None,\n",
       "               colsample_bynode=None, colsample_bytree=None, gamma=None,\n",
       "               gpu_id=None, importance_type='gain', interaction_constraints=None,\n",
       "               learning_rate=None, max_delta_step=None, max_depth=None,\n",
       "               min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "               n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "               random_state=None, reg_alpha=None, reg_lambda=None,\n",
       "               scale_pos_weight=None, subsample=None, tree_method=None,\n",
       "               validate_parameters=None, verbosity=None),\n",
       " LGBMClassifier()]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_origin.models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1_TP_notell_2u</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E_notell_2u</th>\n",
       "      <th>Q2_TP_ppl_nd_dangun</th>\n",
       "      <th>Q2I</th>\n",
       "      <th>Q2E_ppl_nd_dangun</th>\n",
       "      <th>Q3_TN_do_moral</th>\n",
       "      <th>Q3I</th>\n",
       "      <th>Q3E_do_moral</th>\n",
       "      <th>Q4_VN_ppl_good</th>\n",
       "      <th>...</th>\n",
       "      <th>orientation_Homosexual</th>\n",
       "      <th>race_Arab</th>\n",
       "      <th>race_Asian</th>\n",
       "      <th>race_Black</th>\n",
       "      <th>race_Native American</th>\n",
       "      <th>race_Other</th>\n",
       "      <th>race_White</th>\n",
       "      <th>married_Currently married</th>\n",
       "      <th>married_Never married</th>\n",
       "      <th>married_Previously married</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13302</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35701</th>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67361</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68523</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34740</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 228 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "13302              3.0   4.0            7.0                  2.0  17.0   \n",
       "35701              2.0  20.0            6.0                  2.0   3.0   \n",
       "67361              5.0   9.0           16.0                  1.0   2.0   \n",
       "68523              5.0   9.0            8.0                  1.0  11.0   \n",
       "34740              4.0   4.0            7.0                  2.0  17.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "13302                6.0             1.0   9.0           7.0             2.0   \n",
       "35701                7.0             4.0  19.0           7.0             4.0   \n",
       "67361                8.0             4.0  10.0          12.0             1.0   \n",
       "68523                8.0             1.0  20.0          18.0             5.0   \n",
       "34740                4.0             3.0  16.0           3.0             2.0   \n",
       "\n",
       "       ...  orientation_Homosexual  race_Arab  race_Asian  race_Black  \\\n",
       "13302  ...                       0          0           0           0   \n",
       "35701  ...                       0          0           0           0   \n",
       "67361  ...                       0          0           0           0   \n",
       "68523  ...                       0          0           0           1   \n",
       "34740  ...                       0          0           0           0   \n",
       "\n",
       "       race_Native American  race_Other  race_White  \\\n",
       "13302                     0           0           1   \n",
       "35701                     0           0           1   \n",
       "67361                     0           0           1   \n",
       "68523                     0           0           0   \n",
       "34740                     0           0           1   \n",
       "\n",
       "       married_Currently married  married_Never married  \\\n",
       "13302                          0                      1   \n",
       "35701                          0                      1   \n",
       "67361                          0                      0   \n",
       "68523                          0                      1   \n",
       "34740                          0                      1   \n",
       "\n",
       "       married_Previously married  \n",
       "13302                           0  \n",
       "35701                           0  \n",
       "67361                           1  \n",
       "68523                           0  \n",
       "34740                           0  \n",
       "\n",
       "[5 rows x 228 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature_selection=False, scale_robust=False, feature_addition=False\n",
    "X_train_fs, X_test_fs, y_train_fs, y_test_fs = data_origin.nth_preprocesing(feature_selection=True)\n",
    "X_train_fs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q1_TP_notell_2u</th>\n",
       "      <th>Q1I</th>\n",
       "      <th>Q1E_notell_2u</th>\n",
       "      <th>Q2_TP_ppl_nd_dangun</th>\n",
       "      <th>Q2I</th>\n",
       "      <th>Q2E_ppl_nd_dangun</th>\n",
       "      <th>Q3_TN_do_moral</th>\n",
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       "      <th>Q3E_do_moral</th>\n",
       "      <th>Q4_VN_ppl_good</th>\n",
       "      <th>...</th>\n",
       "      <th>orientation_Homosexual</th>\n",
       "      <th>race_Arab</th>\n",
       "      <th>race_Asian</th>\n",
       "      <th>race_Black</th>\n",
       "      <th>race_Native American</th>\n",
       "      <th>race_Other</th>\n",
       "      <th>race_White</th>\n",
       "      <th>married_Currently married</th>\n",
       "      <th>married_Never married</th>\n",
       "      <th>married_Previously married</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>5245</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>15.0</td>\n",
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       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>58402</th>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>17.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
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       "      <th>67361</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>68523</th>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>34740</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46661 rows × 228 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q1_TP_notell_2u   Q1I  Q1E_notell_2u  Q2_TP_ppl_nd_dangun   Q2I  \\\n",
       "32873              1.0  15.0            6.0                  1.0  20.0   \n",
       "14951              1.0  15.0            4.0                  4.0  10.0   \n",
       "28035              2.0  10.0            5.0                  1.0  17.0   \n",
       "5245               5.0  12.0            4.0                  5.0  15.0   \n",
       "58402              4.0  13.0            6.0                  4.0   3.0   \n",
       "...                ...   ...            ...                  ...   ...   \n",
       "13302              3.0   4.0            7.0                  2.0  17.0   \n",
       "35701              2.0  20.0            6.0                  2.0   3.0   \n",
       "67361              5.0   9.0           16.0                  1.0   2.0   \n",
       "68523              5.0   9.0            8.0                  1.0  11.0   \n",
       "34740              4.0   4.0            7.0                  2.0  17.0   \n",
       "\n",
       "       Q2E_ppl_nd_dangun  Q3_TN_do_moral   Q3I  Q3E_do_moral  Q4_VN_ppl_good  \\\n",
       "32873               13.0             5.0   8.0           7.0             5.0   \n",
       "14951                4.0             1.0   9.0           4.0             1.0   \n",
       "28035                6.0             4.0   8.0           7.0             5.0   \n",
       "5245                 5.0             1.0   3.0          10.0             1.0   \n",
       "58402               10.0             3.0   5.0           8.0             1.0   \n",
       "...                  ...             ...   ...           ...             ...   \n",
       "13302                6.0             1.0   9.0           7.0             2.0   \n",
       "35701                7.0             4.0  19.0           7.0             4.0   \n",
       "67361                8.0             4.0  10.0          12.0             1.0   \n",
       "68523                8.0             1.0  20.0          18.0             5.0   \n",
       "34740                4.0             3.0  16.0           3.0             2.0   \n",
       "\n",
       "       ...  orientation_Homosexual  race_Arab  race_Asian  race_Black  \\\n",
       "32873  ...                       0          0           0           0   \n",
       "14951  ...                       0          0           0           0   \n",
       "28035  ...                       0          0           0           0   \n",
       "5245   ...                       0          0           0           0   \n",
       "58402  ...                       0          0           1           0   \n",
       "...    ...                     ...        ...         ...         ...   \n",
       "13302  ...                       0          0           0           0   \n",
       "35701  ...                       0          0           0           0   \n",
       "67361  ...                       0          0           0           0   \n",
       "68523  ...                       0          0           0           1   \n",
       "34740  ...                       0          0           0           0   \n",
       "\n",
       "       race_Native American  race_Other  race_White  \\\n",
       "32873                     0           0           1   \n",
       "14951                     0           0           1   \n",
       "28035                     0           0           1   \n",
       "5245                      0           0           1   \n",
       "58402                     0           0           0   \n",
       "...                     ...         ...         ...   \n",
       "13302                     0           0           1   \n",
       "35701                     0           0           1   \n",
       "67361                     0           0           1   \n",
       "68523                     0           0           0   \n",
       "34740                     0           0           1   \n",
       "\n",
       "       married_Currently married  married_Never married  \\\n",
       "32873                          0                      0   \n",
       "14951                          0                      1   \n",
       "28035                          1                      0   \n",
       "5245                           0                      1   \n",
       "58402                          1                      0   \n",
       "...                          ...                    ...   \n",
       "13302                          0                      1   \n",
       "35701                          0                      1   \n",
       "67361                          0                      0   \n",
       "68523                          0                      1   \n",
       "34740                          0                      1   \n",
       "\n",
       "       married_Previously married  \n",
       "32873                           1  \n",
       "14951                           0  \n",
       "28035                           0  \n",
       "5245                            0  \n",
       "58402                           0  \n",
       "...                           ...  \n",
       "13302                           0  \n",
       "35701                           0  \n",
       "67361                           1  \n",
       "68523                           0  \n",
       "34740                           0  \n",
       "\n",
       "[46661 rows x 228 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_origin.X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NthPreprocessing' object has no attribute 'X_train_ro'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-342e2b878525>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train_ro\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_ro\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_ro\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_ro\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_origin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnth_preprocesing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscale_robust\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mX_train_ro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/dss14th/project/4_ML_3/ml-repo-3/nthpreprocessing.py\u001b[0m in \u001b[0;36mnth_preprocesing\u001b[0;34m(self, feature_selection, scale_robust, feature_addition)\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mscale_robust\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale_robust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfeature_addition\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_addition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/dss14th/project/4_ML_3/ml-repo-3/nthpreprocessing.py\u001b[0m in \u001b[0;36mscale_robust\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0mrbscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRobustScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train_ro\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_cols\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrbscale\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    129\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_cols\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrbscale\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NthPreprocessing' object has no attribute 'X_train_ro'"
     ]
    }
   ],
   "source": [
    "# 클래스 내에서 self로 변수들을 받아서 원본 데이터의 X_train 데이터가 변경이 됨.. ㅠㅠ \n",
    "# 다시 이야기 해보기\n",
    "X_train_ro, X_test_ro, y_train_ro, y_test_ro = data_origin.nth_preprocesing(scale_robust=True)\n",
    "X_train_ro.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_fa, X_test_fa, y_train_fa, y_test_fa = data_origin.nth_preprocesing(feature_addition=True)\n",
    "X_train_fa.tail()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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