credit_ensemble.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ede112d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.utils import resample\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import classification_report,confusion_matrix,roc_curve, auc\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd123025",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family'] = 'Microsoft YaHei' "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abf2162",
   "metadata": {},
   "source": [
    "## 数据概览"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f93933",
   "metadata": {},
   "source": [
    "### 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4942624",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"D:\\BaiduSyncdisk\\机器学习\\shangji\\data\\Credit_card.csv\")  # 读取特征数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2b703ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_label = pd.read_csv(\"D:\\BaiduSyncdisk\\机器学习\\shangji\\data\\Credit_card_label.csv\")  # 读取类别数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "505ca292",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1548, 18)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80e40cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1548, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f063ba24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1548 entries, 0 to 1547\n",
      "Data columns (total 18 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Ind_ID           1548 non-null   int64  \n",
      " 1   GENDER           1541 non-null   object \n",
      " 2   Car_Owner        1548 non-null   object \n",
      " 3   Propert_Owner    1548 non-null   object \n",
      " 4   CHILDREN         1548 non-null   int64  \n",
      " 5   Annual_income    1525 non-null   float64\n",
      " 6   Type_Income      1548 non-null   object \n",
      " 7   EDUCATION        1548 non-null   object \n",
      " 8   Marital_status   1548 non-null   object \n",
      " 9   Housing_type     1548 non-null   object \n",
      " 10  Birthday_count   1526 non-null   float64\n",
      " 11  Employed_days    1548 non-null   int64  \n",
      " 12  Mobile_phone     1548 non-null   int64  \n",
      " 13  Work_Phone       1548 non-null   int64  \n",
      " 14  Phone            1548 non-null   int64  \n",
      " 15  EMAIL_ID         1548 non-null   int64  \n",
      " 16  Type_Occupation  1060 non-null   object \n",
      " 17  Family_Members   1548 non-null   int64  \n",
      "dtypes: float64(2), int64(8), object(8)\n",
      "memory usage: 217.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699531d6",
   "metadata": {},
   "source": [
    "可以看到GENDER，Annual_income，Birthday_count，Type_Occupation列存在缺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7b12f6",
   "metadata": {},
   "source": [
    "各字段意义如下：\n",
    "\n",
    "| 字段| 说明|\n",
    "|:-----|:-----:|\n",
    "| Ind_ID|客户ID|\n",
    "|Gender\t|性别信息|\n",
    "|Car_owner|\t是否有车|\n",
    "|Propert_owner|\t是否有房产|\n",
    "|Children|\t子女数量|\n",
    "|Annual_income|\t年收入|\n",
    "|Type_Income|\t收入类型|\n",
    "|Education|\t教育程度|\n",
    "|Marital_status\t|婚姻状况|\n",
    "|Housing_type|\t居住方式|\n",
    "|Birthday_count\t|以当前日期为0，往前倒数天数|\n",
    "|Employed_days\t|就业开始日期。当前日期为0，往前倒数天数（正值则意味着个人目前未就业）|\n",
    "|Mobile_phone\t|手机号码|\n",
    "|Work_phone\t|工作电话|\n",
    "|Phone\t|电话号码|\n",
    "|EMAIL_ID|\t电子邮箱|\n",
    "|Type_Occupation|\t职业|\n",
    "|Family_Members|\t家庭人数|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f1a862fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1548 entries, 0 to 1547\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   Ind_ID  1548 non-null   int64\n",
      " 1   label   1548 non-null   int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 24.3 KB\n"
     ]
    }
   ],
   "source": [
    "data_label.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b73aacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1548, 19)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(data, data_label, on=\"Ind_ID\")  # 将特征和类别按照对应的ID进行拼接\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5837c1fb",
   "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>Ind_ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>Car_Owner</th>\n",
       "      <th>Propert_Owner</th>\n",
       "      <th>CHILDREN</th>\n",
       "      <th>Annual_income</th>\n",
       "      <th>Type_Income</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Housing_type</th>\n",
       "      <th>Birthday_count</th>\n",
       "      <th>Employed_days</th>\n",
       "      <th>Mobile_phone</th>\n",
       "      <th>Work_Phone</th>\n",
       "      <th>Phone</th>\n",
       "      <th>EMAIL_ID</th>\n",
       "      <th>Type_Occupation</th>\n",
       "      <th>Family_Members</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5008827</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>Pensioner</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-18772.0</td>\n",
       "      <td>365243</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5009744</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5009746</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5009749</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5009752</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>-13557.0</td>\n",
       "      <td>-586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ind_ID GENDER Car_Owner Propert_Owner  CHILDREN  Annual_income  \\\n",
       "0  5008827      M         Y             Y         0       180000.0   \n",
       "1  5009744      F         Y             N         0       315000.0   \n",
       "2  5009746      F         Y             N         0       315000.0   \n",
       "3  5009749      F         Y             N         0            NaN   \n",
       "4  5009752      F         Y             N         0       315000.0   \n",
       "\n",
       "            Type_Income         EDUCATION Marital_status       Housing_type  \\\n",
       "0             Pensioner  Higher education        Married  House / apartment   \n",
       "1  Commercial associate  Higher education        Married  House / apartment   \n",
       "2  Commercial associate  Higher education        Married  House / apartment   \n",
       "3  Commercial associate  Higher education        Married  House / apartment   \n",
       "4  Commercial associate  Higher education        Married  House / apartment   \n",
       "\n",
       "   Birthday_count  Employed_days  Mobile_phone  Work_Phone  Phone  EMAIL_ID  \\\n",
       "0        -18772.0         365243             1           0      0         0   \n",
       "1        -13557.0           -586             1           1      1         0   \n",
       "2             NaN           -586             1           1      1         0   \n",
       "3        -13557.0           -586             1           1      1         0   \n",
       "4        -13557.0           -586             1           1      1         0   \n",
       "\n",
       "  Type_Occupation  Family_Members  label  \n",
       "0             NaN               2      1  \n",
       "1             NaN               2      1  \n",
       "2             NaN               2      1  \n",
       "3             NaN               2      1  \n",
       "4             NaN               2      1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7af20a97",
   "metadata": {},
   "source": [
    "### 绘制箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e498da8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['CHILDREN', 'Annual_income', 'Birthday_count', 'Employed_days', 'Family_Members']  # 对数值型数据绘制箱线图\n",
    "plt.figure(figsize=(20,15))\n",
    "for i,col in enumerate(columns,1):\n",
    "    plt.subplot(2,3,i)\n",
    "    sns.boxplot(y=data[col],palette=\"Set2\")\n",
    "    plt.title(f'箱线图：{col}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed196a26",
   "metadata": {},
   "source": [
    "可以看到：\n",
    "* CHILDREN列有个值为14，可能是异常值\n",
    "* Employed_days列有个很大的正值，换算成年将近1000年，可能为异常值\n",
    "* Family_Members列有个极大值为15，可能是异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086ca176",
   "metadata": {},
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da715c7",
   "metadata": {},
   "source": [
    "#### 1.数据类型转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc324c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Ind_ID'] = data['Ind_ID'].astype(str) #将Ind_ID转化为object格式"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2456fd38",
   "metadata": {},
   "source": [
    "#### 2.缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a1ec2cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_mode = data['GENDER'].mode()[0]\n",
    "data['GENDER'].fillna(gender_mode,inplace=True)  # 对‘GENDER'的缺失值采用众数填充\n",
    "\n",
    "data['Type_Occupation'].fillna('unknown',inplace=True)  # 对‘Type_Occupation'的缺失值填充“unknown”\n",
    "\n",
    "annual_income_median = data['Annual_income'].median()\n",
    "data['Annual_income'].fillna(annual_income_median,inplace=True)  # 对‘Annual_income’的缺失值采用中位数填充\n",
    "\n",
    "birthday_count_median = data['Birthday_count'].median()\n",
    "data['Birthday_count'].fillna(birthday_count_median,inplace=True)  # 对‘Birthday_count’的缺失值采用中位数填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "829f0582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ind_ID             0\n",
       "GENDER             0\n",
       "Car_Owner          0\n",
       "Propert_Owner      0\n",
       "CHILDREN           0\n",
       "Annual_income      0\n",
       "Type_Income        0\n",
       "EDUCATION          0\n",
       "Marital_status     0\n",
       "Housing_type       0\n",
       "Birthday_count     0\n",
       "Employed_days      0\n",
       "Mobile_phone       0\n",
       "Work_Phone         0\n",
       "Phone              0\n",
       "EMAIL_ID           0\n",
       "Type_Occupation    0\n",
       "Family_Members     0\n",
       "label              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21590ce0",
   "metadata": {},
   "source": [
    "#### 3.数据转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58834252",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = (-data['Birthday_count'] / 365).astype(int)  # 将‘Birthday_count’列转化为‘Age’列\n",
    "data.drop('Birthday_count',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "44cddb3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ind_ID             0\n",
       "GENDER             0\n",
       "Car_Owner          0\n",
       "Propert_Owner      0\n",
       "CHILDREN           0\n",
       "Annual_income      0\n",
       "Type_Income        0\n",
       "EDUCATION          0\n",
       "Marital_status     0\n",
       "Housing_type       0\n",
       "Employed_days      0\n",
       "Mobile_phone       0\n",
       "Work_Phone         0\n",
       "Phone              0\n",
       "EMAIL_ID           0\n",
       "Type_Occupation    0\n",
       "Family_Members     0\n",
       "label              0\n",
       "Age                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be4acd3",
   "metadata": {},
   "source": [
    "#### 4.异常数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56670bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[data['CHILDREN'] == 14, 'CHILDREN'] = data['CHILDREN'].median()  # 使用中位数替换异常值\n",
    "\n",
    "data.loc[data['Family_Members'] == 15, 'Family_Members'] = data['Family_Members'].median() # 使用中位数替换异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "89e03b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       261.0\n",
       "mean     365243.0\n",
       "std           0.0\n",
       "min      365243.0\n",
       "25%      365243.0\n",
       "50%      365243.0\n",
       "75%      365243.0\n",
       "max      365243.0\n",
       "Name: Employed_days, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看'Employed_days'正值的情况\n",
    "positive_employed_days = data[data['Employed_days'] > 0]['Employed_days']\n",
    "positive_employed_days.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2324a2c4",
   "metadata": {},
   "source": [
    "可以看出，共有261行数据的“Employed_days”为正值，且值都相同，均为365243。故365243可能代表特殊含义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "189abf51",
   "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>CHILDREN</th>\n",
       "      <th>Annual_income</th>\n",
       "      <th>Employed_days</th>\n",
       "      <th>Mobile_phone</th>\n",
       "      <th>Work_Phone</th>\n",
       "      <th>Phone</th>\n",
       "      <th>EMAIL_ID</th>\n",
       "      <th>Family_Members</th>\n",
       "      <th>label</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>261.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.034483</td>\n",
       "      <td>151277.396552</td>\n",
       "      <td>365243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.306513</td>\n",
       "      <td>0.015326</td>\n",
       "      <td>1.659004</td>\n",
       "      <td>0.134100</td>\n",
       "      <td>59.199234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.237694</td>\n",
       "      <td>81253.131354</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.461931</td>\n",
       "      <td>0.123081</td>\n",
       "      <td>0.563817</td>\n",
       "      <td>0.341414</td>\n",
       "      <td>5.457401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>33750.000000</td>\n",
       "      <td>365243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>99000.000000</td>\n",
       "      <td>365243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>135000.000000</td>\n",
       "      <td>365243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>180000.000000</td>\n",
       "      <td>365243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>630000.000000</td>\n",
       "      <td>365243.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CHILDREN  Annual_income  Employed_days  Mobile_phone  Work_Phone  \\\n",
       "count  261.000000     261.000000          261.0         261.0       261.0   \n",
       "mean     0.034483  151277.396552       365243.0           1.0         0.0   \n",
       "std      0.237694   81253.131354            0.0           0.0         0.0   \n",
       "min      0.000000   33750.000000       365243.0           1.0         0.0   \n",
       "25%      0.000000   99000.000000       365243.0           1.0         0.0   \n",
       "50%      0.000000  135000.000000       365243.0           1.0         0.0   \n",
       "75%      0.000000  180000.000000       365243.0           1.0         0.0   \n",
       "max      3.000000  630000.000000       365243.0           1.0         0.0   \n",
       "\n",
       "            Phone    EMAIL_ID  Family_Members       label         Age  \n",
       "count  261.000000  261.000000      261.000000  261.000000  261.000000  \n",
       "mean     0.306513    0.015326        1.659004    0.134100   59.199234  \n",
       "std      0.461931    0.123081        0.563817    0.341414    5.457401  \n",
       "min      0.000000    0.000000        1.000000    0.000000   35.000000  \n",
       "25%      0.000000    0.000000        1.000000    0.000000   57.000000  \n",
       "50%      0.000000    0.000000        2.000000    0.000000   60.000000  \n",
       "75%      1.000000    0.000000        2.000000    0.000000   63.000000  \n",
       "max      1.000000    1.000000        5.000000    1.000000   68.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_employed_data = data[data['Employed_days'] == 365243]\n",
    "positive_employed_data.describe()  # “Employed_days”为正值的数值型特征概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1be50dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type_Income\n",
      "Pensioner    261\n",
      "Name: count, dtype: int64\n",
      "-----------------\n",
      "Type_Occupation\n",
      "unknown    261\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "explore_data = data[data['Employed_days'] == 365243][['Type_Income', 'Type_Occupation']]\n",
    "type_income_counts = explore_data['Type_Income'].value_counts()\n",
    "type_occupation_counts = explore_data['Type_Occupation'].value_counts()\n",
    "print(type_income_counts) \n",
    "print(\"-----------------\")\n",
    "print(type_occupation_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e550566",
   "metadata": {},
   "source": [
    "发现，所有“Employed_days”为正值的行，其'Type_Income'和'Type_Occupation'均相同，分别为**Pensioner（退休人士）**和**unknown**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "479b60b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将“Employed_days”为正值的行的'Type_Occupation'列设置成成'Pensioner'\n",
    "data.loc[data['Employed_days'] == 365243, 'Type_Occupation'] = 'Pensioner'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60ce1437",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Is_Retired'] = (data['Employed_days'] == 365243).astype(int)  # #创建一个新的二元特征 \"Is_Retired\"，为退休人员设置为1，其他人为0\n",
    "data.loc[data['Employed_days'] == 365243, 'Employed_days'] = 0  # 将Employed_days列中的正值转换为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "92d6c512",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将Employed_days转化为Employed_years\n",
    "data['Employed_years'] = (-data['Employed_days'] / 365).astype(int)\n",
    "#删除'Employed_days'\n",
    "data.drop('Employed_days',axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "08aff0d2",
   "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>Ind_ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>Car_Owner</th>\n",
       "      <th>Propert_Owner</th>\n",
       "      <th>CHILDREN</th>\n",
       "      <th>Annual_income</th>\n",
       "      <th>Type_Income</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Housing_type</th>\n",
       "      <th>Mobile_phone</th>\n",
       "      <th>Work_Phone</th>\n",
       "      <th>Phone</th>\n",
       "      <th>EMAIL_ID</th>\n",
       "      <th>Type_Occupation</th>\n",
       "      <th>Family_Members</th>\n",
       "      <th>label</th>\n",
       "      <th>Age</th>\n",
       "      <th>Is_Retired</th>\n",
       "      <th>Employed_years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5008827</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>Pensioner</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pensioner</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5009744</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5009746</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5009749</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>166500.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5009752</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>Commercial associate</td>\n",
       "      <td>Higher education</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ind_ID GENDER Car_Owner Propert_Owner  CHILDREN  Annual_income  \\\n",
       "0  5008827      M         Y             Y         0       180000.0   \n",
       "1  5009744      F         Y             N         0       315000.0   \n",
       "2  5009746      F         Y             N         0       315000.0   \n",
       "3  5009749      F         Y             N         0       166500.0   \n",
       "4  5009752      F         Y             N         0       315000.0   \n",
       "\n",
       "            Type_Income         EDUCATION Marital_status       Housing_type  \\\n",
       "0             Pensioner  Higher education        Married  House / apartment   \n",
       "1  Commercial associate  Higher education        Married  House / apartment   \n",
       "2  Commercial associate  Higher education        Married  House / apartment   \n",
       "3  Commercial associate  Higher education        Married  House / apartment   \n",
       "4  Commercial associate  Higher education        Married  House / apartment   \n",
       "\n",
       "   Mobile_phone  Work_Phone  Phone  EMAIL_ID Type_Occupation  Family_Members  \\\n",
       "0             1           0      0         0       Pensioner               2   \n",
       "1             1           1      1         0         unknown               2   \n",
       "2             1           1      1         0         unknown               2   \n",
       "3             1           1      1         0         unknown               2   \n",
       "4             1           1      1         0         unknown               2   \n",
       "\n",
       "   label  Age  Is_Retired  Employed_years  \n",
       "0      1   51           1               0  \n",
       "1      1   37           0               1  \n",
       "2      1   42           0               1  \n",
       "3      1   37           0               1  \n",
       "4      1   37           0               1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f702b4ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5Wb1799aff/4pf39/x7jZs2erWrVqmjZtmqS/ptSuXbtWixYt0uTJk3P6UoBM7Ha7rFar0TEKBbvd7tjjoGjRoizdk0/MZjPfa3ik/v3764EHHtBtt90mSWrRooVMJlOWb3xeW7M8J6ZPn65Zs2Zp8eLFKlWqlJ599lk9/fTT2rBhg9O4bdu26ZlnntGbb76piIgIrVixQp07d9YPP/ygO+644+ZfIIB8Y7FYFB8fn6ndZrMpPj5eFotFFStWNCAZAE+2ZMmSbPu8vb1VsmRJNWjQQGXKlMnHVAAAILflqKCyefNmvf3229qxY4caNGjg1PfYY48pMjJS7du31/3336/GjRvf8H3/+YkNX19f2Wy2TOO2bdumNm3a/C+8j4+aN2+uuLi4bO9ttVqd3iBPSUm54VwofKxWq/r27Wt0DCDPxMbGytfX1+gYQI4dOnTIUUyRpKSkpFy7t81m0/Tp0zVu3Dh16NBBkjRz5ky1bdtWiYmJCgsLc4z99ttvVadOHQ0ePFjSXx8Eeffdd3XgwAEKKoCHCA4OVnh4uA4fPuz0O4eXl5fuvvtuBQcHG5gOgKd65plnZLPZZLfb5e3t7fizl9dfC4PYbDb5+Piob9++evvtt+Xt7W1wYgAAcDNytOTXrFmzNGzYsEzFlGtatmypAQMG6PXXX7/pQHa7XQsXLlSjRo2cZqdIUkJCgtObGpIUGhqqU6dOZXu/mJgYBQUFOQ6m2wIA4HkqVKjgdF6xYsXrHn/XoUMHWSyWbO996NAhnTt3Tm3btnW0RUREyMvLK9OHNjp06KATJ05oy5YtysjI0JIlS1SkSBG1bNky2/tbrValpKQ4HQCMYzKZFBkZmWnGpslkUp8+fZjJCeCmLF++XPXq1dOhQ4eUlpam1NRUbdu2TQ0aNNDu3bv1yy+/aObMmVq2bJleeeUVo+MCAICblKMZKnv27FFUVNR1x3Tq1Endu3e/qTBpaWkaMGCAtm3bluX655cvX85UZPH397/uEk1RUVEaMWKE4zwlJYWiCrJlNpsVGxtrdIxCwWq1qn///pKkefPmyWw2G5yocOD7jMJo48aNunz5crb9CQkJkuT0oQ0/Pz+VKVMm04c2atasqZiYGD3wwAMymUzy8vLShg0bVLp06WzvHxMTw9KkgJspX7682rVrp3Xr1jnaHnnkEZUrV87AVAA82ejRo7V48WLVqlVLklSkSBFFRERo/PjxGjhwoPbu3atBgwYpPT1dc+bM0YQJEwxODAAAbkaOCirnz593+UtGuXLldO7cuRwHOXXqlB577DElJCRo69atWc6CMZvNjj0XrklNTc1UZPnnNbyBiBtlMplYDskAZrOZ7zsAw1y+fFleXl6Znhey+tDG1q1bNXz4cL3++utq1qyZVq5cqR49emjPnj2qUaNGlvfnwx2Ae8pqU3oAuFlnzpzJchmv0qVL69ChQ47zZs2aacyYMfkZDQAA5KIcLfn19/U/s72hl1eW+59cz/Hjx9WoUSMFBgbq4MGDuu+++7IcV7FixUxrpiclJalq1ao5+noAAADXmM1m2Ww2paenO7Vn9aGNqKgoRUZGasSIEbr33nv12muvqUGDBpoyZcp171+8eHGnA4CxTp8+rc8//9ypbcOGDTp9+rRBiQB4uqZNm2rMmDH67bffHG2///67Jk+e7LTP2i+//OK0LxwAAPAsOSqoSFKNGjVUtGjRbI8777wzxyF69uypxo0ba8OGDSpbtmy245o2barNmzc7zjMyMrR9+3a1bt06x18TAABAkmPPlb8v72W1WnX27NlMH9qIj49XnTp1nNrq1aun+Pj4PM8JIHfY7XYtXrw4yxkqWbUDwI2YO3euTp8+rcqVK6t+/fpq0qSJKlWqpK+//lqzZs1yjHvnnXcUERFhYFIAAHArcrTk16JFi3I9wPHjx7Vv3z6NGTPGsYb5NWXKlNFjjz2m559/Xl27dtWQIUPUsGFDRUdHq2vXrpo7d65sNpsiIyNzPRcAACgc6tWrJz8/P23evFnPPfecJGnHjh0ymUxq3ry509iKFSvqhx9+cGo7dOiQoygDwP1ZLJYsi6A2m03x8fGyWCz8Pw0gx6pVq6ajR49q5cqVOnTokFJSUvTYY4+pV69e+uijj9SiRQtJ0rJly667bDkAAHBvOSqoPP300y7H2O12Xbly5YbveW1afVYb2b/55ps6cuSILBaLJKlu3bpasWKFxowZo6lTp6phw4batGmTAgMDb/jrAQCAwmXChAnX3TTez89P/fv314QJExQaGqqAgAANHTpU/fr1U4kSJdSmTRvHhzsGDx6sMWPGqGbNmmrUqJFWr16tjRs3av369fn4igDciuDgYIWHh+vw4cNOSxV7eXnp7rvvVnBwsIHpAHgyHx8fPfHEE3r00Ue1du1aLVu2TKNHj5bNZtPAgQMlSSVLljQ4JQAAuBU5KqjciJ07d6pVq1bKyMi4ofHNmze/7rT6oUOHOp1369ZN3bp1u6WMAADAs3zwwQc5Gt+zZ0/Hn8ePH+9y/NSpU3XlyhX16NFD3t7eevLJJzV9+nSlpaU5fbhjyJAhstvteu211/TLL7/ojjvu0AcffKBHHnkkZy8IgGFMJpMiIyM1atSoTO19+vSRyWQyKBkAT7dr1y4tXbpUK1euVHJysmrUqKEpU6aod+/eRkcDAAC5JNcLKgAAALntySefvOGxJpPJqaByI8xms+bOnau5c+dm6jt58qTTvYcNG6Zhw4bl6P4A3Ev58uXVoUMHrVmzRna7XSaTSR07dlS5cuWMjgbAw/z4449aunSpli1bphMnTqhKlSp65plnNHPmTC1btkz16tUzOiIAAMhFOd6UHgAAIL/ZbLYbPm50liyAwq1Tp04qUaKEJOm2225Tx44djQ0EwOPcd999qlmzppYuXaquXbsqLi5OCQkJiomJue5KHAAAwHNRUAEAAABQ6JjNZvXt21elS5dWnz59ZDabjY4EwMMcPHhQJUuWVPv27dWxY0c1bNhQklg6EACAAixHS37dcccdLh8M/vzzz1sKBAAA4Mr58+c1YcIE7d69W5cuXcrUn5CQkP+hAHic+vXrq379+kbHAOChzpw5o5UrV2rp0qWaO3euypUrp0cffVRdunShqAIAQAGVo4JKTtYvBwAAyCvPPvusvvnmGz3xxBN68803NW7cOB05ckRbt25V//79jY4HAAAKgeLFi+uZZ57RM888o5MnT2rZsmVavny53n77bZlMJs2dO1eDBw/WPffcY3RUAACQS3JUUJk4cWJe5QAAALhh27Zt05o1axQREaFVq1apT58+Cg0N1ZtvvqnDhw8bHQ8AABQylStX1tixYzV27Fh98803Wrp0qT766CMtWrRId911lw4dOmR0RAAAkAvYQwUAAHgcf39/FSlSRJJUvXp1ff/995Kk1q1b66OPPjIyGgAAKOTuvfdezZo1SxaLRWvWrFGtWrWMjgQAAHJJjmaoPPTQQzc8dtOmTTkOAwAAcCNatGihdevWqUmTJmrZsqVeeeUV+fj4aMmSJSpTpozR8QAAAOTt7a327durffv2RkcBAAC5JEcFlYoVK+ZVDgAAgBsWExOj3bt3S5IGDRqkDRs26OGHH5a/v7/ef/99g9MBAAAAAICCKEcFlUWLFuVVDgAAgBtWuXJlVa5cWdJfy39t375dFy9eVGBgoHx8cvR4AwAAAAAAcEN4xwEAAHgci8WSZfuVK1ckScHBwfkZBwAAAAAAFAI5Kqj07dv3hsfGxsbmOAwAAMCNqFSpkkwmU7b9GRkZ+ZgGAAAAAAAUBjkqqCQlJWVq2759uxo2bCh/f/9cCwUAAHA927Ztczq32Ww6fvy4Zs2apWHDhhkTCgAAAAAAFGg5Kqhs3rw5U5ufn58WLVqk6tWr51ooAACA64mIiMjU1rJlS0VEROjZZ5/Vc889Z0AqAAAAAABQkHkZHQAAACC3hISE6MCBA0bHAAAAAAAABRCb0gMAAI/zz03pbTabfv31V7322muqVq2aQakAAACQ2+x2+//+nJ4hm4FZAADXZ0//336mf//3uyChoAIAADxOVpvS2+12hYSEaMWKFQalAgAAQG67evWq48+/fbLtOiMBAO7k6tWr8vPzMzpGrsuVgso/39AAAADIS//clN7Ly0tly5bV7bffLh8fPi8CAAAAAAByX47ecShSpEim4kl6erpq1aqVaezfP0EAAACQm7LalB4AAAAFT9GiRR1/LtutpUw+3gamAQBcjz09wzGb8O//fhckOSqoLFiwIK9yAAAA3LD09HTFxsZq586dOnv2bKa1WTdt2mRQMgAAAOSmv3+w1+TjLa8izEYGAHf1932uCuqqVjn6KRQYGKiMjAx179492zGLFy9W1apVbzkYAABAdvr376/Y2Fg1btxYYWFhLPMFAAAAAADyXI7efXjppZf04osvXnfMxYsXNWnSJG3duvWWggEAAGTn008/1dy5c9WvXz+jowAAAAAAgELCKyeDk5KSVKdOneuOadSokb7//vtbyQQAAHBdxYsX17333mt0DAAAAAAAUIjkqKDi5eXlckmN2267TcnJybcUCgAA4Hqio6P1+uuvKy0tzegoAAAAAACgkMjRkl+1atXSV199pfDw8GzHHDx4UOXLl7/lYAAAANlZunSpDh06pEqVKumuu+5SkSJFnPrZlB4AAAAAAOS2HBVUIiMjNWXKFHXt2lUVKlTI1P/HH3/olVdeUYcOHXItIAAAwD9VqlRJlSpVMjoGAAAAAAAoRHJUUHn++ef12Wef6d5779WUKVP0yCOPqGzZsvr999+1bds2jR8/XpcuXdK4cePyKi8AAIAWLVpkdAQAAAAAAFDI5Kig4uXlpXXr1umll17SoEGD9Oyzzzr67Ha7WrdurTVr1qhcuXK5HhQAAODvbDabEhISdPbsWdntdqe+Jk2aGJQKgCfZt2+f3n//fT399NOqX7++0XEAAAAAuLkcFVQkqUiRIpo+fbo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      "text/plain": [
       "<Figure size 2000x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['CHILDREN', 'Annual_income', 'Age', 'Employed_years', 'Family_Members']  # 对数值型数据绘制箱线图\n",
    "plt.figure(figsize=(20,15))\n",
    "for i,col in enumerate(columns,1):\n",
    "    plt.subplot(2,3,i)\n",
    "    sns.boxplot(y=data[col],palette=\"Set2\")\n",
    "    plt.title(f'箱线图：{col}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd18828",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f5f81d",
   "metadata": {},
   "source": [
    "### 1.数值型变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c92fc10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "numerical_columns = ['Annual_income', 'Age', 'Employed_years']\n",
    "plt.figure(figsize=(20, 15))\n",
    "for i, column in enumerate(numerical_columns, 1):\n",
    "    sns.displot(data[column], kde=True, bins=30)\n",
    "    plt.title(f'{column}属性的分布情况', fontsize=15)\n",
    "    plt.xlabel(column, fontsize=12)\n",
    "    plt.ylabel('频次', fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a95eaf4",
   "metadata": {},
   "source": [
    "* 大多数人收入较低，极少数人收入较高\n",
    "* 年龄分布在20岁—70岁间，分布较为均匀\n",
    "* 大多数人工龄较低，其中为0的占多数，这部分可能是由于将退休人员的工龄也设置为0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f4d67b",
   "metadata": {},
   "source": [
    "### 2.分类变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ea220d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EL7bn/gqTs7MzABS6wpOYmIhq1aqhcuXK0Ol0xZYXjKOkciIiIqKSlIvA5OHhgd27dyvdzp8/j6SkJAQGBsLExASNGzc2Kk9NTUVsbCwCAwMBAE2aNDEqz8vLw759+5RyIiIiopI897fkACAkJASTJ0+Gn58fPD09ERwcjE6dOqF27dpKeffu3dGsWTM0atQIM2bMgI+PD4KCggAA77zzDho0aIDQ0FD06NEDX375JfLz8zFkyJAynCsiIiIqL577K0wAMHbsWISEhGD06NFo2bIl3N3dsXr1aqW8c+fOWLhwIUJDQ9G4cWNkZ2cjPDwcWq0WAPDKK69g/fr1WLVqFerVq4czZ87gl19+gZWVVVnNEhEREZUjGhGRsq5EeZCWlgYbGxukpqbC2tq6xH4D3l/1jGr17xM3b1BZV4GIiP5FHuX8XZJycYWJiIiIqCwxMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKspNYEpPT8fbb78NOzs7WFlZoXfv3rh69apSvnTpUnh6ekKv16NVq1ZISEgwGn7Lli2oUaMGLCwsUL9+fcTFxT3rWSAiIqJyqtwEpnHjxmH//v3YvHkzfv75Z1y+fBn9+vUDAGzatAnBwcEIDQ3FwYMHkZOTgy5duiA/Px8AEB0djb59+2LEiBE4cuQI3NzcEBQUhPT09LKcJSIiIionNCIiZV2J0qhVqxZGjhyJcePGAQC2b9+Ofv364c6dO/D390fTpk2xcOFCAMC5c+dQo0YNREREoGXLlujRowfy8vLwww8/AABSUlLg4OCApUuXYujQoaWaflpaGmxsbJCamgpra+sS+w14f9VjzOmLLW7eoLKuAhER/Ys8yvm7JOXmClOfPn2wceNGXL9+HSkpKfjmm2/Qp08fpKSk4MSJE+jQoYPSr6+vLxwdHRETEwMAiIyMNCq3tbWFv7+/Uk5ERERUEtOyrkBpTZkyBXv27IGDgwM0Gg18fX1x5MgRXLx4EQDg6elp1L+bmxuSkpJw+/ZtpKSkFFtenKysLGRlZSnf09LSnuDcEBERUXlSbq4wDR8+HH///Td27tyJvXv3wtLSEn369EFGRgYAwGAwGPVvMBiQlZWlWl6c2bNnw8bGRvm4uro+4TkiIiKi8qJcXGH69ddfsWLFChw9ehT169cHAGzduhXu7u5o0aIFACA7O9tomMzMTBgMBuh0uhLLizN58mSEhIQo39PS0hiaiIiIXlDl4grT6dOnAQB+fn5KN1dXV1SuXBk5OTkAgMTERKNhEhMTUa1aNVSuXBk6na7Y8uLodDpYW1sbfYiIiOjFVC4Ck7OzM4D7V5oKXL16FTdv3oS3tzc8PDywe/dupez8+fNISkpCYGAgTExM0LhxY6Py1NRUxMbGIjAw8NnNBBEREZVb5eKW3GuvvQZ/f38MGTIECxYsgLm5OSZNmgQ3Nzd06tQJf//9NyZPngw/Pz94enoiODgYnTp1Qu3atQEAISEh6N69O5o1a4ZGjRphxowZ8PHxQVBQUBnPGREREZUH5eIKk0ajwc6dO1GzZk306NED7du3h52dHSIiImBpaYmxY8ciJCQEo0ePRsuWLeHu7o7Vq1crw3fu3BkLFy5EaGgoGjdujOzsbISHh0Or1ZbhXBEREVF5UW5eXFnW+OLKZ4MvriQioifphXtxJREREVFZYWAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFSUq8B048YNDBkyBJUqVYJer0e3bt2UsqVLl8LT0xN6vR6tWrVCQkKC0bBbtmxBjRo1YGFhgfr16yMuLu4Z156IiIjKq3ITmNLT09GsWTNcvXoV27ZtQ3R0NPr37w8A2LRpE4KDgxEaGoqDBw8iJycHXbp0QX5+PgAgOjoaffv2xYgRI3DkyBG4ubkhKCgI6enpZTlLREREVE5oRETKuhKl8cEHH2Dz5s04ffo0zM3Njcr8/f3RtGlTLFy4EABw7tw51KhRAxEREWjZsiV69OiBvLw8/PDDDwCAlJQUODg4YOnSpRg6dGippp+WlgYbGxukpqbC2tq6xH4D3l/1P8whAUDcvEFlXQUiIvoXeZTzd0nKzRWmlStX4p133ikUllJSUnDixAl06NBB6ebr6wtHR0fExMQAACIjI43KbW1t4e/vr5QTERERlaRcBKYrV64gOTkZ1tbWaNGiBSpVqoSmTZsiNjYWly5dAgB4enoaDePm5oakpCTcvn0bKSkpxZYXJysrC2lpaUYfIiIiejGVi8B09epVAMD8+fMxZswYhIeHw9bWFu3atVOCjMFgMBrGYDAgKysLGRkZJZYXZ/bs2bCxsVE+rq6uT3KWiIiIqBwpF4EpNzcXAPDuu+/i9ddfR+PGjbFq1SqkpaUhKioKAJCdnW00TGZmJgwGA3Q6XYnlxZk8eTJSU1OVT2Ji4pOcJSIiIipHykVgsre3BwB4e3sr3SpWrAh7e3sUtFl/ONAkJiaiWrVqqFy5MnQ6XbHlxdHpdLC2tjb6EBER0YupXAQmLy8v2NvbGzXSvnnzJv7++2/4+fnBw8MDu3fvVsrOnz+PpKQkBAYGwsTEBI0bNzYqT01NRWxsLAIDA5/pfBAREVH5ZFrWFSgNExMThISEYObMmXByckK1atUwdepUVK9eHUFBQbhy5QomT54MPz8/eHp6Ijg4GJ06dULt2rUBACEhIejevTuaNWuGRo0aYcaMGfDx8UFQUFAZzxkRERGVB+UiMAHAxIkTcffuXYwfPx5paWlo2bIlwsPDYWZmhrFjx+LGjRsYPXo0MjMz0bVrVyxZskQZtnPnzli4cCFCQ0Nx+/ZttGrVCuHh4dBqtWU4R0RERFRelPrFlfv374e3tzecnJxU++3SpQvatGmDcePGPXYFnxd8ceWzwRdXEhHRk/TMX1zZokULeHh4oF+/fiW+8PGLL75AeHg4rl279j9XioiIiOh58kiNvufMmYOkpCS89tprWLWq8FWU7777Du+++y66dOmCjz766IlVkoiIiKgsPVJg6tSpEw4cOICYmBh06tRJ6S4imD9/Pvr164eePXti06ZNT7yiRERERGXlf2r0Xb9+feXvXbt2YeLEiUhISMDcuXPx3nvvPbHKERERET0PHikwZWVlYd26ddBqtdi3bx9+/PFH3LlzB3379sWPP/7Inw8hIiKif6VSByaNRoM7d+5g5MiRuHv3LvLz8wEAgwcPRt++fRmWiIiI6F+rxDZMS5Ysweeff46rV69CRGBnZ4e0tDRkZmbi/PnzWLt2Le7cuYPAwEA0b94c58+ff1b1JiIiInpmSgxMycnJmDVrFtzc3KDRaHD8+HEAgKmpKby9vdGvXz989913OHfuHPR6PerWrcsG30RERPSvU2Jg+vjjj3H9+nVs2bIFHTp0QP/+/dG0aVPs27fPqL9q1aph165dGD9+PPr374/vvvvuadaZiIiI6JlSfa2AVqtFly5dEB4ejpMnT6JixYro3r07bt++XajfOXPmYOTIkRg8eDDOnj37VCpMRERE9Kw90nuYHBwcsH37dvz666/Izc3F7t27C/WzYMEC1K5dGwMGDEBubu4TqygRERFRWSl1YAoNDYWPjw/+/PNPODo64v/+7//Qvn17dOzYEWfOnFH6MzU1xX//+180atQIpfyZOiIiIqLnWqkC06hRoxAaGor//Oc/cHd3B3D/CbpvvvkGCQkJ8PPzw+DBg3H58mUAgL+/P5YuXQozM7OnVnEiIiKiZ0U1ML377rtYs2YNwsPDMXHiRKW7qamp0lbpm2++QVRUFHx8fDB8+HBcvXr1qVaaiIiI6FlSDUz29vb46aef0L59+6JHYGKCQYMG4ffff8eUKVOwa9cuRERE8J1MRERE9K+h+qbvKVOmIDExEQAQFBRUYr9jxozB0KFD4efnhwEDBmDRokVPppZEREREZUg1MD3YcHvXrl149913UaFChSL7tbe3x/jx41GlShXMnTv3ydWSiIiIqAyV6rfkNBqN8vf7778Pe3v7IvvbsWMHfvzxR0RHR0Ov1z+ZGhIRERGVsVIFpo0bN6JLly5KcProo4/w119/QaPRwNraGo6OjggICMD48ePx4YcfIiAg4KlWmoiIiOhZKtVrBaZPnw5vb2/le2ZmJu7du4c7d+7g/PnzWL58OZo1a4a//voLnTt3fmqVJSIiIioLpbrC9Ouvv+Lu3bvw8fGBRqPBzJkzC/Xzxx9/YNSoUWjRogUOHz6MGjVqPPHKEhEREZWFUrdh8vb2hohg4sSJxbZPmjhxIubMmYO+ffvixIkTMDF5pF9eISIiInoulSrRFDwpN2jQIADAvXv3lM/du3eVv7VaLb799ltcvHgRK1aseHq1JiIiInqGVK8wrV+/HhcvXsSuXbuwcuVKnD59GuPHj0dERATy8/PRvHlzdOvWDSEhIUqj8OHDh2Pp0qV46623nvoMEBERET1tqleY+vTpg6tXr+LgwYMAgLt37+LcuXNKee/evTF79my0aNECqampAIB33nkHS5cufUpVJiIiInq2SrzCtH//fly7dg0xMTG4dOkSvvvuO1y4cAEAcPHiRfzzzz9o2LAhli1bhrFjx6JZs2b45Zdf4OnpCU9Pz2cyA0RERERPW4mBac2aNYiOjla+z5w5E/fu3QMAfPjhh9ixY4dR/x4eHujWrRv2798PMzOzp1BdIiIiomevxMD01VdfFep25MgRdO/eHevWrStUdvXqVdSvXx/jx4/Hl19++eRqSURERFSGVBt9//XXX1i2bJnR9wJffvklfvvtN7z55pt45ZVX4OjoiNWrV6N169YYMmQIGjRo8HRqTURERPQMqTb6zsrKQmJiovK5fv26UlajRg1cuXIFDRs2RLNmzXDgwAG0bNkS7dq1Q3Bw8FOtOBEREdGzonqFqVq1akbvVPrtt98wdepUAEDLli3RsmVLJCcnY968eUhOTgYAzJkzBzExMU+pykRERETPlkYK3kpJJUpLS4ONjQ1SU1NhbW1dYr8B7696RrX694mbN6isq0BERP8ij3L+Lgl/u4SIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKkzLugJET9OV0NplXYVyy+3D02VdBSKi5wavMBERERGpYGAiIiIiUsHARERERKSiXAamVatWQaPRYM2aNUq3pUuXwtPTE3q9Hq1atUJCQoLRMFu2bEGNGjVgYWGB+vXrIy4u7llXm4iIiMqpcheYMjMz8eGHHxp127RpE4KDgxEaGoqDBw8iJycHXbp0QX5+PgAgOjoaffv2xYgRI3DkyBG4ubkhKCgI6enpZTELREREVM6Uu8A0c+ZM1K9f36jb7NmzMWLECAwcOBABAQFYvnw5zp49i6ioKADAvHnzEBQUhAkTJqBu3boICwtDamoqNm/eXBazQEREROVMuQpMp06dwtKlS/HZZ58p3VJSUnDixAl06NBB6ebr6wtHR0fExMQAACIjI43KbW1t4e/vr5QTERERlaTcvIcpMzMT/fv3x9SpU+Hi4qJ0v3TpEgDA09PTqH83NzckJSXh9u3bSElJKba8OFlZWcjKylK+p6WlPYnZICIionKo3FxhCg4ORuXKlTFhwgSj7hkZGQAAg8Fg1N1gMCArK0u1vDizZ8+GjY2N8nF1dX0Cc0FERETlUbkITF9//TW2bt2KdevWwcTEuMo6nQ4AkJ2dbdQ9MzMTBoNBtbw4kydPRmpqqvJJTEx8ErNCRERE5VC5uCX30Ucf4datW4Vuq7355ptwd3cHACQmJsLLy0spS0xMRO/evVG5cmXodLpCgScxMREBAQHFTlOn0ylhi4iIiF5s5eIK0549e3DmzBmcPHlS+QD3g9SuXbvg4eGB3bt3K/2fP38eSUlJCAwMhImJCRo3bmxUnpqaitjYWAQGBj7rWSEiIqJyqFxcYXrwytGDHB0d4eXlhZCQEEyePBl+fn7w9PREcHAwOnXqhNq17//wakhICLp3745mzZqhUaNGmDFjBnx8fBAUFPQsZ4OIiIjKqXIRmNSMHTsWN27cwOjRo5GZmYmuXbtiyZIlSnnnzp2xcOFChIaG4vbt22jVqhXCw8Oh1WrLsNZERERUXmhERMq6EuVBWloabGxskJqaCmtr6xL7DXh/1TOq1b9P3LxBT3R8V0JrP9HxvUjcPjxd1lUgInpsj3L+Lkm5aMNEREREVJYYmIiIiIhUMDARERERqWBgIiIiIlLBwERERESkgoGJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCoYmIiIiIhUMDARERERqWBgIiIiIlLBwERERESkgoGJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCoYmIiIiIhUMDARERERqWBgIiIiIlLBwERERESkgoGJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCoYmIiIiIhUMDARERERqWBgIiIiIlLBwERERESkgoGJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCoYmIiIiIhUMDARERERqWBgIiIiIlLBwERERESkgoGJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCoYmIiIiIhUMDARERERqWBgIiIiIlLBwERERESkgoGJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCoYmIiIiIhUMDARERERqWBgIiIiIlLBwERERESkgoGJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCrKTWA6deoU2rZtC4PBAAcHBwwdOhS3bt1SypcuXQpPT0/o9Xq0atUKCQkJRsNv2bIFNWrUgIWFBerXr4+4uLhnPQtERERUTpWbwDR27Fi0aNECMTExCAsLQ1RUFAYNGgQA2LRpE4KDgxEaGoqDBw8iJycHXbp0QX5+PgAgOjoaffv2xYgRI3DkyBG4ubkhKCgI6enpZTlLREREVE6YlnUFSmvt2rVwdXUFANSpUwepqakYOHAg7t69i9mzZ2PEiBEYOHAgAGD58uWoUaMGoqKi0LJlS8ybNw9BQUGYMGECACAsLAwODg7YvHkzhg4dWlazREREROVEubnCVBCWClhYWCA/Px8pKSk4ceIEOnTooJT5+vrC0dERMTExAIDIyEijcltbW/j7+yvlRcnKykJaWprRh4iIiF5M5SYwPUhEEBYWhoYNG+L69esAAE9PT6N+3NzckJSUhNu3byMlJaXY8uLMnj0bNjY2yufhwEZEREQvjnIXmHJycjB8+HBERkZiyZIlyMjIAAAYDAaj/gwGA7KyslTLizN58mSkpqYqn8TExCc8J0RERFRelJs2TACQlJSEPn36ICEhAREREahXrx6OHj0KAMjOzjbqNzMzEwaDATqdrsTy4uh0OmVYIiIierGVmytM58+fR8OGDWFlZYX4+Hg0atQIAODs7AwAha4AJSYmolq1aqhcuTJ0Ol2x5URERERqyk1geuONN9C4cWP89NNPsLe3V7o7OzvDw8MDu3fvVrqdP38eSUlJCAwMhImJCRo3bmxUnpqaitjYWAQGBj7TeSAiIqLyqVzckjt//jzi4uLwn//8p9ALKatUqYKQkBBMnjwZfn5+8PT0RHBwMDp16oTatWsDAEJCQtC9e3c0a9YMjRo1wowZM+Dj44OgoKCymB0iIiIqZ8pFYLp27RoA4PXXXy9UtnjxYowdOxY3btzA6NGjkZmZia5du2LJkiVKP507d8bChQsRGhqK27dvo1WrVggPD4dWq31m80BERETll0ZEpKwrUR6kpaXBxsYGqampsLa2LrHfgPdXPaNa/fvEzRv0RMd3JbT2Ex3fi8Ttw9NlXQUiosf2KOfvkpSbNkxEREREZYWBiYiIiEgFAxMRERGRCgYmIiIiIhUMTEREREQqGJiIiIiIVDAwEREREalgYCIiIiJSwcBEREREpIKBiYiIiEgFAxMRERGRCgYmIiIiIhUMTEREREQqGJiIiIiIVDAwEREREalgYCIiIiJSwcBEREREpIKBiYiIiEgFAxMRERGRCgYmIiIiIhUMTEREREQqGJiIiIiIVDAwEREREalgYCIiIiJSwcBEREREpIKBiYiIiEgFAxMRERGRCgYmIiIiIhUMTEREREQqGJiIiIiIVDAwEREREalgYCIiIiJSwcBEREREpIKBiYiIiEgFAxMRERGRCgYmIiIiIhUMTEREREQqGJiIiIiIVJiWdQWI6MXw2uLXyroK5dahcYfKugpELzxeYSIiIiJSwcBEREREpIKBiYiIiEgFAxMRERGRCgYmIiIiIhUMTEREREQqGJiIiIiIVDAwEREREalgYCIiIiJSwcBEREREpIKBiYiIiEgFAxMRERGRCgYmIiIiIhUMTEREREQqGJiIiIiIVJiWdQWIiOjZimrWvKyrUK413x9V1lWgMsArTEREREQqXqjAJCKYMWMGnJycYGlpie7du+PGjRtlXS0iIiJ6zr1QgWnevHlYtGgRli1bht27d+P333/H4MGDy7paRERE9Jx7Ydow5efnY968eZg6dSo6d+4MAFiwYAE6dOiAS5cuwdPTs4xrSERERM+rF+YK0+nTp3Hz5k106NBB6da8eXOYmJggJiamDGtGREREz7sX5gpTQkICABhdSdLr9ahSpQqSkpIK9Z+VlYWsrCzle2pqKgAgLS1NdVp5Wfcet7ovrNIs30eRnpn3RMf3InnS6yL3Xu4THd+L5Emvizu5XBeP40muj2VTdj6xcb2IRnzcQbWfgvUlIo81rRcmMGVkZMDExAQ6nc6ou8FgMApGBWbPno0ZM2YU6u7q6vrU6kiAzeKRZV0FKjDbpqxrQP8/m0lcF88VG66P58XEL0rfb3p6OmweY929MIFJp9MhPz8fubm5MDX9f7OdmZkJg8FQqP/JkycjJCRE+Z6fn49//vkHlSpVgkajeSZ1ftLS0tLg6uqKxMREWFtbl3V1XnhcH88ProvnB9fF8+Pfsi5EBOnp6XBycnqs8bwwgcnZ2RkAkJSUBA8PDwD3b7vduHED1apVK9S/TqcrdDXK1tb2aVfzmbC2ti7XG/+/DdfH84Pr4vnBdfH8+Desi8e5slTghWn07e/vD71ej927dyvdoqKioNFo0KxZszKsGRERET3vXpgrTHq9HqNGjcKHH34INzc3VKhQAePHj8eIESNgZ2dX1tUjIiKi59gLE5gA4OOPP8a9e/fQu3dvaLVaDBgwAPPmzSvraj0zOp0O06ZNK3SrkcoG18fzg+vi+cF18fzgujCmkcd9zo6IiIjoX+6FacNERERE9L9iYCIiIiJSwcBEREREpIKBqRje3t6YPn16WVej3Js1a5by3qunJTk5GZUqVcLGjRtLPcz06dPh7e39ROvRq1cvdO3atVT9rly50ugFqg9q3bo1hgwZ8gRr9mjq16+Pd955p8ym/6R5eHhg1qxZz2x6Ja3bAo+6jPft2weNRlPkzzjRk3fw4EFoNBpcvny5rKtSpGdxXH3SxwFTU1OsXLnyiY2vLDyTp+QyMjIwd+5cbNiwAYmJiTAYDOjevTvCwsKexeTLpUGDBsHMzOyFXkZDhgxBUlIS9uzZU6jM1NQUX3/9NYYMGQInJyecP3++zF8PsWrVqnL3Fvj8/HwkJCQYhce9e/dCr9eXuPz/zdLS0nD37l04ODg8tWkULOPH8aKun+dVamoqHBwcULt2bRw9erSsq/PYnsQ2+m/zTK4w9e7dG9u2bcNnn32G2NhYrFmzBi4uLs9i0uVSWloatmzZgrfeequsq1JuPA8/WWMwGMrdAWbcuHEYNmyYUTdra2uYmZmVUY3KnqOjI3bt2vVUp/GiL+N/o02bNsHS0hLHjh3D77//XtbVeWzcRgt76oHp1q1b2LlzJ+bMmYNOnTrh5ZdfRlBQUJE/bPtv8bhvali3bh3c3d3x6quvPqEa0dNWXt/OUdQPT7/ouEweT3ndFx7X6tWr0adPH7i5uWH16tVlXZ0XzrPY7p56YNLr9TA1NS3xEmVaWhrefPNN2NraomrVqhg2bBjS0tKU8pSUFIwcORIODg6wsLDAK6+8grt37wIAzp8/j27dusHGxgZ6vR4tWrRAbGysMmxBW5WdO3fC19cXVlZW6NGjB1JSUpR+bty4gT59+sDKygr29vb45JNPCtVx+fLlqFu3LiwtLeHl5YU1a9YUmsY333yDihUr4v3338fcuXNhZWWFzMxMpb/MzExYW1tjxYoVJS6zsLCwEq8uLV68GC+99BIsLCzw0ksv4ezZs0rZN998A29vb1haWuLVV18ttNxXrlyJl19+GTqdDk5OTti+fTsAICcnB1OnToWHhwfMzc3h5eWFzz//XBnu8uXL0Gg02LZtG7p06QJLS0vUrFkTUVFRRuP/7LPP4ObmBr1ej06dOuHWrVtG5fHx8ejUqROqVKmCSpUqYejQocq6LJjG6tWrUbduXWzYsAFZWVnQarVGyxu4fytp9uzZAO7/PqBGo8G+ffsAAMePH0fdunWh1Wqh0Wjg4uKCL7/8EhqNBhqNBkeOHMGqVauQkJCAqlWrwsnJCVZWVujSpQtu3bqF8PBw1K5dG3q9HnZ2dqhQoYLRdhkeHo46depAo9HAysoKLi4usLe3L9T2aNu2bWjQoAF0Oh1MTU1hYWGBtm3b4sKFC0o/d+/eVd42b2Njg5CQEKMdv2CZHDx4sMRuhw8fRsuWLWEwGGBjY4MPP/xQ6bdfv35wdHSEjY0NunXrhhs3bgC437YnLCxM+Ymggro/3H7v6tWrGDhwIMzMzKDVauHi4gI7OztUqVIFkydPVtrsHDt2DC+//DJMTU1hZmYGvV6P1q1bK+M5evQoWrduDUtLS1hZWaFz5864ePGiUj5kyBC0bt0aX375JZycnGBnZ4dPP/0Ut2/fRs+ePWFpaYnq1avj559/Nhqmffv22LBhA7y9vWFhYYGWLVvijz/+QHHOnTuHNm3aQK/Xw8vLC/PmzYOIKO2E8vLyMHToUKNtKjo6Gg0bNoRer4evry9WrVpV7PgLxMbGwt/fH5aWlmjevLlRe5iHl/GFCxfQtm1b6PV6uLu7Y+PGjUW2+fjjjz/QvHlzGAwG7NixA+np6UqZiGDOnDlwcXGBlZUVfHx84OnpCXNzc7i4uMDGxgYffPABunfvDo1Gg6FDhyrD/vDDD9BoNDh9+rTSrXXr1pg8eTKA++0Ee/bsiQoVKsDV1RWTJk1CdnY2gP/Xvur777+Hl5cXGjRoUOTy2LNnDxo2bAiDwQAHBwesXbtWKVNbvsVt3wCwZs0a1KlTBzqdDvb29hg9ejTu3LmjlHt4eGDGjBl47733ULFiRTg7O2PRokVG49+9ezf8/PxgYWGBunXr4vjx40blt27dwvDhw+Hq6ooKFSqgVatWRtuYi4sL9u/fj/j4eCQmJmLFihWoUaOG0dXblJQUBAYGQqPRFDqPzZo1C76+vjAYDKhVqxZ++eUXo+mrHVcfXPfW1taFjjMeHh6YNm0aRo0aBSsrK3h5eWHPnj2Ij49HgwYNYGlpiSZNmiAhIUEZ5uFttKTz8MP++OMPdOjQAXq9Hq6urkUGyOL2wwI7d+5U1mtAQIByDC/Yj4o67wJQjt8GgwF+fn6FrharnSNLJM/Au+++KwCkT58+kpCQUKi8Y8eO0qxZMzl48KBERkZKzZo1ZejQoSIikpOTI/Xr15fq1avLtm3b5MSJE/Lpp59KRkaGXL16VapUqSJt2rSRqKgoOXTokHTt2lVsbW3l77//FhGRadOmibW1tQQFBUl0dLSsX79e9Hq9TJo0SZl+ixYtxNfXV3bt2iWHDx+Wli1bipmZmUybNk3pp1u3brJhwwY5efKkTJgwQczNzeXKlSvKNCpVqiQdO3aUmJgYOX36tCQmJoqJiYls375dGce2bdtEr9dLampqscsqPj5ezMzM5Pr160WW79ixQ0xNTWXdunVy6tQp+fbbb+W3334TEZFNmzaJra2trFq1Sk6cOCGjR48WOzs7+eeff0RE5PPPPxdzc3P5+OOP5fjx47J9+3b55ZdfRERk4MCBUqVKFVm9erWyjM3NzWX58uUiInLp0iUBIL6+vrJ+/Xo5cuSINGrUSFxcXCQnJ0dERFauXCnm5uayYMECOX78uMyaNUvMzc3F3d1dqf+nn34qH330kcTExMj27dvF0tJS5syZYzSNmjVrytatW6V9+/YSGBgorVu3lh49eijjyMnJEQAyZMgQERFJTEwUABIZGSl5eXliZ2cnGo1GevToIUuWLJFx48aJg4ODABAA4ufnJ927dxdLS0txdXUVADJ37lwxGAzyxhtviIWFhSxZskSaNm0q7u7uotVqZc2aNVKzZk1p1aqVaLVaefvttwWAuLu7y3vvvSdRUVESGBgogwcPVur55ptvSu/evcXBwUEGDRokAKRFixZSvXp1Wb58uWi1WhkyZIg4OjrK5s2b5dixY9K7d28xMzNTxlOwTA4cOKCM9+FuR48eFXNzcxk8eLAcPnxY9u/fL998842IiKxbt04mTpwo+/fvl4iICHFycpKRI0eKiMjly5fl9ddflwYNGsiFCxeUbc7Ly0umTZsmgwcPlpYtW4qPj4/4+/tLnTp1pFKlSlKrVi0xNTWVUaNGCQAJCQkRjUYjzZs3FwcHB6levbrY2NhImzZt5JNPPhERkdOnT4ter5f+/ftLdHS0REREyGuvvSbVqlWTzMxMEREZPHiw2NnZydtvvy1xcXEybtw4ASD16tWT+fPnS2xsrLRt21aqVq0qWVlZyjBOTk7Spk0bOXjwoOzZs0dq1qwpAQEByvJyd3eXmTNniohIWlqaODo6yqhRo+T48eOyYcMGsbKykm+//Vbu3r0rFy5cEK1WK3PnzpULFy7I3bt35c8//xRLS0uZPn26nDx5Uv773/+KVquVqKioIvfRFStWiEajkZYtW0pkZKTs3LlT7O3tjbbhgmUsIpKdnS1eXl7SuHFj2bdvn0REREhAQIAAkBUrVoiISGRkpLIsduzYIfv37xdra2uxsrJSxjlv3jxxdXWVbdu2ybBhw0Sr1Yq9vb0cPXpUwsLCRKPRiJWVlcyZM0f69+8vfn5+yrCjR48WvV4vn376qVIng8EgkZGRkpubK3Xr1pWePXvKsWPHJDw8XJycnGTGjBlGdWvUqJHs2bNHDh8+XGiZ/P3332JhYSGTJk2S06dPS3h4uPz0008iIqrLt6TtOywsTExNTWXWrFly/Phx+e6778TJyUn69+9vtP4dHR1l1qxZEhcXJ8OHDxeNRiNnz54VEZHz58+LmZmZDB8+XI4dOyZr164Ve3t7ASCXLl0SEZH9+/fL22+/LXv37pWYmBipVauWtG/fXpmGra2tmJiYyMcffyzLly8XAPLWW29JlSpVJDc3VzmPWVpaSrNmzYzOYyL3z0E//PCDnDhxQvr06SOVKlWSO3fuiEjpjqsPrvu4uDjp0aOHVK9eXbKzs5VlYG9vL4sXL5Zjx45J8+bNpWrVqlKnTh35/vvvJTIyUtzd3aVbt25FbqMlnYcflpOTIy+99JK89tprEhUVJREREeLv72+0PZe0H4qI/P7772JqairDhg2To0ePyrp165RjeME6Keq8e+TIEeX4ffLkSQkNDRUzMzO5cOGCiKifI9U8k8AkIrJ06VKpVKmS6HQ6mTp1quTm5oqISHR0tFhYWBhVeNOmTWJubi55eXmyYcMGoxl+0IcffihVq1ZVNiwRkXv37omjo6NMnz5dRO4vVK1WK9euXVP6eeONN6Rx48YiInLo0CEBICdOnFDK//nnn0KB6UHp6ekCQLZt26ZMQ6PRKCuyQIsWLZTgVzDdvn37lric3nnnHaMD68M+/fRTsbW1LXJD9fHxkaVLlyrfc3NzxcbGRtasWSNZWVliY2OjHOQe9McffwgA2bx5s1H34OBg8fDwEJH/d5KeP3++Uv7LL78IAPn9999FROSll16S8ePHG42jV69eRjv2w3r27Cldu3Y1mkZBHQcPHiwmJiZiamoqAESn04lOpxMzMzMBIF988YWIGAemW7duCQBxc3Mzms7ixYuVwLRixQqZNm2amJiYyF9//SU+Pj4ybtw4GTBggNjY2MikSZOMtstatWrJrFmzZNOmTaLRaGTIkCGF6ioihQJTVlaWWFlZyc6dO0VEpHLlyjJr1iwBIFOmTBETExMxMTFRtqOCYZycnB4pMLVv315atGhR7DJ+0Lvvvit169ZVvr/11lvSvHlzo34eDEw1atQQnU4nf/31lzRv3lxq164tIiL+/v4yePBgcXFxkY4dOwoAGTt2rHLwmTJlijg6OirjHDRokNSpU0fy8/OVbteuXRMLCwtZuXKliNxf3/b29spBPjMzU3Q6nXTq1EkZZv/+/QJA+U/C4MGDxdraWlJSUpR+CvbpuLg4ETEOTLNnz5aGDRsaze+YMWOkdevWynetVqsc2EVERowYIX369DEapmPHjjJs2LAil/GKFSsEgBw5ckTpNnXqVHFwcCi0jEXuh1pzc3NJTk5Wyk+fPl1kYNq4caPSz6uvvioA5N69e0bb2p07d0Sv18uMGTMEgBw8eFBERCpWrCgmJiaSmZkpERERYmJiIrdv3xYREW9vb3nrrbekQ4cOyjKsUKGCZGdny/r168XFxUVZLyL3T9De3t5GdSs42RUlLi5OAMihQ4cKlakt35K2b3d3dxk7dqxRtx9++MHoxOru7m60Dd25c0fMzMxk2bJlIiLy9ttvG+0TIiJLliwxGsfDFi9eLLa2tsp3U1NTcXJyUr57eHhInz59RKPRSFRUlHIeMzc3l127dhU5zgIF677gnKR2XH34OCMicvPmTaN1/3AY2rdvX6HjeWhoqFStWlX5/uA2WtJ5+GFr164VnU4nV69eVbqdOXPGaHtW2w+HDx8uderUMSovOIY/GJgePu+2a9fO6GKIiCjHb5GSz5Gl8cxeKzBy5EgkJCQgJCQEs2fPVi4Hx8XFISsrC46OjrCwsICFhQX69++P7OxsXL16FYcPH0bNmjWLfAQ8NjYWr776KgwGg9LNwsICDRo0MLpN5ezsjKpVqyrfPT09ldsSp06dQsWKFeHn56eUV6xYEa6urkbTOnjwIIYNG4Z69eopDdYfvK1nb29f6DHPAQMGYPv27cjLy0NWVhZ27NiBgQMHFruMsrKysHbt2kKNcB/Uu3dv2NraokaNGpg/fz5SU1MBAHfu3MH58+cxfvx4ZTlaWloiLS0Nly9fxtmzZ5GamoouXboUGmdcXBwAoE2bNkbdmzVrhsuXLxtd3vb391f+9vT0BHD/lua9e/dw8eJFtGrVymgcderUMfp+8+ZNTJs2Da1atYKrqyu2bt1qtBwBGF3Sb9iwIWJiYmBhYYFPP/0UJ0+eRLdu3aDRaIzWewE7OzvY29sjKSkJb775pnKLoW7duko/TZo0AXD/MrqTkxP8/f2RkJAADw8PpKenY8GCBWjatCkyMzNhZ2eHs2fPYtq0aejfvz9ERBn+4bo+7Mcff0R6ejqCgoKg0WiUeS9YDsD9W4sPLjNzc3P4+voWO86iHD58uNjXGdy5cweffPIJ2rdvj2rVquHzzz8vtLxLkp6ejpo1a8LJyQkA8MorrwAAmjZtil9//RXu7u5IT0+HiYkJ/vzzTzRt2hQVK1Y02seA+/tqwe2IAlWrVoWPj4/RvlqjRg2loWnBLZYHt7mCejx4S6J27dqwsbFRvterVw8AjG4vFIiLi0NsbKyyj1hYWGDZsmUlPj4eFxeHLVu2GA2za9euEofRaDQICAhQvnt4eCjr/GGnTp1CzZo14ejoqHR7+eWXodVqC/Vbv3595e8KFSoAuL8sLly4gPT0dHTr1g22tra4d++e8iqFgnpaWFggPz8ff/zxB5o0aQJLS0vs378fly9fRmpqKt5//33s378fOTk52LdvH1q1agUzMzPExcUhOTkZVlZWyvxPmTKl0PyXtC/4+fmhbdu2aNeuHSZMmIBLly4pZWrLt7jt+8aNG/jzzz+LPG4BwK+//qp0e3AbMhgMsLe3NzoHqB23cnNz8eWXX6Jr167w8fFBSEiIsh8dO3YMubm5aNq0KXJzc5Gbm4uePXti165daNy4MbZt24bDhw/D0dERdnZ2RrepC4SHh2PgwIHw8/NDo0aNANw/v5TmuPrgui9Yfs7OzgBgtI4ePAYWnN8e3rcevtVXoKTz8MMKtucHnzKtVauW0fasth8WtU4erH+Bh8+7cXFxWLBggdF4f/vtN+U8VtI5sjSe6Y/vWltb4+OPP4aLiwvGjBmD0NBQ5OTkoGLFijh06FCh/u3t7SEixb7TRKfTwcSk6MxXcH8dQKGW/qampsq90oyMDJibmxca/sGGnxEREWjfvj1mzpyJ0aNHw8vLC5UqVTLqv+Dg9aBevXph7Nix2L9/P9LS0qDX69G2bdsi6wsA33//PfR6Pdq1a1dsPy4uLvj1118RFhaGhQsXYv78+YiMjISTkxNEBIsXL1YOGAUqV66MK1euKPP+sIIfVixuWebk5Ch/P7gsC8YlIrhz5w5EpNCyfHA5Zmdn49VXX4WPjw+mTJmCGjVqYOrUqUYHT8B4WRoMBgQEBKBbt244evQoRo4cicjIyBKfiKtcuTICAwNx8+ZN1K1bF/Pnzzc6wBTUsWBeMjIyYG1trezQkydPRnZ2NpYuXYp169YpddLpdGjUqJHRui9qvQP378/36dMHADBv3jy89tpreP3119G1a1eMHTsWERERRS7Th5dZwTrJz89Xuj34N4AS95GOHTsiOzsbH3zwAWrVqoWVK1c+0rtQNBqN0XbxYF2zs7NhMBiQn5+vrI+Cejy4jwGl31cfng8TExOjaRasowfH/fA2l5GRAeD+8eZhOTk5aNOmDT777DOj7iU9DZSTk4PBgwfjvffeM+peVGB/sN4PniC0Wm2xjVLT0tIK/bhpdnZ2ofX8cD0LlrmIKPvoli1bkJubi27dumHz5s2oXr26URArGLeZmRlatmyJffv24dq1a2jXrh18fHxQqVIlREdHIyoqCt26dVPmv1atWvjuu++KnV+g+H0BuL88du3ahe3bt2PBggXw8fFRGkmrLd/itu+C9f7wdlWwXB7nHPBww/9hw4YhJiYGH330EerWrYv9+/cr7UwL2uds3Lix0LvgatWqpbT7LGir+3AQXrlyJcaNG4dPPvkEEydOhJmZGWrUqAEApTquPrjuvby8jPp7cN0/uAwLltnD+1Zx22hJx5iHFbU8H96e1fbDovaJoh7GeHiby8nJweTJk9GvXz+j7jY2NsjNzS3xHFkaT/0KU1E7fcHTXzk5OfD19cU///wDrVYLX19fo4+ZmRlq166NX3/9FdeuXSs0njp16uDw4cO4d++e0i0rKwvHjh0z+p9YSby9vXH9+nWjxqcJCQlITk5Wvn///fcICAjApEmT4O/vj6tXryIvL0913DY2NujUqRO2bduGTZs2oV+/fiVudGFhYRg6dGixJ5YCer0eY8eOxdmzZ1GhQgV8/fXXsLGxQdWqVXHlypVCy7Fy5crw8fGBubk59u7dW2h8tWvXhkajKfQ+lwMHDsDb2xu2traq81q5cmXY2toWCr4PNkw+c+YMLly4gPnz56N169ZwdnbGb7/9pjpu4P7Vuh07diiNIUsKTN7e3khMTMT27dvxwQcf4KOPPjKq16lTp5S/7927h+joaOV/L2ZmZkrD2tTUVLz00ksICgpCs2bN0LBhQzg5ORVq6F6Un376CRUrVoSpqSlsbGxQs2ZN/P3336hcubLSuLPA4cOHlb9TU1ONGt5WqVIFwP2G1wUebMwJ3F9/Ra3Xf/75B1FRUZg2bRo6duwIDw8PnDt3zqgfExMT5ObmFjsfVlZWOHv2rNH0gfvr9eF9rHbt2oiOjjbaHwvUqVMHERERRgfkGzdu4Pfffy/1vlqcc+fOGYX6vXv3QqvVolatWoX69fX1xblz5+Dl5WW0jzx4onl4mRQM8/B+5ebm9lj1LuDt7a1cAS4QHR39SE/9eHt7w9TUFFevXkXbtm1hYWGBhIQE+Pr6KlffMjMzYW5urpyM27dvj8jISOzevRsdOnQAAAQGBmLXrl04fPiw8h83X19fXLp0CVWrVi20DB6FRqNB165dERUVhT59+igP16gt3+K2bxsbG7i7uxc6bu3fvx8mJiZGV/jUlt3Dx60DBw4Yfd+6dSuCg4PRs2dPeHt74/z58wDuX3nasGEDLC0tMXLkSBw7dkz5+Pr6IjExEVevXoVOp0NKSoqynB8ed6dOnTBq1CjUrl3b6GpHaY6rD677h5fhg1deH0dJ5+GHFWzPD17JPnTokNH2rLYfFrVOirqo8jBfX1/88ccfhZZDwUMvJZ0jS+OpB6YrV66gTZs22Lx5M3799VdERUUhJCQE/v7+8Pb2Rtu2bfHyyy+jV69e2LlzJ44fP44lS5Yol5MHDBgABwcHdOrUCT///DNOnDiBjz76CBkZGRg3bhyys7PRo0cPHDhwANHR0ejXrx9EBCNGjChV/YKCguDq6op+/fohKioK+/btw4ABA4z+d1q1alWcOXMGO3fuxKFDhzBhwoRSv/On4ET/008/lXg77s8//0RERITRkytF+fbbbxEWFoZTp04hIiICt27dwksvvQQACAkJwYIFC7Bo0SLEx8djx44d6Ny5MwDA0tISISEhmDp1KpYsWYL4+Hhs3rwZP/30Ezw9PTFo0CCMHDkSa9euRXx8PD7//HN88cUXj/T6hxEjRmDBggX4+uuvcfz4cYSEhBi9j6RKlSowMTHB8uXLER8fjw8++KDQyb847dq1g6mpKT744AP07du32P6Sk5NhZmaGgwcPYsiQIYiMjIS1tTW+/fZbpZ/33nsP58+fR1ZWFvr164e8vDxle7G1tcX69esRHR0Nb29vtGvXDk2aNEFERASWLFmCevXqYcmSJVi8eDEAICYmRrkK9aCqVavi5s2baNGiBd577z20bNkSWq0WZ8+eVbYDjUaDhg0bYsSIEdi1axeio6PRu3dvWFhYKOPR6/Vo1KgRFi1ahFu3biEpKanQW6s//PBDfP/99wgJCcGxY8cQGRmJ//73v7CysoLBYMCqVatw6tQpfP7554VOPK6urjh9+jQOHTpUKEwVzIebmxu6dOmC27dv48aNGxg1ahTOnTunPJVS4J133sGdO3fQrVs3nDt3DiKC0NBQAPev2p09exaDBw/GkSNHsG/fPuXk8/rrrxe7Pkvjn3/+waBBgxAbG4utW7di/PjxGDhwoHJb4kGjR4/G1atX8cYbbyA6OhqHDh3CpEmTsGPHDqNlsmPHDpw6dQo3btxAcHAwDh06hLFjxyI2Nhb79u3D8OHDceLEiceqd4EBAwZAo9Ggb9++iImJwY8//oiJEyeW+hgTHR2N2NhYBAUFISQkBBs3bsTgwYMxefJkNGzYEPHx8Vi5ciVSU1PRrFkz5X/uHTp0wJkzZ3Dw4EHlynfr1q2xZs0aODo6olq1agCA/v37w2AwoEePHti3bx+OHTuG2bNn46uvvir1PMbFxWHmzJk4duwYjh49inPnzinHLbXlW9z2Ddx/UurLL7/E7NmzcfLkSWzZsgWjR4/GW2+9VahZRXFGjhyJgwcPYuLEiThx4gSWLVtW6KmuqlWrYtOmTTh58iS+/fZbZZ/ftWsXbty4ASsrKzg7O6NevXrKZ+jQodi7dy8CAwOxe/dumJmZ4T//+U+h81jVqlVx4MABHDhwAHv27Cn0CxNqx9UKFSrg7bffxqRJk7By5UrEx8fju+++K/F886hKOg8/rH///gCAfv36KdvzpEmTjK6sqe2Ho0ePLrROHjyGFyckJATr16/H9OnTceLECfzyyy/o1auXcquxpHNkqZSqpdNjSE1NlR49eoizs7OYm5uLm5ubDB8+3KhB2B9//CEdO3YUg8Egtra20rx5c6WxmojIxYsXpXPnzmJpaSnW1tbSsWNH5cma+Ph4adOmjRgMBqlQoYJ06dLFqGHatGnTxMvLy6hOD3c7fvy4NGjQQMzNzcXHx0d27NghPj4+SoO31NRU6dq1q1haWspLL70ke/fuNWoYWtQ0CmRlZYmdnZ34+vqWuJymTZsmgYGBqstz27Zt4uXlpTwlMW3aNKUhbW5urkydOlUcHR3FwsJCqlevLh9//LEybE5Ojvzf//2fODs7i06nk5dffln27dsnIvcbywcHB4uDg4OYmZnJyy+/LOvWrVOGLU3j47t378qwYcPEyspKbG1tZcKECTJz5kyjRt+LFi2SqlWrirW1tUycOFHefPNNpdHxw+MbPHiw0TIZM2aMAJCYmBij5f9go+9//vlHmjZtKubm5qLRaESr1UqbNm1k48aNSqPvtWvXSqVKlZQne44ePaqsAy8vL1m4cKF4eHiImZmZ6PV6MTU1NdouFy5cqDxd5+zsLJs2bRIR40bfOTk5MnToULGyshJLS0upUKGCaDQasbe3l7CwMFmxYoVotVpJSEiQwMBA0el04ubmJmFhYdKuXTujxuNnz56VRo0aicFgkNq1a8u6desKrYv169dLzZo1xdzcXFxdXWXJkiUiIvLdd9+Ju7u7GAwGGTp0qEydOtVofVy7dk0aN24sOp1OJk6cKCLGjb4DAwPl8uXL0qNHD9FqtaLVaqVly5YSGxsrIiLNmzeXpk2bilarFRGR2NhYadGihdIwv+BJRpH7DU0LplWxYkUZMGCA0dOgD69vEeMG2yVtI1OnThVbW1uxsbGRYcOGGT0U8fA4du/eLf7+/mJubi4ODg7Sp08f5YlXEZEtW7aIg4ODVKhQQZnP9evXi6+vr5ibm4uLi4u8/fbbSoPphxWs25K6PdigVkRk7969UqtWLTE3N5eAgACJiYkRExMTWbVqlYj8v4bViYmJyjDt2rVTtumCj4WFhVSqVEn0er1UrVpV7OzsxMzMTLy8vKRixYpGy0HkfiPYBxvfXrt2TTQaTaGG1HFxcdKsWTPR6XRSuXJl6dixo5w5c6bYuj3s/PnzEhAQIBYWFlKlShUZMGCA3Lp1SylXW77Fbd8i9x8oql69upiZmYmLi4tMnTpVeXJXpPD6L6rbggULxNHRUfR6vXTq1Em2bdtm1MB437594uvrKxYWFtKlSxdZtGiRAJDevXtLjRo1ipxGcnKyaLVaefPNNwWAvP/++0WexxITE6VFixai1+vFz89PwsPDleOZSOmOq3fu3JFRo0ZJpUqVlONEWFhYsfNb1PFcbRst6Tz8sN27dyvr65VXXpHo6GjR6XRGD1Oo7YcLFy4UJycn0ev10rVrV+UY/uDT6UWddwuO3+bm5uLp6Snvvfee8sCC2jlSjUbkBX3L2DNUs2ZNDBgwAFOmTCmyPD8/H56enpg7d26JV09edHPnzkVYWJhyOfxRrFu3TvmfT2JiIt80/y/xb/15kOTkZDg7OyM8PBwdO3Ys6+rQYzhy5AheffVV/PnnnzzuPIaCY3h6enqJ7eWeJv747lMWExODCxculHh5dM+ePcjIyED37t2fYc3KFxHBypUrVW9ZFmfr1q1P/ccqiZ6UrVu3QqvVKk/8Ufm1YsUKtG7dmmHpMW3duhW1atUqs7AEPOOn5F4kf/31F/78808EBwdj8ODBJd5Pb9u2bbGPc9L9Bo4///wzbt68iZEjR6r2v337dvz888/o2bMndDodNmzYgC1btmDKlCn4+OOPn0GNiUrv1q1bGDBgAEaOHAlnZ2ccPHgQH3zwAYYOHWr0OhQqX+Lj4/H7779j1apVT/23Cf9tijuGP/yLD88aA9NTcvDgQbz55pvo0KGD0U+M0KPr0KEDnJ2d8cMPP6BixYqq/Xt5eeHs2bNYu3YtMjMzUbNmTaxbtw6Ojo4MTPTcMRgMsLKywvDhw3H79m24uLjgvffew9SpU8u6avQYPvjgAxw9ehSfffZZocfYqWTFHcMffl3As8Y2TEREREQq2IaJiIiISAUDExEREZEKBiYiIiIiFQxMRERERCoYmIiIiIhUMDAR0XPBw8MDrq6u8Pb2hre3N8LCwtCiRQs4ODjAy8sLLi4u6NWrl9GPJ69cuRLt27c3Gs/ly5eNfo8PAL766iv4+fnB1dUVLi4ueOONN4zKp0+fDp1Ohxs3bgAA1qxZo9RDo9HA09MT3t7e+OyzzwqNPysrC1OnToWXlxfc3Nzg7u6OSZMmITMz02je6tSpg+zsbKXbzZs3S/17cURU9hiYiOi5sWnTJly8eBEXL17EW2+9BQD4/PPP8ccff+DKlSvo168fOnTogKioqFKPc8yYMVi7di02b96MxMREJCUlGf2odF5eHr755hu89tprWLlyJYD7PzZaUA/g/g/cXrx4EcHBwYXG37NnT5w9exZHjhzBlStXEB8fjytXrhT6UeGcnBzlx4iJqPxhYCKicsHExAQ9e/bEp59+inHjxpVqmPj4eGzcuBHbtm2Dt7e30v2ll15S/v7pp5/g7OyMKVOmYPny5XiUV9MdOnQIcXFxWLNmDSpXrgwAsLW1xTfffIPY2FgcPnxY6Xfu3Ln45ptvEBcXV+rxE9Hzg4GJiJ4bffv2ha+vL3x9fREfH19kP23atMHp06eRnp6uOr6IiAg0adIEdnZ2xfbz1VdfYdCgQWjVqhXu3buHyMjIUtc3Ojoar732GiwtLY266/V61KtXD7GxsUo3e3t7LFq0CEOGDDG6NUdE5QMDExE9NzZs2IBz587h3LlzqFu3bpH9ZGdnQ6PRwNTUFBqNpsgrQlqtFsD99kU6na7Y6SUlJWHv3r3o06cPTExM0L9/f3z11Velrm9eXl6x7ZA0Go1SjwK9evVCrVq1jG4JElH5wN+SI6JyJTw8HA0bNoRer4eDgwOSkpKMypOTk5Vfhn/llVfwxRdfIDMzs1BDcAD4+uuvkZ+fj5o1awIAcnNzkZ6ejhs3bqBKlSqqdalXrx4WLVqEe/fuQa/XK93v3buHY8eOYdq0aYWG+eKLL1C3bl00bdr0keabiMoWrzARUbmQm5uLtWvXYvr06Vi0aBEAoEGDBrh69SqWLVuGnJwc3Lx5E6GhoejatSsAoG3btqhWrRoGDhyoPAEnIoiPj0deXh7CwsKwefNmXLt2DdeuXcPNmzdRr149pfG3mlatWsHX1xdDhgxBSkoKAOD27dsYOnQoGjdujICAgELDVKpUCV988QXGjBnz+AuFiJ4ZBiYiem68/vrryuP87733HgBgwoQJ8PLygru7O8LDw7Fnzx7Ur18fAFCxYkV8//33CAsLQ+XKlfHKK6/A19cX06dPB3D/ttiPP/4IV1dXBAQEwMPDA97e3li5ciV++uknAECHDh2M6jBmzBh89dVXpWr8rdFosH37djg7O6NOnTpwc3NDQEAAvL29sWHDhmKH69q1K1577bX/ZRERURnRyKM8EkJERET0AuIVJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGpYGAiIiIiUsHARERERKSCgYmIiIhIBQMTERERkQoGJiIiIiIVDExEREREKhiYiIiIiFQwMBERERGp+P8A/AdRh1zRL68AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_columns = ['GENDER', 'Type_Income', 'EDUCATION']\n",
    "\n",
    "for i, column in enumerate(categorical_columns, 1):\n",
    "    sns.countplot(data=data, x=column, order=data[column].value_counts().index)\n",
    "    plt.title(f'{column}的分布', fontsize=10)\n",
    "    plt.xlabel(column, fontsize=8)\n",
    "    plt.ylabel('频次', fontsize=12)\n",
    "    plt.xticks(rotation=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "021e6b99",
   "metadata": {},
   "source": [
    "可以发现：\n",
    "* 数据集中女性数量多于男性\n",
    "* 收入类型中，Working类型占大多数\n",
    "* 学历背景中，大多数人属于中等教育，极少人拥有高学历或低学历"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5313fdee",
   "metadata": {},
   "source": [
    "### 3.申请结果分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5fe76f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7.5))\n",
    "ax = sns.countplot(data=data, x='label', palette=\"Blues_d\")\n",
    "plt.title('申请结果分布', fontsize=15)\n",
    "plt.xlabel('申请结果', fontsize=12)\n",
    "plt.ylabel('频次', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcd9de35",
   "metadata": {},
   "source": [
    "可以明显看出数据集中通过的样本量明显多于不通过的样本量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c8c66fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JTEzUJ598ok8++UT58+dXs2bNVKdOHavXA4CcjMQ4AORw165dU5kyZWQYhoKDg+Xu7m7Rpm3bttq+fbs2bdqkjh07mpb88vPzU8eOHTVu3Dj5+/srLCxMFStWVL9+/TRw4ECricFDhw7p008/1ZEjRxQfH6/nn39e3t7e6tWrV6aNKWVPsBIlSuj69etaunSp5s6dqwsXLqhw4cLq0aOHpkyZYnUfttDQUM2YMUPffvutrly5oty5c6tKlSoaP368WrVqZWqXlJSkFStWaOXKlTpz5ozu3bunUqVKqWXLlhozZozFEm8pe8ONHj1a3t7eGjlypHbt2qXk5GR5enpqzpw5KlasmM6ePauPP/5YBw8eVHx8vBo0aKBZs2ZZXc7s1q1bmjJlirZu3arr16/Lzc1NHTp00IQJE0zJ6Js3b5otWRYTE6P4+HizJdgeZ8mtkiVL6rXXXrMo3717t6T7y+I9uGRYejz4Gaxatar+/PNPizYvvPCCWrRooZkzZ1rUlSpVStL9xLWfn58+/vhjlS1bVhcuXNDcuXP18ssva8mSJRafz0OHDmnGjBlq1KiR3nvvvUf2s0qVKtqyZYtF+VtvvSUXFxetXLnSos5aQJ1ixIgRZvv57du3z5QYX716ta5evaolS5ZY7dvu3bt1/vx5s6UY27Ztm+r+gEFBQVq/fr1ee+01i2D8Qdb2Ojt9+rQmTJggf39/vfvuu/r000/NvsRITk5WSEiIRo0apbVr1+rZZ5+Vv7+/1S9vAAAAADyejh07atiwYZoxY4Y++ugjbd++PVOuGxAQoM2bNyt//vxav3691aT4o6RsxdW1a1e1bt1aY8eO1bp16zRnzpxM23v6QYsXL1ZoaKhq1qypiRMnptl27ty52rJli44fP64ffvjBaqw2ZcoUbdq0SZs3b9b27dvVpk2bR/bhypUrSkhIMCuLiIgwi8EzGiM/6LnnnrMag7u6uiomJsZqXWJiYprXrFatmunnXr16WexHf+LECXXr1k2TJk1S586dLc5PWZ590aJFOnXqlPbs2SMHBwetWLFCe/bsUd26ddWvXz+L827duqVvv/1WXbt2Tdf2fj179tSYMWPMyu7du6fatWtr4MCBFonuGzduWH3xX7q/Ddknn3xiVjZp0iRTYnzq1Kmys7PT/PnzVblyZYvzx40bZ5EYHzhwoO7evWv1focPH9aRI0fUtWtXq3uOS7L6N5acnKydO3dq9OjRunLliiZOnKghQ4aY3TshIUG///673n//fR0/flwtWrTQ0qVL0731AADkNCTGASCHe+aZZ9SiRQt9//332rhxowYNGmRWHxYWph9++EHFihWzCAJv3bqlunXrKiIiQtWrV9fNmzd19uxZDRo0SOfPn9fnn39u1n7p0qX64IMPZBiGqlatKicnJ506dUq9e/fWxYsX5ePjk+njmzBhgj777DPVqVNHZcqU0fnz5/XZZ58pMjJSCxcuNGt74sQJeXp66saNG8qfP79eeuklRUZG6tSpU9q1a5cpMR4bG6t27drpxx9/lKOjo2rWrCkXFxedPn1aCxYs0Pr167Vnzx6z4DLF7du3Vb9+feXNm1fly5fX6dOntXbtWp0/f17Lly/Xq6++KldXV1WpUkWnT5/Wzp07derUKZ07d84sgX358mU1btxYV65cUfHixVWnTh398ccfmjt3rnbt2qWAgADlz59fo0aNsvqm+bPPPmv6+d1337UaPD9NM2bMMHszetiwYTpz5oyOHz9utX3Kixxbt261Wv/OO++od+/ecnBwkJubm/z8/LRu3ToZhiE/P780Z3qnl5OTk6pUqWJRnidPHrm4uFits/YyxqPExMTI19dX5cqVU8+ePa22SUpKkpOTk1lZam0lae/evVq/fr1atmypjz/+ON196dGjhylZX716dYWGhqpLly4KDw/XnTt3FB4eroiICCUnJytXrlwaPXq0JkyYIBcXl3TfAwAAAEDGDB06VDNmzNDOnTsVERGRKfsNp7zo26dPH5UoUSLD58fHx+ubb76Rg4ODOnXqpBIlSqhu3bo6evSotmzZoq5duz52Hx+Wsg+6t7e31f3bH5Q3b1516tRJixYt0vbt260mxl1dXTVv3jy9+eab8vb2VtOmTR/5gkCjRo105coVs7IVK1aYXhKQ7u+7npUiIyNNq6pJ95/F+PHjtXTpUoWGhlq0TxnPqVOnLOLOFD169NCoUaNUuXJlBQYGasGCBfL19ZWHh4fGjRuXKf0uVKiQRZwdHR0t6f5L3Q/XWdv/Oz3OnDmjVatWqXPnzlaT4pL1GDytGesTJ07UkSNH5O3tne6XxuPj41W1alVdunRJktS8eXMdPHhQ/v7+ZjF4VFSUpPurwz24Lz0AwDoS4wAA9e3bV99//73WrVtnkRjfsGGD4uPj1b17d4uknq+vrzp16qQFCxaYEl+bN282LTf2zjvvmJZIO3XqlPr376/8+fNry5YtatKkiSTp+PHjatasmT799FN17drV6szof+v27dtavXq1/vjjD1WoUEGSNHv2bA0fPlxLlizR5MmTTTNdw8PD1a5dO924cUMDBgzQ9OnTTWO6ceOGzp49a7ruqFGj9OOPP6p69eraunWraVZuTEyMvL29tWzZMnXp0kW//fabRTC+bNkyjR071vQSwPHjx/Xaa6/p2LFjatasmd544w2tXr1auXLl0pUrV/Tqq6/qn3/+0dq1a01LlhmGoc6dO+vKlSsaPny4PvvsMzk6Oio6OlpvvfWWfvjhB/n6+uq///2vPvroI1NQ9Pvvv2vQoEGqUKGCvvzyS1Of3N3ddeTIkX/1jKOjoxUcHGxRnjJLPSQk5JFvp0vS+vXrdenSJcXFxSk6Olrdu3fXmTNnUk18pywHllr966+/LkmKi4vToEGD5ODgoPj4eH344YeqVq2aQkNDLZZhTwk29+7dq9jYWLO65s2bWwTZD8+8TxEXFycHBwerdQ/PHEiP6OhoNWnSRK1atVJoaKjVt74TEhIs3lZ/EmrXrq0tW7aoTJkyKliwoBwdHVW5cmUVK1ZMwcHBWr16tZKTk9W4cWMtWLBAVapUeeQXUgAAAAAeT8mSJeXh4aHAwECdOHHCFG8/jv3790v6v9gqo7Zt26awsDA1b97clFh/5513dPToUa1YsSLTE+PJycmmF6sbNWqUrnPq1q1rmuWcmo4dO6pdu3batm2bxo8fr1mzZqV5zW+++cYUT65evVpLly41JdZTVK1a1bTKWkbdvn3bagweFxenxMREq3UPJ7vv3r1r+k4gOjpa+fLl0/jx4/XTTz/p4sWLVu9bt25dhYSEpBqDe3l5KTY2Vn/99ZcGDhyoxMREJSUl6bvvvpOzs7NOnTqlQ4cOmZ1z9OhRSfef08OrlfXq1Uv58+c3K3t45r10f8a4dH9Sx8N1169ft9rXR0lKSlKTJk3k6+urf/75x2oMnpiY+MRj8Ny5c6tatWqKiIgwrYyXL18+eXh4qGjRojp06JB+/PFH2dvb67333pOvr68KFiz4RPsEADYhKzc4BwA8XYGBgYYko0SJEmbl8fHxRtGiRQ07OzsjKCjIrK5hw4aGJOO3334zlfXq1cuQZJQvX96IjY21uE/Pnj0NSUa/fv1MZW+99ZYhyVi0aJFF+7FjxxqSjPHjx2d4TJIMSUZMTIzFOCUZmzZtMmuflJRklC5d2pBk+Pv7m8o/+eQTQ5Lh6emZ5v1u3rxp5M6d27C3tzfOnDljUR8TE2O4u7sbkowdO3aYyidOnGhIMsqUKWPEx8ebndOjRw9DkuHi4mLcvHnTrG78+PGGJKN3796mMn9/f0OSUb9+fSM5Odms/YULFwxJRunSpS361rt3b0OSUb16dYu65cuXG5KMvXv3Wh23JKNXr14WZY9zjBgxItV+PPh5+7du3bplhIeHG/fu3TMqVqxolC1b1oiOjjYMwzB+/fXXDPV1+fLlZtdu1qzZvx53u3btzK6V8tkIDAw0K9+7d6/ZvX/88UfDycnJ+Prrry3G+uqrr1r8Xadl9+7dhiTD19c33ecYhmEkJiZafOaCg4ON3r17G/b29kalSpWMzZs3G4ZhGHfu3DHq1atnTJkyJUP3AAAAAGzZv4kVRo8e/cjr1qtXz5BkfPPNN6ayRo0aWcSm1pQoUcIiJnF1dX2s2MzT09OQZCxdutRUdv36dcPBwcGwt7c3goODrZ6XEgfVrVs31WtbaxMaGmr1+4G0fP/994Yko3LlyqYya9+bBAcHG/nz5zccHByM48ePm8p37NhhSDIaNWpkce2kpCSjQoUKhiRjyJAhFvUp36ukNb4H49CUssc5tm/fbrUfRYoUScfTerRr164Zd+/eNf744w8jV65cxltvvWWqmz59eob6+nB87ODg8K/HPXv2bLNrNWrUyChbtqxF/x+OzSdPnmy4uroa586dM2uXkJBgSDI6d+6c7mczbtw4Q5Jx5MiRdJ+Tcq+HHT161GjQoIEhyfjPf/5jnDp1yjAMwzh16pRRoUIF47vvvsvQPQAgp2HGOABAuXLlUvfu3TVnzhxt2LBBw4YNk3R/H+KDBw+qTp06euGFFyzO69Spk9VltN58802tXLlSJ06ckHT/ze0ff/xRuXLl0jvvvGPRvnbt2pKU5lva/4a9vb08PT0tyqpVq6agoCDduHHDVL5t2zZJ0vDhw9O85r59+xQfH69GjRpZXSo9T5486tSpk+bOnasDBw5Y7M3VqFEji5n3zz33nCSpXr16Zns1SzLNUn5wmfEdO3ZIur9U9sNLglesWFGurq4KCgpSWFiYaa/x69ev6+uvv5Z0/43q9957T1OmTLG6p3xGvPnmm1b3vF69erXWrFmjtWvXqlChQhb1LVu2TPc9XnvtNf3888/pajt69GhNnTpVklSkSBFJ0scff6yLFy9q9+7dpmXvateurZiYmHT3wdoS6BUrVtTy5cstyt977z05Oztr3rx5FnVdunRJ9R5Lliwxe1YPv6n/6quvqkKFCurbt68qVaqkWrVqmeoSEhLM/hb/+9//KiwsLNV7pSyFt2PHDrPPljVdunTRSy+9JElycHAwlYeGhmrq1KlauHChChcurHnz5umDDz6Qo+P9f146OzurdOnSGj9+vH7//XctX75czs7Oad4LAAAAyCn+85//WKyu9OC/8TMqPj5eklJd6jqjUmbi/ptZsdevX9cPP/yg3Llzq2PHjqbyEiVKqEmTJtq9e7dWrlxpsV/04/g3q3OlxNOP2vKqZMmS+u9//6tBgwbpgw8+0NGjR81iI2u2bdtmiunOnz+vgQMHas6cOf9qe60HTZ48WS+//LJF+ahRo3Tnzh0tWbLEou7MmTMaPXp0uq6fmJiYoT7u2LHD9L2Hu7u7kpOT9cEHH8jV1VULFiwwtRs2bJjFHuBpsfY5fvvttzV48GCzspiYGL3++uvq06eP+vbta1YXGhpq9vl7UGRkpGbMmGFWdvjwYbPf33rrLX322Wdq166djh49atqiIOWzltLHO3fu6NNPP01zPCmz5efPn6+NGzem2Xbs2LGm7wZS4mvp/iqAEyZM0JYtW1SrVi3t3LnTbAuAIkWKKE+ePGrTpo0+++wzjRw5Ms37AEBORWIcACDp/j7Tc+bM0bp160yJ8a+//lqGYVgEFylSlid/WOnSpSXJlJi7deuWaWntAgUKpNqHtBJ5/0bRokWVJ08eq+XS/31xIEnnzp2TJNWoUSPNa16+fFmS+R7dD0tZWj0kJMSizloiOmWpq5SlsR6Uspz7g0F+ypLf/fv3Ny2vbs2DifH58+fLzs5Ozz//vG7fvq3t27dr79692rVrl8qXL5/qNR6lXLlyFsl/SQoICJAkNWnSRG5ubv/6+imeffZZffvtt6nWR0VFWf1yICAgQNOmTVO/fv3UtGlTU7mdnZ3Vz0Z67dixQ4ZhWP2SKF++fMqXL5/VfdsvXbqU6v7mD+7xZo2Li4vWr1+vl19+WR07dtTJkydNwXJcXJzZFwdffPGFxb521hw4cEAHDhxIs80LL7xgSoxL9/9Wli1bJj8/P7m6umratGl6//33Tc8zKSlJd+/eVXR0tCZPnqxbt25p3bp1unTpkrZv3/6v9icEAAAAbM233377WDHJw4KCgiTJbNnnlMR7cnJymucahiHJPAlXuHBh3bhxQzdu3FClSpUy1JdVq1YpKSlJrVu3tlja+Z133tHu3bu1YsWKTE2Mp8S+0v1Y3MPD45HnpLwsX7x48Ue2HTBggNasWaOAgADNnz9fQ4YMSbP9zJkzVaNGDf3222+6evWqfvzxRwUGBmrTpk2P9cJwrVq1rMbgU6dOVXx8vNW6f/M569atW5p7g+/Zs8dqonvWrFk6dOiQ1q1bZ/ZcHRwcHvkyQVru3r0rBwcHs8+o9H97jJcpU8ZqDB4TE2M10X/nzh19+OGHad7zueee0/z589W3b1/17NnTNKEiLi5O0v8lxiMiIjRz5sx0jWPNmjWPbOPt7W2K9ZOTk3Xo0CEtXLhQGzZsUM2aNbVt2za1bdvW1D4hIUHR0dEyDEOLFy9Wly5d9OGHH+rPP//U4sWLLZ4ZAOR0/F9FAICk+8mvOnXq6OjRo7p8+bLKlSunNWvWyNnZOdW9v1L7x3V4eLik/0vqpgThuXLlMktOPiwz9xeXlGoC0lp5yh5gj3ozOikpSVLqY3/w+tb2Vk6tT4+qe1DK86xVq5bFDPMHpQS/gYGBmj17tt59912dO3dOjo6OWr16tZo2baoGDRroxx9/TNd9s1Lu3Lkt9vh+UMpn7kGhoaHq0qWLSpcurSFDhmjv3r3666+/dO7cOc2aNUsbN27UzZs303XvDz74QJK0du1aXb16Nc32169fV+7cuU0z11NTpkwZsxUUAgMDTS9VSPdXJ3h4b8Dnn39evr6+GjJkiN59911t3rxZ0v2XPB78siPlBY7UvPrqqzp8+LAKFSqkixcvWp3Vb83Ds/fLli0rPz8/zZw505QMf3h/9hS//vqr6tWrpx07dqT53xIAAABAxpw+fVqhoaEqUKCAatasaSrPly+fJOvxUgrDMEwvsj+YxH7hhRd048YNHTp0SA0aNMhQf7766itJ92dNpxbnnj9/XocPH9Yrr7ySoWunxsnJSc8//7zOnj2rgwcPpisxnvJCd926dR/Z1t7eXl988YVq166tcePG6c0330y17bp16/Tzzz9rw4YN6tKli5o3b66xY8eqe/fuatGihfz9/dM/sCxSsGDBNOO2lMkFD/r555/18ccfq3379qpVq5a+//57nTt3TnZ2dvL29tbixYvTdW8PDw+1atVKkh4ZV6dMeDh06NAj27722mum5HnZsmUt4uZJkyZp8uTJZmV9+vTR9u3btWXLFs2dO1dDhgwx3TMlBi9Xrpzp5RJrIiMj5ebmppiYGDVu3Fh79+5Ns58pQkNDVblyZd25c0fS/e8mEhMT9eGHH2rgwIGmGDy11RKWLVum4OBgbdiwIc1JKgCQ05AYBwCY9O3bV7/++qvWr1+vVq1a6cyZM+rWrZtpuaiHhYaGWi2/cOGCJJlmIhcuXFi5cuVSUlKStm7dmqlvxWcWNzc3BQUF6fz582azYx+W8vZ9yhitSQmuHkxyZqaUGbdeXl6mhG1aPvjgAyUkJOjDDz/Uu+++K+n+lxw7duxQixYt0jWzODWGYSgxMdGiPCV5n5SUZLU+o86ePZvuFwdSzJgxwzS2BwP6Z599VnPmzNG0adN0/PjxR14nb968puf8xRdfaP/+/em6/6NmPzRq1Mjq1gKP4u3trQ0bNig4OFh37txRoUKFFBsbm+ZLEg/69ddfdfjwYdWrV0+//vqrxo4dq4ULF6br3I4dOyomJkalSpVS0aJF5erqqgIFCpiO999/X61atdLo0aNNM+fz58+vfPnyacuWLerbt6+6du2q48ePW31xBAAAAEDGffbZZ5Luz/J98CXulJj02LFjqcYef/31l2JjY+Xu7m5KpEtSq1attGfPHn3xxRcaMWJEupdU//XXX3X27Fnlzp071eR0aGiowsLCtGLFikxLjEtS27ZtdfbsWS1atEg9e/ZMs210dLTWrVsnSWrfvn26rl+tWjWNHDlSvr6+8vb21oABAyzahIWFafDgwapSpYrZMt5du3ZVZGSkPv3008daLS85OdlqjJ2SmLVWl/KCf0YsWLDAbCn09Bg+fLgSEhK0detWbd26VdL9CQDvvvuu4uLiNGjQoHRdp1WrVqbEeHpXFdizZ4/27NmTZpuJEydanVX+KAsXLtT+/fv122+/Sfq/iRXpnfm/ePFixcTE6NVXX9W+ffu0fv16derU6ZHnFStWTK+//rquXr2qUqVKqVChQqbY29XVVf/884+mTZumsWPHytPT0ywGz5s3r0aOHCk/Pz+NHz9ec+fOzfC4AcBW8Y0kAMCka9eucnZ21oYNG7R27VpJSnUZdUn67rvvrJZ/8cUXku7vmSbdf6v1pZdeUnJysml/7OwmZWautf24HtS4cWPZ29tr3759Vt+QjouL0/r16yXJ6hJmmeHVV1+VlPrzf9Dy5cu1e/duDR06VGXLljWrq127ti5evGixD3tGzJo1S7ly5bI4pkyZIun+8vDW6jPq2Wef1Z9//pnq8csvv1ic88orr6h169b68MMPtWzZMh0+fFi3bt3S+fPnzZ6BYRipHt26dTO75r59+6y2u3LliqpWrapKlSqpfPnyqlSpktzc3NSjRw8lJCRYPWffvn0Zfg7S/ZkKmzZtMs34lu7v/5eeoDw+Pl4DBgyQnZ2dFixYoH79+snPz08bNmxI172HDx+u48ePa+vWrapVq5bGjx+vSZMmafjw4aa95kuVKqUGDRqoZs2aOnjwoOzt7ZUvXz716NFD3377rbZu3UpSHAAAAMgkCxcu1Ndff63ChQtr/PjxZnWNGjWSdH9p89u3b6d6viQ1b97crPy9995TkSJFdPnyZQ0aNCjNGbEPWrFihSSpc+fOOnfunNVj2bJlku7PrI6JiUn3WB9l8ODBypcvn44cOaJZs2al2s4wDHl7e+vOnTtq3ry51W25UjNhwgRVrFhR27Zt086dOy3qhw0bpps3b2rmzJkWcY+Xl5d+++23x3qJvl27dlZj7AMHDuivv/6yWvfwf9v06NatW5ox+Pz58y3OadmypTp16qSJEyfqm2++0cmTJxUdHW32PcuIESPSjMEf3ApAUqrtdu/erQIFCpi+d3nllVeUO3duLVy4MNVzJk2alOHnIN2fSPHbb7/pyy+/lHQ//pbSlxgPDAzUp59+Knd3d+3cuVPVq1dXv379zL6XSMs333yjw4cPa8mSJapWrZqmTp2q8ePHa/DgwWrZsqWk+xMfXnnlFT377LPauXOnXF1d5eLiooULF2r58uWaNm3avxo3ANgqZowDAExcXV3VsWNHrV27VqGhofLw8LBYyvlB+/fv17hx4zR58mQ5ODgoOTlZU6ZM0fHjx1WiRAl1797d1HbAgAE6cuSIvL29VaRIETVs2NBUd+3aNc2ePVvTp09/ouNLy4gRI7R27VotWbJEFStW1PDhw037X126dEl//PGHWrdurdKlS6t79+5auXKlunfvrs2bN6tMmTKS7r81PHjwYF27dk0tW7ZUnTp1nkhfO3furDFjxujbb7+Vj4+PxowZY0o2JyQk6JtvvlHJkiXVtGlTnTlzRhUrVrRYDixFaqsBpFfz5s2tLiHn7++v7777Tp999pnVJbvS2hv9QVFRUf+6b+3atVO7du3+9fnpdfDgQXXu3FnOzs7av3+/2rdvr3z58mn9+vVq1KiR2rRpo6+++ipd+9al18PXio6ONm1dkJrk5GS99957OnbsmAYOHKhatWqpfPny2rZtm3r06CFnZ2e1bt36kfeOjY1Vjx49tHHjRp0+fdr0IszDzp49K29vb40ePVoLFy7U22+/bQrcAQAAADyec+fOydfXVytXrpSLi4u2bt0qd3d3szZt27aVh4eHAgMD9frrr2vBggWqW7eu7OzsdOfOHc2ePVvz58+Xo6Ojhg8fbnZu/vz5tXLlSrVp00ZffPGFbt68qWnTpunZZ581a3f27Fnt2LFDI0eOVHx8vL755htJUo8ePVLte6tWreTm5qbr169r8+bNFi8k/1tubm768ssv1bVrV40YMUIhISEaO3as2RLxly5d0siRI7VlyxYVK1ZMy5cvz9A98uTJo8WLF6tZs2by8/OzqE9ZeS+1F9AfNwYfMGCAqlWrZlE+a9YsRUZGWk3+nj9/XrNnz37ktZOSkh7rRYV/m3jOqEWLFmnw4MF6/fXXtXLlShUpUkT/+c9/9P7776tv3766cuWKfHx80r3KQXo888wzpp9T9jV/VAx++/ZttW/fXhEREVqyZIny5cunhQsXqnHjxmrRooV27dqlihUrPvLeV65ckaenp/744w8VKlQo1b+Xr7/+WgMHDtTixYv11VdfqUaNGurdu3f6BwkAOQSJcQCAmb59+2rNmjW6cuWKJk+enOby1R06dJCvr6+WLFmiqlWr6u+//9Y///wjJycnrVq1yiwh2r17d+3cuVNr1qxRo0aN5OHhoXLlyunWrVs6e/as8uTJk6WJ8RdffFGLFi2Sl5eXRo0apenTp+v555/XjRs3dP78eXl7e5uShvPmzdO5c+f0yy+/qEKFCqpVq5acnZ11+vRphYeHq1q1aqa35J8EV1dXrVy5Uh07dtTEiRM1Z84cValSRfb29jpz5oyioqJMs9YHDx6s9u3bp3uJr+PHj6d76fNu3bqpVatWVvegv379ur777jv17NlTbm5uFvWHDh1S7dq1Lcrv3r0r6f7LEj4+PqpcubKk+0vXP84e9NHR0Tp27JiOHj2qo0ePasKECf/6WiliYmLk4+OjadOm6ZVXXtGmTZvMEtbVq1fX7t271b59e1WtWlXTp09Xz54909yf/t+IjY3V3bt3lT9//lTbREVFqU+fPtq0aZMaNGhgmj1RsGBB/fjjj2rUqJHat2+vTz75RKNGjUp1Rvf169fVsWNHHTlyRF5eXlZnCaR4/vnndfToUb399tvq1KmT3nnnHS1atCjNfgIAAAA5Rdu2ba3+u7t79+5mL5lL0vr163Xq1ClJ92Omy5cvKzg4WNL92aJr1661mizNlSuX1q1bpzfeeEMnTpxQ/fr1lS9fPjk7O+v27dtKTk6Wg4ODFixYoBo1alic7+npqc2bN6tXr16m5bErVaqkUqVKKSEhQX///bdCQkJUvXp1jRw5Utu2bVNYWJieeeYZNWvWLNWxOzo6qmfPnpo2bZpWrFiRaYlx6f6L5MnJyXr//fc1Y8YMzZ07Vy+++KJcXV117do108pvzz33nLZt26ZSpUpl+B5NmzZVr169THupP2jKlCmqX79+uq+1e/dui7LTp09blJUoUULdunXT+++/b/W/1TfffCN7e3v169fPou7PP//UzZs3rY41JQb/5Zdf5OXlZdqne82aNVqzZk26x/GwGzdu6JdfftHRo0d1/PjxdK9SlpZr167Jy8tL/v7+GjBggObOnWta1lySevfuLTs7O1ObBQsWmFZNyEy3bt2SpDRj2wsXLqhDhw46e/asRo0apbffflvS/Zntq1evVteuXVW/fn2tWLHCtGy8NUePHlX79u11+/ZtLV26NM2/lb59+6pAgQJ699139fLLL2vKlCkaNWpUhrelAwCbZwAAcozAwEBDklGiRIlU2yQlJRn58uUz7O3tjStXrlht06tXL0OS4efnZ+zatcuoV6+ekSdPHqNw4cJG+/btjZMnT6Z67S+++MJ4+eWXjbx58xq5c+c2PDw8jN69exunTp36V2OSZEgyYmJi0j3OB/v/sICAAKNjx45GsWLFjNy5cxtly5Y1unfvbvz2229m7WJiYozp06cbNWrUMFxcXAxnZ2fjxRdfND799FMjOjra4roTJ040JBmjR4+2qPPz8zMkGb169bKo27JliyHJaNGihUXdiRMnjLfeessoVqyY4ejoaBQuXNho1qyZ8dVXXxlJSUlWx96sWTOjevXqFuXLly83PcvUDmv9S03KeENCQtJ9TnJystG8eXNDkuHo6Gg8//zzxoEDB4xXX33VeP7559M89/r164Yk4+OPPzYMwzASEhKMJUuWGO+9955RrVo1w97e3pBkODg4GDVr1jQuXbpk1K5d26hdu3aa1+3WrZuRN29es7KkpCRj5cqVRunSpQ17e3tj6NChRnx8vKm+du3aRqNGjUy/h4SEGA0bNjQkGWXKlDGmT59u9reV8qxSO5YvX55mH1M+I9Y+W4ZhGD/88INRoUIFQ5Lx+uuvG1FRURZtTp48abi7uxuSjDp16hj79u2zep3ixYsbDg4OxuzZsy3qY2NjDUlG//79zcqjoqKMrl27GpKM8uXLG0ePHk1zPAAAAIAte1TcNXHiRFNba7FCvnz5jAoVKhjdunUzNm3alGrs96CgoCBj6NChRpUqVQxnZ2fDycnJKFeunNGzZ0/jxIkTjzz/9u3bxpQpU4x69eoZhQsXNnLlymW4ubkZNWrUMAYPHmz6N36rVq0MScaHH374yGv+9ddfhiTD3t7euHr1qql87969hiSjbt26qZ6bnjbXr183xo0bZ9SuXdsoWLCgkStXLsPd3d3w9PQ0li1bZiQkJFg9Lz3fmxiGYdy6dcsoVqyYIcks/nuYg4ODMWTIEIvylO8lHicWfFCjRo2MypUrp7t9yhiKFy9u2NvbG/b29oanp6dx6dIlQ5IxcODANM/95ptvDEnGjz/+aBjG/ec9a9Yso3Pnzka5cuVMY3BxcTFatmxpREVFGZKMESNGpHndkiVLGq1atTIri4qKMiZOnGi4uLgYBQoUMJYtW2ZW9/DfzZEjR4wyZcoYkoyXX37ZWL16tREeHm6qb9SoUZrPPTAwMM0+DhkyxJBk7Nixw6IuMTHRmDdvnpE/f35DkjFmzBir1/jqq68MJycnQ5LRtWtX4+LFi2b1ycnJxvTp041cuXIZRYsWNfbu3WtxjZ07dxqSjHXr1pmVX7x40ahRo4YhyfjPf/6Toe9lACAnIDEOADCza9cu0z+eU5NWYhnZ26MS40uWLDFOnjxpcTytxPjrr79uuLm5GUuXLjV9wWMtMR4fH288++yzRvXq1Y06deqYkrqrVq0ytSlbtqyRJ08eo2HDhsbYsWONnTt3GpGRkab62rVrP/KLCEmmxHhcXJwxd+5co3z58oYko3LlysahQ4csxvFwYjxlbCtXrjT1U5LRoEEDIy4uzvSsvL29jbFjx5qO7t27W3wZ8sknnxjvvPOO0b9/f2PYsGFGr169DBcXF0OSceDAAbN77tu3z3j99dcNSUauXLmMCRMmGImJiak+/1u3bhnt27c39e/ll182vvzyS+PWrVvGmjVrDDs7O8PNzc3Yv3+/6Zw5c+YYQ4YMMcaNG2e0adPGkGRMmzbN6vVnzZplODo6Gm5ublaT8wAAAABgax6VGLcWfy9ZsuSpJcbd3d2NmjVrml6OTkhIsJoYP3TokPHss88aNWvWNOrUqWPkz5/fsLe3Ny5fvmwYxv+9UFC4cGGjTZs2xrRp04wjR46YXiJPSWCn50hJjIeEhBgjR440XF1dDUlGy5YtzV6iePC6DybGDcMw7t69a4wbN87IkyeP6eX74cOHm56Vq6urWfw9duxYo0GDBhaJ8a5duxq9evUyBg4caAwdOtRo3bq1YWdnZxQpUsS4c+eOqV18fLyxatUqo2rVqoYko2jRosbGjRvTfP6nT582nn/+edNL/B07djS2bdtmxMbGGh988IEpLn9wzP379zdGjBhhjB071qhevbohyfjll18srn3v3j2jW7duZs8TAHAfS6kDAMzMmzdPUvr3gIZtqVixotVl2Z4GOzs7rVmzRk5OTo9cbjtXrlyqWLGigoKCFBsbq3Llyqlv375my7r/9NNPKlWqVJr7ipUpU0YffvhhqvUrV67UH3/8YbrnqVOndO/ePS1YsEDvv/++aW/39IytR48eevvtt7Vu3TotWbJE48aNM+vbiBEjVK5cOdPv+/bt0+rVq82uc+/ePa1du9asrHjx4poxY4YaNGggSQoPD1fDhg3122+/SZJatGihGTNm6IUXXkizj0WKFNGWLVu0bt06ffLJJ/rll190/PhxFStWTO3bt1f//v01btw4s30Lr169qrlz50q6v9deq1at5OXlZfX6w4YNU82aNeXs7Kx8+fI94okBAAAAgO2zFn+Hh4c/lXsXKVJER44cUenSpVPdTitFxYoVlTt3biUkJCgxMVEvvfSS3n33XZUtW1aSVK5cOZ07d06VKlVKc+nuZs2aqX379qnWjx8/3vSzs7Ozdu/eLQ8PD/n6+uqNN95I99hcXFw0ZcoUDRo0SMuWLdO2bds0bNgwU33BggX1ySefmJ0zadIkHTx40KzsypUrOnz4sOl3Ozs7Pffcc1qwYIFp7/qAgAC1bdtWoaGhypUrl/r37y8fHx8VLVo0zT6++OKLOnbsmGbPnq3Zs2dr8+bNOnz4sH7++WcNHz5cBQoU0Keffmr2vcHRo0d14sQJ0xg+/PBD1alTx+Lazs7OWr16tZo0aaJ27dql76EBQA5hZxiGkdWdAABkD/7+/mrTpo1eeOEFnTlzJtVgpnfv3vrqq6/k5+dndf8qZF/NmzfXrVu3TPvTPSmTJk3S5MmTFRISYnWP8ezgpZdekiQdO3Ys1Tbdu3fX1q1bFR0dLUlKSkpSbGys8ubNm+Z18+XLp3379qWrH2FhYQoLC1O5cuXStf94Sh/i4uLk4OAgV1dXizaff/65Dh06pGHDhmVof7sUhmHo22+/VVxcnDp16pSuPjk4OGT4PgAAAABgyxwdHeXt7a05c+Y80fs0btxY169fN+2hnt1ER0crf/78GjFihGbMmJFqu1KlSqlGjRry9/eXJEVFRSlfvnypfj+Vct2JEydq0qRJ6erLP//8o8TERFNS/1ESEhJMMXj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rZurWrWszrVWrVqZZs2amadOmpkaNGmbjxo0mLCzMlC5d2vTo0cMYY6x7XXBwsPn+++/Ntm3bTLVq1UyBAgVMfHy8McaYihUrmkaNGpldu3aZDRs2mKlTpxpjjN25mp5zo1u3biZ37tymQ4cOZseOHebLL780Tk5OZvLkycYYY27cuGHKlStn2rZta3bs2GGWL19u8uXLZ0aPHm2MMSYsLMxIMtWqVTNr1qwxmzdvfuD7Fkj06quvGkmmffv25siRI9b0y5cvmw0bNhhJZvbs2ebQoUMmPj4+1fvX2bNnzUcffWQkmUOHDpljx44ZY4wZP368KViwoFm8eLGJiIgwbdq0MSVKlDBxcXF25bl586bJnTu3eeGFF8y+ffvMmjVrzLx586wyBQQEmH79+pldu3aZefPmGR8fH/Ptt98aY5Kv+0ZHRxsvLy/z1VdfWetYunSpcXNzM+fPnzfr1683vXr1Mr/++qvZunWreeKJJ0yjRo2svKnVhVO6pwF3K6XzMVFq9774+HhTuXJlU6JECbN48WKze/du8/HHH5vo6Ghz+vRpkytXLlO/fn2zbt06s2nTJtOyZUvj5+dn/v33X2OMMSNHjjTZsmUzTZo0MVu2bDHff/+98fT0NMOGDbPWn3i+r1q1ymzevNnUrl3buLq6mpEjR1p5WrVqZebNm2f27NljXnnlFePm5mZOnDhhrcPf3980bdrUbN261fz222/m5MmTxtnZ2SxdutRaxuLFi42np6eJiopKcV/t3bvXuLq6mrNnzyabvmzZMpMlSxYzd+5cs2/fPvPtt9+aP//80xhjzPz5842fn5/57rvvzO7du03//v1Njhw5zH///WeMMeazzz4zbm5u5r333jO7du0yS5cuNb/88osxxpiuXbuaXLlymVmzZln72M3NzUyZMsUYk746yMyZM42bm5v55JNPzK5du8yYMWOMm5ubCQwMtMr/8ccfm7Fjx5qtW7eapUuXGm9vb6tOn7iO0qVLm0WLFln15Xr16pk2bdpYy4iPjzc5c+a0yna71K61ifWS8uXLm1mzZpmIiAjTr18/4+TkZPbu3WuMMWbbtm3Gw8PDTJo0yezZs8eEhoYaV1dXc+jQIetveK/tBmNuXdddXFxSPA4elOTqoIlcXFzMjBkzjDH29cb09I8knmsNGzY0W7ZsMfPmzTNeXl4mJCTEWkd66rlJ//5JpXXv7d69uwkICDALFiwwO3bsMM8//7xxdXW1/ja39x0lSjpt+/btxs3NzXTr1s1s3rzZrF+/3kyfPt0YY8zcuXPN0KFDzfr1683atWtNvnz5TN++fY0xt9rCzz33nKlSpYo5dOiQdR4XLVrUup6kt8/LycnJ1K1b14SHh5sVK1aYXLlymfbt26fr7/y4SHo816xZ0+TNm9cMGjTIREREmHfeecdIMitXrjTGGHPp0iVTuHBhU6VKFfPzzz+biIgI8+GHHxpj0t82ypEjh+nVq5eJiIgwgwYNMpJMpUqVzEcffWR27txpGjRoYPLkyWNiY2OtefLly2fq169vNm7caNasWWNKly5tKlasaJU7MDDQvPvuu8aY1OuE96vPIfH4ql27tgkLCzMrV640uXPntrnO3n7MxsXFmaJFi5rq1aub8PBws3btWlOxYkUjybpmJF5fK1WqZJYtW2bWr19vSpQoYSpUqGAtM61zNzAw0BQoUMC8//77ZvPmzebixYt3e2g4pNSu32+//bbx9vY2kyZNMrt37zbTpk0zvr6+5q233jLGGDN06FCbY65///7G09PTfPzxx8aYW39jLy8vExYWdl/a+Kn10aV1vKZ2/U1vv2pq/f0HDx40rq6upnfv3mbHjh1mzpw5Jnfu3Db3s/S0o5Ieq2PGjDG5cuUyN27csPK1bNnSpmxI3pgxY0yePHnMzZs3zdatW40km+tXam0BY27V43/66Seze/du0759e+Pv72+uXr1qjElf3TQ916Y77Xe//RqaVvlvFx8fb4oXL26eeeYZs27dOrN27VpToUIFm+ttWn0H6a2zJW27pFUHTqud8SghMI6H4v/+7/+MJPPPP/+YuLg44+PjY1atWmWioqJMlixZzP/93/8ZY25dMMaMGWOmT59u3N3dzT///GOznAoVKpju3bsbY25V4pJeJBMbMmvXrjW1atUyzz77rLl+/bqVnlxgvFmzZtb3q1evGldXV/P1118bY4zp3bu3KVu2rE0ZPv/8cwLjsDNnzhwjySQkJKSaLzAw0AwcONBm2k8//WRzTCW9kYaHhxtJ5qOPPrKmhYaGmjx58tgs907mOXnypHFxcbEa3cYYs2PHDiPJREZGGmNuBcYTOyiMMclWTBIl7TRJatmyZUaS1ahJKzCeVM6cOc2nn35qjLn1YE2DBg3s8kRERBhJZtOmTXZpL7zwgilbtqzN3+fMmTPGw8PDzJw50xhjTHBwsM323i5pB1dq5TPGtkPJGGP69Olj14HRtGlT07NnzxSXCcd3e6P2ypUr5uuvvzYuLi5mzJgxxsPDw6ZiOX/+fOPm5mZu3rxp3etuP79/+eUXI8n89ddfxhhjfHx8zNixY+3WmfRcTc+50a1bN5MzZ06b++nTTz9tOnbsaIwx5vvvvzcFChSwCQKOHz/eFCtWzBjz/xvNiRV04GGbPHmy8ff3N+7u7mbEiBFWR0Va9y5j7O9fs2bNMrc/WxwbG2t8fHyszlZjjDl//ryRZDZu3Gi3vAsXLhhJZs6cOXZp48aNM1WrVrWZNmDAAFOvXj1jTPJ1X2OM6dixo819sWvXrjadmrf7/PPPjZ+fn/U9rbqwMfb3NOBepHQ+btmyJdV737x582w6ZW73zjvvmDx58lidX8YYc/36dRMQEGBGjRpljLnV8ePi4mLOnDlj5enUqZOpXr26McaYTZs2GUlm9+7dVvp///1nFxi/3ZUrV4wks3jxYmsdTk5Odu3EWrVq2dQxO3XqZDp06JDqfnr55ZdTPI+NuRVY9vPzS7YzrWTJktbDa8bceoDN19fXzJ4928TGxhpfX1+rY/t2hw8fNpLMggULbKYPGTLEFC5c2Bhj0lUHKV68uBk8eLDNMtq1a2fT+ZhU27ZtTcuWLW3WkbSMM2fONF5eXtYDOr/88ovx8PAwly5dslteatfaxHrJ7de1hIQEU7JkSTNo0CBjjDENGza0eWjCGGOeeOIJM2bMGGPM/Ws3ZGRg3NnZ2bi7u9t9bt83Se+T6ekfGTlypHF2djanTp2y8nTp0sVUq1bNGJP2uZ7S3/92ad17Ex9ISTw3E+fJly/fHQXGGzVqZGrVqpWuffrqq6+acuXKWd+T9j0ZY9tBfid9Xjt37rTS33zzTRMQEJCuMj0ukguMlylTxvqekJBgChQoYIYPH26MMeb9999PsRM/vW2j3LlzW22fmJgY4+7ublOfWr9+vZFkPbDUrVs3ky1bNpvrVeJ9JyIiwhhjGxhPq05ozL33OSQeX9u2bbOmjRgxwuTNm9f6fvsxO3fuXOPm5mZzbv/222/JBsZ/+OEHK8/UqVONJHP9+vV01ZsDAwNtfrABWykFxq9evWo8PT1t7s/GGDNhwgTj7u5uYmJizNq1a42zs7PVrilWrJh56aWXTOPGjY0xt47JrFmzmri4uPvSxk+tjy6t4zW16296+1VTa+P06tXL5pptjDGTJk1Ktb8/uXZU0mP16NGjxsnJyWqvXb582Xh4eJhVq1Ylu0z8f8HBwaZfv37W98KFC9tcv1JrCySVeG1KrNenVTdN77XpTvvqb7+G3kn558yZY9zd3W1+fPf777/bXG/Tuk+kt86WtO2SVh04tXbGo4ah1PFQ1KpVS+7u7lq7dq22bNmiuLg41ahRQ9myZVOVKlW0du1a/fPPPzp8+LDq1aunnTt3qnTp0sqXL5/Ncv73v//ZDBEnKdnhWoYOHaoTJ07op59+koeHR6plq1ChgvV/Ly8v5c6d2xoCa9++fdb7zxOVK1fujrYdjwcvLy9JshnuMalz587p+PHjql+/vs30GjVqSJL++OMPa9rtx1niUK+3H6v58uWzG/blTubZu3evbt68qaeeekoeHh7y8PDQs88+K0k2Q2fdfn5t3rxZvr6+VnlTY4zRnDlz9Nxzz+mJJ56wXnVw+3CVqfntt980cOBAVa9eXTlz5tT58+eteQcNGqQNGzaoatWq+uGHH6z3w5YvX14NGjRQw4YN9corr+jo0aPW8nbu3Km6devavCslT548KlmypPbv36+LFy/qwIEDatGixT2XLzkRERFauHChta89PDy0atUqXskAhYWFycPDQz4+Pnr33Xc1d+5c+fn5KTY2VgEBAdbx0rlzZ8XFxen06dPWvLef30FBQZJk3b+GDh2qt99+W+3atdPmzZtTXH9a50ai0qVL29xPg4KCrHVFRETo1KlT8vHxscr75ptv2h3fDKGOjNK3b18dOXJEISEhGjdunHr06JFi3ju9fx06dEhXrlxRq1atrOM/f/78kpTsNT5Hjhzq2bOnunfvrhdffNFm6MKIiAjt3LnT5l7x9ddfp3kuderUSWFhYbp06ZLi4uK0dOlSdevWTdKt96h/+eWXatmypUqWLKmQkBC7bUmtLgzcbymdjxEREane+zZv3qzSpUurWLFidsvcuXOnnn76aas+LkkeHh6qUqWKzb0sf/78ypMnj/X99nvZvn37lD17dpUvX95Kz549u1WnTrRx40b17NlTlSpVsl6fdPs5lTt3bus1C4m6dOmipUuX6ubNm4qNjdWyZcus1zIkJzY2VnPmzLEbivN2zz//vPz8/FSqVCl99NFHioqKkiRdvXpVBw8e1ODBg6396O3trcuXL+vYsWPav3+/oqKikq3zJr42Irm2yrFjx2yGKE2pDnL9+nX9/fffdm3osmXL2nw/f/68Ro4cqTp16qhgwYJatGiR3bUp6bWuTZs2kmQNjTl//nw1b95cvr6+dtuS2rU2UWLbR7r1eowKFSpYw19GRETok08+sbke//nnnzp27NgDbzc8LFWrVtWePXvsPi4uLinOk97+kQIFCiggIMD6XrhwYZ0/f15S2ud6otTqjWnde3///XclJCTYlNXNzU3BwcFp7BVbmzdvVsuWLZNNu3r1qj788EM1atRIRYoU0WeffXZHf9f09nk5OzvbXJduv24hZU899ZT1fycnJwUGBlr7bfPmzfrf//6n7Nmz282X3rZRqVKl5OrqKknWUM5J+14k2fTZlClTxuZ6ValSJUmyGXY3UXrrhEnnudM+BycnJ1WsWNH6fvu5mtS+fftUunRpm3P7ySefTPaaUblyZZtlSrf2RXrrzbQb79z+/ft1/fr1ZO/hsbGxOnz4sJ599ll5e3tr/fr1OnbsmKKiovT6669r/fr1io+PV3h4uOrUqSNXV9f70sZPrY8ureM1pevvnfSr3ml/f9K6SnraUUm3v3DhwnrmmWe0ePFiSdKyZcvk5+dn87oG2NuxY4cOHDigNm3a6MaNG7px44batm2r+fPnW68jSq0tIN0a/rtr164qX768qlWrJulWHT09ddP0Xpvupq8+UVrlv13i9TZv3rzWtCeeeMLmepvWfSK9dbakbZfU6sBptTMeNVkyugB4PHh5eenZZ5/V2rVrlS9fPj3zzDPy9PSUdOv9JcuWLdNTTz0lPz8/VapUST/88IOcnZN/buP2d5q6uLgkG/jOly+f9u/fr4MHD1oXw5QkVmYTZcmSxXofw+XLl+Xu7m6THhsbm/YG47GT2FgNCwtTq1atks3j5uYmSXbHdmKj6/ZjO0uW/395Tsx/+7Hq4uJi966VO5knPj5e0q13+iSei4kKFSpk/T9r1qzW/40xNutIzejRo/XNN9/o/fff17vvvquTJ0+qQYMG6Zr3wIEDqly5sl5++WV9+umnKl68uE1ls379+jp8+LAmTJigvn376tNPP1V4eLhVkV66dKk++eQTlSxZUrNmzVL79u3l7u6ermtKerYvrfIlJz4+Xt26ddNrr71mM/32Dlw8nqpWrarp06fLy8tLBQsWlJOTkz777DNlz55dmzZtssufO3du/fPPP5Jsz+/EYzfxHB8xYoSaNm2qjz/+WDVq1NCAAQM0YcIEu+Wl99xI7V4ZHx+vJ554Qj/++GOq23r79QR42LJly6b33ntPBQoU0IABAxQaGprsNf9O71+J99OFCxeqaNGiNmm3dxzebsqUKercubM++eQTlStXTh999JFCQkIUHx+v+vXr69NPP7XJn/RenrTu27BhQ/n6+lodL25ubmrcuLEkqWfPntq6davGjh2rcuXKaf369XbvLE7t/AYehOTOx/j4+FTvfanVQ+/HvSw6Otqqq9/u9rbf2rVr1ahRI7377rvq37+/ihYtKn9/f5v8yd3r2rVrp4EDB2r9+vW6fPmyPD09U72uLFmyRJ6enmrYsGGKeQoUKKA//vhD06ZN04QJE/TRRx8pLCxM+fLlkzFGn3/+ud3DrDlz5tSJEyesbU8qsd2b0r5MvN5JKddBrl69KmOM3b68fT/GxcXp6aefVsmSJfXmm2+qVKlSGjFihE2HuWS/LxPfVb1o0SI1a9ZMixcv1vTp05PfQUr5WpsoaRmjo6OVLVs2a1uHDx+ujh072uS5Paj1oNoND4uXl9cdB4rT2z+S9FxL2g5NTz03tXpjWvfe1atXJ1uO28uaeJwnJCRY027/v5R6+7dp06aKi4vTW2+9pSeeeEIzZ860e89yatJ73XJycrLpCOcenT6pXe+NMXbpie6238DZ2dmuvpa4rkTJXXMkWded26WnTpjcPHfa5+Ds7GxzfCXXz5QoufM/Li7O7rxJWs7b90V66820G+9cWvfwuLg4ubq6qnbt2goPD9eZM2fUsGFDlSxZUv7+/tqyZYvWrVtn9Wfejza+s7Nzin10aR2vKV1/76Rf9U7rfUnvZ+lpRyW3/V26dNG4ceP06aefav78+erUqVOqD51B1vuzkz7wIN16uOC5555L9Z48c+ZMDRo0SB9++KGGDh0qV1dXlSpVSpLSVTdN77XpbvrqE91Jn3pyx2fS621a94n01tmSHsOp1YFv3LiRajvjUcMvxvHQNGzYUJs2bdKaNWtsLnT169fX3r17tWLFCtWuXVsuLi4qW7as9u/fb/PEsHTrCf3bnzxMyeuvv6527dqpefPmOnTo0F2XuVixYnYNtuQacEDhwoVVt25dvf3221YDJylfX18FBgZqzZo1NtPXr18vZ2dnmyd1H7TETpALFy4oODjY5pNSw6lMmTK6cOGC9u7dm+byFy1apG7duumFF15QcHCwDh8+nO6yrVixQtmzZ9eHH36oatWqydXVVZGRkTZ5AgIC9P7772vnzp3atm2b1fnh5OSkli1bat26dWrfvr0+/PBDSbeeBFy7dq1NBeXcuXP666+/VLlyZWXPnl358uXTr7/+el/K5+zsrBs3bljfg4ODdeDAAbt9fftDCHg8JXZKFipUyGrMBQcH67///pOLi4vdMZNaZ0hSTz31lGbPnq2vv/5aEydOTHZEi7TOjfQIDg7W0aNHlSdPHrvyAhkpuY66p59+WtKtBl9iY/b263Va96+k8xQrVkxZsmTR6dOn7Y7/5H7FmKhWrVpaunSp3nrrLY0dO1bS/79XFC1a1GY5SRvnSbm6uqpdu3ZavHixFixYoE6dOlnXikWLFmnIkCFq27atihUrpoMHD6a6rOQkvacBdyOt8zGte1+ZMmX0xx9/6MyZM3bLKVu2rDZv3qzr169b02JjY7Vjx45038uKFSums2fP6u+//7amHTlyRKdOnbK+L1myRBUrVtSwYcNUoUIFnT592hq5KDW+vr5WIHf+/Pnq2LFjqh1j06ZNU48ePVLs3E7k6empgQMHav/+/cqaNaumTp0qX19f5cmTRydOnLDbjzlz5lTJkiXl5uaWbJ23TJkycnJysmurbNiwQcWKFZOfn1+a25ozZ075+fnZtZk3btxo/f/333/XoUOH9NFHH6levXrKnz+//vzzzzSXLd3qbF62bJn1q/FGjRqlmj+5a22iffv2Wf+/fv26tmzZYv2SJvH6n3QfBgQE3Pd2w6PkfvSP3I96blr33sRfY90+alJUVJTNyAG5cuWSJJs+p6R9R2XKlEn27/zff/9p3bp1GjlypJo2barChQvrwIEDNnnSunfea58X7l6ZMmW0ZcsWm3tGovvRNkrJgQMHbB4w+vXXX+Xi4qInnnjCLm966oQPu8+hWLFi1qgjibZs2XJHD2rcbb0ZaStevLg8PDySvYdnzZrVChI2atRIYWFhWr16tfUgbd26dbVq1Spt3rzZeijvfrXxU+qjS+t4Ten6e7/6VZO7n23YsMHm+922o55//nmdPn1a69at06pVq1IdJQi32tXz5s1T9+7dtWPHDptPcHCwFTRPrS2Q+NBkv379VKZMGZtfL6enbvowrk2plT+pxOvt7SMUbNq0yeZ6m9Z94m7rbKnVgdNqZzxqCIzjoWnYsKEOHz6s3bt32wTGq1WrJm9vby1ZssQaWqRTp04qVKiQWrRoodWrV2vHjh3q16+fDhw4oNdffz1d65syZYpKlCihxo0b3/VQU/3799fGjRs1dOhQ7d69W19//bW+/fbbu1oWHN+UKVP033//qVq1avr+++/1xx9/aNOmTfrkk0+sXzOMGjVKX375pcaNG6c9e/Zo4cKF6t+/v1566SW7oRofpODgYDVu3Fg9e/bUwoULtXfvXs2cOVODBw9OcZ569erp6aefVtu2bbV48WLt3btXkyZNSnborzx58mjFihXasWOHlixZookTJ6a7bHny5NG///6ruXPnaufOnerevbvNk3LvvPOOfvrpJ+3fv18rV66Us7OzihYtqoiICL377rvasWOHtm/frgMHDqh48eKSpOHDh2v//v3q1q2btm3bpvDwcKtymzhM7jvvvKNJkyZpzJgx2r17t1auXKm5c+fecfmkW0PqLFu2TPv27dO5c+c0ZMgQbdq0SQMHDtTOnTsVHh6u3r17a/fu3eneL3h8NGjQQE8++aTatWunlStXateuXdaxmV4vvfSSwsPD9dtvv2nTpk0KCAiQj4+PXb70nBtp6dy5s7y8vNSmTRuFh4drx44dGjdunL755pt0lxd4EE6cOKH69etrwYIF+uOPP7Ru3TqFhISoQoUKKlasmHLlyiV3d3ctXLhQu3fv1tWrV9O8fyXeq7/77jvt2rVLWbNmVa9evTRs2DDNnDlTe/fu1Y8//phiB8ipU6f02muvadOmTdqzZ4927dpl3av69++v06dPq1OnTtqyZYs2bdqkYcOGadmyZWlua+fOnbVu3TqFhYVZw6hLt+5Z8+fP1549e/Ttt98me19LS9J7GnA30jof07r3denSRXnz5lWzZs30888/a/fu3Ro7dqyio6M1aNAgxcXFqU2bNtqwYYO2bNmijh07yhijPn36pKt8TZo0UcGCBdWxY0etW7dO4eHh6tKli80v+fLkyaPff/9dK1eu1KZNm/TKK6/YDLebmsSA7ooVK1LtID1+/LjWrl2b6isfJOnbb7/VtGnTtG/fPq1du1YXLlywriUhISH65JNPNHHiRO3du1fLli1T8+bNJUne3t4KCQnRiBEjNGnSJO3du1cLFizQihUrFBQUpBdeeEF9+/bVnDlztHfvXn322Wf64osvNHr06HRtpyT16dNHn3zyiaZOnapdu3YpJCREf/31l5WeK1cuOTs7a8qUKdq7d6/eeuutdD/M3rBhQ2XJkkVvvfWWOnTokGIgNbVrbaLXXntNy5cv17Zt29SxY0fdvHnTOl5CQkL0/fffa9SoUdq9e7d++eUXtWvXzhoe8362Gx4l96N/5H7Uc9O695YvX15Vq1ZVnz59tGrVKm3ZskXPP/+8zYgrnp6eqlatmiZOnKgLFy4oMjLSrgzvvPOOlixZopCQEO3YsUNhYWH66quv5OPjIy8vL3333Xfat2+fPvvsM7sATsGCBa1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      "text/plain": [
       "<Figure size 2000x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relationship_columns = ['Type_Income', 'EDUCATION', 'GENDER', 'Is_Retired']\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "\n",
    "for i, column in enumerate(relationship_columns, 1):\n",
    "    plt.subplot(2, 2, i)\n",
    "    sns.countplot(data=data, x=column, hue='label', palette=\"Blues_d\", order=data[column].value_counts().index)\n",
    "    plt.title(f'{column}和申请结果的关系', fontsize=15)\n",
    "    plt.xlabel(column, fontsize=12)\n",
    "    plt.ylabel('频次', fontsize=12)\n",
    "    plt.xticks(rotation=0)\n",
    "    plt.legend(title='申请结果')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32795a5e",
   "metadata": {},
   "source": [
    "## 用户画像"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a19dd69",
   "metadata": {},
   "source": [
    "### 1.通过申请的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1b5c2f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Gender Distribution': GENDER\n",
      "F    0.640932\n",
      "M    0.359068\n",
      "Name: proportion, dtype: float64, 'Average Annual Income': 190049.14238892935, 'Education Distribution': EDUCATION\n",
      "Secondary / secondary special    0.671522\n",
      "Higher education                 0.270211\n",
      "Incomplete higher                0.045885\n",
      "Lower secondary                  0.010925\n",
      "Academic degree                  0.001457\n",
      "Name: proportion, dtype: float64, 'Car Ownership Distribution': Car_Owner\n",
      "N    0.594319\n",
      "Y    0.405681\n",
      "Name: proportion, dtype: float64, 'Property Ownership Distribution': Propert_Owner\n",
      "Y    0.655499\n",
      "N    0.344501\n",
      "Name: proportion, dtype: float64, 'Average Age': 43.2367079388201, 'Occupation Distribution': Type_Occupation\n",
      "Laborers                 0.174800\n",
      "Pensioner                0.164603\n",
      "unknown                  0.149308\n",
      "Core staff               0.109978\n",
      "Managers                 0.088857\n",
      "Sales staff              0.081573\n",
      "Drivers                  0.056082\n",
      "High skill tech staff    0.042243\n",
      "Medicine staff           0.034232\n",
      "Accountants              0.028405\n",
      "Cleaning staff           0.014567\n",
      "Security staff           0.012382\n",
      "Cooking staff            0.012382\n",
      "Private service staff    0.012382\n",
      "Secretaries              0.006555\n",
      "Low-skill Laborers       0.005098\n",
      "Waiters/barmen staff     0.002913\n",
      "HR staff                 0.002185\n",
      "Realty agents            0.001457\n",
      "Name: proportion, dtype: float64, 'Marital Status Distribution': Marital_status\n",
      "Married                 0.680991\n",
      "Single / not married    0.139840\n",
      "Civil marriage          0.070648\n",
      "Separated               0.059723\n",
      "Widow                   0.048798\n",
      "Name: proportion, dtype: float64}\n"
     ]
    }
   ],
   "source": [
    "approved_data = data[data['label'] == 0] #通过申请的用户数据\n",
    "\n",
    "approved_profile = {\n",
    "    'Gender Distribution': approved_data['GENDER'].value_counts(normalize=True),\n",
    "    'Average Annual Income': approved_data['Annual_income'].mean(),\n",
    "    'Education Distribution': approved_data['EDUCATION'].value_counts(normalize=True),\n",
    "    'Car Ownership Distribution': approved_data['Car_Owner'].value_counts(normalize=True),\n",
    "    'Property Ownership Distribution': approved_data['Propert_Owner'].value_counts(normalize=True),\n",
    "    'Average Age': approved_data['Age'].mean(),\n",
    "    'Occupation Distribution': approved_data['Type_Occupation'].value_counts(normalize=True),\n",
    "    'Marital Status Distribution': approved_data['Marital_status'].value_counts(normalize=True)\n",
    "}\n",
    "print(approved_profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89575266",
   "metadata": {},
   "source": [
    "### 2.未通过申请的用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "56bde3d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Gender Distribution': GENDER\n",
      "F    0.571429\n",
      "M    0.428571\n",
      "Name: proportion, dtype: float64, 'Average Annual Income': 198720.0, 'Education Distribution': EDUCATION\n",
      "Secondary / secondary special    0.622857\n",
      "Higher education                 0.314286\n",
      "Lower secondary                  0.034286\n",
      "Incomplete higher                0.028571\n",
      "Name: proportion, dtype: float64, 'Car Ownership Distribution': Car_Owner\n",
      "N    0.617143\n",
      "Y    0.382857\n",
      "Name: proportion, dtype: float64, 'Property Ownership Distribution': Propert_Owner\n",
      "Y    0.628571\n",
      "N    0.371429\n",
      "Name: proportion, dtype: float64, 'Average Age': 44.85142857142857, 'Occupation Distribution': Type_Occupation\n",
      "Pensioner                0.200000\n",
      "Laborers                 0.160000\n",
      "Core staff               0.131429\n",
      "unknown                  0.125714\n",
      "Managers                 0.080000\n",
      "Sales staff              0.057143\n",
      "Drivers                  0.051429\n",
      "Security staff           0.045714\n",
      "High skill tech staff    0.040000\n",
      "Accountants              0.028571\n",
      "Cooking staff            0.022857\n",
      "Medicine staff           0.017143\n",
      "Cleaning staff           0.011429\n",
      "Low-skill Laborers       0.011429\n",
      "IT staff                 0.011429\n",
      "Waiters/barmen staff     0.005714\n",
      "Name: proportion, dtype: float64, 'Marital Status Distribution': Marital_status\n",
      "Married                 0.651429\n",
      "Single / not married    0.200000\n",
      "Separated               0.080000\n",
      "Widow                   0.045714\n",
      "Civil marriage          0.022857\n",
      "Name: proportion, dtype: float64}\n"
     ]
    }
   ],
   "source": [
    "rejected_data = data[data['label'] == 1] # 申请未通过的用户数据\n",
    "\n",
    "rejected_profile = {\n",
    "    'Gender Distribution': rejected_data['GENDER'].value_counts(normalize=True),\n",
    "    'Average Annual Income': rejected_data['Annual_income'].mean(),\n",
    "    'Education Distribution': rejected_data['EDUCATION'].value_counts(normalize=True),\n",
    "    'Car Ownership Distribution': rejected_data['Car_Owner'].value_counts(normalize=True),\n",
    "    'Property Ownership Distribution': rejected_data['Propert_Owner'].value_counts(normalize=True),\n",
    "    'Average Age': rejected_data['Age'].mean(),\n",
    "    'Occupation Distribution': rejected_data['Type_Occupation'].value_counts(normalize=True),\n",
    "    'Marital Status Distribution': rejected_data['Marital_status'].value_counts(normalize=True)\n",
    "}\n",
    "print(rejected_profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07332df2",
   "metadata": {},
   "source": [
    "### 3.对比分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb54903d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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VOvPMM5t9zj/+8Y/64x//KEk6+eSTZYxpVGyNGjWqWefr1auX+vTpoxtvvFETJ07UxIkTm/X6k08+We+++66GDx++UbFYVlama665Rq7rasKECerbt+8mz7G5419++WXDmDBp/c8tIyNjs3c/S9riXc1vvfWW3nrrra19SzrqqKMazrNhBF92drZee+21rb52S++9ww47SJIWL1680eNVVVVauHBhw9fl5eXyPK/RsVWrVjV6zfHHH6/dd99dxcXFG+0nd8YZZ+j111/XpZdeqnHjxik/P7/ZmXfZZRd9/vnnqq6u3uqe5gAAAADQEqirqas3WLJkicaPH7/F52yom99//33Nmzdvi8898sgjVVBQoB122EGO42j58uUbPaesrKxRXV5dXa1wONzo2Nq1axu95rzzztMxxxyjn3/+Wffcc0+jxy688EJ9+umnuuyyy/Tyyy9vccT+pmRlZWmHHXbY7BZsAIDkQFMcABBzevTooaOPPnqj43vssYdee+21Zq0e35T6+vpNjlVrjvPPP1+nn366vvrqq4a9yJrjsMMOU//+/fXoo4/Kdd2G467r6qqrrtKKFSskSbvuuquOOOKIRq/dMDbt16PIpPVF+9SpUzd6r5KSkq02dx977LGN9l6TpAsuuEAHHnjgJsfnvfXWW3r77be3eN4NpkyZonPPPbdJz5WkmTNnKjU1Vccff7yOP/54SZu+WPHpp5/q008/3ej4pv7+bNC1a1d17dq14Wf8a126dNG9996riy66SNdcc42efvpp2bbd5NzS//YYLykpoSkOAAAAwBfU1dTVG+rqZcuW6YknnmjSaz7++OOG0fKb06NHD11wwQW64IILtGLFCh166KEbPWf06NGNtjXbYEu1eq9evdSrVy8FAoGNHttll100bNgw3XHHHbrjjjsa7WHeVAUFBVq4cKFc193kewAAEh9NcQBAzDn99NM3+1heXp7efPPNRsfC4bAkaeHChXr99dcbPda/f3/l5ubqvPPOazi2YsUKhUKhTa7ifuuttzZZxG5KWlqaDj74YM2dO7fh2PPPP6+7775bo0eP1qBBgzZ6zRNPPKEHH3xQGRkZDcd+XYzdeeedmjJlis4//3w988wz6tu3ry688MJG59iwv9hvj1dXV2+yeC8rK9vianZJ2m+//TbbwO3YsaMOOOCAjY7PmDFji+fclJtvvllDhgzZ7OMjR47UG2+80eTzHXfccbr//vsbvr7//vs1ZsyYRj+HWbNm6aSTTmryOTdcrBg1apQee+wx/f3vf2/yayU1XCgpKytr0n54AAAAABBt1NXU1RsccMABjX6+v/XJJ5/o73//uzzPk2VZeuihh7Z413tTXHzxxbrqqqsavr7qqqu0dOnSRn+3PvjgA11xxRVNPudpp52mSZMmady4cdprr7104oknNitTfn6+jDEqLy/f5P7rAIDER1McABBX5syZo2HDhm3yscmTJ2vy5MmNjl122WUaOnSodtlll4Zjy5YtU9u2bRsdmz17thYvXrzdq4U3jBzb3B5XtbW1ktSoeN/g0Ucf1X//+1/tv//+uvbaa/XMM89sV5YNYmkVdPv27bd4ISEnJ6dZ59uW8elNcd1116moqEiHH354s1+74WfteV6zXwsAAAAALY26uvkSta6eNWuWrr32Wu2xxx6aNm2aBg0apBtuuEFdunTRrrvuus0Zt2V8elPceeeduv3227e4t/vmUKsDAGiKAwDiygEHHKDp06c3OlZbW6shQ4botNNO07XXXtvosVAopJSUlIa7icvKyvT222/ruOOO0+WXX97wvNtuu03Lli3b7iJ31qxZysvLU8eOHTf5eF1dnaSNi3fP8zRp0iT17NlTDzzwQLNHdm9JXl6eSktLt/gcz/PkOM5mH9/UY9tSSDZnFXhTbMv49KZITU3ViBEjtum1JSUlktb/3AEAAAAg1lBXN18i1tUzZ87UhRdeqMGDB+vcc8/VOeecozPOOEPvv/++LrzwQo0cOXKbG+PbMj69KQoKCvTwww9v02s31OrbO/YfABC/aIoDAOJKIBDY7DiyYDC41T2cf/75Z0nSjjvu2Oh4OBxWamrqdmUrKirSjBkzdNhhh232OVVVVbIsa6Pi3bZtPfbYY6qoqGj23dJbk5+fv9W7pffaa6/NPvbqq6/q1VdfjUqWeBifvjlr165VSUnJVvfe23ChZGv7zQEAAACAH6irmy/R6uqvvvpKl1xyiXbccUc98MAD+vHHHxseu/vuu3X22WfrrLPO0kMPPaQDDzyw2RlbYnz65mz4+7i1kf2lpaXKzs5WKBTa7vcEAMQnmuIAgKQyduxYBYNBDR48uNHxysrKRgV1amrqJkexbckzzzwj13V17LHHbvY5lZWVSk9Pl2VZGz3Wtm1btW3btlnv2RR9+vTR3LlztXz58s3ucX3TTTdtsoC8/fbbtddee23ye/rss8/02WefbfX9q6qqmp15e214z/T09IbifmsXdn5r3bp1OuSQQ5SZmamMjAytW7dOkUhkq03xOXPmqGPHjlG/CAMAAAAAsYC6On7ramOMnnrqKY0YMUIDBgzQM888o6ysrEbPyc7O1nPPPafzzjtPF198sc455xxdeeWV273gQVq/5dmGyQM//fSTpObX6jNnztRZZ52lrKwspaena82aNZK0xVq9rq5OixYt0m677bbt4QEAcY+mOAAgacydO1cffPCBDj74YLVv377RY+Xl5Y0KwTvvvFN33nlnk889efJkvfDCC+rbt+8WV7SvXLlSBQUFzQ+/CZFIRMYYpaSkKBwO64cffpC08Qi5wYMH6+2339bUqVM3Kt779u2r4447TkOHDt1s8d6rVy8NHTp0o8fatGmz2VXWNTU1kqS33npL9957b8PPsjk/018bN26cpPV3DTTFxx9/rBtvvLHh6w4dOjQqkD3P06OPPipJmj9//ibP0aZNG6Wnp6ukpETr1q1Tenq6DjjgAF144YWbfd9ly5Zp9erVOu6445qUEwAAAADiCXV1/NbVhYWFuummm/Tll1/qgAMO0IMPPrhRQ3yD3NxcPf/887r++uv17LPP6tNPP9WwYcM2umt8w53fTa3VR40apUceeaTh65133rnRlLW6ujqNHDlSkjRv3rxN/j3YcccdFQ6HVVxcLGOMsrKydMQRR+jkk0/e7Pt+//33ikQi2nvvvZuUEwCQmGiKAwCSQmVlZcNeZ3/7298aPVZfX68FCxZowIABm319RUWFJG1yb7RPPvlE11xzjVJSUnTfffdtdt+y+fPn6+eff9ZBBx20jd9FY/PmzdOJJ56oUCgkY4wcx1G/fv02WmX9u9/9TpZl6YsvvthojPgRRxyhI444Ypvef3OvnTZtmkpLSzVhwgRNnDhRp556asNjI0aM0FFHHbXZc952220aPXp0oxX/L7zwgu6++2717NlT77//vnbccUf97W9/01//+teN9gI777zzdPLJJys9PV3Dhg1TOBxWRkaGDj/88IZV7Z7nadiwYXrjjTc0aNAgjR07Vqmpqbr66qs3uiDw9ddfN+tnMmnSJEna4ig7AAAAAIhH1NXxWVdHIhG98MILeuyxx1RTU6MLLrhAV1999Vb3XM/Oztbjjz+up59+Wg899JAuvPBC7bzzzrr++uu177776l//+peee+459ezZU88995y6dOmioUOH6tprr1WfPn0anevGG2+U4zgKh8PKzMxUOBxWbm5uoz3G6+vr9be//U1TpkzRoEGD9Mgjj8gYowsuuKDRYoOsrKyGu8yb6osvvpBErQ4AyY6mOAAgbkyePFmLFy/e6HgkEpG0fh+pl156aaPH+/fvr9tuu01LlizR9ddfL8/zdMkll6hNmzYKhUKaPn26SkpKdPDBBze85umnn1ZZWZkyMzNVU1Oj0aNHS5K6dOnS8JyVK1dqxIgRevPNN5WRkaEnnnhCffv2lSR9/vnneuWVV1RQUKCMjAzV1NTo448/luM4OuOMM5r0/WZkZGxxPNlOO+2k008/XZ7nyRij/Pz8RoXyBl27dtXBBx+sCRMmaNWqVercuXOT3n97BAIBHXroobruuuvUrVs3TZkyZZPPe/bZZzVmzBilp6fLGKO5c+eqe/fuDQXv+PHjddddd2nPPffUs88+qxEjRuixxx7Ta6+9pt///vfq2LGjZs+erUAgoGAwqEAgoEAgIGNMw3uUl5dr9OjRikQi2nnnnVVYWKg33nhDxx57rO6//3795z//0SOPPKLx48dryJAh6tatm7KyshQKhRrOGwwGZVmWwuGw6uvrVVdXp/r6etXX1+uoo47S3nvvLWOMRo0apTZt2jQq7AEAAAAgVlBXN5YMdfXnn3+u++67T506ddLDDz+s/fffv8kZLMvSBRdcoEMOOUR33nmnpk6dqtTUVD366KN67rnndNRRR+n+++/XtddeqzvuuENPP/20hgwZotWrV2vq1KkNNfqGP57nNZx33bp1evbZZxWJRDRkyBB9+umnmjJlii666CJdddVVuu222/Twww/rxRdf1JAhQ9SlSxelp6c31Ogb6nVJm6zVhw4dqp122kk1NTUaN26c+vbtu9HIfwBAcqEpDgCIG+PHj9fbb7+92ce/+eYbffPNNxsdP+ecczR//nxddNFFOu+881RcXKyffvpJtbW1qq2tVX5+vv761782GmU2Z84cvfPOOxudp1OnTg2Pn3TSSXIcR3vssYf+9a9/qUePHg3PzcvL09dff61IJCLHcRQIBNStWzfdcsst2m+//Zr0/X7//fdbfDwlJUXDhw9v0rnOP/98TZw4US+++GKjseItYa+99tKHH3642X3Wfm2HHXZQIBBQJBJRIBDQAQccoEsuuaTh8f79+2uHHXbQ448/rrS0NN1www064IADNGbMGH3//fcqLi5WXV2dHMdpUraHHnpIhxxyiD7++GPdfffdsixLl1xyiQ488EC9+uqrmj59ur799ltVV1fLcZxGzfVNCQQCDeP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      "text/plain": [
       "<Figure size 2000x2000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(20, 20))\n",
    "plt.rcParams['font.family'] = 'Microsoft YaHei' \n",
    "\n",
    "#性别\n",
    "ax1 = plt.subplot(4, 2, 1)\n",
    "approved_profile['Gender Distribution'].plot(kind='pie', ax=ax1, autopct='%1.1f%%',colors=['#de3163', '#87cefa'])\n",
    "ax1.set_title('性别分布(申请通过)', fontsize=15)\n",
    "ax1.set_ylabel('')\n",
    "\n",
    "ax2 = plt.subplot(4, 2, 2)\n",
    "rejected_profile['Gender Distribution'].plot(kind='pie', ax=ax2, autopct='%1.1f%%',colors=['#de3163', '#87cefa'])\n",
    "ax2.set_title('性别分布(申请未通过)', fontsize=15)\n",
    "ax2.set_ylabel('')\n",
    "\n",
    "# 绘制条形图\n",
    "def bar_plot_with_annotation(ax, series, title, color):\n",
    "    sns.barplot(x=series.index, y=series.values, ax=ax, color=color)\n",
    "    ax.set_title(title, fontsize=15)\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_xlabel('')\n",
    "    for p in ax.patches:\n",
    "        ax.annotate(f'{p.get_height()*100:.2f}%', (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                    ha='center', va='center', fontsize=11, color='black', xytext=(0, 10),\n",
    "                    textcoords='offset points')\n",
    "    ax.set_ylim(0, 1)\n",
    "    \n",
    "    \n",
    "#教育背景\n",
    "ax3 = plt.subplot(4, 2, 3)\n",
    "bar_plot_with_annotation(ax3, approved_profile['Education Distribution'], '教育背景(申请通过)', '#66cdaa')\n",
    "\n",
    "ax4 = plt.subplot(4, 2, 4)\n",
    "bar_plot_with_annotation(ax4, rejected_profile['Education Distribution'], '教育背景(申请未通过)', '#a9a9a9')\n",
    "\n",
    "#是否有车\n",
    "ax5 = plt.subplot(4, 2, 5)\n",
    "bar_plot_with_annotation(ax5, approved_profile['Car Ownership Distribution'], '是否有车(申请通过)', '#66cdaa')\n",
    "\n",
    "ax6 = plt.subplot(4, 2, 6)\n",
    "bar_plot_with_annotation(ax6, rejected_profile['Car Ownership Distribution'], '是否有车(申请未通过)', '#a9a9a9')\n",
    "\n",
    "#是否有房\n",
    "ax7 = plt.subplot(4, 2, 7)\n",
    "bar_plot_with_annotation(ax7, approved_profile['Property Ownership Distribution'], '是否有房(申请通过)', '#66cdaa')\n",
    "\n",
    "ax8 = plt.subplot(4, 2, 8)\n",
    "bar_plot_with_annotation(ax8, rejected_profile['Property Ownership Distribution'], '是否有房(申请未通过)', '#a9a9a9')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96066b1",
   "metadata": {},
   "source": [
    "从上图可以看出：\n",
    "* 女性申请者相较于男性申请者更加容易通过\n",
    "* 有房、有车的申请者相较于无房、无车的申请者更加容易通过\n",
    "* 不过差距不大\n",
    "* 另外一个有意思的点是，Academic degree教育背景的申请者全部通过，这可能与样本资料的限制有关"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c3fc68f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "#年收入\n",
    "ax1 = plt.subplot(3, 2, 1)\n",
    "sns.distplot(approved_data['Annual_income'], ax=ax1, color='#66cdaa', bins=30, kde=True)\n",
    "ax1.set_title('年收入(申请通过)', fontsize=15)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('')\n",
    "\n",
    "ax2 = plt.subplot(3, 2, 2)\n",
    "sns.distplot(rejected_data['Annual_income'], ax=ax2, color='#a9a9a9', bins=30, kde=True)\n",
    "ax2.set_title('年收入(申请未通过)', fontsize=15)\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('')\n",
    "\n",
    "#工作类型，由于涉及工作类型较多，取前8进行可视化\n",
    "ax3 = plt.subplot(3, 2, 3)\n",
    "top_occupations_approved = approved_profile['Occupation Distribution'].head(8)\n",
    "bar_plot_with_annotation(ax3, top_occupations_approved, '工作类型(申请通过)', '#66cdaa')\n",
    "\n",
    "ax4 = plt.subplot(3, 2, 4)\n",
    "top_occupations_rejected = rejected_profile['Occupation Distribution'].head(8)\n",
    "bar_plot_with_annotation(ax4, top_occupations_rejected, '工作类型(申请未通过)', '#a9a9a9')\n",
    "\n",
    "#婚姻状况\n",
    "ax5 = plt.subplot(3, 2, 5)\n",
    "bar_plot_with_annotation(ax5, approved_profile['Marital Status Distribution'], '婚姻状况(申请通过)', '#66cdaa')\n",
    "\n",
    "ax6 = plt.subplot(3, 2, 6)\n",
    "bar_plot_with_annotation(ax6, rejected_profile['Marital Status Distribution'], '婚姻状况(申请未通过)', '#a9a9a9')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b30b2e72",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecfa453",
   "metadata": {},
   "source": [
    "### 1.数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7798de17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除Ind_ID\n",
    "data.drop(columns=['Ind_ID'],axis=1, inplace=True)\n",
    "\n",
    "# 将所有的Y替换成1，所有的N替换成0\n",
    "data.replace({'Y': 1, 'N': 0}, inplace=True)\n",
    "\n",
    "# 将M(男)替换成0，将F(女)替换成1\n",
    "data.replace({'M': 0, 'F': 1}, inplace=True)\n",
    "\n",
    "# 将教育情况列改为有序编码\n",
    "education_mapping = {\n",
    "    'Lower secondary': 0,\n",
    "    'Secondary / secondary special': 1,\n",
    "    'Incomplete higher': 2,\n",
    "    'Higher education': 3,\n",
    "    'Academic degree': 4\n",
    "}\n",
    "data['EDUCATION'] = data['EDUCATION'].map(education_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "523bf34b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对分类数据独热编码:\n",
    "categorical_cols = data.select_dtypes(include=['object']).columns\n",
    "new_data = pd.get_dummies(data, columns=categorical_cols)\n",
    "\n",
    "#对连续变量进行标准化\n",
    "numerical_cols = ['Annual_income', 'Age', 'Employed_years', 'Family_Members', 'CHILDREN','EDUCATION']\n",
    "scaler = StandardScaler() \n",
    "new_data[numerical_cols] = scaler.fit_transform(new_data[numerical_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1dc7da83",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = new_data.drop('label', axis=1)\n",
    "y = new_data['label']\n",
    "\n",
    "#划分训练集与测试集\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1017, stratify=y) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d276e02",
   "metadata": {},
   "source": [
    "由于之前已经发现，样本中类别不平衡，label=0的样本远多于label=1的样本，又因为样本数量较少，因此考虑**过采样**方法使样本平衡"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7e635fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_min= x_train[y_train == 1]\n",
    "y_min = y_train[y_train == 1]\n",
    "x_maj = x_train[y_train == 0]\n",
    "y_maj = y_train[y_train == 0]\n",
    "\n",
    "x_min_resampled = resample(x_min, replace=True, n_samples=len(x_maj), random_state=1017)\n",
    "y_min_resampled = resample(y_min, replace=True, n_samples=len(y_maj), random_state=1017)\n",
    "x_train = pd.concat([x_maj, x_min_resampled])\n",
    "y_train = pd.concat([y_maj, y_min_resampled])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f4afe47",
   "metadata": {},
   "source": [
    "### 2.逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bef395e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=1017)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=1017)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=1017)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression(random_state=1017)\n",
    "logreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "be08979d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.68      0.77       412\n",
      "           1       0.14      0.42      0.21        53\n",
      "\n",
      "    accuracy                           0.65       465\n",
      "   macro avg       0.52      0.55      0.49       465\n",
      "weighted avg       0.81      0.65      0.71       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(x_test)\n",
    "result_rep = classification_report(y_test, y_pred)\n",
    "print(result_rep)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fab32ca",
   "metadata": {},
   "source": [
    "逻辑回归模型评分如下:  \n",
    "1.精确度: 对于类别0，精确度为0.90，对于类别1，精确度为0.14  \n",
    "2.召回率: 对于类别0，召回率为0.68，对于类别1，召回率为0.42  \n",
    "3.F1得分: 对于类别0，F1得分为0.77，对于类别1，F1得分为0.21  \n",
    "4.总体准确率为0.65，模型效果一般"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0f5d6d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制混淆矩阵\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('逻辑回归结果的混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695d630a",
   "metadata": {},
   "source": [
    "### 3.支持向量机(SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a2679797",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(probability=True, random_state=1017)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(probability=True, random_state=1017)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(probability=True, random_state=1017)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf = SVC(kernel='rbf', probability=True, random_state=1017)\n",
    "svm_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "38cd504e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.80      0.85       412\n",
      "           1       0.21      0.42      0.28        53\n",
      "\n",
      "    accuracy                           0.76       465\n",
      "   macro avg       0.56      0.61      0.57       465\n",
      "weighted avg       0.83      0.76      0.79       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_svm = svm_clf.predict(x_test)\n",
    "rep_svm = classification_report(y_test, y_pred_svm)\n",
    "print(rep_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed32baa2",
   "metadata": {},
   "source": [
    "支持向量机模型评分如下:  \n",
    "1.精确度: 对于类别0，精确度为0.91，对于类别1，精确度为0.21  \n",
    "2.召回率: 对于类别0，召回率为0.80，对于类别1，召回率为0.42  \n",
    "3.F1得分: 对于类别0，F1得分为0.85，对于类别1，F1得分为0.28  \n",
    "4.总体准确率为0.76，模型效果一般，但优于逻辑回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bf15e0cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_svm, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('支持向量机结果的混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3871807",
   "metadata": {},
   "source": [
    "### 4.决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6f0a9516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=1017)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=1017)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=1017)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf = tree.DecisionTreeClassifier(random_state=1017) \n",
    "tree_clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5e10a272",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.92      0.92       412\n",
      "           1       0.41      0.43      0.42        53\n",
      "\n",
      "    accuracy                           0.86       465\n",
      "   macro avg       0.67      0.68      0.67       465\n",
      "weighted avg       0.87      0.86      0.87       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_tree = tree_clf.predict(x_test)\n",
    "rep_tree = classification_report(y_test, y_pred_tree)\n",
    "print(rep_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d882e17",
   "metadata": {},
   "source": [
    "决策树模型评分如下:  \n",
    "1.精确度: 对于类别0，精确度为0.93，对于类别1，精确度为0.41  \n",
    "2.召回率: 对于类别0，召回率为0.92，对于类别1，召回率为0.43  \n",
    "3.F1得分: 对于类别0，F1得分为0.92，对于类别1，F1得分为0.42  \n",
    "4.总体准确率为0.86，模型效果良好，优于逻辑回归模型和支持向量机模型  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d6ffd00b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_tree = confusion_matrix(y_test, y_pred_tree)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_tree, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('决策树结果的混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65b7604",
   "metadata": {},
   "source": [
    "### 5.贝叶斯"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "620d32db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bys_clf = GaussianNB()\n",
    "bys_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f2dd43ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.07      0.13       412\n",
      "           1       0.12      0.94      0.21        53\n",
      "\n",
      "    accuracy                           0.17       465\n",
      "   macro avg       0.51      0.51      0.17       465\n",
      "weighted avg       0.81      0.17      0.14       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_bys = bys_clf.predict(x_test)\n",
    "rep_bys = classification_report(y_test, y_pred_bys)\n",
    "print(rep_bys)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d33a92",
   "metadata": {},
   "source": [
    "贝叶斯分类器评分如下:  \n",
    "1.精确度: 对于类别0，精确度为0.9，对于类别1，精确度为0.12  \n",
    "2.召回率: 对于类别0，召回率为0.07，对于类别1，召回率为0.94  \n",
    "3.F1得分: 对于类别0，F1得分为0.13，对于类别1，F1得分为0.21  \n",
    "4.总体准确率为0.17，模型效果极差  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3738dad4",
   "metadata": {},
   "source": [
    "### 6.神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eff5f61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(100, 50), random_state=1017)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(100, 50), random_state=1017)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(100, 50), random_state=1017)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nw_clf = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=200, random_state=1017)\n",
    "nw_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9e443134",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.95      0.94       412\n",
      "           1       0.51      0.42      0.46        53\n",
      "\n",
      "    accuracy                           0.89       465\n",
      "   macro avg       0.72      0.68      0.70       465\n",
      "weighted avg       0.88      0.89      0.88       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_nw = nw_clf.predict(x_test)\n",
    "rep_nw = classification_report(y_test, y_pred_nw)\n",
    "print(rep_nw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd767157",
   "metadata": {},
   "source": [
    "神经网络评分如下:  \n",
    "1.精确度: 对于类别0，精确度为0.93，对于类别1，精确度为0.51  \n",
    "2.召回率: 对于类别0，召回率为0.95，对于类别1，召回率为0.42  \n",
    "3.F1得分: 对于类别0，F1得分为0.94，对于类别1，F1得分为0.46  \n",
    "4.总体准确率为0.89，模型效果较好，目前为止最优  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b267bb43",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_nw = confusion_matrix(y_test, y_pred_nw)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_nw, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('神经网络结果的混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583c4d48",
   "metadata": {},
   "source": [
    "## stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "99211ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, ClassifierMixin, clone\n",
    "\n",
    "class StackingClassifier(BaseEstimator, ClassifierMixin, TransformerMixin):\n",
    "    def __init__(self, base_models, meta_model):\n",
    "        self.base_models = base_models\n",
    "        self.meta_model = meta_model\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        self.base_models_ = [clone(model) for model in self.base_models]\n",
    "        self.meta_model_ = clone(self.meta_model)\n",
    "        \n",
    "        # 训练基础模型\n",
    "        for model in self.base_models_:\n",
    "            model.fit(X, y)\n",
    "        \n",
    "        # 生成基础模型的预测结果作为元特征\n",
    "        meta_features = np.column_stack([\n",
    "            model.predict(X) for model in self.base_models_\n",
    "        ])\n",
    "        \n",
    "        # 训练元模型\n",
    "        self.meta_model_.fit(meta_features, y)\n",
    "        \n",
    "        return self\n",
    "    \n",
    "    def predict(self, X):\n",
    "        meta_features = np.column_stack([\n",
    "            model.predict(X) for model in self.base_models_\n",
    "        ])\n",
    "        return self.meta_model_.predict(meta_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c97b89e9",
   "metadata": {},
   "source": [
    "#### staking(基模型：逻辑回归+随机森林+SVM  元模型：逻辑回归)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "43d2a4f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9204301075268817\n"
     ]
    }
   ],
   "source": [
    "# 定义基础模型\n",
    "base_models = [\n",
    "    LogisticRegression(),\n",
    "    RandomForestClassifier(n_estimators=10, random_state=1017),\n",
    "    SVC()\n",
    "]\n",
    "\n",
    "# 定义元模型\n",
    "meta_model = LogisticRegression()\n",
    "\n",
    "# 定义Stacking模型\n",
    "stacking = StackingClassifier(base_models, meta_model)\n",
    "\n",
    "# 拟合数据\n",
    "stacking.fit(x_train, y_train)\n",
    "\n",
    "# 预测数据\n",
    "y_pred = stacking.predict(x_test)\n",
    "\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e3b32293",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       412\n",
      "           1       0.81      0.40      0.53        53\n",
      "\n",
      "    accuracy                           0.92       465\n",
      "   macro avg       0.87      0.69      0.74       465\n",
      "weighted avg       0.91      0.92      0.91       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rep_sta = classification_report(y_test, y_pred)\n",
    "print(rep_sta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a7bb7f",
   "metadata": {},
   "source": [
    "#### staking(基模型：神经网络+随机森林+SVM 元模型：逻辑回归)¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d4b68a76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9225806451612903\n"
     ]
    }
   ],
   "source": [
    "base_models = [\n",
    "    MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=200, random_state=1017),\n",
    "    RandomForestClassifier(n_estimators=10, random_state=1017),\n",
    "    SVC()\n",
    "]\n",
    "\n",
    "meta_model = LogisticRegression()\n",
    "\n",
    "stacking = StackingClassifier(base_models, meta_model)\n",
    "\n",
    "stacking.fit(x_train, y_train)\n",
    "\n",
    "y_pred = stacking.predict(x_test)\n",
    "\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "052b3a57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       412\n",
      "           1       0.84      0.40      0.54        53\n",
      "\n",
      "    accuracy                           0.92       465\n",
      "   macro avg       0.88      0.69      0.75       465\n",
      "weighted avg       0.92      0.92      0.91       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rep_sta = classification_report(y_test, y_pred)\n",
    "print(rep_sta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "682ec4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mTFFmZqY6d+4sSdqzZ0++9eau5e6771ZAQICOHDmi4OBgp48DcOehqMMd78q8JcmWsgwbNkzff/+9du7cKUn2FOjqx0CdPHlSFy9e1H333WcvLjIzM+13UV7RokULxcXFafPmzWrfvv01+zF58mSdPXtWEyZMkI+Pz3X3d0ZISIiWLVumb775xmGZkdWrV+fbb+jQoapXr57q1q2rOnXqyMfHR7t27ZJUeEJ133332b8+dOiQw7bs7GyNGzdOv/76q9LS0lS3bl0dPXpUsbGxatKkyU29rytzFiXbI7Wuno/29ddf6/Lly4qMjLS3vf322/rb3/4mSSpfvrweeeQRzZw5U5GRkVq9erUCAgIc1rHbv3+/unbtqj59+qhnz55KTk6WJG3atOm6Sd0VGzZs0D333KP+/fvr1Vdftc8zbNy4cb6lXSQVOJ/zivLly+vs2bP68MMPHdqv/AGxevVq+2dQqlQph+FYAHcOijrc8c6cOaORI0eqU6dOCggI0OnTp/X111/bb4q45557JEkLFy5UTk6Oqlatqvr166tcuXJasGCBfHx85OHhoSVLlsjHx8chGXvyySe1adMmPffcc9q3b5/CwsKUnp6urVu3av78+Q79KFWqlN5++209/vjjeu6551S2bFmHwqQoBg0apNWrV2vo0KF6+umn5efnp88//9zhxgDJ9hSF0NBQ+/dpaWnavXt3ka/71VdfaeLEiQoNDdXChQvVt29fhYeH64knntCwYcPUqVMnDR06tFgm9F+8eNFhOHnnzp2qU6eOwzB2zZo18w2Bjhs3zj7cunv3bg0dOlTffPONzp07p9OnT+u+++6TyWRyGNoNCQkptB9Xr4V35swZ/fe//9WIESPUs2dPrV27Vq+88orq16+vMmXKFLi8yvvvv28fgpZsf2zs3LlTFotFP//8s6pXr665c+c6HJOdnS1J+vDDD+037JjNZoo64A5FUYc7nq+vr/0/mFlZWapZs6b69Omjp59+WpLt2awjR47U0qVLNXr0aD3//PNq0qSJZs+erddee00vv/yy/Pz8NHz4cH355Zc6ceKEw7lXrFiht956Sxs2bNAHH3ygatWqFfrs1LJly+q9995T7969NWzYMC1YsMAhDbtRNWrU0OLFizV9+nS99dZbKleunNq3b6/JkyerZ8+e9nRn79692r9/vxITE3Xo0CGdOXNGDRs21L///e8but4333yjOXPmKCkpSWPHjs13w0RERIRWrVql8ePHq2XLlnr44YfVoUMHNW3a1N6Xa90oUZDffvvNPiyZl5enbdu22Z+RerU//vhDa9eutReSVy/D8uOPPyowMFClSpXSzp07VapUKYc5eFccOnSowPmREydOtH+dk5OjF154QZUrV9Zjjz0mk8mkyZMna9KkSYXegStJ9evXd/jew8NDkyZN0m+//aZHH31UXbt2zfd5XplTt3LlSubUAaCoA8qXL3/dlf6HDBmS7zmlERER+ZaueOihh+xzw66oXLnyNe8i/etTG6pUqaLPPvvsmvtMmzYt33UKa2/UqJE++OADh7Yrd8heKTKWLl2qs2fPqmnTpurSpYuaNm0qX19f+/DrtRIqSbpw4YL69++v5ORk9e7dW2+//XahRUbNmjW1YMEC7d69W++//74GDhyoHj162D+fa90o8b///U8rV65Ux44dVadOHR0+fFgHDx60z5P75ptvdPbsWbVq1UqSbb5dXl6e4uLi9M0336hs2bKaOHGiKlasqB07dqhBgwa6dOmSNmzYYH/k1o4dO/S3v/2twCc0eHh4qFSpUsrNzdXx48fl7e2t3Nxc7d+/356qvvXWW/ruu++0ePFi+1y/evXqafHixZozZ85159SNGDHCnirGx8df83MHgKtR1AFuLCcnJ99yH3l5eVq8eLFKly5tX3T37bffvuZ5PvvsswITqiuT/ytWrKhJkyapTp06qlSpklN9i4iIUEREhM6dOydfX197+7VulPDz89Nnn31mL3pLly6tjh072hOs/fv3q3z58vZ08+eff5bJZNKlS5f02muvqWXLlvL09FReXp5eeuklrV69Wh4eHqpfv74GDBggyTZ0er101GQyqWfPnvZ5lrVq1bJ/Fk888YQefPBBNW7cON9xffv2LTBFvFrFihW1c+dOp9fwA4ArTFar1VrSnQDgGvv379eLL76ohx9+WLVq1dK5c+e0fv16HTx4UMOHD9czzzxT0l108Ntvv6l8+fLXfCi91WrVpUuXZLFYVLZs2Xw3Lvz+++/2dfYk200ttWvXvqF+/HWZlJycHJ05c+amliYBAFejqAPc2O+//67p06dr7969Onv2rMqUKaPg4GD17ds33wK2AABjo6gDAABwA9de5h4AAACGQFEHAADgBijqAAAA3MBtt6SJd5OYku4CABdJ+2ZWSXcBgIt4lWBF4cra4eI+4/zeIqkDAABwA7ddUgcAAHBDTGRUEkkdAACAWyCpAwAAxmYylXQPbgskdQAAAMVs7dq1CgoK0rp16+xty5cvV+vWrdWoUSP1799fycnJDsfEx8erQ4cOCg0NVY8ePZSYmHhD16SoAwAAxmbycN2rCC5duqSZM2c6tG3atElTp07V8OHDtXz5cuXm5mrIkCHKy8uTJO3bt0+jRo1Sr169tHLlSvn7+2vQoEHKyspy+roUdQAAwNhMJte9imDOnDlq2LChQ9t7772nXr16qWvXrmrYsKEmT56so0ePas+ePZKkBQsWKDIyUlFRUQoODtaUKVOUmZmp+Ph4p69LUQcAAFAIi8WirKwsh5fFYil0/6SkJH344Yf6z3/+Y2/LyMjQkSNHFBkZaW+rV6+eqlWrpgMHDkiSEhISHLZXqFBBISEh9u3O4EYJAABgbC5c0iQuLk6zZjkuQBwTE6Nhw4bl2/fSpUsaPXq0hgwZopo1a9rbT506JUkKDAx02N/f31+pqalKT09XRkZGvu1+fn5KTU11uq8UdQAAAIUYPHiwoqOjHdrMZnOB+7766quqWLGinnjiCYf27OxsSZK3t7dDu5eXlywWyzW3p6WlOd1XijoAAGBsLlzSxGw2F1rEXW3lypX67LPPtHbtWnl4OCaHV47PyclxaLdYLPL29r7mdi8vL6f7SlEHAABwk959911duHBBbdq0cWgfP368/P39JUkpKSmqXbu2fVtKSorat2+vSpUqyWw2KyUlxeHYlJQUhYSEON0HijoAAGBst8FjwhYtWpQvaevYsaNGjhyptm3bKioqSrt27dL9998vSTp+/LhSU1MVEREhDw8PhYWFaefOnerevbskKTMzU4mJiRo4cKDTfaCoAwAAuElXJ3BXq1atmmrXrq3o6GjNmDFDwcHBCgwM1NSpU9WqVSsFBQVJkqKjoxUTE6NmzZopLCxMs2bNUt26ddWyZUun+0BRBwAAjM0Ajwnr16+fzp8/r4kTJ+rSpUtq06aNJkyYYN/eunVrjR8/XrNnz1ZGRobCw8MVFxcnT09Pp69hslqtVld0vqi8m8SUdBcAuEjaN7OuvxMAQ/IqwZjIO+J5l5374u5pLjt3cSOpAwAAxnYbzKm7HVDUAQAAYzPA8OutQGkLAADgBkjqAACAsTH8KomkDgAAwC2Q1AEAAGNjTp0kkjoAAAC3QFIHAACMjTl1kkjqAAAA3AJJHQAAMDaSOkkUdQAAwOg8uFFCYvgVAADALZDUAQAAY2P4VRJJHQAAgFsgqQMAAMbG4sOSSOoAAADcAkkdAAAwNubUSSKpAwAAcAskdQAAwNiYUyeJog4AABgdw6+SGH4FAABwCyR1AADA2Bh+lURSBwAA4BZI6gAAgLExp04SSR0AAIBbIKkDAADGxpw6SSR1AAAAboGkDgAAGBtz6iRR1AEAAKNj+FUSw68AAABugaQOAAAYG8OvkkjqAAAA3AJJHQAAMDaSOkkkdQAAAG6BpA4AABgbd79KIqkDAABwCyR1AADA2JhTJ4miDgAAGB3Dr5IYfgUAAHALJHUAAMDYGH6VRFIHAADgFkjqAACAsTGnThJJHQAAgFsgqQMAAIZmIqmTRFIHAADgFijqAACAoZlMJpe9bsTGjRvVqVMnNW7cWP/85z81a9YsWa1WSdLu3bsVFBTk8Bo8eLDD8fHx8erQoYNCQ0PVo0cPJSYm3tD1GX4FAADGdpuMvv788896+umnVa9ePe3fv1+TJk1S5cqV1adPH6Wnp6tGjRpaunSpfX9vb2/71/v27dOoUaM0evRohYeHa/bs2Ro0aJC2bNkiHx8fp65PUQcAAFAMYmJi7F8HBwfr888/165du+xFXdWqVVWnTp0Cj12wYIEiIyMVFRUlSZoyZYpatGih+Ph49ejRw6nrM/wKAAAM7XYZfv2ry5cvq2LFipKk9PR0VapUqdB9ExISFBkZaf++QoUKCgkJ0YEDB5y+HkkdAABAISwWiywWi0Ob2WyW2Wwu9Jjs7Gxt2LBBBw8e1OjRoyVJaWlp2rVrl8LCwuTn56c2bdpoyJAhKleunNLT05WRkaHAwECH8/j5+Sk1NdXpvlLUAQAAQ3PlkiZxcXGaNWuWQ1tMTIyGDRtW4P6hoaGyWCzy8fHRyy+/rODgYEnSY489pocfflhWq1X79+/XzJkzdfLkSc2cOVPZ2dmSHOfYSZKXl5fS0tKc7itFHQAAQCEGDx6s6Ohoh7ZrpXRr165VVlaWEhMT9corr+jo0aMaNWqUateubd+nYcOG8vHx0dixY3X+/Hn7+XJychzOZbFY5OXl5XRfKeoAAIChuTKpu95Q61/Vq1dPktS4cWN5eXnpxRdf1NChQ/MVZyEhIZKk06dPKyQkRGazWSkpKQ77pKSk2PdzBjdKAAAAuICnp6esVqsuX76cb9vBgwfl4eGhwMBAeXh4KCwsTDt37rRvz8zMVGJioiIiIpy+HkkdAAAwtNvhMWFZWVmaNGmSOnfurOrVqyspKUmxsbF66KGHVK5cOb355psKDQ1VYGCgDh48qNjYWPXq1ct+R2x0dLRiYmLUrFkzhYWFadasWapbt65atmzpdB8o6gAAgLGVfE0ns9ms3NxcjR07VpmZmfL391e/fv00YMAA+z4TJkxQVlaWatWqpaFDh+rxxx+3b2vdurXGjx+v2bNnKyMjQ+Hh4YqLi5Onp6fTfTBZrzy/4jbh3STm+jsBMKS0b2ZdfycAhuRVgjGRb5+l19+piNKXP379nW4TJHUAAMDQbofh19sBN0oAAAC4AZI6AABgaCR1NiR1AAAAboCkDgAAGBpJnQ1JHQAAgBsgqQMAAIZGUmdDUQcAAIyNmk4Sw68AAABugaQOAAAYGsOvNiR1AAAAboCkDgAAGBpJnQ1JHQAAgBsgqQMAAIZGUmdDUgcAAOAGSOoAAICxEdRJuomiLi0tTRaLRd7e3qpQoUJx9gkAAAA36IaKus2bN+uDDz7QwYMHZbFY7O3ly5dXeHi4nnrqKYWGhhZ7JwEAAArDnDobp4u6999/X7GxsWrfvr3Gjx8vPz8/eXt769KlS0pJSdHu3bvVt29fzZgxQw8++KAr+wwAAGBHUWfjdFG3cOFCTZw4UT179ixw+yOPPKIVK1ZQ1AEAAJQAp4u6rKys6w6tNm7cWKdOnbrpTgEAADiLpM7G6SVNmjdvrjfeeENpaWkFbs/KytKcOXP097//vdg6BwAAAOc4ndRNnDhRTz31lCIjIxUSEqKAgAB5e3srJydHKSkpOnTokCpWrKh58+a5sr8AAAAOSOpsnC7qatasqXXr1mn79u3as2ePkpOTde7cOZnNZtWpU0fdu3dX+/bt5eXl5cr+AgAAoAA3tKSJh4eH2rRpozZt2riqPwAAADeGoE4SjwkDAABwCzwmDAAAGBpz6mwo6gAAgKFR1Nkw/AoAAOAGnErqPvnkkxs6aadOnYrUGQAAgBtFUmfjVFE3evRop09oMpko6gAAAG4xp4q6pKQkV/cDAACgaAjqJDGnDgAAwC0U6e7XtLQ0zZw5U/v371dGRka+7du2bbvpjgEAADiDOXU2RUrqXnjhBW3btk3h4eFKTU1Vt27d1KhRI2VnZ6tLly7F3UcAAABcR5GKuoSEBMXGxmrs2LHy8/NT9+7d9eabb2rw4MH69ddfi7uPMLA+DzfXxX2z1LtjM3vbUz0f0Pefvqzzu2doU9ww3RVQxeGYUU88qB82TlLa129q+6JRah561y3uNYDi8Mm6tWocEqRPP1lX0l2BmzOZTC57GUmRijovLy+VKmUbub3rrrt09OhRSVJERIQ2btxYfL2DoZUxl9KEIQ85tHV/sIlee7aHJr27QW0GvKnSpTy16q3B9h+cfp3u1/jBHfXSO+sV2T9WP574TetmD5Wvj3dJvAUARXTp0iXNmTWzpLuBOwRFnU2Rirr7779f27dvt389d+5c7dy5U/Pnz1flypWLtYMwrnFPddC3h39xaHtuQFst+O9X+nDDN9r3fbKGTv5QIfX99f/urS9JujekjrbsOqKPNu3VoR9P67nXV6li+bKqX7taSbwFAEX03tw5CmnYsKS7AdxRilTUjRo1SsHBwZKkfv36qVSpUnryySe1detWjRkzplg7CGNqeI+/nur5gMbErra3+fp4q8nfail+5xF7248nflXK7+lq3qiuJGnN1n0Kb3y3GjUIUKlSHhrSu6WSfk7VwR9P3/L3AKBofvwhSR9/9KFGj/1PSXcFdwiSOpsi3f0aEBCggIAASZK3t7eWLl2q9PR0lStXzj4siztXGXMpLZwSpenz43X6twv29itz506cPuewf3LKeQVUryhJ2rH3qJau/1oJK8YpLy9P2X/mqFXUG8rJvXyrug/gJly6dEnjxo7WU4OHqEbNmiXdHeCOUqQKrLCbIf78809JUo0aNYreIxjea8/10LkLWXrng+0O7T5ly0iSLv5pcWjP/tOiMmbbP8U+DzfXoJ7/T4NfXqbvj6XoqZ7/T6vfflr/6PuazqZl3Zo3AKDIXp/+qipWrKh+/Z8o6a7gTmKsQM1lilTUtWzZ8pqR5Pfff1/kDsHYorpFqEvrxgrvPU1Wq9Vh2yVLriSpdGnHf3ZeZUrbC71XhnfR9PmbtWTd15Kkb4+c1L7/vqBnHvunJs759Ba8AwBFtXrVSm3b+pk+/u9aeXiwtj1wqxWpqFuyZInD93l5eTpx4oSWLl2qJ57gr7M72dgn26mKbzl9/+lEh/a4l/vqZMp5SVJgjUo6fuqsfVtgjUr675bvVL1yeflV89WBH/5v/lxenlWHfjythg0Cbs0bAFBk8957V+kXLqhj2zYO7S+9MF7vzZ2j9RviS6hncHdGm/vmKkUq6po3b56vLTw8XM2aNdMLL7ygRx999KY7BmPq+PQslS7l6dB2YM2LennWJ1r7+QFtnBujNuHB+vJb2zI49WtXV2DNStq+50elZWQr+6JFf7u7pj5P+L/nDf+tnp+++u6nW/o+ANy49+YvUm5ujkNb104dNWzESLV5sG0J9Qq4cxTrXQ1+fn5KSkq6/o5wW1cncFdLOZuh46fOauay7Zo0rLMO/nBKJ86c02vPdteG/x3S4Z/OSJIW/PcrjRvUQb+dz9TRX37VgB4PqH7taor+z6Jb+C4AFEWt2rULbK9arVqh24DiQFJnUyw3SlitVv3222+aP3++avODi2t496P/qWolH731n0flZS6tT784qH9PW2nf/p+31yrr4iVN/XdXVfYtp8M/nVG3YXNZ0gQAUKjbpabbuHGj3n33XZ08eVKVKlXSI488omeeecZedC5fvlzz58/X2bNnFRYWpilTpqhWrVr24+Pj4/XWW2/p1KlTatCggSZOnKiGN7Deo8n619nsTggODs5XFVutVvn5+emNN95Q06ZNb/SUdt5NYop8LIDbW9o3s0q6CwBcxKsEVzSr/9wml537p9gOTu87a9Ys1a1bV/Xq1dP+/fs1adIkvfDCC+rTp482bdqkMWPGaPLkyapfv75effVVZWRkaP369fLw8NC+ffvUr18/jR49WuHh4Zo9e7a+/fZbbdmyRT4+Pk5dv0hF3Z49exy+9/DwUOXKlVW7du2bXqeOog5wXxR1gPsqyaLuntGbXXbuo6+3L/KxgwYNktls1qxZs9StWzfde++9euGFFyRJx44dU8eOHbV48WKFh4crJiZGly9f1rvvvitJysjIUIsWLfTyyy+rR48eTl2vSPecnzlzRg0bNlTz5s3VvHlz3Xfffbr77rt19uxZ++PDAAAA7mSXL19WxYoVlZGRoSNHjigyMtK+rV69eqpWrZoOHDggSUpISHDYXqFCBYWEhNi3O6NIRd24ceN07ty5fO0XLlzQqFGjinJKAACAIjGZXPeyWCzKyspyeFkslmv2Jzs7WytXrtTBgwfVr18/nTp1SpIUGBjosJ+/v79SU1OVnp6ujIyMfNv9/PyUmprq9OfgdFiampqqVatWSbLNn1uyZIl8fX3t2y9fvqxdu3apatWqTl8cAADgdhYXF6dZsxynjsTExGjYsGEF7h8aGiqLxSIfHx+9/PLLCg4O1t69eyXZHq16NS8vL1ksFmVnZxe6PS0tzem+Ol3UVa5cWZs3b5bFYpHJZNK2bdsc5s+ZTCb5+flp+vTpTl8cAADgZrlySZPBgwcrOjraoc1sNhe6/9q1a5WVlaXExES98sorOnr0qB588EFJUk6O4zqOFotF3t7e9vMVtN3Ly8vpvjpd1JnNZn36qe0xTcHBwVq8eLHDbbgAAADuxmw2X7OI+6t69epJkho3biwvLy+9+OKL6tu3ryQpJSXFYem3lJQUtW/fXpUqVZLZbFZKSorDuVJSUhQSEuL0tYs0p27Dhg2qVKlSvvaLFy/mqzIBAABcyZVz6m6Gp6enrFarfHx8FBAQoF27dtm3HT9+XKmpqYqIiJCHh4fCwsK0c+dO+/bMzEwlJiYqIiLC6esVqagbPXq0fX7d1RYvXlzoGDMAAIAreHiYXPZyVlZWlsaMGaOvvvpKP/74o9avX6/Y2Fg99NBDKleunKKjo7VkyRJt2rRJhw4d0vjx49WqVSsFBQVJkqKjo7Vp0yZ99NFHSkpK0rhx41S3bl21bNnS6T4UaVWZn376SeHh4fnaIyMjtWDBgqKcEgAAwLDMZrNyc3M1duxYZWZmyt/fX/369dOAAQMkSf369dP58+c1ceJEXbp0SW3atNGECRPsx7du3Vrjx4/X7NmzlZGRofDwcMXFxcnT07OwS+ZTpKKuQoUKOnPmjIKDgx3az58/r8uXLxfllAAAAEVyOzwmzGw2a8aMGYVuN5lMGjFihEaMGFHoPn379rXPvyuKIg2/tm7dWq+//rqSk5PtbefOndObb75ZYIIHAAAA1ypSUjd69GhFR0erY8eOql+/vjw9PXXs2DFVqVJFb731VjF3EQAAoHCuXNLESIpU1JUvX14rVqxQfHy89u3bJ6vVql69eqlz584qU6ZMcfcRAAAA11Hkx+96enqqY8eO6tixo73twoULWrVq1U2NBwMAANwIgjqbIhd1V1gsFm3fvl3r1q3Tjh07JImiDgAA4BYrclG3d+9erV+/Xps3b1ZmZqbq1q2rkSNHqkuXLsXZPwAAgGtiTp3NDRV1J06c0Lp167R+/XqdPn1aAQEB6tGjhxYtWqTXX3/9hh5lAQAAUBwo6mycLuoeffRRHTp0SH5+fmrXrp06dOigRo0aKTc3VwsXLnRlHwEAAHAdThd1SUlJ8vX1VatWrdS6dWs1atRIEtUxAAAoWZQiNk4Xdbt27dKmTZu0fv16LV++XFWqVFG7du304IMPUtgBAACUMKefKOHj46OePXtq6dKl2rp1q/r27avdu3crOjpakrR8+XIlJSW5rKMAAAAFMZlMLnsZSZEeExYQEKAhQ4Zo48aN+vjjj9W3b19t375d3bp1U6dOnYq7jwAAALiOm16nrlGjRmrUqJHGjRunHTt2aP369cXRLwAAAKcYLFBzmZsu6q7w9PRUq1at1KpVq+I6JQAAAJxUbEUdAABASTDa3DdXoagDAACGRk1nU6QbJQAAAHB7IakDAACGxvCrDUkdAACAGyCpAwAAhkZQZ0NSBwAA4AZI6gAAgKExp86GpA4AAMANkNQBAABDI6izoagDAACGxvCrDcOvAAAAboCkDgAAGBpBnQ1JHQAAgBsgqQMAAIbGnDobkjoAAAA3QFIHAAAMjaDOhqQOAADADZDUAQAAQ2NOnQ1JHQAAgBsgqQMAAIZGUmdDUQcAAAyNms6G4VcAAAA3QFIHAAAMjeFXG5I6AAAAN0BSBwAADI2gzoakDgAAwA2Q1AEAAENjTp0NSR0AAIAbIKkDAACGRlBnQ1EHAAAMzYOqThLDrwAAAMUiKSlJAwYMUOPGjdWiRQuNGzdOaWlpkqTdu3crKCjI4TV48GCH4+Pj49WhQweFhoaqR48eSkxMvKHrU9QBAABDM5lc97oRkydPVvPmzbVixQpNmTJFe/bs0dixYyVJ6enpqlGjhrZs2WJ/TZ482X7svn37NGrUKPXq1UsrV66Uv7+/Bg0apKysLKevz/ArAABAMYiNjZWfn58kKTg4WJmZmRozZowuXryo9PR0Va1aVXXq1Cnw2AULFigyMlJRUVGSpClTpqhFixaKj49Xjx49nLo+SR0AADA0k8nksteNuFLQXVGmTBnl5eVJsiV1lSpVKvTYhIQERUZG2r+vUKGCQkJCdODAAaevT1IHAABQCIvFIovF4tBmNptlNpuveZzVatWqVavUuHFjeXt7Ky0tTbt27VJYWJj8/PzUpk0bDRkyROXKlVN6eroyMjIUGBjocA4/Pz+lpqY63VeKOgAAYGgeLrz5NS4uTrNmzXJoi4mJ0bBhwwo9JicnRxMnTlRCQoKWLVsmSXrsscf08MMPy2q1av/+/Zo5c6ZOnjypmTNnKjs7W5Lk7e3tcB4vLy/7jRbOoKgDAAAoxODBgxUdHe3Qdq2ULjU1VSNHjtSpU6e0ePFihYaGSpJq165t36dhw4by8fHR2LFjdf78efv5cnJyHM5lsVjk5eXldF8p6gAAgKG58jFhzgy1XnH8+HH1799fDRo00Lp161SlSpVC9w0JCZEknT59WiEhITKbzUpJSXHYJyUlxb6fM7hRAgAAGNrtsqTJs88+qyZNmmjevHnXLOgk6eDBg/Lw8FBgYKA8PDwUFhamnTt32rdnZmYqMTFRERERTl+fpA4AAOAmHT9+XIcPH9agQYOUnJzssK1y5cqaP3++QkNDFRgYqIMHDyo2Nla9evWy3xEbHR2tmJgYNWvWTGFhYZo1a5bq1q2rli1bOt0HijoAAGBoJpX8Y8LOnj0rSRoxYkS+bS+++KIkacKECcrKylKtWrU0dOhQPf744/Z9WrdurfHjx2v27NnKyMhQeHi44uLi5Onp6XQfTFar1XqT76NYeTeJKekuAHCRtG9mXX8nAIbkVYIx0cNx37js3J8Obuaycxc3kjoAAGBorlzSxEi4UQIAAMANkNQBAABDc+WSJkZCUgcAAOAGSOoAAIChEdTZUNQBAABD86Cqk8TwKwAAgFsgqQMAAIZGUGdDUgcAAOAGSOoAAIChsaSJDUkdAACAGyCpAwAAhkZQZ0NSBwAA4AZI6gAAgKGxTp0NRR0AADA0Sjobhl8BAADcAEkdAAAwNJY0sSGpAwAAcAMkdQAAwNA8COokkdQBAAC4BZI6AABgaMypsyGpAwAAcAMkdQAAwNAI6mwo6gAAgKEx/GrD8CsAAIAbIKkDAACGxpImNiR1AAAAboCkDgAAGBpz6mxI6gAAANwASR0AADA0cjobkjoAAAA3QFIHAAAMzYM5dZIo6gAAgMFR09kw/AoAAOAGSOoAAIChsaSJDUkdAACAGyCpAwAAhkZQZ0NSBwAA4AZI6gAAgKGxpIkNSR0AAIAbIKkDAACGRlBnQ1EHAAAMjSVNbBh+BQAAcAO3XVJ3csdbJd0FAC7yZ87lku4CABfxKuVZYtcmobLhcwAAAHADFHUAAMDQTCaTy143IikpSQMGDFDjxo3VokULjRs3Tmlpafbty5cvV+vWrdWoUSP1799fycnJDsfHx8erQ4cOCg0NVY8ePZSYmHhD16eoAwAAKAaTJ09W8+bNtWLFCk2ZMkV79uzR2LFjJUmbNm3S1KlTNXz4cC1fvly5ubkaMmSI8vLyJEn79u3TqFGj1KtXL61cuVL+/v4aNGiQsrKynL7+bTenDgAA4EZ43CY3v8bGxsrPz0+SFBwcrMzMTI0ZM0YXL17Ue++9p169eqlr166SbAVgx44dtWfPHoWHh2vBggWKjIxUVFSUJGnKlClq0aKF4uPj1aNHD6euT1IHAABQDK4UdFeUKVNGeXl5ysjI0JEjRxQZGWnfVq9ePVWrVk0HDhyQJCUkJDhsr1ChgkJCQuzbnUFSBwAADM2VSZ3FYpHFYnFoM5vNMpvN1zzOarVq1apVaty4sc6dOydJCgwMdNjH399fqampSk9PV0ZGRr7tfn5+Sk1NdbqvFHUAAMDQXLn4cFxcnGbNmuXQFhMTo2HDhhV6TE5OjiZOnKiEhAQtW7ZM2dnZkiRvb2+H/by8vGSxWK65/eobLa6Hog4AAKAQgwcPVnR0tEPbtVK61NRUjRw5UqdOndLixYsVGhqqgwcPSrIVe1ezWCzy9va2n6+g7V5eXk73lTl1AADA0DxMrnuZzWb5+Pg4vAor6o4fP66ePXuqXLlyWrduncLCwiRJNWrUkCSlpKQ47J+SkqLAwEBVqlRJZrO5wO21atVy/nO4gc8MAAAAhXj22WfVpEkTzZs3T1WqVLG316hRQwEBAdq1a5e97fjx40pNTVVERIQ8PDwUFhamnTt32rdnZmYqMTFRERERTl+f4VcAAGBoLpxS57Tjx4/r8OHDGjRoUL5FhStXrqzo6GjNmDFDwcHBCgwM1NSpU9WqVSsFBQVJkqKjoxUTE6NmzZopLCxMs2bNUt26ddWyZUun+0BRBwAAcJPOnj0rSRoxYkS+bS+++KL69eun8+fPa+LEibp06ZLatGmjCRMm2Pdp3bq1xo8fr9mzZysjI0Ph4eGKi4uTp6fzz9Q1Wa1W682/leLze2ZuSXcBgIuULnUb/DkNwCUqejtffBS35zf+6LJzT+vYwGXnLm7MqQMAAHADDL8CAABDI6Gy4XMAAABwAyR1AADA0G6Hu19vBxR1AADA0Dyo6iQx/AoAAOAWSOoAAIChEdTZkNQBAAC4AZI6AABgaB4kdZJI6gAAANwCSR0AADA07n61IakDAABwAyR1AADA0AjqbCjqAACAoXGjhA3DrwAAAG6ApA4AABiaSUR1EkkdAACAWyCpAwAAhsacOhuSOgAAADdAUgcAAAyNpM6GpA4AAMANkNQBAABDM7H6sCSKOgAAYHAMv9ow/AoAAOAGSOoAAIChMfpqQ1IHAADgBkjqAACAoXkQ1UkiqQMAAHALJHUAAMDQuPvVhqQOAADADZDUAQAAQ2NKnQ1FHQAAMDQPUdVJDL8CAAC4BZI6AABgaAy/2pDUAQAAuAGSOgAAYGgsaWJDUgcAAOAGSOoAAICh8ZgwG5I6AAAAN0BSBwAADI2gzoaiDgAAGBrDrzYMvwIAALgBkjoAAGBoBHU2JHUAAABugKQOAAAYGgmVDZ8DAACAG6CoAwAAhmYymVz2KoqkpCR169ZNe/futbft3r1bQUFBDq/Bgwc7HBcfH68OHTooNDRUPXr0UGJi4g1dl+FXAACAYnD48GG99957+uKLL/Tnn386bEtPT1eNGjW0dOlSe5u3t7f963379mnUqFEaPXq0wsPDNXv2bA0aNEhbtmyRj4+PU9cnqQMAAIZmcuHrRmzZskVms1lz587Nty09PV1Vq1ZVnTp17K/q1avbty9YsECRkZGKiopScHCwpkyZoszMTMXHxzt9fZI6AABgaK5cfNhischisTi0mc1mmc3mfPuOHDlSJpNJp06dyrctPT1dlSpVKvQ6CQkJGjVqlP37ChUqKCQkRAcOHFCPHj2c6itJHQAAQCHi4uJ07733Orzi4uIK3Pdac/DS0tK0a9cuhYWFqUOHDoqNjdUff/whyVbwZWRkKDAw0OEYPz8/paamOt1XkjoAAGBorlx7ePDgwYqOjnZoKyilu57HHntMDz/8sKxWq/bv36+ZM2fq5MmTmjlzprKzsyU5zrGTJC8vL6WlpTl9DYo6AACAQhQ21Hqjateubf+6YcOG8vHx0dixY3X+/Hn7+XNychyOsVgs8vLycvoaDL8CAABDM5lc93KVkJAQSdLp06dVqVIlmc1mpaSkOOyTkpKiWrVqOX1OijoAAIBb7ODBg/Lw8FBgYKA8PDwUFhamnTt32rdnZmYqMTFRERERTp+T4VcAAGBoRV0k+FZ68803FRoaqsDAQB08eFCxsbHq1auX/Y7Y6OhoxcTEqFmzZgoLC9OsWbNUt25dtWzZ0ulrUNQBAADcAhMmTFBWVpZq1aqloUOH6vHHH7dva926tcaPH6/Zs2crIyND4eHhiouLk6enp9PnN1mtVqsrOl5Uv2fmlnQXALhI6VK3/1/TAIqmorfzxUdxW7HvtMvO3atJgMvOXdxI6gAAgKEZYfj1VuBGCQAAADdAUgcAAAyNnM6GpA4AAMANkNQBAABDY06dDUkdAACAGyCpAwAAhkZCZcPnAAAA4AZI6gAAgKExp86Gog4AABgaJZ0Nw68AAABugKQOAAAYGqOvNiR1AAAAboCkDgAAGJoHs+okkdQBAAC4BZI6AABgaMypsyGpAwAAcAMkdQAAwNBMzKmTRFIHAADgFkjqAACAoTGnzoaiDgAAGBpLmtg4XdR17dpVFy9edGrf+Pj4IncIAAAAN87poq53796aMmWKBgwYoNKlS7uyTwAAAE5j+NXG6aKuV69eWrZsmXx9fTVgwABX9gkAAAA3yOm7X00mk/r3768NGza4sj8AAAA3xGRy3ctITFar1VrSnbja75m5Jd0FAC5SupTBfkMCcFpFb88Su/aW73932bnb/q2ay85d3Lj7FQAAGBqLD9uw+DAAAIAbIKkDAACG5kFQJ4miDgAAGBzDrzYMvwIAALgBp5K6Tz755IZO2qlTpyJ1BgAA4EYZbekRV3FqSZPg4GDnT2gy6fvvvy9yh1jSBHBfLGkCuK+SXNJk+w/nXHbuVkFVXHbu4uZUUpeUlOTqfgAAABQJc+psmFMHAADgBop092taWppmzpyp/fv3KyMjI9/2bdu23XTHAAAAnMGSJjZFSupeeOEFbdu2TeHh4UpNTVW3bt3UqFEjZWdnq0uXLsXdRwAAAFxHkZK6hIQEzZkzR82bN9dnn32m7t27y9/fX4sWLdLRo0eLu49wA9u2bNKS99/TqeST8q1YUZ269lDUwCEymUxKTTmjd2ZM1zd7dqtUqVIKa9pMw0eNVU0//5LuNoDrOPrjD5o543Ud2P+dypUrp4gW/08jRo2Rb8WK9n1On0rW1Mkv6aHOXdXhoc4l11m4LebU2RQpqfPy8lKpUrZ68K677rIXchEREdq4cWPx9Q5u45cTx/V49FOKW7Rc/QcM1qL5c7X2vyskSe/OnCH/wFp6J26RXpn+plLPnNbYfz+jvLy8Eu41gOuJnfaK7r2vmRYs+VDjX3pF+77dq5dfeF6SlHzyF02Z+KL69equbxK+LuGewp2ZTK57GUmRkrr7779f27dvV9OmTXX//fdr7ty5KlWqlNauXavKlSsXdx/hBgYMGmr/uv49Qdq5Y7u+Sditbo/01tARz6pGTT/79n+PGa8hT/bTqZO/qPZddUuiuwCcNOnV1+w/v/c0CNIfWZl6+YXn9efFi/omYbfS0s4rbsESPd67Rwn3FHB/RSrqRo0apf3790uS+vXrpx07dujJJ5+Ut7e3pk2bVpz9g5u6nHdZVX19JcmhoJMkc5ky9n0A3N4K+vm9krJ3e6SXuvfsXRLdwh3GYIGayxSpqAsICFBAQIAkydvbW0uXLlV6errKlStnH5YFCnLxYra2xW/SkcRDGjJsVIH7fLJmlarXqKlate+6tZ0DcFOsVqvWr/mvQkIbycvbu6S7A9xxilSB/frrrwW2//nnn5KkGjVqFL1HcFut/9FEFotF5cr56NlxL+qeBo5PKrFarVo4712tX7NSr8bO5A8EwEByc3L02quT9e3ePYpbsKSku4M7jIfRJr+5SJH+q9myZUuZrvEB3sxjwuC+Fi7/r7KyspR0JFFvvz5Vx4/9pMHPjJQkZWZmaMpL/9F33+7Rq7Ez9UBkq5LtLACn/fprql4Y86xOn07W7PcW6m8hDUu6S8AdqUhF3ZIljn+F5eXl6cSJE1q6dKmeeOKJYukY3E+du+6WJIU0bCQvL2+9NuUlRT35tLIvZmvY4Ch5eXlp4QerFBBYu4R7CsBZJ385oaEDo1TvngZa9vEaVa5snOdkwn2Q09kUaUmT5s2bO7zCw8PVu3dvzZw5U2vWrCnuPsINeXp6ymq16nLeZb0xdZJ8fMpr9rylFHSAwbw4brQaNg7Tm7PmUtAB/7+kpCR169ZNe/fudWhfvny5WrdurUaNGql///5KTk522B4fH68OHTooNDRUPXr0UGJi4g1dt1if/ern56ekpKTiPCXcwB9ZWZo8YZz2fL1TP/90VFs2fap3Z76hB9t1lIeHh77833a1frCdfv8tVaeSf7G/0i9cKOmuA7iGk7+cUNKRw/pXuw46fSpZySd/sb+yMjNLunu4k5hc+LoBhw8f1ogRI9SrVy8dOXLEYdumTZs0depUDR8+XMuXL1dubq6GDBliv1t83759GjVqlHr16qWVK1fK399fgwYNUlZWltPXL5YbJaxWq3777TfNnz9ftWuTtMCRuYxZubk5euWlccrKylLNmv7q0auveveL0vnzZ3X5cq5mzpiumTOmOxzXu1+UYkaOLqFeA7iec2fPSpL+M/rf+bY99/x49ezd91Z3CXeo2+WJElu2bJHZbNbcuXMVFRXlsO29995Tr1691LVrV0nS5MmT1bFjR+3Zs0fh4eFasGCBIiMj7cdNmTJFLVq0UHx8vHr0cG6dx2K7UcJqtcrPz09vvPFGUU4JN1a6tFkTX40tcFvNmv76au/hW9wjAMWhyb33KWH/kevvKDm9H3C7sVgsslgsDm1ms1lmsznfviNHjpTJZNKpU6cc2jMyMnTkyBH9+9//9wdQvXr1VK1aNR04cEDh4eFKSEjQqFH/t9RXhQoVFBISogMHDri2qPvrjRIeHh6qXLmyateuzTIUAADglnLliiZxcXGaNWuWQ1tMTIyGDRtWQD8K7siVIi8wMNCh3d/fX6mpqUpPT1dGRka+7X5+fkpNTXW6r0WqwM6cOaO2bduqbNmyDu2pqan6/vvv1aoVy1EAAADjGzx4sKKjox3aCkrpriU7O1uS7YENV/Py8pLFYrnm9rS0NKevU6QbJcaNG6dz587la79w4YJDdAgAAOBqrrxPwmw2y8fHx+F1o0Xdlf1zcnIc2i0Wi7y9va+53cvLy+nrOJ3UpaamatWqVZJs8+eWLFki3///2Z2SdPnyZe3atUtVq1Z1+uIAAADu7sqTtlJSUhxuKE1JSVH79u1VqVIlmc1mpaSkOByXkpKikJAQp6/jdFFXuXJlbd68WRaLRSaTSdu2bXOYP2cymeTn56fp06df4ywAAADF7Pa4+bVQNWrUUEBAgHbt2qX7779fknT8+HGlpqYqIiJCHh4eCgsL086dO9W9e3dJUmZmphITEzVw4ECnr+N0UWc2m/Xpp59KkoKDg7V48WLVqlXrRt4TAADAHSk6OlozZsxQcHCwAgMDNXXqVLVq1UpBQUH27TExMWrWrJnCwsI0a9Ys1a1bVy1btnT6GkW6UWLDhg2qVKlSvvaLFy+qVKlSKl26dFFOCwAAcMNul3XqrqVfv346f/68Jk6cqEuXLqlNmzaaMGGCfXvr1q01fvx4zZ49WxkZGQoPD1dcXJw8PT2dvobJarVab7Rj3bt3V+fOnfMtrDd37lzt379fc+fOvdFT2v2emVvkYwHc3kqXuv1/8QIomorezhcfxe3bExkuO/e9d1Vw2bmLW5Hufv3pp58UHh6erz0yMlLffvvtTXcKAAAAN6ZIRV2FChV05syZfO3nz5/X5cuXb7pTAAAAzrpNHv1a4opU1LVu3Vqvv/66kpOT7W3nzp3Tm2++WWCCBwAAANcq0py6zMxMRUdH64cfflD9+vXl6empY8eOqUqVKlq4cOFN3RXLnDrAfTGnDnBfJTmn7rtfXDenrmkd48ypK1JRJ9kWG46Pj9e+fftktVoVFBSkzp07q0yZMjfVIYo6wH1R1AHui6Ku5BW5qCvIhQsXtGHDBvXt27fI56CoA9wXRR3gvkqyqNv3S6bLzt2kTnmXnbu4FWmduqtZLBZt375d69at044dOyTppoo6AAAA3LgiF3V79+7V+vXrtXnzZmVmZqpu3boaOXKkunTpUpz9AwAAuCYTgwCSbrCoO3HihNatW6f169fr9OnTCggIUI8ePbRo0SK9/vrrN/TQWQAAgOJATWfjdFH36KOP6tChQ/Lz81O7du3UoUMHNWrUSLm5uVq4cKEr+wgAAIDrcLqoS0pKkq+vr1q1aqXWrVurUaNGkiQTmScAAChJlCKSbqCo27VrlzZt2qT169dr+fLlqlKlitq1a6cHH3yQwg4AAKCEFWlJk9OnT2v9+vX65JNP9PPPP8tkMql79+56/PHHFRwcfFMdYkkTwH2xpAngvkpySZODyVkuO3ejWj4uO3dxu+l16g4ePKj169dr48aNSktLU/369fXJJ58U+XwUdYD7oqgD3BdFXckrtsWHL1++rB07dmj9+vV68803i3weijrAfVHUAe6rJIu6Q6dcV9SFBt6BRV1xoagD3BdFHeC+KOpK3k0/UQIAAKAk8eeiDUUdAAAwNqo6SZJHSXcAAAAAN4+kDgAAGJqJqE4SSR0AAIBbIKkDAACGxoOtbEjqAAAA3ABJHQAAMDSCOhuSOgAAADdAUgcAAIyNqE4SRR0AADA4ljSxYfgVAADADZDUAQAAQ2NJExuSOgAAADdAUgcAAAyNoM6GpA4AAMANkNQBAABjI6qTRFIHAADgFkjqAACAobFOnQ1JHQAAgBsgqQMAAIbGOnU2FHUAAMDQqOlsGH4FAABwAyR1AADA2IjqJJHUAQAAuAWSOgAAYGgsaWJDUgcAAOAGSOoAAIChsaSJDUkdAACAG6CoAwAAhmZy4etGrFq1SkFBQQ6vSZMm2bcvX75crVu3VqNGjdS/f38lJycX9S0XiOFXAABgbLfJ8Gt6eroaN26s119/3d5Wvnx5SdKmTZs0depUTZ48WfXr19err76qIUOGaP369fLwKJ6MjaIOAACgGKSnp6tGjRqqU6dOvm3vvfeeevXqpa5du0qSJk+erI4dO2rPnj0KDw8vlusz/AoAAAzN5ML/3YgLFy6oUqVK+dozMjJ05MgRRUZG2tvq1aunatWq6cCBAzf9/q8gqQMAACiExWKRxWJxaDObzTKbzfn2vXDhgrZu3apPPvlEgYGB6ty5s6KionTq1ClJUmBgoMP+/v7+Sk1NLba+UtQBAABDc+WSJnFxcZo1a5ZDW0xMjIYNG5Zv3+HDh2vIkCGyWCzavXu33nnnHZ0/f15t2rSRJHl7ezvs7+Xlla9gvBkUdQAAAIUYPHiwoqOjHdoKSukkqX79+vavGzdurMuXL2v+/Plq3769JCknJ8dhf4vFkq/QuxnMqQMAAIbmyiVNzGazfHx8HF6FFXV/FRISouzsbFWtWlWSlJKS4rA9JSUl35DszaCoAwAAcIGDBw/K19dXfn5+CggI0K5du+zbjh8/rtTUVEVERBTb9Rh+BQAAhna7PCZs8uTJatmypapXr65du3Zp/vz5GjZsmDw8PBQdHa0ZM2YoODhYgYGBmjp1qlq1aqWgoKBiuz5FHQAAMLjbo6rLzs7W6NGjdenSJd11112aNGmSfV26fv366fz585o4caIuXbqkNm3aaMKECcV6fZPVarUW6xlv0u+ZuSXdBQAuUrrU7fGLF0Dxq+jtWWLXPpVWfHeQ/lVgJefmz90OSOoAAICh3S7DryWNGyUAAADcAEkdAAAwNII6G5I6AAAAN0BSBwAADI05dTYkdQAAAG6ApA4AABiaiVl1kijqAACA0VHTSWL4FQAAwC2Q1AEAAEMjqLMhqQMAAHADJHUAAMDQWNLEhqQOAADADZDUAQAAQ2NJExuSOgAAADdAUgcAAIyNoE4SRR0AADA4ajobhl8BAADcAEkdAAAwNJY0sSGpAwAAcAMkdQAAwNBY0sSGpA4AAMANkNQBAABDY06dDUkdAACAG6CoAwAAcAMMvwIAAENj+NWGpA4AAMANkNQBAABDY0kTG5I6AAAAN0BSBwAADI05dTYkdQAAAG6ApA4AABgaQZ0NSR0AAIAbIKkDAADGRlQniaIOAAAYHEua2DD8CgAA4AZI6gAAgKGxpIkNSR0AAIAbIKkDAACGRlBnQ1IHAADgBkjqAACAsRHVSSKpAwAAcAskdQAAwNBYp86Gog4AABgaS5rYMPwKAADgBkxWq9Va0p0AAADAzSGpAwAAcAMUdQAAAG6Aog4AAMANUNQBAAC4AYo6AAAAN0BRBwAA4AYo6gAAANwARR0AAIAboKgDAABwAxR1KHZz5sxR69at7d8PHz5cQ4YMuWXXT01NVVBQkBISEm7ZNYE7BT/fwO2Lou4O8vzzzysoKEhBQUFq1KiRunTponXr1rn8utOnT9ebb77p1L65ublKTk52cY+klJQUDR48WGFhYXrggQe0YMECl18TcCV+vh3l5eXpo48+UpcuXW7J9YDbQamS7gBurSZNmmj69OnKysrStm3bNGbMGHl7e6tt27Yuu6a3t7fT+w4dOlSVK1fWtGnTXNafy5cva9CgQapevbo++OADHT58WC+99JL8/PzUsWNHl10XcDV+vm0+/vhjLV68WL/88ov8/Pxcei3gdkJRd4fx8vJSnTp1JEkhISE6cuSIVq5cWeAvfavVKpPJdEv7Z7FYXH6NL774QsePH9eiRYtUpUoVhYSEaPfu3Vq+fDlFHQyNn2+bjz76SN26dVNmZqY2btx4S64J3A4Yfr3D1atXTykpKZKkd955R//617+0atUqNWvWTK+99pokafv27erUqZMaN26sLl26aMeOHQ7nWLRokf75z3+qUaNGGjx4sC5cuOCwPSoqSs8//7z9e4vFotjYWP3zn/9Uw4YN1b59e50+fVpBQUHavXu31qxZo6CgIL3zzjuSpF9//VXDhg1TkyZN1LJlS73++usO/3FITExU7969FRoaqrZt2+qrr7665ntOSEjQ3//+d1WpUsXeFh4ergMHDshqtd74hwjcpu7En29J+u9//6uBAweqVClyC9xZ+Bd/hztx4oQCAwPt32dkZOizzz7T/Pnz5e3trYMHD2rEiBEaO3as7r33Xm3btk1Dhw7Vhg0bVKdOHa1Zs0axsbF67rnn1Lx5c/3vf//T7NmzVb169UKv+e9//1sHDhzQmDFj1KBBAx06dEhWq1VbtmzR2LFjVa1aNT333HPy9fW1D5XWqVNHS5Ys0blz5/Tiiy/K29tbMTExSk9P14ABA9SsWTMtXbpU58+f19SpU6/5npOTkx3esyT5+fnJYrEoLS1NlStXvrkPFbhN3Ik/35JueQIJ3C4o6u5Qf/zxhz799FNt377d4SaB9PR0vfjii/b/EDz55JPq37+/+vbtK0kKDg7Wpk2btHHjRg0ZMkRz587VY489pqioKEnS3//+d33//fdKTEws8LoHDhzQ1q1btWzZMjVr1sx+ziu8vLxUrlw5+xDShg0bdOHCBa1atUqlS5eWZEsGVqxYoZiYGH344YcqXbq0ZsyYoTJlykiSPDw8NHjw4ELfe3Z2tipVquTQdmVe0K0aHgJc6U7++QbuZBR1d5iEhASFhobKYrGoZs2aio2NVUREhH17lSpVHP6yP3z4sBISErR48WJ7W25urk6fPq0///xTv/zyi8LDwx2uERQUVOgv/X379ql8+fL2X/jXk5iYqN9++01Nmza1t1mtVvsw6Q8//KAmTZrYf+Ffuf61mM1m5eTkOLRdunRJku0/OoBR8fMN3Nko6u4wjRs31pQpU+Tt7S0/P798wxRly5Z1+D43N1eDBg3SQw895NBevnx5ZWdny2q12v/CvuJaaZfVapWnp6fT/c3NzVX9+vX11ltvFbg9Ozs7X5+vl7bVqFFDv/zyi0NbSkqKypcvr4oVKzrdN+B2w883cGejqLvDeHl5qV69ek7vf/fdd+vkyZOFHlOhQgV99913ioyMtLd99913hZ6vQYMGunDhgpKSkhyGZa7w8PBQbm6uw/VXrVqlqlWrytfXN9/+tWvX1rZt25Sbm2ufFL13795rvqd7771Xn3zyiTIzM1W+fHlJ0tdff+2QaABGxM83cGfj7ldcU1RUlDZs2KB33nlHR44c0VdffaXhw4crLS1NktSrVy8tWrRIK1eu1OHDhzV16lQdP3680PP94x//UJMmTTRs2DB99tlnSkpK0rJly+wLktasWVP79u1TYmKiTp48qU6dOtknTSckJOjgwYOKi4vTihUrJEmPPvqoUlJSNH78eB06dEgbNmzQwoULr/me2rdvr4oVK2rcuHFKSkrSihUrFB8fr4EDBxbTpwYYgzv+fAN3Moo6XFPHjh01btw4rVmzRo8++qheeuklBQQEyMfHR5IUExOjhx9+WFOnTlVUVJSsVqv69OlT6PlMJpPmzJmjxo0b6z//+Y8ee+wxxcfH2+eyPfnkkypbtqwee+wxffnll/Lx8dG8efNktVo1cOBADRo0SN999519Ds4999yj119/XXv37lWfPn20fPlyvfTSS9d8T97e3po3b55+//13PfLII1q4cKFmzJihxo0bF9OnBhiDO/58A3cyk5WFuQAAAAyPpA4AAMANUNQBAAC4AYo6AAAAN0BRBwAA4AYo6gAAANwARR0AAIAboKgDAABwAxR1AAAAboCiDgAAwA1Q1AEAALgBijoAAAA3QFEHAADgBijqAAAA3MD/B3SivEUD+S6HAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_st = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_st, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('stacking结果的混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab44730",
   "metadata": {},
   "source": [
    "## voting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c13aa05",
   "metadata": {},
   "source": [
    "#### 三模型voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "667b7f30",
   "metadata": {},
   "outputs": [],
   "source": [
    "three_predictions = np.array([y_pred_svm, y_pred_tree, y_pred_nw])\n",
    "three_pred = np.apply_along_axis(lambda x: np.argmax(np.bincount(x)), axis=0, arr=three_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b8092895",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.93       412\n",
      "           1       0.48      0.43      0.46        53\n",
      "\n",
      "    accuracy                           0.88       465\n",
      "   macro avg       0.70      0.69      0.69       465\n",
      "weighted avg       0.88      0.88      0.88       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rep_three = classification_report(y_test, three_pred)\n",
    "print(rep_three)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db6101de",
   "metadata": {},
   "source": [
    "#### 四模型voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "176b63e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "four_predictions = np.array([y_pred, y_pred_svm, y_pred_tree, y_pred_nw])\n",
    "four_pred = np.apply_along_axis(lambda x: np.argmax(np.bincount(x)), axis=0, arr=four_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8555c192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.93       412\n",
      "           1       0.47      0.36      0.41        53\n",
      "\n",
      "    accuracy                           0.88       465\n",
      "   macro avg       0.70      0.65      0.67       465\n",
      "weighted avg       0.87      0.88      0.87       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rep_four = classification_report(y_test, four_pred)\n",
    "print(rep_four)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "549edd53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_four = confusion_matrix(y_test, four_pred)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_four, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('4模型voting结果的混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce07d8af",
   "metadata": {},
   "source": [
    "可以看到voting后模型的**效果不升反降**，特别是在**不通过**类型的识别上表现较差"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8e247c",
   "metadata": {},
   "source": [
    "## bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "379351e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "class Bagging:\n",
    "    def __init__(self, base_estimator, n_estimators):\n",
    "        self.base_estimator = base_estimator\n",
    "        self.n_estimators = n_estimators\n",
    "        self.estimators = []\n",
    "        self.selected_features = []\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        for _ in range(self.n_estimators):\n",
    "            # 从原始训练集中有放回地抽样生成子训练集\n",
    "            indices = np.random.choice(len(X), size=900, replace=True)\n",
    "            X = np.array(X)\n",
    "            y = np.array(y)\n",
    "            X_subset = X[indices]\n",
    "            y_subset = y[indices]\n",
    "            \n",
    "            # 随机选择特征\n",
    "            num_features = X.shape[1]\n",
    "            selected_features = np.random.choice(num_features, size=30, replace=False)\n",
    "            self.selected_features.append(selected_features)\n",
    "            X_subset = X_subset[:, selected_features]\n",
    "\n",
    "            # 训练学习器并加入到集成模型中\n",
    "            estimator = clone(self.base_estimator)\n",
    "            estimator.fit(X_subset, y_subset)\n",
    "            self.estimators.append(estimator)\n",
    "\n",
    "    def predict(self, X):\n",
    "        predictions = np.zeros((len(X), self.n_estimators))\n",
    "        for i, estimator in enumerate(self.estimators):\n",
    "            X_subset = X.iloc[:, self.selected_features[i]]\n",
    "            # 使用每个学习器进行预测\n",
    "            predictions[:, i] = estimator.predict(X_subset)\n",
    "\n",
    "        # 使用投票策略集成预测结果\n",
    "        ensemble_predictions = np.apply_along_axis(\n",
    "            lambda x: np.argmax(np.bincount(x.astype(int))),\n",
    "            axis=1,\n",
    "            arr=predictions\n",
    "        )\n",
    "        return ensemble_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a55317",
   "metadata": {},
   "source": [
    "### 决策树bagging+随机特征选择（RF）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ac53d0a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.98      0.95       412\n",
      "           1       0.69      0.42      0.52        53\n",
      "\n",
      "    accuracy                           0.91       465\n",
      "   macro avg       0.81      0.70      0.73       465\n",
      "weighted avg       0.90      0.91      0.90       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "base_clf = tree.DecisionTreeClassifier()\n",
    "bagging = Bagging(base_clf, n_estimators=100)\n",
    "\n",
    "bagging.fit(x_train, y_train)\n",
    "y_pred_bag = bagging.predict(x_test)\n",
    "rep_bag = classification_report(y_test, y_pred_bag)\n",
    "print(rep_bag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48fc06d7",
   "metadata": {},
   "source": [
    "bagging效果良好，对申请不通过的区分能力有了很大的提升"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c62ef9",
   "metadata": {},
   "source": [
    "**使用sklearn包中的随机森林**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d26cdb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=1017)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=1017)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=1017)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(random_state=1017)\n",
    "rf_clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b7b07571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       412\n",
      "           1       0.84      0.40      0.54        53\n",
      "\n",
      "    accuracy                           0.92       465\n",
      "   macro avg       0.88      0.69      0.75       465\n",
      "weighted avg       0.92      0.92      0.91       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf = rf_clf.predict(x_test)\n",
    "class_report_rf = classification_report(y_test, y_pred_rf)\n",
    "print(class_report_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4bf77d4",
   "metadata": {},
   "source": [
    "随机森林模型的效果很好，与自己编写的RF相比更优"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba2ca229",
   "metadata": {},
   "source": [
    "下面进行其他模型的bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "516a9338",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Bagging:\n",
    "    def __init__(self, base_estimator, n_estimators):\n",
    "        self.base_estimator = base_estimator\n",
    "        self.n_estimators = n_estimators\n",
    "        self.estimators = []\n",
    "        self.selected_features = []\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        for _ in range(self.n_estimators):\n",
    "            # 从原始训练集中有放回地抽样生成子训练集\n",
    "            indices = np.random.choice(len(X), size=900, replace=True)\n",
    "            X = np.array(X)\n",
    "            y = np.array(y)\n",
    "            X_subset = X[indices]\n",
    "            y_subset = y[indices]\n",
    "            \n",
    "            # 随机选择特征(size=特征数时，即为49时，则是普通bagging)\n",
    "            num_features = X.shape[1]\n",
    "            selected_features = np.random.choice(num_features, size=49, replace=False)\n",
    "            self.selected_features.append(selected_features)\n",
    "            X_subset = X_subset[:, selected_features]\n",
    "\n",
    "            # 训练学习器并加入到集成模型中\n",
    "            estimator = clone(self.base_estimator)\n",
    "            estimator.fit(X_subset, y_subset)\n",
    "            self.estimators.append(estimator)\n",
    "\n",
    "    def predict(self, X):\n",
    "        predictions = np.zeros((len(X), self.n_estimators))\n",
    "        for i, estimator in enumerate(self.estimators):\n",
    "            X_subset = X.iloc[:, self.selected_features[i]]\n",
    "            # 使用每个学习器进行预测\n",
    "            predictions[:, i] = estimator.predict(X_subset)\n",
    "\n",
    "        # 使用投票策略集成预测结果\n",
    "        ensemble_predictions = np.apply_along_axis(\n",
    "            lambda x: np.argmax(np.bincount(x.astype(int))),\n",
    "            axis=1,\n",
    "            arr=predictions\n",
    "        )\n",
    "        return ensemble_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "653462be",
   "metadata": {},
   "source": [
    "### 支持向量机bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fd37761e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.80      0.85       412\n",
      "           1       0.20      0.38      0.26        53\n",
      "\n",
      "    accuracy                           0.75       465\n",
      "   macro avg       0.55      0.59      0.56       465\n",
      "weighted avg       0.83      0.75      0.79       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "base_clf = SVC(kernel='rbf', probability=True)\n",
    "bagging_svm = Bagging(base_clf, n_estimators=10)\n",
    "\n",
    "bagging_svm.fit(x_train, y_train)\n",
    "y_pred_bag_svm = bagging_svm.predict(x_test)\n",
    "rep_bag_svm = classification_report(y_test, y_pred_bag_svm)\n",
    "print(rep_bag_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d032c29b",
   "metadata": {},
   "source": [
    "基于SVM的bagging效果并不好"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e020eef",
   "metadata": {},
   "source": [
    "### 神经网络bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5b45c95f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.94      0.93       412\n",
      "           1       0.49      0.45      0.47        53\n",
      "\n",
      "    accuracy                           0.88       465\n",
      "   macro avg       0.71      0.70      0.70       465\n",
      "weighted avg       0.88      0.88      0.88       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "base_clf = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=200)\n",
    "bagging_nw = Bagging(base_clf, n_estimators=30)\n",
    "\n",
    "bagging_nw.fit(x_train, y_train)\n",
    "y_pred_bag_nw = bagging_nw.predict(x_test)\n",
    "rep_bag_nw = classification_report(y_test, y_pred_bag_nw)\n",
    "print(rep_bag_nw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64103f0",
   "metadata": {},
   "source": [
    "可以发现，神经网络做bagging的效果相较于单纯的神经网络模型并没有提升"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5394ba",
   "metadata": {},
   "source": [
    "## boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2245425a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Boosting:\n",
    "    def __init__(self, base_classifier, n_estimators=5):\n",
    "        self.n_estimators = n_estimators\n",
    "        self.models = []\n",
    "        self.weights = []\n",
    "        self.base_classifier = base_classifier\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        n_samples, n_features = X.shape\n",
    "        # 初始化样本权重\n",
    "        sample_weights = np.ones(n_samples) / n_samples\n",
    "\n",
    "        for i in range(self.n_estimators):\n",
    "            model = clone(self.base_classifier)\n",
    "            # 使用带权重的样本进行训练\n",
    "            model.fit(X, y, sample_weight=sample_weights)\n",
    "            \n",
    "            predictions = model.predict(X)\n",
    "            \n",
    "            error = np.sum(sample_weights * (predictions != y)) / np.sum(sample_weights)\n",
    "            model_weight = 0.5 * np.log((1 - error) / error)\n",
    "            \n",
    "            # 更新样本权重\n",
    "            sample_weights *= np.exp(-model_weight * y * predictions)\n",
    "            sample_weights /= np.sum(sample_weights)\n",
    "            \n",
    "            # 保存模型和权重\n",
    "            self.models.append(model)\n",
    "            self.weights.append(model_weight)\n",
    "\n",
    "    def predict(self, X):\n",
    "        # 对每个模型的预测结果进行加权平均\n",
    "        predictions = np.zeros(X.shape[0])\n",
    "        for model, weight in zip(self.models, self.weights):\n",
    "            predictions += weight * model.predict(X)\n",
    "        \n",
    "        # 使用符号函数将结果映射到二分类标签\n",
    "        return np.sign(predictions).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e44e2c",
   "metadata": {},
   "source": [
    "### 决策树boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "bc2d66db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.75      0.83       412\n",
      "           1       0.23      0.58      0.33        53\n",
      "\n",
      "    accuracy                           0.73       465\n",
      "   macro avg       0.58      0.67      0.58       465\n",
      "weighted avg       0.85      0.73      0.77       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "base_clf = tree.DecisionTreeClassifier()\n",
    "boost_tree = Boosting(base_clf, 10)\n",
    "boost_tree.fit(x_train, y_train)\n",
    "\n",
    "y_pred_boost_tr = boost_tree.predict(x_test)\n",
    "rep_boost_tr = classification_report(y_test, y_pred_boost_tr)\n",
    "print(rep_boost_tr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4137c2c7",
   "metadata": {},
   "source": [
    "### 调用adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b4f11d9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.69      0.78       412\n",
      "           1       0.15      0.42      0.22        53\n",
      "\n",
      "    accuracy                           0.66       465\n",
      "   macro avg       0.52      0.55      0.50       465\n",
      "weighted avg       0.82      0.66      0.72       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_ada = AdaBoostClassifier(n_estimators=10, learning_rate=1)\n",
    "model_ada.fit(x_train, y_train)\n",
    "\n",
    "y_pred_ada = model_ada.predict(x_test)\n",
    "class_report_ada = classification_report(y_test, y_pred_ada)\n",
    "print(class_report_ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef85ff6",
   "metadata": {},
   "source": [
    "可以发现，无论是自己编写的决策树boosting集成算法还是调用sklearn包中的adaboost，模型都未取得良好的效果"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c6f8c6f",
   "metadata": {},
   "source": [
    "### 支持向量机boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "32d0783d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.45      0.61       412\n",
      "           1       0.14      0.72      0.24        53\n",
      "\n",
      "    accuracy                           0.48       465\n",
      "   macro avg       0.54      0.59      0.42       465\n",
      "weighted avg       0.84      0.48      0.57       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "base_clf = SVC(kernel='rbf', probability=True, random_state=1017)\n",
    "boost_svm = Boosting(base_clf, 10)\n",
    "boost_svm.fit(x_train, y_train)\n",
    "\n",
    "y_pred_boost_svm = boost_svm.predict(x_test)\n",
    "rep_boost_svm = classification_report(y_test, y_pred_boost_svm)\n",
    "print(rep_boost_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf3ef42",
   "metadata": {},
   "source": [
    "对支持向量机使用boosting集成方法效果极差"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b47e27a",
   "metadata": {},
   "source": [
    "### 调用xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d2a3611a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.96      0.95       412\n",
      "           1       0.60      0.45      0.52        53\n",
      "\n",
      "    accuracy                           0.90       465\n",
      "   macro avg       0.77      0.71      0.73       465\n",
      "weighted avg       0.89      0.90      0.90       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "xgb_clf = xgb.XGBClassifier(random_state=1017, use_label_encoder=False, eval_metric='logloss')\n",
    "xgb_clf.fit(x_train, y_train)\n",
    "\n",
    "y_pred_xgb = xgb_clf.predict(x_test)\n",
    "class_report_xgb = classification_report(y_test, y_pred_xgb)\n",
    "print(class_report_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e44ec1e",
   "metadata": {},
   "source": [
    "xgboost模型的效果较好"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75078639",
   "metadata": {},
   "source": [
    "## 参数调整"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57df4ae2",
   "metadata": {},
   "source": [
    "从以上模型的混淆矩阵可以看出，随机森林模型、xgboost模型、stacking模型具有良好的效果，故使用网格搜索进行调参"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc3b02e",
   "metadata": {},
   "source": [
    "### Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8bd95028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "base_models = [\n",
    "    ('mlp', MLPClassifier()), \n",
    "    ('rf', RandomForestClassifier()),\n",
    "    ('svm', SVC()),\n",
    "]\n",
    "\n",
    "meta_model = LogisticRegression()\n",
    "\n",
    "stacking_model = StackingClassifier(estimators=base_models, final_estimator=meta_model)\n",
    "\n",
    "# 定义参数网格\n",
    "param_grid = {\n",
    "   'rf__n_estimators': [10, 50],\n",
    "    'svm__kernel': ['linear', 'rbf'],\n",
    "}\n",
    "\n",
    "# 实例化网格搜索对象\n",
    "grid_search = GridSearchCV(stacking_model, param_grid, cv=5, n_jobs=-1,verbose=1)\n",
    "\n",
    "# 进行网格搜索\n",
    "grid_search.fit(x_train, y_train)\n",
    "\n",
    "# 选择最佳模型\n",
    "best_stacking_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9271408c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       412\n",
      "           1       0.87      0.38      0.53        53\n",
      "\n",
      "    accuracy                           0.92       465\n",
      "   macro avg       0.90      0.69      0.74       465\n",
      "weighted avg       0.92      0.92      0.91       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_sta = best_stacking_model.predict(x_test)\n",
    "class_report_sta = classification_report(y_test, y_pred_sta)\n",
    "print(class_report_sta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "835e2795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_sta = confusion_matrix(y_test, y_pred_sta)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_sta, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('Stacking混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae3b2cb",
   "metadata": {},
   "source": [
    "### 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6f790268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n",
      "Best parameters found: {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'n_estimators':[10,50,100], #决策树的数量\n",
    "    'max_depth':[None,10,20,30],#决策树的最大深度\n",
    "    'min_samples_split':[2,5,10],#决定节点分裂的最小样本数量\n",
    "    'min_samples_leaf':[1,2,4],#决定叶节点的最小样本数量\n",
    "    'max_features':['auto','sqrt','log2'] #考虑分裂时的特征数量\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "grid_search = GridSearchCV(estimator=rf,param_grid=param_grid,cv=5,n_jobs=-1,verbose=1) \n",
    "grid_search.fit(x_train,y_train)\n",
    "print('Best parameters found:',grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "175ea23e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       412\n",
      "           1       0.81      0.40      0.53        53\n",
      "\n",
      "    accuracy                           0.92       465\n",
      "   macro avg       0.87      0.69      0.74       465\n",
      "weighted avg       0.91      0.92      0.91       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_rf = grid_search.best_estimator_\n",
    "y_pred_orf = best_rf.predict(x_test)\n",
    "class_report_orf = classification_report(y_test, y_pred_orf)\n",
    "print(class_report_orf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "9f77a2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0UXp6uoYPH67XX39d//rXvzRu3Dh99913mjx5srp3765XX31Vy5cv19ixY9WhQwf99ttvuu+++zR79mwFBwdr/Pjx8vHxcdhHb/Xq1Vq1apV9KvjPAgICNG7cOE2ZMkUPPfSQbrrpJn366acOGyybTCZ7Sib9300JK1eutG+Z8u9//1tfffWVFi5cqD179qh69eoKDw932Gj5yy+/vOhdtOds2rRJ3377raZPny4/Pz/17NnT/tgvX1/fMolbo0aNtHr1aoeNg3NycrRr1y7t3btX1atX1++//64lS5Y4vC8nJ0enT592aP/b3/5GUQfAJSjqgCtk0qRJqlevngICAnTo0CF9+OGH9hsUzjlx4oR69+6t6tWr6+6771adOnXO29+GDRuUkZGh2bNnO7Sf26IjKytLJ06cUJ06dbRv3z499dRTSkxMVGhoqP3c+++/X59//rleffVVzZs3T82aNbMfe+ihh2Q2m/XAAw8oMTGxzOPLJOmuu+5S/fr17fu0lScmJkahoaG66aab9O233+rw4cMaPXp0mfOys7OVkpJif0TXH9e4HTx40D61u337drVu3dpe8J7Trl07LVu2rNwxnLu5Q5J++uknTZs2TT169LBP6Y4dO1YZGRnn/Qxms9leYJ7z+++/a/DgwfL19dXkyZPVp0+fMvvjJSYmateuXVqzZs15+waAykJRB1whPj4+atq0qU6fPq0RI0aof//+2rJliwYPHqwHH3xQo0ePltlsVmpqqs6ePavS0lLt379fzZs3d1jbJkmZmZkaP368WrRooTvvvNPhWOPGjRUSEqKhQ4dKsm3ZERISotmzZ+uWW25xONfT01N33HGHxo0bV+6NAjExMQoLC3NYR/dH1apVu2BBd85NN90kSUpNTVWNGjXUuXNnSbZ94s6ePauRI0dqy5YtuuGGG9SjRw+ZTCZt2rRJHTt21MmTJ5WWlqYpU6bIarXq008/Pe/mwufWB+bn5+uXX36Rl5eXfv75Z504ccK+1Ul8fLy8vLw0efJk+/v69eunfv36adCgQRddU/fmm29Ksk2jVtYzcQGgMlDUAVfY5s2bFRISoqeeekpDhw7V66+/rhdffFEnTpxQaWmpw7ne3t769NNPy/TRqFEjNWjQQElJSWWeiODh4aF33nlHx48fV40aNex31/65oDunvATuj/6cUF2OvXv3KiIiwl5gHTp0SCaTSTVr1tTbb79tTxGHDRummTNn6rffflP16tV1yy236LbbblN2drZKSkrKPHnjz77//nvdf//9kmyFa3h4uDp06CBJGjdunAIDA8u9Y/jFF1/U77//fsG+mzRpovXr1593g2MAqCom6x/nfgBcEVartdwNg8/tG1daWmp/YHx5D4WXbCnX+TbavVpZrVbl5OSofv36kqTS0lJlZWVd0l2jZ8+e1dmzZ+2pnGRbi/jbb79VaFsSAHAXFHUAAABugPkDAAAAN0BRBwAA4AYo6gAAANwARR0AAIAbuOq2NPFuG1/VQwDgIrmfO/9MVQDG4lWFFYUra4fTe4zz7xZJHQAAgBu46pI6AACAS2Iio5JI6gAAACrd6tWrFRQU5PDs55SUFEVFRSksLEwDBw7U0aNHHd6TmpqqHj16KDQ0VNHR0Rd8JnV5KOoAAICxmUyue1XAmTNnNGfOHIe2jRs3avr06Xr88ceVkpKikpISDRs2TGfPnpUk7dmzRwkJCYqJidHKlSvl7++voUOHqrCw0OnrUtQBAABUotdee02tW7d2aJs/f75iYmLUt29ftW7dWlOnTtXBgwe1a9cuSdLChQsVGRmpQYMGKTg4WImJiSooKFBqaqrT16WoAwAAxmbycN3rEmVmZuqtt97SM888Y2/Lz8/XgQMHFBkZaW9r2bKlGjZsqH379kmS0tLSHI77+voqJCTEftwZ3CgBAACMrYLTpM6wWCyyWCwObWazWWazucy5Z86c0ejRozVs2DA1adLE3n7s2DFJUmBgoMP5/v7+ys7OVl5envLz88sc9/PzU3Z2ttNjJakDAAA4j+TkZN18880Or+Tk5HLPnTZtmurUqaOHH37Yob2oqEiS5O3t7dDu5eUli8Vy0ePOIqkDAADG5sItTeLi4hQbG+vQVl5Kt3LlSn3wwQdavXq1PDw8yj2/uLjYod1iscjb2/uCx728vJweK0UdAADAeZxvqvXPXn/9dZ06dUrdunVzaB8/frz8/f0lSVlZWWrWrJn9WFZWlrp37666devKbDYrKyvL4b1ZWVkKCQlxeqwUdQAAwNhcuKbOWYsXLy6TtPXs2VOjRo3SHXfcoUGDBmnHjh3q2LGjJOnQoUPKzs5Wp06d5OHhofDwcG3fvl39+vWTJBUUFCgjI0NDhgxxegwUdQAAAJfpjwncHzVs2FDNmjVTbGysZs2apeDgYAUGBmr69Onq2rWrgoKCJEmxsbGKj49X+/btFR4erqSkJLVo0UJdunRxegwUdQAAwNgM8JiwAQMGKCcnR5MnT9aZM2fUrVs3TZo0yX48KipK48eP19y5c5Wfn6+IiAglJyfL09PT6WuYrFar1RWDryjvtvFVPQQALpL7eVJVDwGAi3hVYUzkHTHWZX2f/uxFl/Vd2UjqAACAsV0Fa+quBld/XgkAAICLIqkDAADGZoA1dVcCRR0AADA2pl8lMf0KAADgFkjqAACAsTH9KomkDgAAwC2Q1AEAAGNjTZ0kkjoAAAC3QFIHAACMjTV1kkjqAAAA3AJJHQAAMDaSOkkUdQAAwOg8uFFCYvoVAADALZDUAQAAY2P6VRJJHQAAgFsgqQMAAMbG5sOSSOoAAADcAkkdAAAwNtbUSSKpAwAAcAskdQAAwNhYUyeJog4AABgd06+SmH4FAABwCyR1AADA2Jh+lURSBwAA4BZI6gAAgLGxpk4SSR0AAIBbIKkDAADGxpo6SSR1AAAAboGkDgAAGBtr6iRR1AEAAKNj+lUS068AAABugaQOAAAYG9OvkkjqAAAA3AJJHQAAMDaSOkkkdQAAAG6BpA4AABgbd79KIqkDAABwCyR1AADA2FhTJ4miDgAAGB3Tr5KYfgUAAHALJHUAAMDYmH6VRFIHAADgFkjqAACAsbGmThJJHQAAgFsgqQMAAIZmIqmTRFIHAABQKTZs2KDevXurTZs2uvXWW5WUlCSr1SpJ2rlzp4KCghxecXFxDu9PTU1Vjx49FBoaqujoaGVkZFzS9UnqAACAoV0tSd0PP/ygxx57TC1bttTevXs1ZcoU1atXTw8++KDy8vLUuHFjLVu2zH6+t7e3/es9e/YoISFBo0ePVkREhObOnauhQ4dq8+bN8vHxcer6FHUAAMDYro6aTvHx8favg4OD9eGHH2rHjh32oq5BgwZq3rx5ue9duHChIiMjNWjQIElSYmKiOnfurNTUVEVHRzt1faZfAQAAXKC0tFR16tSRJOXl5alu3brnPTctLU2RkZH27319fRUSEqJ9+/Y5fT2SOgAAYGiunH61WCyyWCwObWazWWaz+bzvKSoq0vr165Wenq7Ro0dLknJzc7Vjxw6Fh4fLz89P3bp107Bhw1SzZk3l5eUpPz9fgYGBDv34+fkpOzvb6bFS1AEAAJxHcnKykpKSHNri4+M1YsSIcs8PDQ2VxWKRj4+PnnvuOQUHB0uSHnjgAd11112yWq3au3ev5syZoyNHjmjOnDkqKiqS5LjGTpK8vLyUm5vr9Fgp6gAAgKG5MqmLi4tTbGysQ9uFUrrVq1ersLBQGRkZev7553Xw4EElJCSoWbNm9nNat24tHx8fjR07Vjk5Ofb+iouLHfqyWCzy8vJyeqwUdQAAAOdxsanWP2vZsqUkqU2bNvLy8tLEiRM1fPjwMsVZSEiIJOn48eMKCQmR2WxWVlaWwzlZWVn285zBjRIAAMDQTCaTy16Xw9PTU1arVaWlpWWOpaeny8PDQ4GBgfLw8FB4eLi2b99uP15QUKCMjAx16tTJ6euR1AEAAFymwsJCTZkyRX369FGjRo2UmZmpmTNnqlevXqpZs6Zmz56t0NBQBQYGKj09XTNnzlRMTIz9jtjY2FjFx8erffv2Cg8PV1JSklq0aKEuXbo4PQaKOgAAYGhXw+bDZrNZJSUlGjt2rAoKCuTv768BAwZo8ODB9nMmTZqkwsJCNW3aVMOHD9dDDz1kPxYVFaXx48dr7ty5ys/PV0REhJKTk+Xp6en0GEzWc8+vuEp4t42/+EkADCn386SLnwTAkLyqMCaq/eCyi59UQXkpD138pKsEa+oAAADcANOvAADA0K6G6derAUkdAACAGyCpAwAAhkZSZ0NSBwAA4AZI6gAAgKGR1NmQ1AEAALgBkjoAAGBoJHU2FHUAAMDYqOkkMf0KAADgFkjqAACAoTH9akNSBwAA4AZI6gAAgKGR1NmQ1AEAALgBkjoAAGBoJHU2JHUAAABugKQOAAAYG0GdpMso6nJzc2WxWOTt7S1fX9/KHBMAAAAu0SUVdZs2bdKbb76p9PR0WSwWe3utWrUUERGhRx99VKGhoZU+SAAAgPNhTZ2N00Xdf/7zH82cOVPdu3fX+PHj5efnJ29vb505c0ZZWVnauXOn+vfvr1mzZum2225z5ZgBAADsKOpsnC7qFi1apMmTJ+vee+8t9/g///lPrVixgqIOAACgCjhd1BUWFl50arVNmzY6duzYZQ8KAADAWSR1Nk5vadKhQwe9/PLLys3NLfd4YWGhXnvtNd10002VNjgAAAA4x+mkbvLkyXr00UcVGRmpkJAQBQQEyNvbW8XFxcrKytJXX32lOnXq6I033nDleAEAAByQ1Nk4XdQ1adJEa9as0bZt27Rr1y4dPXpUJ0+elNlsVvPmzdWvXz91795dXl5erhwvAAAAynFJW5p4eHioW7du6tatm6vGAwAAcGkI6iTxmDAAAAC3wGPCAACAobGmzoaiDgAAGBpFnQ3TrwAAAG7AqaRu3bp1l9Rp7969KzQYAACAS0VSZ+NUUTd69GinOzSZTBR1AAAAV5hTRV1mZqarxwEAAFAxBHWSWFMHAADgFip092tubq7mzJmjvXv3Kj8/v8zxrVu3XvbAAAAAnMGaOpsKJXUTJkzQ1q1bFRERoezsbN1zzz0KCwtTUVGR7r777soeIwAAAC6iQkldWlqaXnvtNXXo0EEffPCB+vXrJ39/fy1evFgHDx6s7DHCwB68q4MWTh2o2PFL9PaGzyVJj957ixIevk2N6/sqLf2Qhk1J0eHjJyVJp/ckldvP4tU7NGxyyhUbN4CKee+/q/TcxPEObTEP9NczEyZV0YhwLSCps6lQUefl5aVq1Wxvvf7663Xw4EH5+/urU6dOeuWVV5SYmFipg4Qx1TBX06RhvRza+t3WVi89Ga3hU1OU+UO2ZjwVrVX/jlP7+6bLarUqpM9zDufXrXWdtvznCa3c9OUVHDmAisrPy1NoWBtNe2GGva2Wb60qHBGuBRR1NhUq6jp27Kht27apXbt26tixo+bNm6dq1app9erVqlevXmWPEQY17tEe+mL/j2ruX9/e9tTgO7Tw3U/11npbajd86lva995E/ePmVvp490H9cPRXhz4mDe+lPV8f0Ydp3IENGEFeXp4aNWqsZs2bV/VQgGtOhdbUJSQkKDg4WJI0YMAAVatWTY888oi2bNmiMWPGVOoAYUyt/+qvR++9RWNm/tfeVtvHW21vbKrU7Qfsbd8e/klZv+SpQ1iLMn3UqeWt4fffqsTkjVdkzAAuX17eKdWpW7eqh4FrjMlkctnLSCqU1AUEBCggIECS5O3trWXLlikvL081a9a0T8vi2lXDXE2LEgfpxQWpOv7zKXv79QG2xO7c+rlzjmblKKBRnTL9DPnnLTrxyylt/YyUDjCKvFOn9OHWLdq4fp0CAgPV664+GjBwkKpXr17VQwPcXoUqsJ9++qnc9t9//12S1Lhx44qPCIb30lPROnmqUK++uc2h3ee6GpKk079bHNqLfreohtnxP0WTyaRHojtrznLHPgBc3Yb963ENGTpMlmKLdn22U6/PfVU5OTl6cvTYqh4a3JmxAjWXqVBR16VLlwtGkl9//XWFBwRjG3RPJ90d1UYR978gq9XqcOyMpUSSVL264392XjWqlyn07uh8o/wa1lbK+7tcO2AAlaplq1b2r8PC2qi0tFSLFi5QwlNjDDeVBRhNhYq6pUuXOnx/9uxZHT58WMuWLdPDDz9cKQODMY195E7Vr11TX78/2aE9+bn+OpKVI0kKbFxXh4793w0RgY3r6t3Njne39rutnbbt+lZ5haddP2gALnPjTSE6fbpIp07lqm5dbqSDa/AHg02FiroOHTqUaYuIiFD79u01YcIE3XfffZc9MBhTz8eSVL2ap0Pbvvcm6rmkdVr94T5tmBevbhHB+uQL236GrZo1UmCTutq261v7+R4eJvWMbK1nk9Zd0bEDqHwZX6XL17e2ateuU9VDAdxepT771c/PT5mZLGq/lh069qu+PfyTw0uSsn7N16Fjv2rO8m3614O3qt9tbdXupmaa9+yDWv/RV9r/3Ql7H61b+atBXR9t3/NdVX0MABU0PXGqtn/ysb7JzNSSxf/R4v8s0OAhj8rDg0eNw3WulrtfN2zYoN69e6tNmza69dZblZSU5LAUKSUlRVFRUQoLC9PAgQN19OhRh/enpqaqR48eCg0NVXR0tDIyMi7p+pVyo4TVatXPP/+sBQsWqFmzZhXpEteI19/+SA3q+ujfz9wnL3N1vf+/dD3xwkqHc9re1Ey/nT6jgz/+XEWjBFBRp08XadzY0TpjOaPmza/XxOemqHefvlU9LLi5q2X29YcfftBjjz2mli1bau/evZoyZYrq1aunBx98UBs3btT06dM1depUtWrVStOmTdOwYcO0du1aeXh4aM+ePUpISNDo0aMVERGhuXPnaujQodq8ebN8fHycur7J+ufV7E4IDg4uU71arVb5+fnp5ZdfVrt27S61SzvvtvEVfi+Aq1vu5+U/Bg6A8XlV4Y5mrZ5y3X6m383sUeH3Dh06VGazWUlJSbrnnnt08803a8KECZKk77//Xj179tSSJUsUERGh+Ph4lZaW6vXXX5ck5efnq3PnznruuecUHR3t1PUq5UYJDw8P1atXT82aNWOfOgAAcEVdrTdKlJaWqk6dOsrPz9eBAwf0xBNP2I+1bNlSDRs21L59+xQREaG0tDQlJCTYj/v6+iokJET79u1zbVF34sQJ3XHHHbruuusc2rOzs/X111+ra9euFekWAADgqmKxWGSxOG67ZTabZTabz/ueoqIirV+/Xunp6Ro9erSOHTsmSQoMDHQ4z9/fX9nZ2crLy1N+fn6Z435+fsrOznZ6rBUq6saNG6ebb765TFF36tQpJSQkaM+ePRXpFgAA4JK5MqhLTk5WUpLj0pH4+HiNGDGi3PNDQ0NlsVjk4+Oj5557TsHBwdq9e7ck21O4/sjLy0sWi0VFRUXnPZ6bm+v0WJ0u6rKzs7Vq1SpJtvVzS5cuVe3ate3HS0tLtWPHDjVo0MDpiwMAAFzN4uLiFBsb69B2oZRu9erVKiwsVEZGhp5//nkdPHhQt912mySpuLjY4VyLxSJvb297f+Ud9/LycnqsThd19erV06ZNm2SxWGQymbR161aH9XMmk0l+fn568cUXnb44AADA5XLlmrqLTbX+WcuWLSVJbdq0kZeXlyZOnKj+/ftLkrKyshx2CcnKylL37t1Vt25dmc1mZWVlOfSVlZWlkJAQp6/tdFFnNpv1/vvvS7Ld/bpkyRI1bdrU6QsBAABcSzw9PWW1WuXj46OAgADt2LFDHTt2lCQdOnRI2dnZ6tSpkzw8PBQeHq7t27erX79+kqSCggJlZGRoyJAhTl+vQmvq1q9fr7p165ZpP336tKpVq6bq1atXpFsAAIBLdjXc/FpYWKgpU6aoT58+atSokTIzMzVz5kz16tVLNWvWVGxsrGbNmqXg4GAFBgZq+vTp6tq1q4KCgiRJsbGxio+PV/v27RUeHq6kpCS1aNFCXbp0cXoMFSrqRo8erT59+mjQoEEO7UuWLNHevXs1b968inQLAABwyTw8qr6qM5vNKikp0dixY1VQUCB/f38NGDBAgwcPliQNGDBAOTk5mjx5ss6cOaNu3bpp0qRJ9vdHRUVp/Pjxmjt3rvLz8xUREaHk5GR5enqe75JlVGjz4bCwML3zzjsKDg52aD9w4IAefvhhff7555fapR2bDwPui82HAfdVlZsP3/TMZpf1fWDaHS7ru7JV6GF8vr6+OnHiRJn2nJwclZaWXvagAAAAnGUyue5lJBUq6qKiojRjxgyHB9GePHlSs2fPVkRERKUNDgAAAM6p8Jq62NhY9ezZU61atZKnp6e+//571a9fX//+978reYgAAADnd7U+JuxKq1BRV6tWLa1YsUKpqanas2ePrFarYmJi1KdPH9WoUaOyxwgAAICLqPCyRk9PT/Xs2VM9e/a0t506dUqrVq2yb7IHAADgagR1Npd9r4rFYtG2bdu0Zs0affzxx5JEUQcAAHCFVbio2717t9auXatNmzapoKBALVq00KhRo3T33XdX5vgAAAAuiDV1NpdU1B0+fFhr1qzR2rVrdfz4cQUEBCg6OlqLFy/WjBkzLun5ZAAAAJWBos7G6aLuvvvu01dffSU/Pz/deeed6tGjh8LCwlRSUqJFixa5cowAAAC4CKeLuszMTNWuXVtdu3ZVVFSUwsLCJFEdAwCAqkUpYuN0Ubdjxw5t3LhRa9euVUpKiurXr68777xTt912G4UdAABAFXP6iRI+Pj669957tWzZMm3ZskX9+/fXzp07FRsbK0lKSUlRZmamywYKAABQHpPJ5LKXkVToMWEBAQEaNmyYNmzYoHfeeUf9+/fXtm3bdM8996h3796VPUYAAABcxGXvUxcWFqawsDCNGzdOH3/8sdauXVsZ4wIAAHCKwQI1l7nsou4cT09Pde3aVV27dq2sLgEAAOCkSivqAAAAqoLR1r65CkUdAAAwNGo6mwrdKAEAAICrC0kdAAAwNKZfbUjqAAAA3ABJHQAAMDSCOhuSOgAAADdAUgcAAAyNNXU2JHUAAABugKQOAAAYGkGdDUUdAAAwNKZfbZh+BQAAcAMkdQAAwNAI6mxI6gAAANwASR0AADA01tTZkNQBAAC4AZI6AABgaAR1NiR1AAAAboCkDgAAGBpr6mxI6gAAANwASR0AADA0kjobijoAAGBo1HQ2TL8CAAC4AZI6AABgaEy/2pDUAQAAuAGSOgAAYGgEdTYkdQAAAG6ApA4AABgaa+psSOoAAADcAEkdAAAwNII6G4o6AABgaB5UdZKYfgUAAKgUmZmZGjx4sNq0aaPOnTtr3Lhxys3NlSTt3LlTQUFBDq+4uDiH96empqpHjx4KDQ1VdHS0MjIyLun6FHUAAMDQTCbXvS7F1KlT1aFDB61YsUKJiYnatWuXxo4dK0nKy8tT48aNtXnzZvtr6tSp9vfu2bNHCQkJiomJ0cqVK+Xv76+hQ4eqsLDQ6esz/QoAAFAJZs6cKT8/P0lScHCwCgoKNGbMGJ0+fVp5eXlq0KCBmjdvXu57Fy5cqMjISA0aNEiSlJiYqM6dOys1NVXR0dFOXZ+kDgAAGJrJZHLZ61KcK+jOqVGjhs6ePSvJltTVrVv3vO9NS0tTZGSk/XtfX1+FhIRo3759Tl+fpA4AAOA8LBaLLBaLQ5vZbJbZbL7g+6xWq1atWqU2bdrI29tbubm52rFjh8LDw+Xn56du3bpp2LBhqlmzpvLy8pSfn6/AwECHPvz8/JSdne30WCnqAACAoXm48ObX5ORkJSUlObTFx8drxIgR531PcXGxJk+erLS0NC1fvlyS9MADD+iuu+6S1WrV3r17NWfOHB05ckRz5sxRUVGRJMnb29uhHy8vL/uNFs6gqAMAADiPuLg4xcbGOrRdKKXLzs7WqFGjdOzYMS1ZskShoaGSpGbNmtnPad26tXx8fDR27Fjl5OTY+ysuLnboy2KxyMvLy+mxUtQBAABDc+VjwpyZaj3n0KFDGjhwoG644QatWbNG9evXP++5ISEhkqTjx48rJCREZrNZWVlZDudkZWXZz3MGN0oAAABDu1q2NHnyySfVtm1bvfHGGxcs6CQpPT1dHh4eCgwMlIeHh8LDw7V9+3b78YKCAmVkZKhTp05OX5+kDgAA4DIdOnRI+/fv19ChQ3X06FGHY/Xq1dOCBQsUGhqqwMBApaena+bMmYqJibHfERsbG6v4+Hi1b99e4eHhSkpKUosWLdSlSxenx0BRBwAADM2kqn9M2K+//ipJGjlyZJljEydOlCRNmjRJhYWFatq0qYYPH66HHnrIfk5UVJTGjx+vuXPnKj8/XxEREUpOTpanp6fTYzBZrVbrZX6OSuXdNr6qhwDARXI/T7r4SQAMyasKY6K7kj93Wd/vx7V3Wd+VjaQOAAAYmiu3NDESbpQAAABwAyR1AADA0Fy5pYmRkNQBAAC4AZI6AABgaAR1NhR1AADA0Dyo6iQx/QoAAOAWSOoAAIChEdTZkNQBAAC4AZI6AABgaGxpYkNSBwAA4AZI6gAAgKER1NmQ1AEAALgBkjoAAGBo7FNnQ1EHAAAMjZLOhulXAAAAN0BSBwAADI0tTWxI6gAAANwASR0AADA0D4I6SSR1AAAAboGkDgAAGBpr6mxI6gAAANwASR0AADA0gjobijoAAGBoTL/aMP0KAADgBkjqAACAobGliQ1JHQAAgBsgqQMAAIbGmjobkjoAAAA3QFIHAAAMjZzOhqQOAADADZDUAQAAQ/NgTZ0kijoAAGBw1HQ2TL8CAAC4AZI6AABgaGxpYkNSBwAA4AZI6gAAgKER1NmQ1AEAALgBkjoAAGBobGliQ1IHAADgBkjqAACAoRHU2VDUAQAAQ2NLExumXwEAANzAVZfUHfn431U9BAAu8ntxaVUPAYCLeFXzrLJrk1DZ8HMAAABwAxR1AADA0Ewmk8telyIzM1ODBw9WmzZt1LlzZ40bN065ubn24ykpKYqKilJYWJgGDhyoo0ePOrw/NTVVPXr0UGhoqKKjo5WRkXFJ16eoAwAAqARTp05Vhw4dtGLFCiUmJmrXrl0aO3asJGnjxo2aPn26Hn/8caWkpKikpETDhg3T2bNnJUl79uxRQkKCYmJitHLlSvn7+2vo0KEqLCx0+vpX3Zo6AACAS+Fxldz8OnPmTPn5+UmSgoODVVBQoDFjxuj06dOaP3++YmJi1LdvX0m2ArBnz57atWuXIiIitHDhQkVGRmrQoEGSpMTERHXu3FmpqamKjo526vokdQAAAJXgXEF3To0aNXT27Fnl5+frwIEDioyMtB9r2bKlGjZsqH379kmS0tLSHI77+voqJCTEftwZJHUAAMDQXJnUWSwWWSwWhzaz2Syz2XzB91mtVq1atUpt2rTRyZMnJUmBgYEO5/j7+ys7O1t5eXnKz88vc9zPz0/Z2dlOj5WiDgAAGJorNx9OTk5WUlKSQ1t8fLxGjBhx3vcUFxdr8uTJSktL0/Lly1VUVCRJ8vb2djjPy8tLFovlgsf/eKPFxVDUAQAAnEdcXJxiY2Md2i6U0mVnZ2vUqFE6duyYlixZotDQUKWnp0uyFXt/ZLFY5O3tbe+vvONeXl5Oj5U1dQAAwNA8TK57mc1m+fj4OLzOV9QdOnRI9957r2rWrKk1a9YoPDxcktS4cWNJUlZWlsP5WVlZCgwMVN26dWU2m8s93rRpU+d/DpfwMwMAAMB5PPnkk2rbtq3eeOMN1a9f397euHFjBQQEaMeOHfa2Q4cOKTs7W506dZKHh4fCw8O1fft2+/GCggJlZGSoU6dOTl+f6VcAAGBoLlxS57RDhw5p//79Gjp0aJlNhevVq6fY2FjNmjVLwcHBCgwM1PTp09W1a1cFBQVJkmJjYxUfH6/27dsrPDxcSUlJatGihbp06eL0GCjqAAAALtOvv/4qSRo5cmSZYxMnTtSAAQOUk5OjyZMn68yZM+rWrZsmTZpkPycqKkrjx4/X3LlzlZ+fr4iICCUnJ8vT0/ln6pqsVqv18j9K5fmloKSqhwDARapXuwr+nAbgEnW8nS8+KtvTG751Wd8v9LzBZX1XNtbUAQAAuAGmXwEAgKGRUNnwcwAAAHADJHUAAMDQroa7X68GFHUAAMDQPKjqJDH9CgAA4BZI6gAAgKER1NmQ1AEAALgBkjoAAGBoHiR1kkjqAAAA3AJJHQAAMDTufrUhqQMAAHADJHUAAMDQCOpsKOoAAIChcaOEDdOvAAAAboCkDgAAGJpJRHUSSR0AAIBbIKkDAACGxpo6G5I6AAAAN0BSBwAADI2kzoakDgAAwA2Q1AEAAEMzsfuwJIo6AABgcEy/2jD9CgAA4AZI6gAAgKEx+2pDUgcAAOAGSOoAAICheRDVSSKpAwAAcAskdQAAwNC4+9WGpA4AAMANkNQBAABDY0mdDUUdAAAwNA9R1UlMvwIAALgFkjoAAGBoTL/akNQBAAC4AZI6AABgaGxpYkNSBwAA4AZI6gAAgKHxmDAbkjoAAAA3QFIHAAAMjaDOhqIOAAAYGtOvNky/AgAAuAGSOgAAYGgEdTYkdQAAAG6ApA4AABgaCZUNPwcAAAA3QFEHAAAMzWQyuexVEZmZmbrnnnu0e/due9vOnTsVFBTk8IqLi3N4X2pqqnr06KHQ0FBFR0crIyPjkq7L9CsAAEAl2L9/v+bPn6///e9/+v333x2O5eXlqXHjxlq2bJm9zdvb2/71nj17lJCQoNGjRysiIkJz587V0KFDtXnzZvn4+Dh1fZI6AABgaCYXvi7F5s2bZTabNW/evDLH8vLy1KBBAzVv3tz+atSokf34woULFRkZqUGDBik4OFiJiYkqKChQamqq09cnqQMAAIZ2tWw+PGrUKJlMJh07dqzMsby8PNWtW/e8701LS1NCQoL9e19fX4WEhGjfvn2Kjo526voUdQAAAOdhsVhksVgc2sxms8xmc5lzL7QGLzc3Vzt27FB4eLj8/PzUrVs3DRs2TDVr1lReXp7y8/MVGBjo8B4/Pz9lZ2c7PVaKOgAAYGiuzOmSk5OVlJTk0BYfH68RI0ZcUj8PPPCA7rrrLlmtVu3du1dz5szRkSNHNGfOHBUVFUlyXGMnSV5eXsrNzXX6GhR1AAAA5xEXF6fY2FiHtvJSuotp1qyZ/evWrVvLx8dHY8eOVU5Ojr2/4uJih/dYLBZ5eXk5fQ2KOgAAYGiuXFJ3vqnWyxUSEiJJOn78uEJCQmQ2m5WVleVwTlZWlv08Z3D3KwAAwBWWnp4uDw8PBQYGysPDQ+Hh4dq+fbv9eEFBgTIyMtSpUyen+ySpAwAAhlbRTYKvpNmzZys0NFSBgYFKT0/XzJkzFRMTY78jNjY2VvHx8Wrfvr3Cw8OVlJSkFi1aqEuXLk5fg6IOAADgCpg0aZIKCwvVtGlTDR8+XA899JD9WFRUlMaPH6+5c+cqPz9fERERSk5Olqenp9P9m6xWq9UVA6+oXwpKqnoIAFykerWr/69pABVTx9v54qOyrdhz3GV9x7QNcFnflY2kDgAAGJoRpl+vBG6UAAAAcAMkdQAAwNDI6WxI6gAAANwASR0AADA01tTZkNQBAAC4AZI6AABgaCRUNvwcAAAA3ABJHQAAMDTW1NlQ1AEAAEOjpLNh+hUAAMANkNQBAABDY/bVhqQOAADADZDUAQAAQ/NgVZ0kkjoAAAC3QFIHAAAMjTV1NiR1AAAAboCkDgAAGJqJNXWSSOoAAADcAkkdAAAwNNbU2VDUAQAAQ2NLExuni7q+ffvq9OnTTp2bmppa4QEBAADg0jld1N1///1KTEzU4MGDVb16dVeOCQAAwGlMv9o4XdTFxMRo+fLlql27tgYPHuzKMQEAAOASOX33q8lk0sCBA7V+/XpXjgcAAOCSmEyuexmJyWq1Wqt6EH/0S0FJVQ8BgItUr2awfyEBOK2Ot2eVXXvz17+4rO87bmzosr4rG3e/AgAAQ2PzYRs2HwYAAHADJHUAAMDQPAjqJFHUAQAAg2P61YbpVwAAADfgVFK3bt26S+q0d+/eFRoMAADApTLa1iOu4tSWJsHBwc53aDLp66+/rvCA2NIEcF9saQK4r6rc0mTbNydd1nfXoPou67uyOZXUZWZmunocAAAAFcKaOhvW1AEAALiBCt39mpubqzlz5mjv3r3Kz88vc3zr1q2XPTAAAABnsKWJTYWSugkTJmjr1q2KiIhQdna27rnnHoWFhamoqEh33313ZY8RAAAAF1GhpC4tLU2vvfaaOnTooA8++ED9+vWTv7+/Fi9erIMHD1b2GOEGtm7eqKX/ma9jR4+odp066t03WoOGDJPJZFJ21gm9OutFfb5rp6pVq6bwdu31eMJYNfHzr+phA7iIg99+ozmzZmjf3i9Vs2ZNder8D41MGKPaderYzzl+7KimT31Wvfr0VY9efapusHBbrKmzqVBS5+XlpWrVbPXg9ddfby/kOnXqpA0bNlTe6OA2fjx8SA/FPqrkxSkaODhOixfM0+p3V0iSXp8zS/6BTfVq8mI9/+JsZZ84rrFP/Etnz56t4lEDuJiZLzyvm//WXguXvqXxzz6vPV/s1nMTnpYkHT3yoxInT9SAmH76PO2zKh4p3JnJ5LqXkVQoqevYsaO2bdumdu3aqWPHjpo3b56qVaum1atXq169epU9RriBwUOH279u9dcgbf94mz5P26l7/nm/ho98Uo2b+NmPPzFmvIY9MkDHjvyoZte3qIrhAnDSlGkv2X9//3pDkH4rLNBzE57W76dP6/O0ncrNzVHywqV66P7oKh4p4P4qVNQlJCRo7969kqQBAwbo448/1iOPPCJvb2+98MILlTk+uKnSs6VqULu2JDkUdJJkrlHDfg6Aq1t5v7/nUvZ7/hmjfvfeXxXDwjXGYIGay1SoqAsICFBAQIAkydvbW8uWLVNeXp5q1qxpn5YFynP6dJG2pm7UgYyvNGxEQrnnrHtvlRo1bqKmza6/soMDcFmsVqvWvveuQkLD5OXtXdXDAa45FarAfvrpp3Lbf//9d0lS48aNKz4iuK2ov7eVxWJRzZo+enLcRP31BscnlVitVi1643WtfW+lps2cwx8IgIGUFBfrpWlT9cXuXUpeuLSqh4NrjIfRFr+5SIX+r9mlSxeZLvADvJzHhMF9LUp5V4WFhco8kKFXZkzXoe+/U9y/RkmSCgrylfjsM/ryi12aNnOObonsWrWDBeC0n37K1oQxT+r48aOaO3+RbgxpXdVDAq5JFSrqli51/Cvs7NmzOnz4sJYtW6aHH364UgYG99P8+r9IkkJah8nLy1svJT6rQY88pqLTRRoRN0heXl5a9OYqBQQ2q+KRAnDWkR8Pa/iQQWr51xu0/J33VK+ecZ6TCfdBTmdToS1NOnTo4PCKiIjQ/fffrzlz5ui9996r7DHCDXl6espqtar0bKlenj5FPj61NPeNZRR0gMFMHDdarduEa3bSPAo64P/LzMzUPffco927dzu0p6SkKCoqSmFhYRo4cKCOHj3qcDw1NVU9evRQaGiooqOjlZGRcUnXrdRnv/r5+SkzM7Myu4Qb+K2wUFMnjdOuz7brh+8OavPG9/X6nJd125095eHhoU8+2qao2+7ULz9n69jRH+2vvFOnqnroAC7gyI+HlXlgv26/s4eOHzuqo0d+tL8KCwqqeni4lphc+LoE+/fv18iRIxUTE6MDBw44HNu4caOmT5+uxx9/XCkpKSopKdGwYcPsd4vv2bNHCQkJiomJ0cqVK+Xv76+hQ4eqsLDQ6etXyo0SVqtVP//8sxYsWKBmzUha4Mhcw6ySkmI9/+w4FRYWqkkTf0XH9Nf9AwYpJ+dXlZaWaM6sFzVn1osO77t/wCDFjxpdRaMGcDEnf/1VkvTM6CfKHHvq6fG69/7+V3pIuEZdLU+U2Lx5s8xms+bNm6dBgwY5HJs/f75iYmLUt29fSdLUqVPVs2dP7dq1SxEREVq4cKEiIyPt70tMTFTnzp2Vmpqq6Gjn9nmstBslrFar/Pz89PLLL1ekS7ix6tXNmjxtZrnHmjTx16e791/hEQGoDG1v/pvS9h64+ImS0+cBRjZq1CiZTCYdO3bMoT0/P18HDhzQE0/83x9ALVu2VMOGDbVv3z5FREQoLS1NCQn/t9WXr6+vQkJCtG/fPtcWdX++UcLDw0P16tVTs2bN2IYCAABcUa7c0cRischisTi0mc1mmc3mcsZR/kDOFXmBgYEO7f7+/srOzlZeXp7y8/PLHPfz81N2drbTY61QBXbixAndcccduu666xzas7Oz9fXXX6trV7ajAAAAxpecnKykpCSHtvj4eI0YMcLpPoqKiiTZHtjwR15eXrJYLBc8npub6/R1KlTUjRs3TjfffHOZou7UqVNKSEjQnj17KtItAADAJXPlirq4uDjFxsY6tJWX0l3IufOLi4sd2i0Wi7y9vS943MvLy+nrOF3UZWdna9WqVZJs6+eWLl2q2v//2Z2SVFpaqh07dqhBgwZOXxwAAOBqdr6p1ktx7klbWVlZDjeUZmVlqXv37qpbt67MZrOysrIc3peVlaWQkBCnr+N0UVevXj1t2rRJFotFJpNJW7dudVg/ZzKZ5OfnpxdffPECvQAAAFSyq+Pm1/Nq3LixAgICtGPHDnXs2FGSdOjQIWVnZ6tTp07y8PBQeHi4tm/frn79+kmSCgoKlJGRoSFDhjh9HaeLOrPZrPfff1+SFBwcrCVLlqhp06aX8pkAAACuSbGxsZo1a5aCg4MVGBio6dOnq2vXrgoKCrIfj4+PV/v27RUeHq6kpCS1aNFCXbp0cfoaFVpTt379etWtW7dM++nTp1WtWjVVr169It0CAABcsqtln7oLGTBggHJycjR58mSdOXNG3bp106RJk+zHo6KiNH78eM2dO1f5+fmKiIhQcnKyPD09nb6GyWq1Wi91YP369VOfPn3KbKw3b9487d27V/PmzbvULu1+KSip8HsBXN2qV7v6/+EFUDF1vJ0vPirbF4fzXdb3zdf7uqzvylahx4R99913ioiIKNMeGRmpL7744rIHBQAAgEtToaLO19dXJ06cKNOek5Oj0tLSyx4UAACAs66SR79WuQoVdVFRUZoxY4aOHj1qbzt58qRmz55dboIHAAAA16rQmrqCggLFxsbqm2++UatWreTp6anvv/9e9evX16JFiy7rrljW1AHuizV1gPuqyjV1X/7oujV17ZobZ01dhYo6ybbZcGpqqvbs2SOr1aqgoCD16dNHNWrUuKwBUdQB7ouiDnBfFHVVr8JFXXlOnTql9evXq3///hXug6IOcF8UdYD7qsqibs+PBS7ru23zWi7ru7JVaJ+6P7JYLNq2bZvWrFmjjz/+WJIuq6gDAADApatwUbd7926tXbtWmzZtUkFBgVq0aKFRo0bp7rvvrszxAQAAXJCJSQBJl1jUHT58WGvWrNHatWt1/PhxBQQEKDo6WosXL9aMGTMu6aGzAAAAlYGazsbpou6+++7TV199JT8/P915553q0aOHwsLCVFJSokWLFrlyjAAAALgIp4u6zMxM1a5dW127dlVUVJTCwsIkSSYyTwAAUJUoRSRdQlG3Y8cObdy4UWvXrlVKSorq16+vO++8U7fddhuFHQAAQBWr0JYmx48f19q1a7Vu3Tr98MMPMplM6tevnx566CEFBwdf1oDY0gRwX2xpArivqtzSJP1oocv6Dmvq47K+K9tl71OXnp6utWvXasOGDcrNzVWrVq20bt26CvdHUQe4L4o6wH1R1FW9Stt8uLS0VB9//LHWrl2r2bNnV7gfijrAfVHUAe6rKou6r465rqgLDbwGi7rKQlEHuC+KOsB9UdRVvct+ogQAAEBV4s9FG4o6AABgbFR1kiSPqh4AAAAALh9JHQAAMDQTUZ0kkjoAAAC3QFIHAAAMjQdb2ZDUAQAAuAGSOgAAYGgEdTYkdQAAAG6ApA4AABgbUZ0kijoAAGBwbGliw/QrAACAGyCpAwAAhsaWJjYkdQAAAG6ApA4AABgaQZ0NSR0AAIAbIKkDAADGRlQniaQOAADALZDUAQAAQ2OfOhuSOgAAADdAUgcAAAyNfepsKOoAAIChUdPZMP0KAADgBkjqAACAsRHVSSKpAwAAcAskdQAAwNDY0sSGpA4AAMANkNQBAABDY0sTG5I6AAAAN0BSBwAADI2gzoakDgAAGJvJha9LsGrVKgUFBTm8pkyZYj+ekpKiqKgohYWFaeDAgTp69GiFP3J5SOoAAAAqQV5entq0aaMZM2bY22rVqiVJ2rhxo6ZPn66pU6eqVatWmjZtmoYNG6a1a9fKw6NyMjaKOgAAYGhXy5YmeXl5aty4sZo3b17m2Pz58xUTE6O+fftKkqZOnaqePXtq165dioiIqJTrM/0KAABQCU6dOqW6deuWac/Pz9eBAwcUGRlpb2vZsqUaNmyoffv2Vdr1SeoAAIChuXJLE4vFIovF4tBmNptlNpvLnHvq1Clt2bJF69atU2BgoPr06aNBgwbp2LFjkqTAwECH8/39/ZWdnV1pY6WoAwAAOI/k5GQlJSU5tMXHx2vEiBFlzn388cc1bNgwWSwW7dy5U6+++qpycnLUrVs3SZK3t7fD+V5eXmUKxstBUQcAAAzNlSvq4uLiFBsb69BWXkonSa1atbJ/3aZNG5WWlmrBggXq3r27JKm4uNjhfIvFUqbQuxysqQMAADgPs9ksHx8fh9f5iro/CwkJUVFRkRo0aCBJysrKcjielZVVZkr2clDUAQAAQzOZXPe6HOnp6apdu7b8/PwUEBCgHTt22I8dOnRI2dnZ6tSp02V++v/D9CsAADC4q2NLk6lTp6pLly5q1KiRduzYoQULFmjEiBHy8PBQbGysZs2apeDgYAUGBmr69Onq2rWrgoKCKu36FHUAAACVoKioSKNHj9aZM2d0/fXXa8qUKfZ96QYMGKCcnBxNnjxZZ86cUbdu3TRp0qRKvb7JarVaK7XHy/RLQUlVDwGAi1SvdnX8NQ2g8tXx9qyyax8/VXl3kP5ZQB3n1s9dDVhTBwAA4AaYfgUAAIbGHIANSR0AAIAbIKkDAACG5srHhBkJSR0AAIAbIKkDAACGZmJVnSSKOgAAYHTUdJKYfgUAAHALJHUAAMDQCOpsSOoAAADcAEkdAAAwNLY0sSGpAwAAcAMkdQAAwNDY0sSGpA4AAMANkNQBAABjI6iTRFEHAAAMjprOhulXAAAAN0BSBwAADI0tTWxI6gAAANwASR0AADA0tjSxIakDAABwAyR1AADA0FhTZ0NSBwAA4AYo6gAAANwA068AAMDQmH61IakDAABwAyR1AADA0NjSxIakDgAAwA2Q1AEAAENjTZ0NSR0AAIAbIKkDAACGRlBnQ1IHAADgBkjqAACAsRHVSaKoAwAABseWJjZMvwIAALgBkjoAAGBobGliQ1IHAADgBkjqAACAoRHU2ZDUAQAAuAGSOgAAYGxEdZJI6gAAANwCSR0AADA09qmzoagDAACGxpYmNky/AgAAuAGT1Wq1VvUgAAAAcHlI6gAAANwARR0AAIAboKgDAABwAxR1AAAAboCiDgAAwA1Q1AEAALgBijoAAAA3QFEHAADgBijqAAAA3ABFHSrda6+9pqioKPv3jz/+uIYNG3bFrp+dna2goCClpaVdsWsC1wp+v4GrF0XdNeTpp59WUFCQgoKCFBYWprvvvltr1qxx+XVffPFFzZ4926lzS0pKdPToURePSMrKylJcXJzCw8N1yy23aOHChS6/JuBK/H47Onv2rN5++23dfffdV+R6wNWgWlUPAFdW27Zt9eKLL6qwsFBbt27VmDFj5O3trTvuuMNl1/T29nb63OHDh6tevXp64YUXXDae0tJSDR06VI0aNdKbb76p/fv369lnn5Wfn5969uzpsusCrsbvt80777yjJUuW6Mcff5Sfn59LrwVcTSjqrjFeXl5q3ry5JCkkJEQHDhzQypUry/1H32q1ymQyXdHxWSwWl1/jf//7nw4dOqTFixerfv36CgkJ0c6dO5WSkkJRB0Pj99vm7bff1j333KOCggJt2LDhilwTuBow/XqNa9mypbKysiRJr776qm6//XatWrVK7du310svvSRJ2rZtm3r37q02bdro7rvv1scff+zQx+LFi3XrrbcqLCxMcXFxOnXqlMPxQYMG6emnn7Z/b7FYNHPmTN16661q3bq1unfvruPHjysoKEg7d+7Ue++9p6CgIL366quSpJ9++kkjRoxQ27Zt1aVLF82YMcPhfw4ZGRm6//77FRoaqjvuuEOffvrpBT9zWlqabrrpJtWvX9/eFhERoX379slqtV76DxG4Sl2Lv9+S9O6772rIkCGqVo3cAtcW/ou/xh0+fFiBgYH27/Pz8/XBBx9owYIF8vb2Vnp6ukaOHKmxY8fq5ptv1tatWzV8+HCtX79ezZs313vvvaeZM2fqqaeeUocOHfTRRx9p7ty5atSo0Xmv+cQTT2jfvn0aM2aMbrjhBn311VeyWq3avHmzxo4dq4YNG+qpp55S7dq17VOlzZs319KlS3Xy5ElNnDhR3t7eio+PV15engYPHqz27dtr2bJlysnJ0fTp0y/4mY8ePerwmSXJz89PFotFubm5qlev3uX9UIGrxLX4+y3piieQwNWCou4a9dtvv+n999/Xtm3bHG4SyMvL08SJE+3/I3jkkUc0cOBA9e/fX5IUHBysjRs3asOGDRo2bJjmzZunBx54QIMGDZIk3XTTTfr666+VkZFR7nX37dunLVu2aPny5Wrfvr29z3O8vLxUs2ZN+xTS+vXrderUKa1atUrVq1eXZEsGVqxYofj4eL311luqXr26Zs2apRo1akiSPDw8FBcXd97PXlRUpLp16zq0nVsXdKWmhwBXupZ/v4FrGUXdNSYtLU2hoaGyWCxq0qSJZs6cqU6dOtmP169f3+Ev+/379ystLU1Lliyxt5WUlOj48eP6/fff9eOPPyoiIsLhGkFBQef9R3/Pnj2qVauW/R/8i8nIyNDPP/+sdu3a2dusVqt9mvSbb75R27Zt7f/gn7v+hZjNZhUXFzu0nTlzRpLtfzqAUfH7DVzbKOquMW3atFFiYqK8vb3l5+dXZpriuuuuc/i+pKREQ4cOVa9evRzaa9WqpaKiIlmtVvtf2OdcKO2yWq3y9PR0erwlJSVq1aqV/v3vf5d7vKioqMyYL5a2NW7cWD/++KNDW1ZWlmrVqqU6deo4PTbgasPvN3Bto6i7xnh5eally5ZOn/+Xv/xFR44cOe97fH199eWXXyoyMtLe9uWXX563vxtuuEGnTp1SZmamw7TMOR4eHiopKXG4/qpVq9SgQQPVrl27zPnNmjXT1q1bVVJSYl8UvXv37gt+pptvvlnr1q1TQUGBatWqJUn67LPPHBINwIj4/Qaubdz9igsaNGiQ1q9fr1dffVUHDhzQp59+qscff1y5ubmSpJiYGC1evFgrV67U/v37NX36dB06dOi8/f39739X27ZtNWLECH3wwQfKzMzU8uXL7RuSNmnSRHv27FFGRoaOHDmi3r172xdNp6WlKT09XcnJyVqxYoUk6b777lNWVpbGjx+vr776SuvXr9eiRYsu+Jm6d++uOnXqaNy4ccrMzNSKFSuUmpqqIUOGVNJPDTAGd/z9Bq5lFHW4oJ49e2rcuHF67733dN999+nZZ59VQECAfHx8JEnx8fG66667NH36dA0aNEhWq1UPPvjgefszmUx67bXX1KZNGz3zzDN64IEHlJqaal/L9sgjj+i6667TAw88oE8++UQ+Pj564403ZLVaNWTIEA0dOlRffvmlfQ3OX//6V82YMUO7d+/Wgw8+qJSUFD377LMX/Eze3t5644039Msvv+if//ynFi1apFmzZqlNmzaV9FMDjMEdf7+Ba5nJysZcAAAAhkdSBwAA4AYo6gAAANwARR0AAIAboKgDAABwAxR1AAAAboCiDgAAwA1Q1AEAALgBijoAAAA3QFEHAADgBijqAAAA3ABFHQAAgBugqAMAAHADFHUAAABu4P8BW/eC34/VsWsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_orf = confusion_matrix(y_test, y_pred_orf)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_orf, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('随机森林混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "cfed9fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制ROU曲线\n",
    "fpr_orf, tpr_orf, _ = roc_curve(y_test, best_rf.predict_proba(x_test)[:, 1])\n",
    "roc_auc_orf = auc(fpr_orf, tpr_orf)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_orf, tpr_orf, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_orf)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('FPR')\n",
    "plt.ylabel('TPR')\n",
    "plt.title('随机森林ROC曲线')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c6bc5d2",
   "metadata": {},
   "source": [
    "### xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "189a0c74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 972 candidates, totalling 4860 fits\n",
      "Best parameters found: {'colsample_bytree': 0.8, 'gamma': 0, 'learning_rate': 0.05, 'max_depth': 10, 'min_child_weight': 1, 'n_estimators': 1000, 'objective': 'binary:logistic', 'subsample': 1.0}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'learning_rate':[0.01,0.05,0.1],\n",
    "    'n_estimators':[100,500,1000],\n",
    "    'max_depth':[3,5,10],\n",
    "    'min_child_weight':[1,3,5],\n",
    "    'gamma':[0,0.1,0.2],\n",
    "    'subsample':[0.8,1.0],\n",
    "    'colsample_bytree':[0.8,1.0],\n",
    "    'objective':['binary:logistic']\n",
    "}\n",
    "best_xgb = xgb.XGBClassifier()\n",
    "grid_search = GridSearchCV(estimator=best_xgb,param_grid=param_grid,cv=5,n_jobs=-1,verbose=1)\n",
    "grid_search.fit(x_train,y_train)\n",
    "print('Best parameters found:',grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "a1a67ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95       412\n",
      "           1       0.67      0.42      0.51        53\n",
      "\n",
      "    accuracy                           0.91       465\n",
      "   macro avg       0.80      0.69      0.73       465\n",
      "weighted avg       0.90      0.91      0.90       465\n",
      "\n"
     ]
    }
   ],
   "source": [
    "best_xgb = grid_search.best_estimator_\n",
    "y_pred_oxgb = best_xgb.predict(x_test)\n",
    "class_report_oxgb = classification_report(y_test, y_pred_oxgb)\n",
    "print(class_report_oxgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "b0592bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_oxgb = confusion_matrix(y_test, y_pred_oxgb)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm_oxgb, annot=True, fmt='g', cmap='Blues', \n",
    "            xticklabels=['Predicted 0', 'Predicted 1'], \n",
    "            yticklabels=['Actual 0', 'Actual 1'])\n",
    "plt.title('xgboost混淆矩阵')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "99449533",
   "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>Feature</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.157169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Annual_income</td>\n",
       "      <td>0.142510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Employed_years</td>\n",
       "      <td>0.128894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Family_Members</td>\n",
       "      <td>0.044000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>0.043111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHILDREN</td>\n",
       "      <td>0.029716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Feature  Importance\n",
       "11             Age    0.157169\n",
       "4    Annual_income    0.142510\n",
       "13  Employed_years    0.128894\n",
       "10  Family_Members    0.044000\n",
       "5        EDUCATION    0.043111\n",
       "3         CHILDREN    0.029716"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_feature_importance = best_rf.feature_importances_\n",
    "feature_names = x_train.columns\n",
    "rf_feature_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': rf_feature_importance\n",
    "})\n",
    "sorted_rf_feature_df = rf_feature_df.sort_values(by='Importance', ascending=False).head(6) #筛选出重要特征\n",
    "\n",
    "sorted_rf_feature_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae64e52",
   "metadata": {},
   "source": [
    "可以看出在特征中，前6个重要特征为：Age>Annual_income>Employed_years>Family_Members>EDUCATION>CHILDREN，这对未来银行的信用识别具有一定的指导意义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6685de6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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