teach_Feature_Engineering（Binary）.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9017584",
   "metadata": {},
   "source": [
    "# 特征工程（二分类数据）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc4ffee",
   "metadata": {},
   "source": [
    "## 1. 课题    \n",
    "特征工程实践操作    \n",
    "    \n",
    "## 2. 课前准备    \n",
    "2.1 学员已完成数据预处理实践课程，掌握缺失值处理、重复值处理、数据离散化等常用数据预处理操作。       \n",
    "2.2 准备好用于实践的数据集（本次使用数据预处理实践课程得到的已清洗数据）。\n",
    "    \n",
    "## 3. 教学目标    \n",
    "3.1 熟练掌握特征处理、特征选择的基本流程和方法。      \n",
    "3.2 学会根据实际需求选择合适的特征处理方法。    \n",
    "3.3 能够分析和解决特征处理过程中出现的问题。      \n",
    "    \n",
    "## 4. 教学重点    \n",
    "4.1 特征编码：将文本类型数据通过编码变为分类型或数值型数据。       \n",
    "4.2 特征变换：将原始特征进行转换，如标准化、归一化、离散化等，以增强数据的可学习性。    \n",
    "4.3 特征选择：通过业务知识和相关性、特征重要性等统计数据，筛选出相关性最大的特征集合。    \n",
    "    \n",
    "## 5. 教学难点    \n",
    "5.1 根据实际需求选择合适的特征处理方法。    \n",
    "5.2 特征处理过程中对不符合预期的结果如何进一步处理。    \n",
    "5.3 如何避免漏掉非线性相关特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e4e089",
   "metadata": {},
   "source": [
    "## 实施步骤    \n",
    "### 步骤 1: 启动Jupyter Notebook    \n",
    "+ 在搜索栏输入\"cmd\"命令，启动命令提示符窗口。    \n",
    "+ 输入\"jupyter notebook\"命令，并按回车键启动Jupyter Notebook。   \n",
    "    \n",
    "### 步骤 2: 创建新的Notebook    \n",
    "+ 在Jupyter的Web界面中，点击右上角的 \"New\" 按钮。    \n",
    "+ 选择 \"Python 3\"内核来创建一个新的Python 3 Notebook。    \n",
    "    \n",
    "### 步骤 3: 导入必要的库\n",
    "+ 我们通常使用numpy和pandas库来进行数据清洗，使用sklearn库来进行数据的分类、离散化等特征处理和特征选择，使用matplotlib和seaborn来进行数据的可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a962dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# 定义字体，在图表中正常显示汉字\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# 在图表中正常显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec9070c",
   "metadata": {},
   "source": [
    "### 步骤 4: 导入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "728d8923",
   "metadata": {},
   "outputs": [
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       "     乘客ID  是否幸存  仓位等级                                                 姓名  \\\n",
       "0       1     0     3                            Braund, Mr. Owen Harris   \n",
       "1       2     1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2       3     1     3                             Heikkinen, Miss. Laina   \n",
       "3       4     1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4       5     0     3                           Allen, Mr. William Henry   \n",
       "..    ...   ...   ...                                                ...   \n",
       "710   886     0     3               Rice, Mrs. William (Margaret Norton)   \n",
       "711   887     0     2                              Montvila, Rev. Juozas   \n",
       "712   888     1     1                       Graham, Miss. Margaret Edith   \n",
       "713   890     1     1                              Behr, Mr. Karl Howell   \n",
       "714   891     0     3                                Dooley, Mr. Patrick   \n",
       "\n",
       "         性别    年龄  兄弟姐妹个数  父母子女个数              船票信息       票价    客舱 登船港口  年龄段  \n",
       "0      male  22.0       1       0         A/5 21171   7.2500   NaN    S    2  \n",
       "1    female  38.0       1       0          PC 17599  71.2833   C85    C    3  \n",
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       "711    male  27.0       0       0            211536  13.0000   NaN    S    2  \n",
       "712  female  19.0       0       0            112053  30.0000   B42    S    2  \n",
       "713    male  26.0       0       0            111369  30.0000  C148    C    2  \n",
       "714    male  32.0       0       0            370376   7.7500   NaN    Q    3  \n",
       "\n",
       "[715 rows x 13 columns]"
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用Pandas导入已清洗好的数据文件\n",
    "data_clean = pd.read_csv('data_clean.csv')\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810d3400",
   "metadata": {},
   "source": [
    "### 步骤 5: 对文本变量进行转换    \n",
    "5.1 使用“one-hot（独热）”编码方式对“性别”特征进行类别编码    \n",
    "首先查看这个特征包含多少个类别，每个类别数量多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "56940652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      453\n",
       "female    261\n",
       "0           1\n",
       "Name: 性别, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['性别'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "050a9d68",
   "metadata": {},
   "source": [
    "此时发现异常值“0”，为了避免与后面的独热编码相混淆，我们把“0”修改为“unknown”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f8ddd2c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male       453\n",
       "female     261\n",
       "unknown      1\n",
       "Name: 性别, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 注意此时“0”为文本格式，需要加上引号，否则无法完成替换\n",
    "data_clean.loc[data_clean['性别'] == '0','性别'] = 'unknown'\n",
    "data_clean['性别'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214aa28e",
   "metadata": {},
   "source": [
    "“性别”特征分类数量较少，可以使用独热编码（One-Hot Encoding）进行编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "99802254",
   "metadata": {},
   "outputs": [
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       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>715 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     乘客ID  是否幸存  仓位等级                                                 姓名  \\\n",
       "0       1     0     3                            Braund, Mr. Owen Harris   \n",
       "1       2     1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2       3     1     3                             Heikkinen, Miss. Laina   \n",
       "3       4     1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4       5     0     3                           Allen, Mr. William Henry   \n",
       "..    ...   ...   ...                                                ...   \n",
       "710   886     0     3               Rice, Mrs. William (Margaret Norton)   \n",
       "711   887     0     2                              Montvila, Rev. Juozas   \n",
       "712   888     1     1                       Graham, Miss. Margaret Edith   \n",
       "713   890     1     1                              Behr, Mr. Karl Howell   \n",
       "714   891     0     3                                Dooley, Mr. Patrick   \n",
       "\n",
       "       年龄  兄弟姐妹个数  父母子女个数              船票信息       票价    客舱 登船港口  年龄段  \\\n",
       "0    22.0       1       0         A/5 21171   7.2500   NaN    S    2   \n",
       "1    38.0       1       0          PC 17599  71.2833   C85    C    3   \n",
       "2    26.0       0       0  STON/O2. 3101282   7.9250   NaN    S    2   \n",
       "3    35.0       1       0            113803  53.1000  C123    S    3   \n",
       "4    35.0       0       0            373450   8.0500   NaN    S    3   \n",
       "..    ...     ...     ...               ...      ...   ...  ...  ...   \n",
       "710  39.0       0       5            382652  29.1250   NaN    Q    3   \n",
       "711  27.0       0       0            211536  13.0000   NaN    S    2   \n",
       "712  19.0       0       0            112053  30.0000   B42    S    2   \n",
       "713  26.0       0       0            111369  30.0000  C148    C    2   \n",
       "714  32.0       0       0            370376   7.7500   NaN    Q    3   \n",
       "\n",
       "     性别分类_female  性别分类_male  性别分类_unknown  \n",
       "0              0          1             0  \n",
       "1              1          0             0  \n",
       "2              1          0             0  \n",
       "3              1          0             0  \n",
       "4              0          1             0  \n",
       "..           ...        ...           ...  \n",
       "710            1          0             0  \n",
       "711            0          1             0  \n",
       "712            1          0             0  \n",
       "713            0          1             0  \n",
       "714            0          1             0  \n",
       "\n",
       "[715 rows x 15 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean = pd.get_dummies(data_clean, columns=['性别'], prefix=['性别分类'])\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a72a6548",
   "metadata": {},
   "source": [
    "5.2 直接使用数值对“登船港口”特征进行类别编码    \n",
    "首先查看这个特征包含多少个类别，每个类别数量多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0b4339ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    554\n",
       "C    130\n",
       "Q     28\n",
       "0      1\n",
       "Name: 登船港口, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['登船港口'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c61af0",
   "metadata": {},
   "source": [
    "再次发现异常值“0”，修改为“unknown”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e521e68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    554\n",
       "C    130\n",
       "Q     28\n",
       "0      1\n",
       "Name: 登船港口, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean.loc[data_clean['登船港口'] == 0,'登船港口'] = 'unknown'\n",
    "data_clean['登船港口'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a194991d",
   "metadata": {},
   "source": [
    "使用replace()函数将类别替换为数值1234"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "16ce3ca5",
   "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>乘客ID</th>\n",
       "      <th>是否幸存</th>\n",
       "      <th>仓位等级</th>\n",
       "      <th>姓名</th>\n",
       "      <th>年龄</th>\n",
       "      <th>兄弟姐妹个数</th>\n",
       "      <th>父母子女个数</th>\n",
       "      <th>船票信息</th>\n",
       "      <th>票价</th>\n",
       "      <th>客舱</th>\n",
       "      <th>登船港口</th>\n",
       "      <th>年龄段</th>\n",
       "      <th>性别分类_female</th>\n",
       "      <th>性别分类_male</th>\n",
       "      <th>性别分类_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>715 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     乘客ID  是否幸存  仓位等级                                                 姓名  \\\n",
       "0       1     0     3                            Braund, Mr. Owen Harris   \n",
       "1       2     1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2       3     1     3                             Heikkinen, Miss. Laina   \n",
       "3       4     1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4       5     0     3                           Allen, Mr. William Henry   \n",
       "..    ...   ...   ...                                                ...   \n",
       "710   886     0     3               Rice, Mrs. William (Margaret Norton)   \n",
       "711   887     0     2                              Montvila, Rev. Juozas   \n",
       "712   888     1     1                       Graham, Miss. Margaret Edith   \n",
       "713   890     1     1                              Behr, Mr. Karl Howell   \n",
       "714   891     0     3                                Dooley, Mr. Patrick   \n",
       "\n",
       "       年龄  兄弟姐妹个数  父母子女个数              船票信息       票价    客舱 登船港口  年龄段  \\\n",
       "0    22.0       1       0         A/5 21171   7.2500   NaN    1    2   \n",
       "1    38.0       1       0          PC 17599  71.2833   C85    2    3   \n",
       "2    26.0       0       0  STON/O2. 3101282   7.9250   NaN    1    2   \n",
       "3    35.0       1       0            113803  53.1000  C123    1    3   \n",
       "4    35.0       0       0            373450   8.0500   NaN    1    3   \n",
       "..    ...     ...     ...               ...      ...   ...  ...  ...   \n",
       "710  39.0       0       5            382652  29.1250   NaN    3    3   \n",
       "711  27.0       0       0            211536  13.0000   NaN    1    2   \n",
       "712  19.0       0       0            112053  30.0000   B42    1    2   \n",
       "713  26.0       0       0            111369  30.0000  C148    2    2   \n",
       "714  32.0       0       0            370376   7.7500   NaN    3    3   \n",
       "\n",
       "     性别分类_female  性别分类_male  性别分类_unknown  \n",
       "0              0          1             0  \n",
       "1              1          0             0  \n",
       "2              1          0             0  \n",
       "3              1          0             0  \n",
       "4              0          1             0  \n",
       "..           ...        ...           ...  \n",
       "710            1          0             0  \n",
       "711            0          1             0  \n",
       "712            1          0             0  \n",
       "713            0          1             0  \n",
       "714            0          1             0  \n",
       "\n",
       "[715 rows x 15 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['登船港口'] = data_clean['登船港口'].replace(['S','C','Q','unknown'],[1,2,3,4])\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b791da9e",
   "metadata": {},
   "source": [
    "发现替换后的数值不是整数，而是浮点数，说明该特征存在缺失值。    \n",
    "此时将缺失值也替换为“unknown”，然后再替换为数值4。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "960b335d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1          554\n",
       "2          130\n",
       "3           28\n",
       "unknown      2\n",
       "0            1\n",
       "Name: 登船港口, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['登船港口'].fillna('unknown',inplace=True)\n",
    "data_clean['登船港口'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "516beb48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>886</td>\n",
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       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
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       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
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       "      <th>714</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
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       "<p>715 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "     乘客ID  是否幸存  仓位等级                                                 姓名  \\\n",
       "0       1     0     3                            Braund, Mr. Owen Harris   \n",
       "1       2     1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2       3     1     3                             Heikkinen, Miss. Laina   \n",
       "3       4     1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4       5     0     3                           Allen, Mr. William Henry   \n",
       "..    ...   ...   ...                                                ...   \n",
       "710   886     0     3               Rice, Mrs. William (Margaret Norton)   \n",
       "711   887     0     2                              Montvila, Rev. Juozas   \n",
       "712   888     1     1                       Graham, Miss. Margaret Edith   \n",
       "713   890     1     1                              Behr, Mr. Karl Howell   \n",
       "714   891     0     3                                Dooley, Mr. Patrick   \n",
       "\n",
       "       年龄  兄弟姐妹个数  父母子女个数              船票信息       票价    客舱  登船港口  年龄段  \\\n",
       "0    22.0       1       0         A/5 21171   7.2500   NaN     1    2   \n",
       "1    38.0       1       0          PC 17599  71.2833   C85     2    3   \n",
       "2    26.0       0       0  STON/O2. 3101282   7.9250   NaN     1    2   \n",
       "3    35.0       1       0            113803  53.1000  C123     1    3   \n",
       "4    35.0       0       0            373450   8.0500   NaN     1    3   \n",
       "..    ...     ...     ...               ...      ...   ...   ...  ...   \n",
       "710  39.0       0       5            382652  29.1250   NaN     3    3   \n",
       "711  27.0       0       0            211536  13.0000   NaN     1    2   \n",
       "712  19.0       0       0            112053  30.0000   B42     1    2   \n",
       "713  26.0       0       0            111369  30.0000  C148     2    2   \n",
       "714  32.0       0       0            370376   7.7500   NaN     3    3   \n",
       "\n",
       "     性别分类_female  性别分类_male  性别分类_unknown  \n",
       "0              0          1             0  \n",
       "1              1          0             0  \n",
       "2              1          0             0  \n",
       "3              1          0             0  \n",
       "4              0          1             0  \n",
       "..           ...        ...           ...  \n",
       "710            1          0             0  \n",
       "711            0          1             0  \n",
       "712            1          0             0  \n",
       "713            0          1             0  \n",
       "714            0          1             0  \n",
       "\n",
       "[715 rows x 15 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['登船港口'] = data_clean['登船港口'].replace('unknown',4).astype('int')\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a0e7a4",
   "metadata": {},
   "source": [
    "5.3 使用sklearn.preprocessing的LabelEncoder对“船票信息”和“客舱”特征进行类别编码    \n",
    "首先查看这两个特征分别包含多少个类别，每个类别数量多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8a503e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "347082           7\n",
       "3101295          6\n",
       "CA 2144          6\n",
       "347088           6\n",
       "S.O.C. 14879     5\n",
       "                ..\n",
       "2693             1\n",
       "349233           1\n",
       "349236           1\n",
       "SC/Paris 2163    1\n",
       "370376           1\n",
       "Name: 船票信息, Length: 543, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['船票信息'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eca3df7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G6             4\n",
       "C23 C25 C27    4\n",
       "B96 B98        4\n",
       "F33            3\n",
       "C22 C26        3\n",
       "              ..\n",
       "C124           1\n",
       "C32            1\n",
       "E34            1\n",
       "C7             1\n",
       "C148           1\n",
       "Name: 客舱, Length: 135, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['客舱'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ce8429",
   "metadata": {},
   "source": [
    "这两个特征都类别数量较多，使用sklearn内置的类别编码模块LabelEncoder来进行编码会更加简便。    \n",
    "首先观察并处理缺失值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f77c1774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "乘客ID              0\n",
       "是否幸存              0\n",
       "仓位等级              0\n",
       "姓名                0\n",
       "年龄                0\n",
       "兄弟姐妹个数            0\n",
       "父母子女个数            0\n",
       "船票信息              0\n",
       "票价                0\n",
       "客舱              529\n",
       "登船港口              0\n",
       "年龄段               0\n",
       "性别分类_female       0\n",
       "性别分类_male         0\n",
       "性别分类_unknown      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "55febc7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown        529\n",
       "B96 B98          4\n",
       "G6               4\n",
       "C23 C25 C27      4\n",
       "F2               3\n",
       "              ... \n",
       "C124             1\n",
       "C32              1\n",
       "E34              1\n",
       "C7               1\n",
       "C148             1\n",
       "Name: 客舱, Length: 136, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean['客舱'].fillna('unknown',inplace=True)\n",
    "data_clean['客舱'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd71e04d",
   "metadata": {},
   "source": [
    "循环对两个特征进行命名并编码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5e249587",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>乘客ID</th>\n",
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       "      <th>船票信息_labelEncode</th>\n",
       "    </tr>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>unknown</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>715 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     乘客ID  是否幸存  仓位等级                                                 姓名  \\\n",
       "0       1     0     3                            Braund, Mr. Owen Harris   \n",
       "1       2     1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2       3     1     3                             Heikkinen, Miss. Laina   \n",
       "3       4     1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4       5     0     3                           Allen, Mr. William Henry   \n",
       "..    ...   ...   ...                                                ...   \n",
       "710   886     0     3               Rice, Mrs. William (Margaret Norton)   \n",
       "711   887     0     2                              Montvila, Rev. Juozas   \n",
       "712   888     1     1                       Graham, Miss. Margaret Edith   \n",
       "713   890     1     1                              Behr, Mr. Karl Howell   \n",
       "714   891     0     3                                Dooley, Mr. Patrick   \n",
       "\n",
       "       年龄  兄弟姐妹个数  父母子女个数              船票信息       票价       客舱  登船港口  年龄段  \\\n",
       "0    22.0       1       0         A/5 21171   7.2500  unknown     1    2   \n",
       "1    38.0       1       0          PC 17599  71.2833      C85     2    3   \n",
       "2    26.0       0       0  STON/O2. 3101282   7.9250  unknown     1    2   \n",
       "3    35.0       1       0            113803  53.1000     C123     1    3   \n",
       "4    35.0       0       0            373450   8.0500  unknown     1    3   \n",
       "..    ...     ...     ...               ...      ...      ...   ...  ...   \n",
       "710  39.0       0       5            382652  29.1250  unknown     3    3   \n",
       "711  27.0       0       0            211536  13.0000  unknown     1    2   \n",
       "712  19.0       0       0            112053  30.0000      B42     1    2   \n",
       "713  26.0       0       0            111369  30.0000     C148     2    2   \n",
       "714  32.0       0       0            370376   7.7500  unknown     3    3   \n",
       "\n",
       "     性别分类_female  性别分类_male  性别分类_unknown  客舱_labelEncode  船票信息_labelEncode  \n",
       "0              0          1             0             135               409  \n",
       "1              1          0             0              74               472  \n",
       "2              1          0             0             135               533  \n",
       "3              1          0             0              50                41  \n",
       "4              0          1             0             135               374  \n",
       "..           ...        ...           ...             ...               ...  \n",
       "710            1          0             0             135               378  \n",
       "711            0          1             0             135                84  \n",
       "712            1          0             0              27                13  \n",
       "713            0          1             0              54                 9  \n",
       "714            0          1             0             135               372  \n",
       "\n",
       "[715 rows x 17 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for feat in ['客舱', '船票信息']:\n",
    "    lbl = LabelEncoder()  \n",
    "    label_dict = dict(zip(data_clean[feat].unique(), range(data_clean[feat].nunique())))\n",
    "    data_clean[feat + \"_labelEncode\"] = data_clean[feat].map(label_dict)\n",
    "    data_clean[feat + \"_labelEncode\"] = lbl.fit_transform(data_clean[feat].astype(str))\n",
    "data_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba17e4c",
   "metadata": {},
   "source": [
    "### 步骤 6: 特征选择    \n",
    "6.1 删去非数值型的特征、顺序编号的ID等无用特征及已完成离散化的原特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "61002475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 715 entries, 0 to 714\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   是否幸存              715 non-null    int64  \n",
      " 1   仓位等级              715 non-null    int64  \n",
      " 2   兄弟姐妹个数            715 non-null    int64  \n",
      " 3   父母子女个数            715 non-null    int64  \n",
      " 4   票价                715 non-null    float64\n",
      " 5   登船港口              715 non-null    int32  \n",
      " 6   年龄段               715 non-null    int64  \n",
      " 7   性别分类_female       715 non-null    uint8  \n",
      " 8   性别分类_male         715 non-null    uint8  \n",
      " 9   性别分类_unknown      715 non-null    uint8  \n",
      " 10  客舱_labelEncode    715 non-null    int32  \n",
      " 11  船票信息_labelEncode  715 non-null    int32  \n",
      "dtypes: float64(1), int32(3), int64(5), uint8(3)\n",
      "memory usage: 44.1 KB\n"
     ]
    }
   ],
   "source": [
    "data_clean = data_clean.drop(['乘客ID','姓名','年龄','船票信息','客舱'],axis=1)\n",
    "data_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb1a933",
   "metadata": {},
   "source": [
    "6.2 查看特征与“是否幸存”之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f959aef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,6))\n",
    "sns.heatmap(data_clean.corr().loc[:,['是否幸存']], annot=True)\n",
    "plt.title('“是否幸存”相关性热力图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ee4b32",
   "metadata": {},
   "source": [
    "6.3 使用sklearn.ensemble的RandomForestClassifier模块输出使用随机森林模型求出的特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2288d5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(data_clean.iloc[:,1:],data_clean.iloc[:,0])\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(data_clean.columns[1:],rf.feature_importances_)\n",
    "plt.xticks(data_clean.columns[1:],rotation=45)\n",
    "plt.title('“是否幸存”特征重要性图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0eff96",
   "metadata": {},
   "source": [
    "### 步骤 7: 保存完成特征处理的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "23ba1a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean.to_csv('data_FE.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c5c3217",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}