Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 泰坦尼克号生存预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 一、目标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对泰坦尼克号失事的乘客进行生还概率的预测分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二、数据集字段（特征）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PassengerId**——乘客ID\n",
    "\n",
    "**Pclass**——乘客舱位等级\n",
    "\n",
    "**Name**——乘客姓名\n",
    "\n",
    "**Sex**——性别\n",
    "\n",
    "**Age**——年龄\n",
    "\n",
    "**SibSp**——堂兄弟/妹个数\n",
    "\n",
    "**Parch**——父母与小孩个数\n",
    "\n",
    "**Ticket**——船票信息\n",
    "\n",
    "**Fare**——票价\n",
    "\n",
    "**Cabin**——客舱\n",
    "\n",
    "**Embarked**——登船港口\n",
    "\n",
    "**Survived**——是否生还（0表示死亡，1表示存活）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**导入数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_raw = pd.read_csv(\"./titanic/train.csv\")\n",
    "data_test = pd.read_csv(\"./titanic/test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*复制一份data_raw作为data_train，当要进行数据清洗时，对其进行，而不是对data_raw进行*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data_raw.copy(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data_clean存储data_train和data_test，用于统一的数据清洗和编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_clean = [data_train, data_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**查看data_train，data_test的基本信息 (head(), tail(), sample(), info(), describe())**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</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",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</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>C</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</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>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()  # 查看data_train的前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>886</td>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
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       "      <td>887</td>\n",
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       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>888</td>\n",
       "      <td>889</td>\n",
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       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.tail()  # 后5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <td>424</td>\n",
       "      <td>425</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rosblom, Mr. Viktor Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>370129</td>\n",
       "      <td>20.2125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>522</td>\n",
       "      <td>523</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lahoud, Mr. Sarkis</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2624</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
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       "      <td>360</td>\n",
       "      <td>361</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Skoog, Mr. Wilhelm</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>347088</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>451</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>West, Mr. Edwy Arthur</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 34651</td>\n",
       "      <td>27.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                            Name   Sex  \\\n",
       "424          425         0       3     Rosblom, Mr. Viktor Richard  male   \n",
       "522          523         0       3              Lahoud, Mr. Sarkis  male   \n",
       "27            28         0       1  Fortune, Mr. Charles Alexander  male   \n",
       "360          361         0       3              Skoog, Mr. Wilhelm  male   \n",
       "450          451         0       2           West, Mr. Edwy Arthur  male   \n",
       "\n",
       "      Age  SibSp  Parch      Ticket      Fare        Cabin Embarked  \n",
       "424  18.0      1      1      370129   20.2125          NaN        S  \n",
       "522   NaN      0      0        2624    7.2250          NaN        C  \n",
       "27   19.0      3      2       19950  263.0000  C23 C25 C27        S  \n",
       "360  40.0      1      4      347088   27.9000          NaN        S  \n",
       "450  36.0      1      2  C.A. 34651   27.7500          NaN        S  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.sample(5)  # 随机取样5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()  # 查看字段类型、数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zabour, Miss. Hileni</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                  Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                   891   891   \n",
       "unique          NaN         NaN         NaN                   891     2   \n",
       "top             NaN         NaN         NaN  Zabour, Miss. Hileni  male   \n",
       "freq            NaN         NaN         NaN                     1   577   \n",
       "mean     446.000000    0.383838    2.308642                   NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                   NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                   NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                   NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                   NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                   NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                   NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch  Ticket        Fare Cabin Embarked  \n",
       "count   714.000000  891.000000  891.000000     891  891.000000   204      889  \n",
       "unique         NaN         NaN         NaN     681         NaN   147        3  \n",
       "top            NaN         NaN         NaN  347082         NaN    G6        S  \n",
       "freq           NaN         NaN         NaN       7         NaN     4      644  \n",
       "mean     29.699118    0.523008    0.381594     NaN   32.204208   NaN      NaN  \n",
       "std      14.526497    1.102743    0.806057     NaN   49.693429   NaN      NaN  \n",
       "min       0.420000    0.000000    0.000000     NaN    0.000000   NaN      NaN  \n",
       "25%      20.125000    0.000000    0.000000     NaN    7.910400   NaN      NaN  \n",
       "50%      28.000000    0.000000    0.000000     NaN   14.454200   NaN      NaN  \n",
       "75%      38.000000    1.000000    0.000000     NaN   31.000000   NaN      NaN  \n",
       "max      80.000000    8.000000    6.000000     NaN  512.329200   NaN      NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.describe(include=\"all\")\n",
    "# 查看数据统计信息，窥探平均数，中位数，判断偏态分布，判断是否异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
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       "      <td>47.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>894</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>413</td>\n",
       "      <td>1305</td>\n",
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       "      <td>Spector, Mr. Woolf</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
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       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
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       "      <td>415</td>\n",
       "      <td>1307</td>\n",
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       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>416</td>\n",
       "      <td>1308</td>\n",
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       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>417</td>\n",
       "      <td>1309</td>\n",
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       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                          Name     Sex   Age  SibSp  \\\n",
       "413         1305       3            Spector, Mr. Woolf    male   NaN      0   \n",
       "414         1306       1  Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
       "415         1307       3  Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
       "416         1308       3           Ware, Mr. Frederick    male   NaN      0   \n",
       "417         1309       3      Peter, Master. Michael J    male   NaN      1   \n",
       "\n",
       "     Parch              Ticket      Fare Cabin Embarked  \n",
       "413      0           A.5. 3236    8.0500   NaN        S  \n",
       "414      0            PC 17758  108.9000  C105        C  \n",
       "415      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416      0              359309    8.0500   NaN        S  \n",
       "417      1                2668   22.3583   NaN        C  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Rheims, Mr. George Alexander Lucien</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17607</td>\n",
       "      <td>39.6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>273</td>\n",
       "      <td>1165</td>\n",
       "      <td>3</td>\n",
       "      <td>Lennon, Miss. Mary</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>370371</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>191</td>\n",
       "      <td>1083</td>\n",
       "      <td>1</td>\n",
       "      <td>Salomon, Mr. Abraham L</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111163</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                 Name     Sex   Age  \\\n",
       "269         1161       3                    Pokrnic, Mr. Mate    male  17.0   \n",
       "259         1151       3             Midtsjo, Mr. Karl Albert    male  21.0   \n",
       "290         1182       1  Rheims, Mr. George Alexander Lucien    male   NaN   \n",
       "273         1165       3                   Lennon, Miss. Mary  female   NaN   \n",
       "191         1083       1               Salomon, Mr. Abraham L    male   NaN   \n",
       "\n",
       "     SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
       "269      0      0    315095   8.6625   NaN        S  \n",
       "259      0      0    345501   7.7750   NaN        S  \n",
       "290      0      0  PC 17607  39.6000   NaN        S  \n",
       "273      1      0    370371  15.5000   NaN        Q  \n",
       "191      0      0    111163  26.0000   NaN        S  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>418</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418</td>\n",
       "      <td>417.000000</td>\n",
       "      <td>91</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>363</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lahtinen, Rev. William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.627188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.907576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId      Pclass                    Name   Sex         Age  \\\n",
       "count    418.000000  418.000000                     418   418  332.000000   \n",
       "unique          NaN         NaN                     418     2         NaN   \n",
       "top             NaN         NaN  Lahtinen, Rev. William  male         NaN   \n",
       "freq            NaN         NaN                       1   266         NaN   \n",
       "mean    1100.500000    2.265550                     NaN   NaN   30.272590   \n",
       "std      120.810458    0.841838                     NaN   NaN   14.181209   \n",
       "min      892.000000    1.000000                     NaN   NaN    0.170000   \n",
       "25%      996.250000    1.000000                     NaN   NaN   21.000000   \n",
       "50%     1100.500000    3.000000                     NaN   NaN   27.000000   \n",
       "75%     1204.750000    3.000000                     NaN   NaN   39.000000   \n",
       "max     1309.000000    3.000000                     NaN   NaN   76.000000   \n",
       "\n",
       "             SibSp       Parch    Ticket        Fare            Cabin Embarked  \n",
       "count   418.000000  418.000000       418  417.000000               91      418  \n",
       "unique         NaN         NaN       363         NaN               76        3  \n",
       "top            NaN         NaN  PC 17608         NaN  B57 B59 B63 B66        S  \n",
       "freq           NaN         NaN         5         NaN                3      270  \n",
       "mean      0.447368    0.392344       NaN   35.627188              NaN      NaN  \n",
       "std       0.896760    0.981429       NaN   55.907576              NaN      NaN  \n",
       "min       0.000000    0.000000       NaN    0.000000              NaN      NaN  \n",
       "25%       0.000000    0.000000       NaN    7.895800              NaN      NaN  \n",
       "50%       0.000000    0.000000       NaN   14.454200              NaN      NaN  \n",
       "75%       1.000000    0.000000       NaN   31.500000              NaN      NaN  \n",
       "max       8.000000    9.000000       NaN  512.329200              NaN      NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**统计data_train，data_test缺失值数量**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集各特征缺失率：\n",
      " PassengerId    0.000000\n",
      "Survived       0.000000\n",
      "Pclass         0.000000\n",
      "Name           0.000000\n",
      "Sex            0.000000\n",
      "Age            0.198653\n",
      "SibSp          0.000000\n",
      "Parch          0.000000\n",
      "Ticket         0.000000\n",
      "Fare           0.000000\n",
      "Cabin          0.771044\n",
      "Embarked       0.002245\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"训练集各特征缺失率：\\n\", data_train.isnull().sum()/data_train.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出Cabin的缺失率十分高！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集各特征的缺失率：\n",
      " PassengerId    0.000000\n",
      "Pclass         0.000000\n",
      "Name           0.000000\n",
      "Sex            0.000000\n",
      "Age            0.205742\n",
      "SibSp          0.000000\n",
      "Parch          0.000000\n",
      "Ticket         0.000000\n",
      "Fare           0.002392\n",
      "Cabin          0.782297\n",
      "Embarked       0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"测试集各特征的缺失率：\\n\", data_test.isnull().sum()/data_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同样地，测试集中Cabin特征缺失严重！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**查看存活信息**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "存活率：\n",
      " 0    0.616162\n",
      "1    0.383838\n",
      "Name: Survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"存活率：\\n\", data_train[\"Survived\"].value_counts()/data_train.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只有38.3%的人幸存，多数人死亡！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四、数据分析&数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]  # 显示中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**查看每个特征的类型，前面有看过，这里再输出一次**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PassengerId, Survived, Pclass, SibSp, Parch——整数类型，离散数据**\n",
    "\n",
    "**Name, Sex, Ticket, Cabin, Embarked——对象类型，离散数据**\n",
    "\n",
    "**Age, Fare——浮点数类型，即连续数据**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先对于PassengerId, Ticket这两类离散数据，代表一种序号信息，对预测生存应该没有作用，先将这两个特征drop掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset.drop(columns=[\"PassengerId\", \"Ticket\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 10 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Name        891 non-null object\n",
      "Sex         891 non-null object\n",
      "Age         714 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Fare        891 non-null float64\n",
      "Cabin       204 non-null object\n",
      "Embarked    889 non-null object\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 69.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      "Pclass      418 non-null int64\n",
      "Name        418 non-null object\n",
      "Sex         418 non-null object\n",
      "Age         332 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Fare        417 non-null float64\n",
      "Cabin       91 non-null object\n",
      "Embarked    418 non-null object\n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 29.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "其次，对于Cabin，缺失率太高，有两种选择：1.直接将该特征drop掉；2.将该特征的缺失值NaN也作为一个特征值。这里选择将该特征drop掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G6             4\n",
       "C23 C25 C27    4\n",
       "B96 B98        4\n",
       "D              3\n",
       "F33            3\n",
       "              ..\n",
       "C111           1\n",
       "C32            1\n",
       "D19            1\n",
       "C62 C64        1\n",
       "A34            1\n",
       "Name: Cabin, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw[\"Cabin\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset.drop(columns=[\"Cabin\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Name        891 non-null object\n",
      "Sex         891 non-null object\n",
      "Age         714 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Fare        891 non-null float64\n",
      "Embarked    889 non-null object\n",
      "dtypes: float64(2), int64(4), object(3)\n",
      "memory usage: 62.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "Pclass      418 non-null int64\n",
      "Name        418 non-null object\n",
      "Sex         418 non-null object\n",
      "Age         332 non-null float64\n",
      "SibSp       418 non-null int64\n",
      "Parch       418 non-null int64\n",
      "Fare        417 non-null float64\n",
      "Embarked    418 non-null object\n",
      "dtypes: float64(2), int64(3), object(3)\n",
      "memory usage: 26.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**接下来分析Embarked, Pclass, Sex对Survived的影响，因为它们的类型少**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 8))\n",
    "plt.suptitle(\"定类/定序分析：Embarked, Pclass, Sex\")  # 大标题\n",
    "plt.subplot(131)  \n",
    "sns.countplot(x=\"Embarked\", hue=\"Survived\", data=data_train)\n",
    "plt.subplot(132)\n",
    "sns.countplot(x=\"Pclass\", hue=\"Survived\", data=data_train)\n",
    "plt.subplot(133)\n",
    "sns.countplot(x=\"Sex\", hue=\"Survived\", data=data_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived\n",
       "Embarked          \n",
       "C         0.553571\n",
       "Q         0.389610\n",
       "S         0.336957"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Embarked\", \"Survived\"]].groupby([\"Embarked\"]).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Pclass          \n",
       "1       0.629630\n",
       "2       0.472826\n",
       "3       0.242363"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Pclass\", \"Survived\"]].groupby([\"Pclass\"]).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Sex             \n",
       "female  0.742038\n",
       "male    0.188908"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Sex\", \"Survived\"]].groupby([\"Sex\"]).mean()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的图表可以看出：\n",
    "对于Embarked（港口），S港口人数最多，但获救率最低，C港口获救率最高。\n",
    "对于Pclass（乘客等级），一等舱乘客的获救率最高，三等舱乘客的获救率最低，舱位应该和社会地位有关，地位高有钱的人坐一等舱，获救率更高\n",
    "对于Sex，女性获救率明显高于男性，说明女士优先"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**其次分析Parch, SibSp对Survived的影响**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 8))\n",
    "plt.suptitle(\"定类/定序分析：Parch, SibSp\")\n",
    "plt.subplot(211)\n",
    "sns.countplot(x=\"Parch\", hue=\"Survived\", data=data_train)\n",
    "plt.subplot(212)\n",
    "sns.countplot(x=\"SibSp\", hue=\"Survived\", data=data_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "Parch          \n",
       "0      0.343658\n",
       "1      0.550847\n",
       "2      0.500000\n",
       "3      0.600000\n",
       "4      0.000000\n",
       "5      0.200000\n",
       "6      0.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Parch\", \"Survived\"]].groupby([\"Parch\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived\n",
       "SibSp          \n",
       "0      0.345395\n",
       "1      0.535885\n",
       "2      0.464286\n",
       "3      0.250000\n",
       "4      0.166667\n",
       "5      0.000000\n",
       "8      0.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"SibSp\", \"Survived\"]].groupby([\"SibSp\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图表可以明显看出：\n",
    "Parch=0的人数最多（单人旅行），但获救率低；对SibSp也类似，这两个特征可以合并起来。这里构建一个新特征Family = Parch + SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"Family\"] = dataset[\"Parch\"] + dataset[\"SibSp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae52c51448>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Family\", hue=\"Survived\", data=data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Family</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.303538</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Family          \n",
       "0       0.303538\n",
       "1       0.552795\n",
       "2       0.578431\n",
       "3       0.724138\n",
       "4       0.200000\n",
       "5       0.136364\n",
       "6       0.333333\n",
       "7       0.000000\n",
       "10      0.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Family\", \"Survived\"]].groupby([\"Family\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上述图表可以看出，Family=1, 2, 3的存活率明显高于其它。对于Family这个特征，还可以继续处理，如将Family划分两个区间，将Family=1,2,3划分为一个区间，其余的划进另一个区间，再分别对这两个区间编码。这里先不采用这个方法，直接保留Family这个特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**上面的分析是针对单个离散变量的，现对多个离散变量进行分析，分析其之间的关系**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析不同Pclass, Sex, Embarked对Survived的影响，利用sns.FacetGrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the countplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ae5304e8c8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 696x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(data=data_train, row=\"Sex\", col=\"Pclass\", hue=\"Survived\")\n",
    "grid.map(sns.countplot, \"Embarked\")\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"9\" valign=\"top\">female</td>\n",
       "      <td rowspan=\"3\" valign=\"top\">1</td>\n",
       "      <td>C</td>\n",
       "      <td>0.976744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">2</td>\n",
       "      <td>C</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.910448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">3</td>\n",
       "      <td>C</td>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"9\" valign=\"top\">male</td>\n",
       "      <td rowspan=\"3\" valign=\"top\">1</td>\n",
       "      <td>C</td>\n",
       "      <td>0.404762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.354430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">2</td>\n",
       "      <td>C</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.154639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">3</td>\n",
       "      <td>C</td>\n",
       "      <td>0.232558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.128302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Survived\n",
       "Sex    Pclass Embarked          \n",
       "female 1      C         0.976744\n",
       "              Q         1.000000\n",
       "              S         0.958333\n",
       "       2      C         1.000000\n",
       "              Q         1.000000\n",
       "              S         0.910448\n",
       "       3      C         0.652174\n",
       "              Q         0.727273\n",
       "              S         0.375000\n",
       "male   1      C         0.404762\n",
       "              Q         0.000000\n",
       "              S         0.354430\n",
       "       2      C         0.200000\n",
       "              Q         0.000000\n",
       "              S         0.154639\n",
       "       3      C         0.232558\n",
       "              Q         0.076923\n",
       "              S         0.128302"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Sex\", \"Pclass\", \"Embarked\", \"Survived\"]].groupby([\"Sex\", \"Pclass\", \"Embarked\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.968085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.921053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.368852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.157407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.135447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Survived\n",
       "Sex    Pclass          \n",
       "female 1       0.968085\n",
       "       2       0.921053\n",
       "       3       0.500000\n",
       "male   1       0.368852\n",
       "       2       0.157407\n",
       "       3       0.135447"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Sex\", \"Pclass\", \"Survived\"]].groupby([\"Sex\", \"Pclass\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">female</td>\n",
       "      <td>C</td>\n",
       "      <td>0.876712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.689655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"3\" valign=\"top\">male</td>\n",
       "      <td>C</td>\n",
       "      <td>0.305263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Q</td>\n",
       "      <td>0.073171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>S</td>\n",
       "      <td>0.174603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Survived\n",
       "Sex    Embarked          \n",
       "female C         0.876712\n",
       "       Q         0.750000\n",
       "       S         0.689655\n",
       "male   C         0.305263\n",
       "       Q         0.073171\n",
       "       S         0.174603"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Sex\", \"Embarked\", \"Survived\"]].groupby([\"Sex\", \"Embarked\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上述图表可知：\n",
    "对于女性，不管是Pclass=1,2,3，Embarked=S,C,Q，获救率都很高，细化来看，Pclass=1,2的女性基本全获救了。\n",
    "对于男性，Pclass=1的获救率最高，其余Pclass的男性获救率较低。\n",
    "不论男女，在C港口登船的获救率都是最高的，此外，对于Pclass=1,2的男女，在Q港口登船的人数十分少，男性更是如此。港口可能也和社会地位有关。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>female</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2</td>\n",
       "      <td>female</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3</td>\n",
       "      <td>female</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>43</td>\n",
       "      <td>39</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Embarked        C   Q    S\n",
       "Pclass Sex                \n",
       "1      female  43   1   48\n",
       "       male    42   1   79\n",
       "2      female   7   2   67\n",
       "       male    10   1   97\n",
       "3      female  23  33   88\n",
       "       male    43  39  265"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([data_train[\"Pclass\"], data_train[\"Sex\"]], data_train[\"Embarked\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Embarked</th>\n",
       "      <th>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>127</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>164</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>353</td>\n",
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      ],
      "text/plain": [
       "Embarked   C   Q    S\n",
       "Pclass               \n",
       "1         85   2  127\n",
       "2         17   3  164\n",
       "3         66  72  353"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data_train[\"Pclass\"], data_train[\"Embarked\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以明显看出，Pclass=1,2的人极少去Q港口登船的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**离散变量中剩余Name没有分析，现对Name进行分析**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch     Fare Embarked  Family  \n",
       "0    male  22.0      1      0   7.2500        S       1  \n",
       "1  female  38.0      1      0  71.2833        C       1  \n",
       "2  female  26.0      0      0   7.9250        S       0  \n",
       "3  female  35.0      1      0  53.1000        S       1  \n",
       "4    male  35.0      0      0   8.0500        S       0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>888</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
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       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     Survived  Pclass                                      Name     Sex   Age  \\\n",
       "886         0       2                     Montvila, Rev. Juozas    male  27.0   \n",
       "887         1       1              Graham, Miss. Margaret Edith  female  19.0   \n",
       "888         0       3  Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN   \n",
       "889         1       1                     Behr, Mr. Karl Howell    male  26.0   \n",
       "890         0       3                       Dooley, Mr. Patrick    male  32.0   \n",
       "\n",
       "     SibSp  Parch   Fare Embarked  Family  \n",
       "886      0      0  13.00        S       0  \n",
       "887      0      0  30.00        S       0  \n",
       "888      1      2  23.45        S       3  \n",
       "889      0      0  30.00        C       0  \n",
       "890      0      0   7.75        Q       0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Taylor, Mr. Elmer Zebley</td>\n",
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       "      <td>640</td>\n",
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       "      <td>Jensen, Mr. Hans Peder</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>66.6000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johansson, Mr. Gustaf Joel</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6542</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Kantor, Mr. Sinai</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass                               Name     Sex   Age  SibSp  \\\n",
       "712         1       1           Taylor, Mr. Elmer Zebley    male  48.0      1   \n",
       "640         0       3             Jensen, Mr. Hans Peder    male  20.0      0   \n",
       "151         1       1  Pears, Mrs. Thomas (Edith Wearne)  female  22.0      1   \n",
       "103         0       3         Johansson, Mr. Gustaf Joel    male  33.0      0   \n",
       "99          0       2                  Kantor, Mr. Sinai    male  34.0      1   \n",
       "\n",
       "     Parch     Fare Embarked  Family  \n",
       "712      0  52.0000        S       1  \n",
       "640      0   7.8542        S       0  \n",
       "151      0  66.6000        S       1  \n",
       "103      0   8.6542        S       0  \n",
       "99       0  26.0000        S       1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，Name中有共同点，即Mrs. Mr. Miss.等，将这些用正则表达式提取出来作为新特征Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"Title\"] = dataset[\"Name\"].str.extract(\" ([A-Za-z]+)\\.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr          517\n",
       "Miss        182\n",
       "Mrs         125\n",
       "Master       40\n",
       "Dr            7\n",
       "Rev           6\n",
       "Mlle          2\n",
       "Major         2\n",
       "Col           2\n",
       "Countess      1\n",
       "Mme           1\n",
       "Jonkheer      1\n",
       "Sir           1\n",
       "Don           1\n",
       "Ms            1\n",
       "Capt          1\n",
       "Lady          1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[\"Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，Mr, Miss, Mrs, Master最多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Capt</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Col</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Countess</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Don</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dr</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jonkheer</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lady</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Major</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Miss</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mlle</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mme</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mrs</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ms</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rev</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sir</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data_train[\"Title\"], data_train[\"Sex\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对Title继续划分，划分为Mr, Miss, Mrs, Master，此外，将Mme划进Mrs，将Mlle, Ms划进Miss，剩余的都划进Rare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"Title\"] = dataset[\"Title\"].replace([\"Mme\"], \"Mrs\")\n",
    "    dataset[\"Title\"] = dataset[\"Title\"].replace([\"Mlle\", \"Ms\"], \"Miss\")\n",
    "    dataset[\"Title\"] = dataset[\"Title\"].replace([\"Lady\", \"Countess\", \"Capt\",\n",
    "                                                \"Col\", \"Don\", \"Dr\", \"Major\",\n",
    "                                                \"Rev\", \"Sir\", \"Jonkheer\",\n",
    "                                                \"Dona\"], \"Rare\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Survived\n",
       "Title           \n",
       "Master  0.575000\n",
       "Miss    0.702703\n",
       "Mr      0.156673\n",
       "Mrs     0.793651\n",
       "Rare    0.347826"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"Title\", \"Survived\"]].groupby([\"Title\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，Master, Miss, Mrs的存活率都较高，而Mr, Rare较低"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**现对连续变量Age, Fare进行分析**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**对Age的分析**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "E:\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "E:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "E:\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 8))\n",
    "plt.subplot(211)\n",
    "plt.title(\"不同年龄对存活率的影响\")\n",
    "sns.kdeplot(data=data_train.loc[data_train[\"Survived\"]==0, \"Age\"], shade=True, label=\"not survived\")\n",
    "sns.kdeplot(data=data_train.loc[data_train[\"Survived\"]==1, \"Age\"], shade=True, label=\"survived\")\n",
    "plt.subplot(212)\n",
    "sns.distplot(data_train.loc[data_train[\"Survived\"]==0, \"Age\"], bins=10, kde=False, label=\"not survived\")\n",
    "sns.distplot(data_train.loc[data_train[\"Survived\"]==1, \"Age\"], bins=10, kde=False, label=\"survived\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图可以看出，0-15年龄段的获救率最高，其他的则不那么明显"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ae59cb0848>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 696x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(data=data_train, row=\"Sex\", col=\"Pclass\", hue=\"Survived\", xlim=[0,80])\n",
    "grid.map(sns.kdeplot, \"Age\", shade=True)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae5be2ccc8>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=data_train, x=\"Pclass\", y=\"Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图可以看出，Pclass越高，年纪越大，这和我们的直觉是一样的，即成功人士往往年纪较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**对Age进行分段分析，前面分析到0-15的存活率最大，年龄范围是0-80，因此这里可以分为5个区间**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"AgeBand\"] = pd.cut(dataset[\"Age\"], bins=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">(0.34, 16.336]</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2</td>\n",
       "      <td>female</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.270270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">(16.336, 32.252]</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.968750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2</td>\n",
       "      <td>female</td>\n",
       "      <td>0.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.165563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">(32.252, 48.168]</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>female</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.435897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2</td>\n",
       "      <td>female</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">(48.168, 64.084]</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>female</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.269231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2</td>\n",
       "      <td>female</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3</td>\n",
       "      <td>female</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"6\" valign=\"top\">(64.084, 80.0]</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>male</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Survived\n",
       "AgeBand          Pclass Sex             \n",
       "(0.34, 16.336]   1      female  0.833333\n",
       "                        male    1.000000\n",
       "                 2      female  1.000000\n",
       "                        male    0.818182\n",
       "                 3      female  0.545455\n",
       "                        male    0.270270\n",
       "(16.336, 32.252] 1      female  0.968750\n",
       "                        male    0.444444\n",
       "                 2      female  0.916667\n",
       "                        male    0.062500\n",
       "                 3      female  0.461538\n",
       "                        male    0.165563\n",
       "(32.252, 48.168] 1      female  1.000000\n",
       "                        male    0.435897\n",
       "                 2      female  0.909091\n",
       "                        male    0.074074\n",
       "                 3      female  0.250000\n",
       "                        male    0.055556\n",
       "(48.168, 64.084] 1      female  0.941176\n",
       "                        male    0.269231\n",
       "                 2      female  0.833333\n",
       "                        male    0.090909\n",
       "                 3      female  1.000000\n",
       "                        male    0.000000\n",
       "(64.084, 80.0]   1      female       NaN\n",
       "                        male    0.166667\n",
       "                 2      female       NaN\n",
       "                        male    0.000000\n",
       "                 3      female       NaN\n",
       "                        male    0.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"AgeBand\", \"Pclass\", \"Sex\", \"Survived\"]].groupby([\"AgeBand\", \"Pclass\", \"Sex\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，不论男女，在(0.34, 16.336]的存活率都较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**现对Fare进行分析**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先看Fare与存活率的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(20, 8))\n",
    "plt.title(\"不同Fare下的存活率\")\n",
    "plt.subplot(211)\n",
    "sns.kdeplot(data_train.loc[data_train[\"Survived\"]==0, \"Fare\"], shade=True, label=\"not survived\")\n",
    "sns.kdeplot(data_train.loc[data_train[\"Survived\"]==1, \"Fare\"], shade=True, label=\"survived\")\n",
    "plt.subplot(212)\n",
    "sns.distplot(data_train.loc[data_train[\"Survived\"]==0, \"Fare\"], bins=20, label=\"not survived\", kde=False)\n",
    "sns.distplot(data_train.loc[data_train[\"Survived\"]==1, \"Fare\"], bins=20, label=\"survived\", kde=False)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以明显地看出，低票价的存活率低，票价越高，存活率越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ae5c885588>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 696x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(data=data_train, col=\"Pclass\", hue=\"Survived\")\n",
    "grid.map(sns.distplot, \"Fare\", kde=False, bins=10)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图可以看出Pclass越高，高票价越多，存活率越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**对Fare划分区间，利用四分位数来划分，因为Fare的分布不是标准的正态分布，而是偏态**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"FareBand\"] = pd.qcut(data_train[\"Fare\"], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FareBand</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>51</td>\n",
       "      <td>70</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>159</td>\n",
       "      <td>22</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass             1   2    3\n",
       "FareBand                     \n",
       "(-0.001, 7.91]     6   6  211\n",
       "(7.91, 14.454]     0  86  138\n",
       "(14.454, 31.0]    51  70  101\n",
       "(31.0, 512.329]  159  22   41"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data_train[\"FareBand\"], data_train[\"Pclass\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很明显，Pclass越高，高票价人数越大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FareBand</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Survived\n",
       "FareBand                 \n",
       "(-0.001, 7.91]   0.197309\n",
       "(7.91, 14.454]   0.303571\n",
       "(14.454, 31.0]   0.454955\n",
       "(31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train[[\"FareBand\", \"Survived\"]].groupby([\"FareBand\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "票价越高，存活率越大；票价越高，越有可能是Pclass 1，存活率越大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 五、数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      2\n",
       "Family        0\n",
       "Title         0\n",
       "AgeBand     177\n",
       "FareBand      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       0\n",
       "Name         0\n",
       "Sex          0\n",
       "Age         86\n",
       "SibSp        0\n",
       "Parch        0\n",
       "Fare         1\n",
       "Embarked     0\n",
       "Family       0\n",
       "Title        0\n",
       "AgeBand     86\n",
       "FareBand     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**目前，Embarked, Fare, Age都有缺失**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对Embarked的填充，有两种：1. 根据众数来填充 2. 根据特征相似的样本的Embarked来填充，比如找Pclass和Sex与缺失样本一样的Embarked来填充。这里采用1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"Embarked\"].fillna(dataset[\"Embarked\"].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Embarked      0\n",
       "Family        0\n",
       "Title         0\n",
       "AgeBand     177\n",
       "FareBand      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对Fare的填充，有两种：1. 采用中位数来填充，注意到Fare的分布是偏态分布，不合适用平均值来填充 2. 根据特征相似样本的Fare的中位数来填充。这里采用1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：填充完以后要对Fare重新划分区间，之前没有对Fare填充缺失值就先划分了区间，这时需要再划分一次**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"Fare\"].fillna(dataset[\"Fare\"].median(), inplace=True)\n",
    "    dataset[\"FareBand\"] = pd.qcut(dataset[\"Fare\"], 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass       0\n",
       "Name         0\n",
       "Sex          0\n",
       "Age         86\n",
       "SibSp        0\n",
       "Parch        0\n",
       "Fare         0\n",
       "Embarked     0\n",
       "Family       0\n",
       "Title        0\n",
       "AgeBand     86\n",
       "FareBand     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对Age的填充，同样有两种：1. 中位数填充，Age的分布也是偏态分布 2. 用特征相似的样本的Age的中位数来填充。这里采用1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意：填充完以后要对Age重新划分区间，之前没有对Age填充缺失值就先划分了区间，这时需要再划分一次**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "for dataset in data_clean:\n",
    "    dataset[\"Age\"].fillna(dataset[\"Age\"].median(), inplace=True)\n",
    "    dataset[\"AgeBand\"] = pd.cut(dataset[\"Age\"], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Name        0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "Family      0\n",
       "Title       0\n",
       "AgeBand     0\n",
       "FareBand    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass      0\n",
       "Name        0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "Family      0\n",
       "Title       0\n",
       "AgeBand     0\n",
       "FareBand    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 六、编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Name        891 non-null object\n",
      "Sex         891 non-null object\n",
      "Age         891 non-null float64\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Fare        891 non-null float64\n",
      "Embarked    891 non-null object\n",
      "Family      891 non-null int64\n",
      "Title       891 non-null object\n",
      "AgeBand     891 non-null category\n",
      "FareBand    891 non-null category\n",
      "dtypes: category(2), float64(2), int64(5), object(4)\n",
      "memory usage: 78.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**需要编码的字段有Sex，Embarked，Title，AgeBand，FareBand**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = LabelEncoder()\n",
    "for dataset in data_clean:\n",
    "    dataset[\"Sex_Code\"] = label.fit_transform(dataset[\"Sex\"])\n",
    "    dataset[\"Embarked_Code\"] = label.fit_transform(dataset[\"Embarked\"])\n",
    "    dataset[\"Title_Code\"] = label.fit_transform(dataset[\"Title\"])\n",
    "    dataset[\"AgeBand_Code\"] = label.fit_transform(dataset[\"AgeBand\"])\n",
    "    dataset[\"FareBand_Code\"] = label.fit_transform(dataset[\"FareBand\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 18 columns):\n",
      "Survived         891 non-null int64\n",
      "Pclass           891 non-null int64\n",
      "Name             891 non-null object\n",
      "Sex              891 non-null object\n",
      "Age              891 non-null float64\n",
      "SibSp            891 non-null int64\n",
      "Parch            891 non-null int64\n",
      "Fare             891 non-null float64\n",
      "Embarked         891 non-null object\n",
      "Family           891 non-null int64\n",
      "Title            891 non-null object\n",
      "AgeBand          891 non-null category\n",
      "FareBand         891 non-null category\n",
      "Sex_Code         891 non-null int32\n",
      "Embarked_Code    891 non-null int32\n",
      "Title_Code       891 non-null int32\n",
      "AgeBand_Code     891 non-null int32\n",
      "FareBand_Code    891 non-null int32\n",
      "dtypes: category(2), float64(2), int32(5), int64(5), object(4)\n",
      "memory usage: 96.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 七、提取X_train, y_train, X_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_columns = [\"Pclass\", \"Family\", \"Sex_Code\", \"Embarked_Code\", \"Title_Code\",\n",
    "               \"AgeBand_Code\", \"FareBand_Code\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data_train[calc_columns]\n",
    "y_train = data_train[\"Survived\"]\n",
    "X_test = data_test[calc_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pclass', 'Family', 'Sex_Code', 'Embarked_Code', 'Title_Code',\n",
       "       'AgeBand_Code', 'FareBand_Code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:211: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "E:\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:180: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae5e1b2dc8>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yw7jVc5ex+6ftAATWrMHJ45ku6d+V+/Isvzqh+NYOJmNbsktyLK2qt1lJUV1j2GjLZeeabbTo1MowLnHu9+wqlYtRmyuYo+Io+HU1AAW7NmNuHmscd1lbOJ2HyjpxXruE1EJqhFL0528uycdhrrtddZXxqOk1m/EiMhLYBbwGvAksB5aKSAQQAAwDkoArgQnAvx1c98fAbSKyGWillFpXOkBERgGjAEYHdeKqgOaGK/IK8CXviPWFejojm5CGjR2KiRzWi6x9Kex7+xua3dsf/wa1KMw9XaZt/wfLHPyTPM9tL40ioum5T55RXWP4+XPriCAnI5tasU3LXb5p+5ZYgi0kb3HNG4Ir9+XZ/dZsxFXsn7PcJflB9dpmd7w0ijpN65+Xy5rPVxTnEhbbxIFcAtlfIhejtooQXz+KMo4DoHJOYoo0+Hds9sL3ulvJfWcSAQ8mnPcrn76DOJ242CW5OEWfvVYlXlRKzQXr6AUYCYwAagL+wDGlVLaI/AkUAuLEulcBU4HrgK+NApRS7wLvAiyoc6uyt6KCnHxMfj4AeFn8EFPZQaVRTEjrxhyYu5L81EwOffkT0c/cRN6xjDJtl3LRmTfu3fOe3zRxBN627eQX4IdJ7O/SgOBAhifcw7/HTHdZPq7cl/s/WAYihPWIZufLn7ksx+q0zf5bKpfhE0fgUyIXEfsTLJbgQG5NuJd3xrxabltFqbxTiI81J/H1A1PZ7eNzzc2cTvwGTuWc/wsRzFFx5H/1kcvycZiy+5bjMTx6eg24F/gCuAXIuUDsBSmlioAfgOnA3IqsK2N7cvE0THBMJDmHUh2KyU4+iqVROAAhcU3JTUkzbPsnOZj0B807WqeHGkQ3Ji2l7LYEMHt7Mfqdx/hq2iecOOy6beTKfQkQ1jWK9M2/uyw/I1W9zUr6M2k/zTu2suXSiLSUv+3mcv87j7Ng2rziXIzaXKHo4G/FU2rmhk0pSjtWJsarVXt8+g4i4IlXMTdsht+dj1rjW8RSmLzHZbk4RR/TqXI/AM8CZ4/G1i8n1lHzgYNKqT8rspK/vttIw6E9aZ1wO/UHduXk3hSinx5WbszR5Vv485NEwnrE0Pur52h6dz9++9cSwzZ3mv3WNLf2V9rWZb/SZUhvhk24iw7XdSNp1WbqNm/A4MeHnxfX4+bLaRjbhGvGDuGxTxPoOKC7S/p35b4EiIiPI21d5b5pVfU2K2nLsg10G9KbmybcRafruhfncn2pXHrdfDmRsU24buyNPPnpJDoN6G7Y5gpntvyCd9cr8L1pNF4d+1D015/4Xn/3eTG5rz5+7qSBQ3+Q9/FrAHjFdKRwX5JL8nDaJVB0RF0CwzVXEZErgGnAeKXU0gvFlze9BuAdbCG8Tyxpa/eQn2p8ANSRGGcN3DHFJetxtbEdn77oZQNqWGjVqw2/bdhNVmqGC7OC/nneF4ypqn35vd/F3z+lMrdZIc69bwTUsBDdqw37KiGXkmb2y3IiqUC8ottTuC8JlZVeaTmdVeO9Zc5M9Rs6NXe8wxve//YXK9xfZfDEYzqVRim1AnDZ6WFnMnM4bDujqSIxGuRm5bBpydoq698T92VVb7OScrNy2FhNcimWm03Bxh+rOgvnFBZWdQYVpouOpmmap6jG02aO0kVH0zTNU+iio2maprlNNf7Sp6N00dE0TfMQqsjzT/zSRUfTNM1T6Ok1TdM0zW302Wuapmma2+iRjqZpmuY2l0DR8fTL4Giapv1zKOX44wJE5AMRWSsiEy4Q946IDHTVn6CLjqZpmqdw0bXXRGQIYFZKdQOaikgLO3G9gDpKqW9c9SfooqNpmuYpipTjj/LFA5/bfl4G9CwdICLewHvAAREZ7Ko/QR/TqYCa6uIvxliZKnJhzcr01sapVZ2CXatixlV1Cob65ftUdQqGvKrphYIfXBZY1SnY9V9XrMSJs9dK3nDS5l3b/cAALMBh288ngPYGq7gT680ypwEPiUikUupNp3MuRRcd7R+vuhYcTStNOXEiQckbThrIxnrTS4BAjGe92mEtVEdFZC7wItY7NVeInl7TNE3zFK6bXtvEuSm1OOCAQczvwNn7nHcEKnSPsbP0SEfTNM1TuO7aawuBNSJSD7gGGC4iU5RSJc9k+wD4UESGA97AUFd0rIuOpmmap3DRtdeUUlkiEg/0A6YppY4C20rFnASGGSxeIbroaJqmeYoC110GRymVzrkz2NxGFx1N0zRPoW9toGmaprmNvrWBpmma5i7OnDJdXemio2ma5in0SEfTNE1zG110NE3TNLfRN3HTNE3T3EXpkY6maZrmNrroaJqmaW5zCZy9pi/4Wc35hIcQ2rs1ZotfVaeiaVpVc90FP6uMLjpuEvXaGNoteZFGjw6xG+NdO5i2iyYXP/dvWpfodx+lRqco2i6chHhX/sD0jqljeHrBi1w71n6efkEBPDR7HA9/PIH7//MkZjfkVVraiXTuHPOE2/sFiH5tNJ2XTKbJozfYjfGpHUynRQnntVla1KPtnMrLucOM++j7TQKXPXK90zHtXr6buv3aVUpe7WbeR+/Fk2hZTl6lYxrfdSU9F0yg54IJ9F3+EnHT7q2U3ABGTnuA5796mcEP2b+epX9QAE/MmcBT/32eh//z9Hmv+Rphwbzw7fRKy+88uui4hohYROQrEVktIv8VEang+l4QkV9s67zgXZ1EJMF28btKEXZtZ8RkYst14/FrFIF/kzplYryCLbR6YyzmAN/itsDoRux5+B3+nPEFeX/+jV9keGWlCEC7/p0xmU1MHTKesMgIwhuXzROgy+CeLH9/Ma/fOYWs1Axi+rSt1LxKy8w6yfgpMziVl+fWfgHCr+2EmExsuO55AhqFE2BnX8a+Mea8fenfKIKWE2/HKyigUvKqd21HxGxi1cAELJG1CWwS4XBMWJco/MJDOPLDFpfnVffaTojZxI8DJmJpFI7FYHsZxRyYs5yfhkzhpyFTOL5+L3/OW+ny3AA6Xt0Fk8nE5BuepXZkBBGN6xrGdb++N0vf+4Zpd0wmMzWdNn3OFehbxt+Fj697branCoscflRX1aLoAHcAa5VSfYB8rPduuCgi0h3oBfTAehvWUeUvUflCesTw99e/AJCeuI3gLpeViVGFRewc9RqFJ08Vt6UuXkd+Sio1r2yPV4iFU8lHKzXPll1j2LTYmueuNdto3qlsngCr5y5j90/bAQisWYOTxzMrNa/SzGYT0yc/i8VSOW/g5QntEc2xr9cCcDxxOyFdosrEqMIito96nYIS+7Iw5xRb75lZaXnV7hZNytfrADi2OolancvmZRQjXmbaTx9JzqFU6vbv4PK8wrq34vDX6wH4OzGJWgbbq7wYvzqh+NYOJmNbsstzA2jVNZb1S6yv+R0/bqVlp1aGcSv+u5QdP1kvwhxUK5gs22s+unss+bn5ZKZmVEp+ZVwCI53qciLBYeAuEflKKTVSRAJE5AsgHEhSSj0oIj2Bh4GbgZ+Bm5RShwzW1R/4VimlROR7IEZEfIHZQD0gBRiB9Xat8wEzIECiiAQAH5fst/TKS94C9rGg9gz0b1o6pAxzgB/5R04AcCY9m6A2ZZcpzD5Vpg3AbPEjfFA38lJSwcW3CL7tpVFENK1X/Dyqaww/f279RJmTkU2t2PL/tqbtW2IJtpC85TeX5nUhgRaLW/sryRzgR17xvswhqE3tMjFG+/J0Wlal5uUV4Mupo+nWvDJysLQum5dRTKNhPTm57zB7315M83uvIqB+Lf74cJlr87Jtr9MZ2YQ0bOxUTJMRV5E8Z7nL8hnx0v3ULfGab9UtltWfrQAgOyObxrHlzyY0t73m/9iyD7O3F4P/bxivj5rKI+8+47Icy6NPmXYRpdQ3IuIPLBCRVVjvULdDKZUgIgtEpI1S6icRuRd4C1hop+AARAAbbevdD+wXkbG29d0iIgnAPUAwsFgpNUtEfrAtO8qg3+2lci2+BWxixDCHXgGFOXmY/a3Db7PFD0yOzx4WZOWy5//e5rK3HiKoXTNObv7d4WUvZN648+9ke9PEEXj7WfP0C/DDVM4sZ0BwIMMT7uHfY9w0l11NnL8vfRFT9ZgsKMjNw+xXMq+y+84oJiS2MfvnriQ/NZODX/5M7DM3ubToFOTkF/fpZfEz3F52Y0So3SOa3S9/5rJ8Phr37/Oe3z7xnnOveTv5nWUJDuSOSSN54/5XARg45gZWfLyU3Kxcl+V3QZdA0akW/2JEpAWwFGgL1AbGAjeISCLW26XWt4W+DdwH/Kec1WVhvec3ItJZRJ4EooH1tt+vA1oBTTh306KNtv9H2em3Qk5u+4PgztapqsCYxuQdSnVouRZT7yO4q3W47xUcQEFm5b64Dyb9QfOO1jwbRDcmLcU4T7O3F6PfeYyvpn3CicNplZpTdZO1bT8htn0ZFNOIUw7uy8qWvj2ZsM4tAQiJbkTOobL7xSgm+8AxLLZjhaFxTclNce3+zNieXDxdFhwTSa7B9rIXU6trFOku/JBlJDlpP1G2KbXIVo1JS/nbMM7s7cVD/3qC+VPncfywNb+Ynm248s5rGPfpZCKjm3Dv1AcqNVcAipx4VFPVougAI4EblFKFwA7g38AspVQ8MAE4aIubALwMPFfOun7Gejc8gD7AKWAn0NXW1tX2/CAQY2s7eyR8r51+KyTtu1+JGNabZpPuovagbuTsOUSTZ4ZfcLlDby+i6bhbabtoMic3/86pP/5yRTp2bV32K12G9GbYhLvocF03klZtpm7zBgx+/Pxce9x8OQ1jm3DN2CE89mkCHQd0r9S8qpO/v9tIvWG9aDnpDiIGdSN7zyGaP3NTVafFX99tInJoT9ok3EaDQV3I2ptCzNPDyo05snwLBz5JJLxHNH2+eo5md13J3n8tcWleR77bSMOhPYlNuJ36A7uStTeFVqXyKh1zdLn1hIbw+DjS1u1xaT6lbVq2nh439OHW5+6m84AebF25iXotGjD0iVvOi4u/+QoaxzZl0NgbGffpZLoM6MGLNz3HS8Of56Xhz3NwVzIfPP1OpeYKoAqKHH5UV6JcfJzgopKw3qd7HtZjK5nArcBHQB2sI5dbgauAy5VSD4jIUmCcUmqzwboEeAPoAKQBtwAFWI/p1AcOYT2mUwPrMR3Bev/v8cCvpftVStmdjHd0eg2sZzSF9mlD5trdnK7kg47/87/4ZQNqWGjVqw2/bdhNlovzfGvjVJeuz1VWxYxzKt4r2EKtPq1JX7ub06mVexJFlsnscKx3cAARvVuTum4P+XbyciTGEV5OvG94B1uo3SeW42vLy+vCMY740tf5MxoDaliI7RXH3g27KvWEgP/+uaBCZ+UCZNzc1+ENH/LZqgr3VxmqRdHxVM4UHXeqSNGpTJdK0XEnZ4qOOzlTdNzpYoqOu7ii6KQPi3d4w4fOT6yWRadanEhwsWzHXkrKVEoNropcNE3TKl31nTVzmEcXHduxF03TtH8Efcq0pmma5j56pKNpmqa5iyqo6gwqThcdTdM0D6H0SEfTNE1zG110NE3TNHfRIx1N0zTNbXTR0TRN09xGFVbL73s6RRcdTdM0D6FHOpqmaZrbqCI90vlH8zFVz48d/fN8LxxUBarzNc767nypqlMwNDfu+apOwVBBxe4oX2laUE0vPOgi7h7piEhNrBdP3qKUcsl9L6rLrQ00rcpU14KjaaUpJQ4/LkREPhCRtSIywc7vQ4HFQGdglYiUvR3tRdBFR9M0zUOoIscf5RGRIYBZKdUNaGq7kWZpbYDHlFIvAt8D7V3xN+iio2ma5iGKCsXhh4iMEpGNJR6jSqwqHvjc9vMyoGfpvpRSq5VS60SkN9bRzlpX/A36mI6maZqHcOZEAqXUu8C7dn5tAQ7bfj6BnVGM7aaYNwPpwBmHOy+HHulomqZ5CFUkDj8uIBuKz7oIxE4tUFYPAtuBQa74G3TR0TRN8xBKOf64gE2cm1KLAw6UDhCRp0XkTtvTEMAl9/LWRUfTNM1DuHCksxC4Q0RmAjcBO0VkSqmYd20xPwJmrMd+Kkwf09E0TfMQjpwK7dh6VJaIxAP9gGlKqaPAtlIx6bbfu5QuOpqmaR6i0IXXXrMVlc8vGOhiuuhomqZ5CFeNdKqSLjqapmkeQl97TdM0Te8ScXUAACAASURBVHMbB85Kq/Z00dE0TfMQeqSjaZqmuU1hked/y8Xz/wJN0y5pviEW6veKxTc0sKpTqXIu/HJoldFFx02azXiA1t+8RINHbrQb4x0WTOzCF8q0XzbnWQJiGrssl/Yz76PP4klEPXK90zFtXxlBnX7WyzQ1uetKei2YQK8FE7h8+Uu0m3avy3KMfm00nZdMpsmjN9iN8akdTKdFCee1WVrUo+2cJ1yWx8VIO5HOnWMqN4ce00dy3dcTiXt4sFMxpdvEbGLYhllcPX88V88fT+hlDRAvM/H/GstVnzzN1Z8/i09wgFO59Z4+kkGLJtLu/+znZi+udJtPcAD9Zz9O7bZNGTB/PH41g4pj/cNqcMPS0t9ndMzAafdxz1cJ9HrI/r8Bo7iQhrW55aMnuHv+c/SbcJvdtspSpMThR3V1waIjIgkisltEEm2PtgYxiRebgG398U4uM1tEGtv5nVlE3hWRNSIyR0Qc+Rvtrs8Val7bBTGbSBo4Dt/ICPya1C0TYw620PyNhzAF+J3XHjakF3l/HiV35wGX5FLv2k6I2cTqAROxNArH0qSOwzG1ukThWzuYoz9sBiB5znLWDJnCmiFTOL5+L8nzVrokx/BrOyEmExuue56ARuEEGOToFWwh9o0xmAPO3bDOv1EELSfejleQc2+SrpSZdZLxU2ZwKi+v0vpodE1HxGxiyaBJBEaGU6NJhEMxRm01W0WSvHAtS4e9yNJhL5K+J4UGfeNIWbWdZbdO5XBiEs1uLHMBYrsaX9MRMZn4evAkguzkZi/OqK1mq0jWTZ7H1je/JiVxO2GtGxevo8tzt+Ll5+P09rvsams/H96QQEhkbWo2Ns7RKO6KZ4bz4xsLmT3sBWrUqUmjrq0M2yqLK++nU1UcHem8qJSKtz22VmpGFXcz4KuU6gUcBcr/KOMGwd1iSPv6FwAyVm+jRufLygYVFrFv9EwKT+YWN3mFBNJ44l0UZGRTo3usS3IJ696KlK/XA/B3YhJhXaIcihEvM+2n30fuoTTq9u9wXrxfnVB8aweTsS3ZJTmG9ojm2NfWq6gfT9xOiEGOqrCI7aNep+DkqeK2wpxTbL1npktyuFhms4npk5/FYqm8wlenWysOfGPdP3+tTiK8U9ntYxRj1Fa7Q3MaXNmOAYsn0WP6SMRs4tAPm/n98x8B8KsVRF5alsO51e3Wiv2LrX2k/JhEHYPc7MUZtR1dt4e/N/9BnS5R1G7bjGObfgegXvdoCnLzyU3NdDi3sxp1jWbXknUA7P8xiYZ2cjSKq9WkLkd2WF/nOccz8Q0KMGyrLP/I6TUR2S8iP4nIZyKyVUSutbXPFpENIvK47Xk9W9waEXnR1tZYROaJyEci8lGp9fYVkUUi4iUiESLynYj8IiLP2n7fxPZ8ORBdTor9gSW2nz8DUkWkpoh8Y8tllr31GfVr8PcX36NiUa5jb7KmAD9OHz0OQEFGNt61Q8rEFGafOq/gANQdNYDj36zl2H9/IHxYH0Kv6uhQf+XxCvAl78gJAE5nZOMbVsOhmMhhvcjal8K+t78htF0zmt57VXF8sxFXsX/O8grndpY5wK+4/zPpOfjUDi4TU5h96ryCA3A6LQt1usBleVyMQIuFoEBLpfbhFeBLztF0APIzsvE32D5GMUZtaVv38/3wV1g8YCImbzMNLj83kREYWZu6PWI48O2vdnPp+co9XDd/fPEj9p7+5BwpPzcA7wDfMnFGbWc1HdiV/MwcigoKMHmbaffI9Wx4+TOHttd1L93DnZ+OL350GdGfk7btcCojh8Aw4xx9AnzLxO3+bgN9HhlCyyva0axPHMk/7zBsqyyXwvSao2evjReRkbafBbgL+AB4nHNXKv0Q+AXYKCJzgPrAM8BWW/t4W9xA4Cql1LoS648B7gbilVIFtjf8z5RSs0VkvYi8CzwFTMN6+9SkcnKNwHp/CJRSmwFEZDrwqVLqbMG7GhhssL4y/Sqljpdcecl7VPxS90aHPk8U5uZhsk0DmC1+YHLsBWGJbcKfkz/mTGoGad/8QkjvONKXbXRoWXsKcvKLc/Gy+CGmsp87jGJCWjfmwNyV5KdmcujLn4h+5ib2f7AMRAjrEc1OB98AHFGYk4fZ/+z28jXM8Z+sICcfLz9vALwtfojB68koxqjtxO6DFNkKddq2ZGo0tU41mXy86PXaaH5+6gNUQaHdXH565sPznnebdAde/iX6EOPX+pmc/DJxRm1n/TJhDh2eGEqjfu0JblaXXXOWczor13DdpS0Zd36O/SfeUTwt52PxNdx+AKdz8srErXlzIQ07tqT76AFs/3INZ3LzDdsqyz/p7LXi6TXgT6AQ66WwC7EWIYD1SqkCYDfQCCjAWnTeB4JKrGtZqYID8CCQCdSyPY8CxtiOFVmAekATYJutj/Km+LKw3h8CEbleRG7HOpJZfzZPoJWd9Rn1W2E52/+gRmfrPK8lujH5h/52aLm8A0fxjbS+CQTGNSM/JbXCuWRsTy6eUguOiSTnUNl1GsVkJx/F0igcgJC4puSmpAEQ1jWK9M2/VzivkrK27SfENgUZFNOIUwY5/pOlJSUTYZsSCo2OJNtg+xjFGLX1fmMModGRiElodHUHTuw6CEDPmaP4/fMfOb7duSnTtO3n+qgZHclJO69ZozijtrgHBtDCdkzJNziA01m51O8ZS/Td/bhu/nhqRUfS69WRhn3YcyQpmchOLQGIaNWIDNtr2dG4o7v+pEb9Wqx979viWKO2yqCceFRXrvyeTgcRWQdcBhwCZgAvY71y6fYScdkGyz6G9c50L2AdRe0FFimlVtmKxgngIBAjIgeB1uXk8TPWK6MutP3/O2An0BX43fb/T7AWntLrM+q3wk58t4HYhVPwqVOTkMvbse/+mTR8+hYOTf1fucsdfnshzWc8QIOHb6ToVD577p1W4Vz++m4jvRc9j19EKBGXx/Hr/W8S/fQwdk2dbzcm8brnoUjRftZoGgzuhnh7sX7kLAAi4uNIW7enwnmV9Pd3G+n8dQK+dUIJu7wt20e/TvNnbuL3V9x+bcJq6eDSTVz71QT864TSoG8bEh94m/ZPDWXztC/sxiwemACKMm0ndh+iz1sPICIc/GEzR9bspH7fNjS6uiMBEaE0G9qLg0s3suuD7x3K7cD3mxi4YAKWCGsfiwYlENKiHs2v787GV78oNw5FmTYxCVf86yGiboknfW8KKauTSFl9bqLjuvnjWfPk+05tvz3LNnH3/OcIigilWXwcH14/kbAW9Wk9uDurps8vNw6g++gBrHvvOwryThfHGrVVhuo8beYoURc44iQiCVgPzh+zNUUA1wAJwGys99qOx3rr0xbAAqXUKyJyMzABSAXCsR5r8QYSlFJ3l1p/olIqUUQW2tZ7FOv0XQiQjHXqrTEwF8i1tQ9RSh0wyNcf61RfQ9uyd9niP7b9f6NS6hERaV56fUBe6X5tIyFDjk6vgfXstJDecWSt28WZVJfcC8muo8q33N97B1sI7xNL2to95Ns5EOtIjLMCVZHDsV7BFmr1aU362t2cdlH/9vTd+VKlrr8i5sY9b9juExxAvd6tObZuD6fsbB+jGEeWc0RBOe99PsEBNOjVmiPry+/DKM7RZe05Ynbsn6RfjQCa9mrNnxv2kFNOP47GOeL5P+dVuGL8XGeow+85PY5+US0r1AWLjmafM0XHnS5UdKqKM0XHnTyx6FS18opOVXK06FQFVxSdNU4UnV7VtOh47GVwRKQO8Gmp5r1KqdFVkY+maVplU1TLOuIUjy06tjvdxVd1Hpqmae5ScAkc0/HYoqNpmvZPo0c6mqZpmttUz6OiztFFR9M0zUPokY6maZrmNnqko2maprlNoR7paJqmae5yCdytWhcdTdM0T1GkRzqapmmau1Tf6y04ThcdTdM0D6FPJNA0TdPcpsjO/Yk8iS46FfCJb/XcfGc4U9UpGOqX7/z97N2hul5UE+D2bZOrOgVDuY/eV9UpGFq8om5Vp1Cp7N9Oz3NUz3dNTdM0rQx99pqmaZrmNvrsNU3TNM1tLoWz10xVnYCmaZrmmCJx/HEhIvKBiKwVkQkViXGWLjqapmkeosiJR3lEZAhgVkp1A5qKSIuLibkYuuhomqZ5iEJx/CEio0RkY4nHqBKrigc+t/28DOhp0J0jMU7Tx3Q0TdM8hDNfDlVKvQu8a+fXFuCw7ecTQPuLjHGaLjqapmkewoVXJMgG/G0/B2I86+VIjNP09JqmaZqHUOL44wI2cW66LA44cJExTtMjHU3TNA/hwpHOQmCNiNQDrgGGi8gUpdSEcmK6uqJjPdLRNE3zEIVOPMqjlMrCeqLAOqCvUmpbqYJjFJPpir9Bj3Q0TdM8hCsvg6OUSufc2WkXHeMsXXQ0TdM8hL61gaZpmuY2l0LR0cd0NO0fIu1EOneOeaKq0/BYPiEW6vSOxbdmYJXloJx4VFd6pONGt04dTd0WDdixcgvfv7XAblxQWDD3vvMos25KAKBWw3BufWUUltAgtn63nqVv2l+2ou6YOoZ6LRqQtHIT39rJ0S8ogPvefASTyUT+qXzeG/sahWcKXJpHhxn3UaNlfY6s2MqeWQudimn38t0cXbmNIz9sqVAOPaaPJKRlfVJWbGXb64scjindJmYTQ9fO5OSfqQCsf24OGb8foc+bY/AJtmDyMrHyvtc5nZlboXzLk5l1kvFTZnAqL6/S+rDHf+QTmOo1omDbOvIXzbMbJzVCsTz5MtnP3V9uW0V1mTGS4Bb1+WvFVnbY2a+lY3yCA4j/+HEOL99Kh4m3sXzYS5zOzGHwuplk2/brxglzyNiT4rI8jVwKtzao1JGOiIwVkUQROWX7/w229lml4tqKSFuD5RMvst8XROQXEflKRC74sUREEkQk/mL6clRc/86YzCZmDHmOsMhwajeuYxjnX8PCHTMewDfAr7itz139WTJzPq9c+zStescRWDOoUnJsZ8tx6pDxhEVGEG4nxy6De7L8/cW8fucUslIziOlTZtdVSL1rOyJmE6sGJmCJrE1gkwiHY8K6ROEXHlLhgtPoGuv6lwyaRGBkODUMcjCKMWqr2SqS5IVrWTrsRZYOe5H0PSk06BtHyqrtLLt1KocTk2h2o0uuMGKX2Wxi+uRnsVgCKrWf0rw69gSTiZzJD2GqXRdTRH27sX63jAYf3wu2VUTDazoiJhPLBk0isFE4QQb71SgmpFUkmxLmsfONr/lr9XZqtm5MSHQkBxauZfnQF1k+9MVKLzjgurPXqlKlFh2l1FtKqXjgsFIqXin1la39kVKhbW2PChOR7kAvoAfW6wWNKn8Jp9dffD2jnSf/cHi5Fl2j2bx4LQB71mynWafLDONUUREfjp1FXva5T7056dnUaxVJUFgwXj7e5GZVzifill1j2LT4FwB2rdlGczs5rp67jN0/bQcgsGYNTh53yZmUxWp3iybl63UAHFudRK3OUQ7FiJeZ9tNHknMolbr9O1QohzrdWnHgm/UA/LU6ifBOZXMwijFqq92hOQ2ubMeAxZPoMX0kYjZx6IfN/P75jwD41QoiLy2rQvleSKDFQlCgpVL7MOLVKo4z6xMBKNixCXPLWMM4c3RbVH4eKvNEuW0VFd69FQdt++fI6iRqG7y2jGL+XreH45v/ILxLFGFtm5G66XfC2jen/pXt6L9kEl1mWPdrZStCOfyorqpkek1EEm3FCBF5GTg7ArpDKXWFnWUCgI+BcCBJKfWgndX3B75VSikR+R6IERFfYDZQD0gBRmC9rtB8wAwIkOhIHyWvZzS28c129+zwl+4joum5W+e26BrD2s9XAZCTkU3D2CaGy+VlnyrTtmv1VuJHXENonZrsW7uDogLXfI657aVRRDStV/w8qmsMP3++sjjHWrFNy12+afuWWIItJG/5zSX5nOUV4Mupo+kAnMnIwdK6tkMxjYb15OS+w+x9ezHN772KgPq1+OPDZRedQ45t/fkZ2dRqXXZ/GcUYtR35aSfff/cKp/7OoNfro2lweVsO/bAZgMDI2tTtEcPGl116Vmq1Ib7+FKWnAaCyszA3NrhQsdkLv8F3kPP681gemWy/zQW8AnzJLbF/atrZr/ZiGg3uSn5mDqqggONb97Pi5lfI+zuDbq+Ppt4VbTm8bLPLcjVyKZxIUOXHdJRSz4rIXtvPs8sJHQXsUEoliMgCEWmjlNpuEBcBbLStbz+wX0TG2pa9RUQSgHuAYGCxUmqWiPzgZB8X9Om49857fuPEu/D28wHAN8APEccnZ/uNuZ4PH3wNgKET7+ayXm3Ys+ai0jrPvHHnXwvwpokjinP0C/DDVE6OAcGBDE+4h3+PmV7hPEoryM3DbMvDbPFFTGXzMIoJiW3M/rkryU/N5OCXPxP7zE0XXXQKcvLx8vMGwNviZ5yDQYxR24ndByk6bT3mlbYtmRpNrVM6Jh8ver02mp+f+gDlog8S1Y3KO4V426bH/PxByo4GfAfeQv6KRZCbU26bKxTk5GM+u3/s/DssL+bXcXNo8+RQGlzVnkNLNxXv1xPbkw2n6lyt+o5fHOdJZ69FATfYjvM0BexNDmdhvTgdItJZRJ4EooH1tt+vA1oBTYBttraNTvbhtENJyTTraB3K149uxImUVIeXDWtYm9B6tfDy9baOkFTlvPQOJv1B847WKbUG0Y1Js5Oj2duL0e88xlfTPuHE4TSX55G+PZmwzi0BCIluRM6hsn0YxWQfOIYlMhyA0Lim5KZcfG5pSclE2KbUQqMjyT5UdlsYxRi19X5jDKHRkYhJaHR1B07sOghAz5mj+P3zHzm+Pfmi86zuCpP3YY6yTqmZI5tRlHa0TIxXTHt8rxyMZdwMzJHN8b/3ccM2VzixPZlw25RaSEwkOQavcaOY6AcH0GSo9bibT3AApzNz6f7GGEJs+7VB/w5k2PZrZXLV/XSqUpWPdGxOAbUARESUMnxX3QtsUEp9JCIDAHt7+GesI5ZZQB/bundivW7Qctv/dwJhQAywCuvxpO+d6MNp25f9yiPzJxEcEUp0fDum3zCeOs3r03FwTxbP+KzcZZe8Np+HP51IYM0a7Fi5mb2/7HBVWufZuuxXnpg/mZCImsTEt+WVG8ZTt3kDOg/uyaIZnxbH9bj5chrGNuGasUO4ZuwQfpy7jI22Y0Gu8Nd3m4hf+Bx+dUKpc3kc6+9/i5inh7Fz6ny7MSuvmwhFio6vjaLh9d0weZlZe9/rF53DwaWbuParCfjXCaVB3zYkPvA27Z8ayuZpX9iNWTwwARRl2k7sPkSftx5ARDj4w2aOrNlJ/b5taHR1RwIiQmk2tBcHl25k1wffV2SzOWT2W9MqvY+Szmz6mcDnZmEKCcOrTSdy35mC79AR5H/xUXFMzouPFv9sGTeDUx/MOG8dRm0X69DSTVz11QT8I0Kpd3kbfhrzNnFPDWVbif1aOub7AQmISej5n4dofms8GXtSOLI6idyj6fR427pfU5Zt5uianS7JsTwF4vljHTF+f3dxJyK/K6Wal3hefEzH9rwm1kst+APPKqV+LB0nIhbgI6AO1tHMrbZrA5XuS4A3gA5AGnALUID1mE594BDWYzo1sB7TEcAbGA/86kgfZ5V3TMeIfw0Ll/Vqze8bdnMy1bUH30s6U4HPOQE1LLTq1YbfNuwmKzXDhVlBv3wfh2O9gwOI6N2a1HV7yLezrRyJccRJg6kzsH6irde7NcfW7eGUnfUbxTiynKNu3+a64xmulPvofY4HBwTiHduBgr3bUZnplZcUsHhF3QvG+AQHUKd3a/5et4e8cvbrhWKcddtfcyt8wvP4xrc6/J7z4oFPquUJ1m4pOpcqZ4uOu1Sk6FQmZ4qOO9krOtXBJVF03MiRolNVXFF0nnWi6LxcTYtOdZleuygG3+PJVEoNropcNE3TKlt1PhXaUR5ddEpO0Wmapl3qPL/keHjR0TRN+yepnhPnztFFR9M0zUMUXgJjHV10NE3TPIQe6Wiapmluo/RIR9M0TXMXPdLRNE3T3EafMq1pmqa5jeeXHF10NE3TPEbBJVB2dNHRNE3zEPpEgn+4nQWuu6OhKzXzCq7qFAx5VdPr/BU4cW8jd6uu1zgLeO29CwdVgVox46o6hUqlTyTQNE3T3EaPdDRN0zS30SMdTdM0zW0Kq+kUtTN00dE0TfMQ+ns6mqZpmtvoYzqapmma2+hjOpqmaZrb6Ok1TdM0zW1cOb0mIsuxXwNSlFK3O7GumkAHYItSKq28WF10NE3TPISLz157RSm13OgXInK97f8fANHAEqXUFDuxocBiYAkwU0QuV0ql2uvUVOG0NU3TNLcoQjn8EJFRIrKxxGOUM32JyBDArJTqBjQVkRZ2QtsAjymlXgS+B9qXt1490tE0TfMQzpxIoJR6F3i3At3FA5/bfl4G9AR+M+hnNYCI9AY6A5PLW6ke6WiapnkI5cR/LmABDtt+PgFE2AsUEQFuBtKBM+WtVBcdTdM0D+HM9JoLZAP+tp8DKadeKKsHge3AoPJWqouOpmmah1BKOfxwgU1Yp9QA4oADRkEi8rSI3Gl7GgJklLdSXXSqoaCQIDr0ak+N0BpVnYpWCXxDLNTvFYtvaGBVp1ItpJ1I584xT1R1Gh6hEOXwwwUWAneIyEzgJmCJiESLSOmz2N61xf0ImLEe/7FLn0jgRk9Of4xGLRqxbsV65r7xiWFMYHAgL81+gXUrNvDAxPt57KYnyTyRCcAjLz7E+lW/snb5OpfmddfUMdRr0YDtKzez5K0vDWP8gwIY9eajmEwm8k/l8Z+xr+Hj51OmrfBMgUtyajfzPoJaNuDo8i3sm7XQoZjGd11Jg8FdAfCuEcCJzX+w7akPKpxL7+kjCWlRn0MrtrLljUVOxZVu8wkOoP/sxzm4YitdJ97GkpteIu/ESQD8w2pw9dyn+OrqCReVp//IJzDVa0TBtnXkL5pnN05qhGJ58mWyn7u/3DZ3yMw6yfgpMziVl+fWfkuLfW00gS3r8/fyLex/7SvDGJ/awbR9/1E2DE5wb3IluPjLoaNFxN6LbatSaqGIxAP9gGlKqUwgEzhvGaVUui3GIRc10hGRmiJyUkT8nFhmtohsEZFEEflMRMwX2XeCbUPY+/0LIvKLiHwlIhf8KHmh9blKr2t6YjKZGTv4YepF1qV+k/qGcc1aNeWdyf9h3puf8GviRlq2tp6l2LpzLKHhNV1ecNr374LJbOLlIeOpHRlBeOM6hnFdBvfih/e/4bU7XyArNYPYPm0N21yh7rWdELOJHwdMxNIoHEuTsjkZxRyYs5yfhkzhpyFTOL5+L3/OW1nhXBpf0xExmfh68CSCIsOp0cT4WKpRnFFbzVaRrJs8j61vfk1K4nbCWjcuXkeX527Fy8/novL06tgTTCZyJj+EqXZdTBHGry8Av1tGg4/vBdvcwWw2MX3ys1gsAW7v+6yIazshJhPrrnuegEbhBBi83ryCLbR+YwzmAPdvo5JcOb2mlBqmlIq383jEFpOulPpcKXXUVX/DxU6v9QP8gN5OLveQUioe6xkOV11k33aJSHegF9AD6xDPqfPSK1Pbbm1IXLwagF9/3ETrTrGGcdvWbWf35t206dKay9pGsXPTLsxeZp6Y9ijHDh2lx1XdXJpXVNcYNi7+BYCda7bRolMrw7jEud+z66ftAATWrMHJ45mGba4Q1r0Vh79eD8DfiUnU6hLlVIxfnVB8aweTsS25wrnU7daK/Yut/aT8mESdTmVzsRdn1HZ03R7+3vwHdbpEUbttM45t+h2Aet2jKcjNJzf14rahV6s4zqxPBKBgxybMLY1fX+botqj8PFTmiXLb3CXQYiEo0OL2fkuq2SOaI1+vBSAtcTuhBq83VVjEtlGvU3jylLvTO4+bTySoFBc7vXY18DZwtYisARYANYE/gB3ALOBjIBxIsp3VUFIYkGMbiXyB9dS835VSI0QkAfDGWjxq2PrKB+ZjnS8UINFOXv2Bb5VSSkS+B2JExBeYDdQDUoARtv7OW5+IBFwgZwBsX7AaBdAy5DLqWRoYJvLYKw/TsFnD4udtu8Xx7f+WAnAy4yR1Wts9+xCAvgPjyc7MpqCggP5D+3Hgt4P871+fM2TE9YTXD+erj+xP85TnjpdGUafpuU/BUV1jWPP5CgByMrIJi21S7vJN27fEEhzI/i2/ldtWEV4Bvpw6Yn0DPJ2RTUjDxk7FNBlxFclzDL9ofUE9X7mH4GZ1i5/X69aKvf+zfljIz8gmqLXx9vEO8CXnSPp5cUZtZzUd2JX8zByKCgoweZtp98j1/DByFv0+ePSi8hZff4rSrVcfUdlZmBsbfI/P7IXf4DvIef15LI9Mtt/2D2MO8CPf9lo6k56Df5vaZWIKs6u22Jz1T77KdDesZzWsAP6L9c18EPCzUupWEXkE2KGUShCRBSLSxrbcmyLij/Wc77VAY+BNYDmwVETOvhM3V0r1FpHngcuBhsBipdQsEfmhnLwigI0ASqn9wH4RGWvL5RZbQbsHCDZY36jSOSultpfuoOQXrvo26Gf3FTDzmdfPez520gP4+FuH5v4Wf0xS/iDz9QlvMuKJu+jRrxvNY5uzeN4S0lPT+WHBCu59esRFF53/jjv/u2LDJ47Axzal4xfgh5STlyU4kFsT7uWdMa+W21ZRBTn5mG05eVn8EFPZnOzGiFC7RzS7X/7sovr+6ZkPz3vebdIdePl7A+Bt8cP6dYSyzuTkl4kzajvrlwlz6PDEUBr1a09ws7rsmrOc01m5F5UzgMo7hXjbpn78/MFgP/oOvIX8FYsgN6fctn+agpw8TP5nX0u+hq+36uJSuImb01vXVkDCsI5QGmMtIB2AH4Gz77RRwA0ikgg0Bc5+tH4I63V8fgWewfolopHAPKwjpbPnhH9s+/9BwAdoAmyztW0sJ70srOeTIyKdReRJW3/rbb9fB7Sysz57ObvEvu37aN0pBoBm0U05mmI8dxTFYgAAG7RJREFURTr8gZu56sYrAetJBdlZORxOPky9SOun76i4lhxLOeayvP5M2k/zjtYptQbRjUhL+dswzuztxf3vPM6CafM4cTjNbpsrZGxPLp4uC46JJPdQ2cs42Yup1TWK9M2/uyyXtO3JRNim1GpGR3IyxfiSUkZxRm1xDwygxY3Ws1B9gwM4nZVL/Z6xRN/dj+vmj6dWdCS9Xh3pdJ6FyfswR1mn1MyRzShKK/v68oppj++Vg7GMm4E5sjn+9z5u2PZPk7VtP6GdLwMgKKYRpwxeb9XFP3V6rT/wklJqpoi8ANwFvKCUKnnKx15gg1LqIxEZgLV4AKCUKhKRdCAIuBdr8focWF1i+dIfuw4CMcAqoC3W6/sY+RnriGUW0Ac4BewEumIdTXW1PQ8zWJ/dnF3hp+9/4Y0FMwmLqEXnvp15cNBDNGoRyRXXX86Hr84ujls8bwkT/zWBa2+5hgN7D/Dr6o3s2LiTp2Y8Qd9B8Xh5e5Ew2nXTIFuWbeDp+S8QEhFK6/h2vHTDOOo2b0CXwT1ZOOPT4rheN19OZGwTrht7I9eNvZHEud9jCQks0/ar7fhQRRz5biO9Fj2PX0QoEZfH8ev9b9Lq6WHsnjrfbszq654HIDw+jrR1eyqcw1kHvt/EwAUTsESE0qBvGxYNSiCkRT2aX9+dja9+UW4cijJtYhKu+NdDRN0ST/reFFJWJ5GyOql4PdfNH8+aJ993Os8zm34m8LlZmELC8GrTif9v787jpKrONI7/3m42WaTZQRJZghoREBIQlKUx4pIYRScu4xL3YOLEieOAUVED7nHJJG4Tt3GLk4mSaIxEIy6AC2jUKK6JmQyKCTGoCCqu8Mwf5zYUTVV1N11d51T7fv3cT1fdunXr8VZRp845956z5qpzaX/A0Xw054b127x/3oamu06nX8oH11+60T7yrSuXG6+4KMrrArxxz5OMvWsWHfp2o+dXRvLs8T9hm1MP4pULb2v4yWWWcmHSWNbUi4iy4bCnS3rGzHYFvguMBf4E/AM4F/g/4AagL6H2cShwGeECo7o2hEOBAcBVhBMLqoEZhJMU5kuab2ZHZdveTeiDMUJ/z0xJ8/Nks+x1vgy8CRwCfEro0+kPLCP06WxZf3+E2tdGmSWtLnYsijWv5dO5a2dGT/wSzz7+HCtXrGzKU5vkC226Nmn7jlt2YujEEfzpiZdYvaLodV3N8vUPG39mVtuunehVO4y3Fr3MRwU61xuzTWOsaFO8wt+ua0c+N3E4yx9/mQ+KvE6+7Rr73EIO2nV54zfu2Jm2w77Mp39cgla13OcLoON/XNui+99cD+xw+mY9r03XTvSsHc7bi17i42Z8lorZ643/yd822wTjtprc6O+cxX+b3+zXawlNLnQ22YHZtwhf7p9kyyX5CoTWqKmFTrk0tdApl6YUOuXUUKETU5MKnTJqbYVOOZSi0Nlpq9pGf+c88bcFSRY6zb44VNK1QNk/gVnfS65VkqaWO4dzzpXLZ/nsteiy632cc+4zY62aMrlBmiq20HHOuc+aEg3kGZUXOs45VyFaw9lrXug451yF8D4d55xzZbPOm9ecc86Vi9d0nHPOlY2fveacc65svHnNOedc2XjzmnPOubJpDTWdZo+99lnWpl3/JA/e20fuEDtCXv9yX4Ozh0ezzfpZNdLyhU+SHD6LHms/jR0hr91eOD92hILa9hzc7DdzcM9Rjf7O+cubf0jyw+M1HfeZl2qB41x9a7U2doRm80LHOecqRGtomfJCxznnKoQPg+Occ65svKbjnHOubFrD2Wte6DjnXIXw63Scc86VjQ+D45xzrmy8T8c551zZeJ+Oc865svGajnPOubLx63Scc86VTSlrOmZ2P4XLgNclHV6yF8vhhY5zzlWIEp+9dqGk+/M9YGb7ZX+vB4YCcyWdW2xnZnYVcI+k3xTbrmozwzrnnCuzdVKjFzObZmZP5izTmvJaZvZPQLWknYHBZrZNkW0nAn0bKnDAC52K0a1bDVN2m0iPHt1iR3GtQLuaTvSdNIz23dOdbsJtSlJTlmskjc5Zrmniy00Gbstu3wdMyLeRmbUFrgWWmtnUhnbqhU4ZXXP1JTyy8C5OP+17DW57+WXn8/W9dwegpqYrd915E2PGjOL+ebfTs2f3kmXqcOTJdDz1x7Tb+9Ci21mXGjqdeRUAbWu/TsfpF9Nx+sV0Ous/6XB4w/8/m+u4i07grDsuYOqJBxTcZosuHZl+0xmccstZfO/q71PddkOr8ZY9u3LOby8pSZZ9LvoWx9wxi4kn7tek7Wo+34tDbpjOUbefye5nHFZw3eYYe+lx7HHXDxj2vcL/1utv065rRybf/O/0GDmYKbfPpH33Llh1Ffv9/sdMmTOTKXNmUvPFz212pmKG/cfxjJt7NoP/bf+C27Tr1ZWdfj2rRV6/Kd58eyVHfGd67BgbURP+K4FOwF+z228DfQpsdwTwInARsJOZnVhspw0WOmY2y8xeMrP52fLdJoSuv68bzewP2X5+YWbVm7mfWWY2ucjj55jZY2Z2h5k1+FOuof2Vwn77fZXq6momTNqXQYO2ZsiQQQW3nTB+J/r27cXdc+cBMGL49kyfMZsLLryMefct4EujhpckU5tR46GqijUXnkRVz75U9d6q4LbtD5wGbdsD8MmCu1lzyQzWXDKDta88x8cP/7YkeeobvddYqqqqOHv/0+i1dR/6DOyXd7td9pvEvdf+hou+eTarVqxkRO2o9Y8dMvNI2rVv1+wsX9xrNFZVxX/tP4uarXvRfWD+f3/5ttvt1H9m4WV3cuOB57Bl3+4MGLd93nVN9fmvhte6b9/ZdB7Qmy6DNs2Ub5ua7bfmqVm38sJld/G3BUvoPnwgNUO3Zumdi7j/gPO4/4DzeOfl15ucpyF9vjYGq6pi8d5n0XFAbzoO6rvJNm26dmL4Zd+humP7kr9+U6xa/S4zz72UDz78MGqO+ppS0ymB92D9ZFOdKVxejAKukfR34GfArsV22tiaznmSJmfLFY18TiEnSpoMrAT2aOa+NmFmuwATgfGEKmGT2jFbSu2knZkzJzR3zrt/AeN3GZN3uzZt2vDTn17Mq0tfZ599wuFZ+PBiHn/iaSZOGMuYMSNZtPipkmSq3m5HPv39AgA+ffFpqocMy7/dF0fCxx+i1W9vtN5qemBbdmPdq6+UJE99248bxuNzHwPg+YXPsO2Y/F/MD9xyL88/8iwAXXp0ZfVbqwAYusswPlrzEatWvNPsLAPGDeXFuYsB+MvC5/j8mO0avV2PQf1Y/vz/AfD+W6to36Vj3nVN1XuX7XntN48DsHzBc/TaadNM+bb5x+KXeevp/6X32O3oOfILrHjqz/T80hD6TxnFnnNnM/bS47Dq0jeCdB8/lOV3LQLgzflL6DZ207xau45np/2Ete9+UPLXb4rq6iouOfs0OnVq+vvSkprSp1MCT7GhSW1HYGmB7f4MDM5ujwZeLbrXRpSWs4DDc+53Bu4FHgZuyFk/H7gY+F12vyMwB1gIXJmtuxGYkN2eA0zKt7/sNc/LnvsM0BfoBtwPPJS91uQCeWcDp2S3BwP7AO2BnwMLgFuBdvn2ly9znv1PA57MlmlFjt3VkubnLJK0Y/bYHpJOrb/f7Paxkn4lqa+k8ySdmK03SVdK+rWkLZrYtltoub5Ypmxpt3Tp0pcl1WT/H7mPnS9p1xJlac4xq1t2lvRAXW5JDxXIXfIsucf0sMMOO7vedmdk7+U+kl6R1LnAupZ4/9ZvM3PmzB/nbFP/8zRGUr/ssZsl7VvC97XBvHney815z0q+DBo06I+xM7TUAkwp8th+wJbAs8CPgJeAroQz2c6tt20X4Pbse3MR0L/Y6zb258zMrEnsKqAfcDkwBRhoZnV1+nHAIkl7ZvenAc9LmgT0M7MR2frLzexlYKssYKH9Dcme+yvgK9n+7pa0K/BJkax9CO2PSPqLwtkU38qy1AKvAMcU2F+hzOtp4865Yh1zxxMKsrrlMopXVetqZKOAa4D6VVUB/wIsAfYt8rpN0Zjq86mzZ89uC9SvLlRl2eaXKAts/jED6E74HB2T3T8VuIpNc7dUljrvLV++/MB6250L3AMcB9xEOO751jVVY96/9dssW7bs6znb1P88LQGWZ489CRQ8U6kZiuVNokWivrVr1xZuc658x+d0m2y0EH7UryZ89hcDu0paJelFSWfk7kTSu5IOlDRJ4YffXzd9qQ2a2rx2AuEL+jhCjaE7Gz5Ez0v6Vc5ztgP2z/4HBgP9s/UnEkrL3xO+GArt7+bs72uEmskgQqkL4R9FIasJH2jMbCczm5G93uPZ44uB7Qvsr1DmUmhOVfX7hM46gBo2/4t0czJN+fa3v92bULiMBK7L1k8kHNOWvES6scesHeGX1mlsqNpPIXypzmfj3C2d5ana2trOebZ7Btia8KuRIutKnWn9NiNHjuyYbZPv83RLto9qwq/cZ+vvqAQaewxdGWQFxeQCy0nZNisl3abQX1MSm9NweyyhCeoQ4P2c9fV/qf0R+LFC/80ZhMIDAEnrCH06XYrsL/c22fN3yG6PLJLvUWD37HYt8AHwAqEmRvb3hQL7K5i5BO4Evkn4gjkImEtWVa233fWEGsRC4ATgEkLN55vZumpCX1VLZHohT55JY8eO/SPhF88zhB8IAHtmeVpSY4/ZscCXgJmEQuZgQtPtZDbN3dJZ7jz44IN71NsOYEa2bk3OtvnWNSdTvvdv/TZTp07tluXJ93k6m1DwPENogch70WAzNSava+0a0e43i437dCYBzxP6YB4Dxmfr59d7XifCOd4LgbsJ7YM3An8gFAyPAgPy7S97zcnZfo7Klp5s6H95lMJ9OkZoZnkMuCvLUdens5ANfTqb7C9f5hK3o3aTdJBCf0393MX6h1pyKZgpgWypHbMGj5Uktt1225Mas10ZM3WTdNDo0aOnR3wfi+aN/BkruKSaq5IXyw6sc8451+Iqeuy1rO8l1ypJDV4R65xzLg6v6TjnnCsbHwbHOedc2Xih45xzrmwquk+nNTCzKsJ1RWsI1748KenduKnCNU6SnoidoxAzG0a4juo1YJmkzbm4suTMrBvhwue3gTek0k6AsrkSPl5J5oK0s1Uy79OJzMx+CdxAuO6lO9BH0pS4qcDMbgC+SDjt/GeSlkWOtJ6ZXU74Yh8EnAkcL6lUozRsNjP7PrA/YTilHwJ7Sjqi+LNaXsLHK8lckHa2SufNa/H1kHQ3sI2kw9gwIkNUko4mXEP1EvCgmT1gZrs38LRyGS7pG8A7kuYSxoRKwT6SxgFvSbqVDSNLxJbq8Uo1F6SdraJ581p875rZncBTZvY1IHrTGoTmNeBwYGfCEDO3E65knxczV2aFmZ0FdDOzIwnj1KVgtZkdAXQws1pKN1xRc6V6vFLNBWlnq2jevBaZmXUAhkp62sx2BJZKWpVArhsJ4989pOxDYmZfkfRg1GAhxxaEASK3A14GrpO0uUPJlIyZ9SaM/1aX64eS3oibKunjlWQuSDtbpfNCJ7JUTyRIkZlNKvSYpJYeB64gM9u60GOSSjl+X5MkfLySzAVpZ2stvHktvtvZ+ESCmYQRkt2m6qZ5qCWMTv4UYbDWLoQCO5bZ2d/tsyzPACOAjwgjhceS6vFKNRekna1V8JpOZGY2X9JkM7tX0l5m9qik8RHzPMSm0xUYIElfiRBpE2b2gKTdcu4/mEI2M5tHOGNtXTYV+325OSPmSvV4JZkL0s5W6bymE19SJxIoTGqXunVm9q+EOV/yz7EdR0dgbzNbQpg2I5W5jlM9XqnmgrSzVTSv6USW6okEKTOzGkIn70DChXvXSnoraijAzAYCp7Ah18WS/jdiJCDp45VkLkg7W6Xzmk5kkj40s2VZZ/RKwsRgiyLHSt17wCrgY+DN7HZ0kpaa2a8JfTvPp1DgZJI8XqSbC9LOVtG8phOZmV1PuOq5G+EMNkmaUPxZn21mdgvwCmG67HHAEEnfjJsKzOxHhIsInyDkWinp5Lipkj5eSeaCtLNVOq/pxDeEML32rYQpu6NeB2Nmp0i6KBsGZ6NfJJKOiRSrvs/lfAH8Ls+8SrF8WVJtdvtqM0vlFNtUj1equSDtbBXNC5341gC7EeaqP5BQ44nppuzvrJghGrDczE4j/ArdGfhb5Dx1VpnZIWzIlUqTTKrHK9VckHa2iubNa5GZWSegH+GagGOBeZIejpsqbWbWDvgWoe/kBeB6SR/HTbV+hOnT2ZDrQkkr46ZK+nglmQvSzlbpvKYTmaT3gT9nd8+KmSWXmU0jtGXDhut0ojavmVlHwhlFr0i60symZ9mqY+YCMLP9gBWSZpjZYYT+nKgFTqrHK9VckHa21sJHmXaFHA2cQ7ja/gek0dx2C/Ah8GJ2fwHhWpifR0vE+mkgxgHvZ6veA75mZjcVflZZJHm8SDcXpJ2tVfDmtUhSv/I/u2B1OPAqieQqNFpDAqM4LJS0yZhdhdaXS8LHK8lcxTKkkK218Oa1SCrgyv+2hDlFUhpZ9wEzexD4LWFmzs6EM/+ejJoKXspqO3PZONfrUVOle7xSzQVpZ2sVvKbj8jKzp4AOwPqh+WPXdADMbGfC4Kh9gNXAo5LuipypCji4fi7gGkkfRs6W3PGCdHNB2tlaAy90IsuGwRkm6UkzOxa4JZWzZLL5YepmMu0v6bGYeRpiZndI2j92jvrM7D8lfSd2jvoSPl5J5oK0s1UKb16L7zbgl4Tqex/CRaIHRk3E+pESBhKmW1hD6H9KfaSEmtgBCtgudoACUj1eqeaCtLNVBD97Lb5ukm4CkHQ+0DNynjpDgK8STueuBdbFjdMoXm1vmlSPV6q5IO1sFcFrOvG9bmbfJ4zXNQb4R+Q8dVIbKcE51wp4TScSM+trZucRrnb+ADgg+3tk5FwnZDcPAP4E/BvhquwTCj4pHRY7QAGeq2lSzQVpZ6sIfiJBJNkskzcSahBjUxnBNneGRDO7QtJ3Y2eqz8z6S/przv2Rkp6JmGfrQo9Jes3M+kh6o9A25WBmw4D+hLlhlkl6L2KWgtcuSYo6SGrK2VoLb16Lp52kWwHM7IDYYQoYGjtAAbeb2R3A1YShg4YCX4uYZ3b2d3ugC2G2yeHAR8DoBAqcy4GtCFNonAn8ENg3YqS6a9RqgU8JJ9GMJBy7ibFCZVLO1ip4oRNPLzM7lFBd753dBkDSf8eLRd+cXH0TypVrPHAFsAK4QFLMAgdJR8P62usuktaZWTVwX8xcOYZLmpzVYuea2Skxw0iaDWBmD0jao259dlFmVClnay280InnF8A2eW7Hbu9MNVeuXwDvArsAZyTUDNgR2NvMlgA7ZPdTsMLMzgK6mdmRwN9jB8qsM7N/JdQMh8UOU0/K2Sqa9+m4imNm4yU9mnP/G5J+GTNTlmMgcAqhGetV4OIUpqw2sy0IIydvB7wMXJfC8EZmVkPINZDQ13SNpLejhsqknK3SeaHjKlJKHeO5sjl1+hPG7fq7pEq4vimKbMSL3YB22SpJujlipPVSzlbp/JRpV3GyjvHZwAXAYCCJvqbseqt7CHkmE85OdIXdC3wh535KpyOnnK2ieU3HVRwzm591jD8kaVczWyCpNoFcj0iakJPrEUnRhg6qgOkzknjf8kk5W6XzEwlcJarrGK9JrGN8tZkdAXQws1rgnZhhKmD6jIfN7OfAzWQT4CV0LUzK2SqaN6+5imJmXwaOIMzO2RXoDRwVM1OOo4BRwEpgKnBs1DTp+4RwYsNOhOtjJkdNs7GUs1U0b15zFcPMbgY+lDTNzH4C9AKeB8akMNy8mVXlnjhgZiMkLYmZKR8zmyDpkdg5AMysFxtPn7EoZp5cKWerZN685irJ57O+kiGEX587SlJCF+790swOIYwUfi6wJfBPcSOFi1Yl7Z6z6gISuLo+mz5jEGEoqKSmz0g5W6XzQsdVkpVmNh3YHzgH6GRm0b/Uc/wEWEjoy5kp6fcxw5jZCEJzX/+srwmgExB1NtMcQwhTQd8KHAKk8uMB0s5W0bxPx1WSwwkjEVwg6XagL2HctcNihjKzSdlAkesIp0n3J1z9X3DwyDKxPH/fAg6KE2cTKU+fkXK2iuZ9Os41k5n9oMBDknR2WcPkYWbnSzo9do76zKwT0I/QaX8sME/Sw3FTBSlnq3Re6DjnysrMDNiDcCryIuBUwijOV0pa5tlaNy90nCsRM7tekp8m3YDs+pd3CCdc9CNMH7ASmCIp6gkOKWdrLfxEAudKx8xsTOwTCOqY2Y8knVxvZIIURiToI+kQADN7SdJJ2e3diz+tLFLO1ip4oeNc6bQD5pnZfYTmGUk6JlYYSSdnf1MbmaDGzHYmFIAfZLer2HBNTEwpZ2sVvHnNuRIxswH110l6NUYWADObKunXsV6/EDO7odBjdRPixZJyttbCCx3nWoiZ9ZO0POLrP5jCwJ7FmNlwwlTay4DXUpmiAtLOVsm80HGuRMzsXGAfoHO26n1JIyLmWQO8Xn81odlv2wiRNg4SpqjYinDl/5nA8ZL2jZsqSDlbpfOLQ50rnYmEKbSfAEYAK+LG4XFJ29ZbtkmhwMkMl/QN4B1JcwkDuKYi5WwVzQsd50qnCtiRUNMZQRiQNKY5kV+/IXVTVHRLbIoKSDtbRfPmNedKxMz6EYbA+RQ4Gbhb0m1xU6XLzLYApgHbEaYRuE7SmripgpSzVTovdJwrITPrDgwAlkl6M3aelJnZqZIuzLk/FOjpk6W1bt685lyJmNnJwAPADOB3ZjYjcqTUDTezxWb2z9n9M4HpMQPVMbN7YmdorbzQca50DgJGSzoUGAN8I3Ke1A0mzFFzYna/N9A2XpyNPGdmU2OHaI280HGudF4CxplZW8I0x69EzpO6lcCVQIfsC35b0il0xgD/Y2ZPmNlDCU0UWPG8T8e5ZsoZ28zYMMYZAKlfnBmTmXUgdNSvBI4knPl3r6TrogZzLcrHXnOumerGNjOzrxJ+IVvxZ3y2mVk7YBKwFzCFcOLFZcAVkuZHjLYRM+vFhjHX+ktaFDNPa+E1HedKxMyeBv4dWFu3zs/E2pSZrQbaE5rWzgPmpDYoqZldTxiNoBthFlFJmhA3VevgfTrOlc6fgLOAo7PlqKhp0jUAOAKoAR4BhpnZSWYWbcigPIYQamJ/BmoJU5G7EvDmNedKZytC38Tahjb8LJO0EvhFttQNrLkncAlh1s4UrAF2A6qBAwk1HlcC3rzmXDOZ2QhJS8zsL4QTCV4l9Ov0lrRD3HSuKczsBElXmVknoC9hdIljgXmSHo6brnXwQse5ZsqdQsDMHso5seCh1PoqXHH13ssrJH03dqbWxvt0nCstFbjtKs/Q2AFaI+/Tca75+prZoYQmtdzbfeLGcpsh33sJgKT/jher9fDmNeeaycx+UOgxSbPLmcU1T5H3UpLOLmuYVsoLHeecc2XjfTrOOefKxgsd55xzZeOFjnPOubLxQsc551zZ/D8K7GF0T5JDPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X_train.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 八、构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, cross_val_score, train_test_split, ShuffleSplit, cross_validate\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtc = DecisionTreeClassifier()\n",
    "rfc = RandomForestClassifier()\n",
    "abc = AdaBoostClassifier()\n",
    "gbc = GradientBoostingClassifier()\n",
    "lr = LogisticRegression()\n",
    "p = Perceptron()\n",
    "ls = LinearSVC()\n",
    "s = SVC(probability=True)\n",
    "gnb = GaussianNB()\n",
    "mnb = MultinomialNB()\n",
    "knc = KNeighborsClassifier()\n",
    "mlpc = MLPClassifier()\n",
    "xgb = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_list = [dtc, rfc, abc, gbc, lr, p, ls, s, gnb, mnb, knc, mlpc, xgb]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "clf_compare = pd.DataFrame(columns=[\"name\", \"params\", \"mean_test_score\", \"test_score 3*sigma\", \"mean_train_score\", \"mean_fit_time\"])\n",
    "cv_splits = ShuffleSplit(n_splits=10, train_size=0.7, test_size=0.3, random_state=42)\n",
    "row_index = 0\n",
    "for clf in clf_list:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    clf_compare.loc[row_index, \"name\"] = clf_name\n",
    "    clf_compare.loc[row_index, \"params\"] = str(clf.get_params())\n",
    "    cv_results = cross_validate(clf, X_train, y_train, cv=cv_splits, return_train_score=True)\n",
    "    clf_compare.loc[row_index, \"mean_test_score\"] = np.mean(cv_results[\"test_score\"])\n",
    "    clf_compare.loc[row_index, \"test_score 3*sigma\"] = 3*np.std(cv_results[\"test_score\"])\n",
    "    clf_compare.loc[row_index, \"mean_train_score\"] = np.mean(cv_results[\"train_score\"])\n",
    "    clf_compare.loc[row_index, \"mean_fit_time\"] = np.mean(cv_results[\"fit_time\"])\n",
    "    row_index += 1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>name</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>test_score 3*sigma</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>mean_fit_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>{'class_weight': None, 'criterion': 'gini', 'm...</td>\n",
       "      <td>0.815299</td>\n",
       "      <td>0.0756322</td>\n",
       "      <td>0.893419</td>\n",
       "      <td>0.00649552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>{'bootstrap': True, 'class_weight': None, 'cri...</td>\n",
       "      <td>0.819403</td>\n",
       "      <td>0.0729246</td>\n",
       "      <td>0.889406</td>\n",
       "      <td>0.0367773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>{'algorithm': 'SAMME.R', 'base_estimator': Non...</td>\n",
       "      <td>0.816045</td>\n",
       "      <td>0.0515533</td>\n",
       "      <td>0.819101</td>\n",
       "      <td>0.0810959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>{'criterion': 'friedman_mse', 'init': None, 'l...</td>\n",
       "      <td>0.823881</td>\n",
       "      <td>0.0600317</td>\n",
       "      <td>0.8626</td>\n",
       "      <td>0.0806928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>{'C': 1.0, 'class_weight': None, 'dual': False...</td>\n",
       "      <td>0.783955</td>\n",
       "      <td>0.0242422</td>\n",
       "      <td>0.789567</td>\n",
       "      <td>0.00468762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Perceptron</td>\n",
       "      <td>{'alpha': 0.0001, 'class_weight': None, 'early...</td>\n",
       "      <td>0.707836</td>\n",
       "      <td>0.325592</td>\n",
       "      <td>0.702408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>{'C': 1.0, 'class_weight': None, 'dual': True,...</td>\n",
       "      <td>0.78694</td>\n",
       "      <td>0.0280745</td>\n",
       "      <td>0.789567</td>\n",
       "      <td>0.0626321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SVC</td>\n",
       "      <td>{'C': 1.0, 'cache_size': 200, 'class_weight': ...</td>\n",
       "      <td>0.833209</td>\n",
       "      <td>0.0573086</td>\n",
       "      <td>0.834029</td>\n",
       "      <td>0.060446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>{'priors': None, 'var_smoothing': 1e-09}</td>\n",
       "      <td>0.798507</td>\n",
       "      <td>0.0493046</td>\n",
       "      <td>0.792777</td>\n",
       "      <td>0.0032984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>{'alpha': 1.0, 'class_prior': None, 'fit_prior...</td>\n",
       "      <td>0.704851</td>\n",
       "      <td>0.0465462</td>\n",
       "      <td>0.715409</td>\n",
       "      <td>0.00319791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>{'algorithm': 'auto', 'leaf_size': 30, 'metric...</td>\n",
       "      <td>0.803731</td>\n",
       "      <td>0.0799727</td>\n",
       "      <td>0.843178</td>\n",
       "      <td>0.00239859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>MLPClassifier</td>\n",
       "      <td>{'activation': 'relu', 'alpha': 0.0001, 'batch...</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.0440567</td>\n",
       "      <td>0.829053</td>\n",
       "      <td>0.897625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>{'objective': 'binary:logistic', 'base_score':...</td>\n",
       "      <td>0.829478</td>\n",
       "      <td>0.0645091</td>\n",
       "      <td>0.88748</td>\n",
       "      <td>0.0672372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  \\\n",
       "0       DecisionTreeClassifier   \n",
       "1       RandomForestClassifier   \n",
       "2           AdaBoostClassifier   \n",
       "3   GradientBoostingClassifier   \n",
       "4           LogisticRegression   \n",
       "5                   Perceptron   \n",
       "6                    LinearSVC   \n",
       "7                          SVC   \n",
       "8                   GaussianNB   \n",
       "9                MultinomialNB   \n",
       "10        KNeighborsClassifier   \n",
       "11               MLPClassifier   \n",
       "12               XGBClassifier   \n",
       "\n",
       "                                               params mean_test_score  \\\n",
       "0   {'class_weight': None, 'criterion': 'gini', 'm...        0.815299   \n",
       "1   {'bootstrap': True, 'class_weight': None, 'cri...        0.819403   \n",
       "2   {'algorithm': 'SAMME.R', 'base_estimator': Non...        0.816045   \n",
       "3   {'criterion': 'friedman_mse', 'init': None, 'l...        0.823881   \n",
       "4   {'C': 1.0, 'class_weight': None, 'dual': False...        0.783955   \n",
       "5   {'alpha': 0.0001, 'class_weight': None, 'early...        0.707836   \n",
       "6   {'C': 1.0, 'class_weight': None, 'dual': True,...         0.78694   \n",
       "7   {'C': 1.0, 'cache_size': 200, 'class_weight': ...        0.833209   \n",
       "8            {'priors': None, 'var_smoothing': 1e-09}        0.798507   \n",
       "9   {'alpha': 1.0, 'class_prior': None, 'fit_prior...        0.704851   \n",
       "10  {'algorithm': 'auto', 'leaf_size': 30, 'metric...        0.803731   \n",
       "11  {'activation': 'relu', 'alpha': 0.0001, 'batch...           0.825   \n",
       "12  {'objective': 'binary:logistic', 'base_score':...        0.829478   \n",
       "\n",
       "   test_score 3*sigma mean_train_score mean_fit_time  \n",
       "0           0.0756322         0.893419    0.00649552  \n",
       "1           0.0729246         0.889406     0.0367773  \n",
       "2           0.0515533         0.819101     0.0810959  \n",
       "3           0.0600317           0.8626     0.0806928  \n",
       "4           0.0242422         0.789567    0.00468762  \n",
       "5            0.325592         0.702408             0  \n",
       "6           0.0280745         0.789567     0.0626321  \n",
       "7           0.0573086         0.834029      0.060446  \n",
       "8           0.0493046         0.792777     0.0032984  \n",
       "9           0.0465462         0.715409    0.00319791  \n",
       "10          0.0799727         0.843178    0.00239859  \n",
       "11          0.0440567         0.829053      0.897625  \n",
       "12          0.0645091          0.88748     0.0672372  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**可以看出SVC的测试准确率最高**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "s.fit(X_train, y_train)\n",
    "y_test = s.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv(\"./titanic/test.csv\")\n",
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_data[\"PassengerId\"],\n",
    "        \"Survived\": y_test\n",
    "    })\n",
    "submission.to_csv('./titanic/submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 九、调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grids = [\n",
    "    {\"criterion\": [\"gini\", \"entropy\"], \n",
    "     \"splitter\": [\"best\", \"random\"],\n",
    "    \"max_depth\": [3, 4, 5, 6, 7],\n",
    "    \"max_features\": [\"sqrt\", \"log2\"]},  # DecisionTreeClassifier\n",
    "    {\"n_estimators\": [50, 100, 150, 200, 250, 300],\n",
    "    \"criterion\": [\"gini\", \"entropy\"],\n",
    "    \"max_depth\": [3, 4, 5, 6, 7],\n",
    "    \"max_features\": [\"sqrt\", \"log2\"]},  # RandomForest\n",
    "    {\"n_estimators\": [50, 100, 150, 200, 250, 300],\n",
    "    \"learning_rate\": [0.01, 0.03, 0.1, 0.3],\n",
    "    \"algorithm\": [\"SAMME\", \"SAMME.R\"]},  # AdaBoost\n",
    "    {\"loss\": [\"deviance\", \"exponential\"],\n",
    "    \"learning_rate\": [0.01, 0.03, 0.1, 0.3],\n",
    "    \"n_estimators\": [50, 100, 150, 200, 250, 300],\n",
    "    \"subsample\": [0.1, 0.3, 0.5, 0.7, 1.0],\n",
    "    \"max_features\": [\"sqrt\", \"log2\"]},  # GradientBoost\n",
    "    {\"penalty\": [\"l2\"],\n",
    "    \"tol\": [0.01, 0.001, 0.0001, 0.00001],\n",
    "    \"C\": [0.3, 0.7, 1.0, 1.3, 1.7],\n",
    "    \"solver\": [\"lbfgs\", \"liblinear\"],\n",
    "    \"max_iter\": [100, 300, 500]},  # Logistic\n",
    "    {\"penalty\": [\"l2\", \"l1\"],\n",
    "    \"alpha\": [0.0001, 0.001, 0.01, 0.1, 1],\n",
    "    \"max_iter\": [300,500, 700, 1000, 2000],\n",
    "    \"tol\": [0.01, 0.001, 0.0001]},  # Perceptron\n",
    "    {\"penalty\": [\"l2\"],\n",
    "    \"loss\": [\"hinge\", \"squared_hinge\"],\n",
    "    \"tol\": [0.01, 0.001, 0.0001],\n",
    "    \"C\": [0.01, 0.03, 0.1, 0.3, 1, 3]},  # LinearSVC\n",
    "    {\"C\": [0.01, 0.03, 0.1, 0.3, 1, 3],\n",
    "    \"kernel\": [\"rbf\", \"poly\", \"sigmoid\"],\n",
    "    \"tol\": [0.01, 0.001, 0.0001]},  # SVC\n",
    "    {\"var_smoothing\": [1e-7, 1e-8, 1e-9]},  # GaussianNB\n",
    "    {\"alpha\": [0, 0.3, 1.0, 1.3]},  # MultinomialNB\n",
    "    {\"n_neighbors\": [3, 5, 7, 9],\n",
    "    \"weights\": [\"uniform\", \"distance\"],\n",
    "    \"algorithm\": [\"ball_tree\", \"kd_tree\"],\n",
    "    \"p\": [1, 2]},  # KNN\n",
    "    {\"hidden_layer_sizes\": [(50,), (100,), (150,), (200,)],\n",
    "    \"activation\": [\"identity\", \"logistic\", \"tanh\", \"relu\"],\n",
    "    \"solver\": [\"lbfgs\", \"sgd\", \"adam\"],\n",
    "    \"alpha\": [0.0001, 0.001, 0.01, 0.1],\n",
    "    \"max_iter\": [200, 300, 400, 500]},  # NeuralNetwork\n",
    "    {\"max_depth\": [3, 5, 7, 9],\n",
    "    \"learning_rate\": [0.001, 0.003, 0.01, 0.03, 0.1, 0.3],\n",
    "    \"booster\": [\"gbtree\", \"gblinear\", \"dart\"]}  # XGBoost\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "gridsearch_compare = pd.DataFrame(columns=[\"name\", \"mean_train_score\", \"mean_test_score\", \"std_test_score*3\", \"best_params\"])\n",
    "row_index = 0\n",
    "for clf, param_grid in zip(clf_list, param_grids):\n",
    "    print(row_index)\n",
    "    grid_clf = GridSearchCV(clf, param_grid, cv=cv_splits, n_jobs=-1, return_train_score=True)\n",
    "    #grid_clf = RandomizedSearchCV(clf, param_grid, cv=cv_splits, n_jobs=-1, return_train_score=True, random_state=42, n_iter=30)\n",
    "    grid_clf.fit(X_train, y_train)\n",
    "    gridsearch_compare.loc[row_index, \"name\"] = clf.__class__.__name__\n",
    "    cv_results = grid_clf.cv_results_\n",
    "    gridsearch_compare.loc[row_index, \"mean_train_score\"] = np.mean(cv_results[\"mean_train_score\"])\n",
    "    gridsearch_compare.loc[row_index, \"mean_test_score\"] = np.mean(cv_results[\"mean_test_score\"])\n",
    "    gridsearch_compare.loc[row_index, \"std_test_score*3\"] = np.mean(cv_results[\"std_test_score\"])*3\n",
    "    gridsearch_compare.loc[row_index, \"best_params\"] = str(grid_clf.best_params_)\n",
    "    row_index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>name</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score*3</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>0.807909</td>\n",
       "      <td>0.792752</td>\n",
       "      <td>0.099503</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7, 'max_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.84662</td>\n",
       "      <td>0.829876</td>\n",
       "      <td>0.0629479</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5, 'max_fea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "      <td>0.808852</td>\n",
       "      <td>0.812648</td>\n",
       "      <td>0.0492202</td>\n",
       "      <td>{'algorithm': 'SAMME.R', 'learning_rate': 0.1,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.841209</td>\n",
       "      <td>0.81762</td>\n",
       "      <td>0.0704187</td>\n",
       "      <td>{'learning_rate': 0.01, 'loss': 'exponential',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.78945</td>\n",
       "      <td>0.785886</td>\n",
       "      <td>0.0351244</td>\n",
       "      <td>{'C': 0.3, 'max_iter': 100, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Perceptron</td>\n",
       "      <td>0.668732</td>\n",
       "      <td>0.66658</td>\n",
       "      <td>0.210356</td>\n",
       "      <td>{'alpha': 0.001, 'max_iter': 300, 'penalty': '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.784051</td>\n",
       "      <td>0.783841</td>\n",
       "      <td>0.0393233</td>\n",
       "      <td>{'C': 0.03, 'loss': 'hinge', 'penalty': 'l2', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>SVC</td>\n",
       "      <td>0.729442</td>\n",
       "      <td>0.723425</td>\n",
       "      <td>0.0897</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'poly', 'tol': 0.01}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.792777</td>\n",
       "      <td>0.798507</td>\n",
       "      <td>0.0493046</td>\n",
       "      <td>{'var_smoothing': 1e-07}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.71549</td>\n",
       "      <td>0.704851</td>\n",
       "      <td>0.0465462</td>\n",
       "      <td>{'alpha': 0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.868469</td>\n",
       "      <td>0.811742</td>\n",
       "      <td>0.0749316</td>\n",
       "      <td>{'algorithm': 'ball_tree', 'n_neighbors': 7, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>MLPClassifier</td>\n",
       "      <td>0.809464</td>\n",
       "      <td>0.785216</td>\n",
       "      <td>0.0554658</td>\n",
       "      <td>{'activation': 'relu', 'alpha': 0.01, 'hidden_...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>XGBClassifier</td>\n",
       "      <td>0.812208</td>\n",
       "      <td>0.78891</td>\n",
       "      <td>0.0601226</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.03, '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name mean_train_score mean_test_score  \\\n",
       "0       DecisionTreeClassifier         0.807909        0.792752   \n",
       "1       RandomForestClassifier          0.84662        0.829876   \n",
       "2           AdaBoostClassifier         0.808852        0.812648   \n",
       "3   GradientBoostingClassifier         0.841209         0.81762   \n",
       "4           LogisticRegression          0.78945        0.785886   \n",
       "5                   Perceptron         0.668732         0.66658   \n",
       "6                    LinearSVC         0.784051        0.783841   \n",
       "7                          SVC         0.729442        0.723425   \n",
       "8                   GaussianNB         0.792777        0.798507   \n",
       "9                MultinomialNB          0.71549        0.704851   \n",
       "10        KNeighborsClassifier         0.868469        0.811742   \n",
       "11               MLPClassifier         0.809464        0.785216   \n",
       "12               XGBClassifier         0.812208         0.78891   \n",
       "\n",
       "   std_test_score*3                                        best_params  \n",
       "0          0.099503  {'criterion': 'entropy', 'max_depth': 7, 'max_...  \n",
       "1         0.0629479  {'criterion': 'gini', 'max_depth': 5, 'max_fea...  \n",
       "2         0.0492202  {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...  \n",
       "3         0.0704187  {'learning_rate': 0.01, 'loss': 'exponential',...  \n",
       "4         0.0351244  {'C': 0.3, 'max_iter': 100, 'penalty': 'l2', '...  \n",
       "5          0.210356  {'alpha': 0.001, 'max_iter': 300, 'penalty': '...  \n",
       "6         0.0393233  {'C': 0.03, 'loss': 'hinge', 'penalty': 'l2', ...  \n",
       "7            0.0897          {'C': 0.1, 'kernel': 'poly', 'tol': 0.01}  \n",
       "8         0.0493046                           {'var_smoothing': 1e-07}  \n",
       "9         0.0465462                                       {'alpha': 0}  \n",
       "10        0.0749316  {'algorithm': 'ball_tree', 'n_neighbors': 7, '...  \n",
       "11        0.0554658  {'activation': 'relu', 'alpha': 0.01, 'hidden_...  \n",
       "12        0.0601226  {'booster': 'gbtree', 'learning_rate': 0.03, '...  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridsearch_compare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如果用最好的分类器，那由表可知RandomForest最好，参数设置在best_params中，可用该参数的RandomForest来拟合预测。**\n",
    "\n",
    "**此外，对于训练集，死亡率约为67.5%，也就是我们如果预测100%的人死亡，准确率也有67.5%，从这个角度来说，Perceptron的表现不尽人意。**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**利用VotingClassifier来构建更强的分类器**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_clf = [\n",
    "    (\"decisiontree\", DecisionTreeClassifier(criterion=\"entropy\", max_depth=7, max_features=\"log2\", splitter=\"best\")),\n",
    "    (\"randomforest\", RandomForestClassifier(criterion=\"gini\", max_depth=5, max_features=\"log2\", n_estimators=50)),\n",
    "    (\"adaboost\", AdaBoostClassifier(algorithm=\"SAMME.R\", learning_rate=0.1, n_estimators=100)),\n",
    "    (\"gradientboost\", GradientBoostingClassifier(learning_rate=0.01, loss=\"exponential\", max_features=\"sqrt\", n_estimators=250, subsample=1.0)),\n",
    "    (\"logistic\", LogisticRegression(C=0.3, max_iter=100, penalty=\"l2\", solver=\"lbfgs\", tol=0.01)),\n",
    "    (\"linearsvc\", LinearSVC(C=0.03, loss=\"hinge\", penalty=\"l2\", tol=0.01)),\n",
    "    (\"svc\", SVC(C=0.1, kernel=\"poly\", tol=0.01)),\n",
    "    (\"gaussian\", GaussianNB(var_smoothing=1e-07)),\n",
    "    (\"multinomial\", MultinomialNB(alpha=0)),\n",
    "    (\"knn\", KNeighborsClassifier(algorithm=\"ball_tree\", n_neighbors=7, p=1, weights=\"uniform\")),\n",
    "    (\"mlp\", MLPClassifier(activation=\"relu\", alpha=0.01, hidden_layer_sizes=(50,), max_iter=300, solver=\"adam\")),\n",
    "    (\"xgb\", XGBClassifier(booster=\"gbtree\", learning_rate=0.03, max_depth=3))\n",
    "]\n",
    "voting = VotingClassifier(voting_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n"
     ]
    }
   ],
   "source": [
    "voting_cv_results = cross_validate(voting, X_train, y_train, cv=cv_splits, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([1.30519176, 1.27760386, 1.27121425, 1.53324866, 1.28551054,\n",
       "        1.28420639, 1.55535507, 1.28954101, 1.33639932, 1.30239964]),\n",
       " 'score_time': array([0.08494639, 0.08294892, 0.08294821, 0.08294821, 0.08294916,\n",
       "        0.08194876, 0.081949  , 0.08294845, 0.08294868, 0.08294821]),\n",
       " 'test_score': array([0.8358209 , 0.84701493, 0.84701493, 0.81343284, 0.83955224,\n",
       "        0.8358209 , 0.82462687, 0.79477612, 0.85820896, 0.84701493]),\n",
       " 'train_score': array([0.84911717, 0.83306581, 0.82985554, 0.84751204, 0.83627608,\n",
       "        0.83467095, 0.84430177, 0.85874799, 0.82504013, 0.83146067])}"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_cv_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8343283582089551"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(voting_cv_results[\"test_score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py:485: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10\n",
      "  'setting alpha = %.1e' % _ALPHA_MIN)\n",
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "voting.fit(X_train, y_train)\n",
    "y_test = voting.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv(\"./titanic/test.csv\")\n",
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_data[\"PassengerId\"],\n",
    "        \"Survived\": y_test\n",
    "    })\n",
    "submission.to_csv('./titanic/submission.csv', index=False)\n",
    "# 20200225 提交准确率：0.80382"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 十、总结 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2020/02/25 目前的最高准确率是0.80382，Top 8%，还可以继续加强，从特征工程方面继续着手，比如对Family是否继续划分并编码等，对缺失值的更精细化填充是否更好？"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### 十一、参考资料"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目前参考了[Kaggle上Titanic的某个kernel][1]、[秋刀鱼的分析][2]、[wLsq的分析][3]\n",
    "\n",
    "[1]: https://www.kaggle.com/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy\n",
    "\n",
    "[2]: https://segmentfault.com/a/1190000016030922\n",
    "\n",
    "[3]: https://mp.weixin.qq.com/s/bHmUmuug2NfJqrbzCDBu9g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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