Python59-kaggle之Titanic案例.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[原文：从官方Titanic案例打开kaggle机器学习之路（上）](https://mp.weixin.qq.com/s/FbQ2ZEks5bif_uBHZRaMUg)、\n",
    "\n",
    "这个案例的原文地址是：\n",
    "\n",
    "https://www.kaggle.com/startupsci/titanic-data-science-solutions\n",
    "\n",
    "Titanic乘客生存预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入需要的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据分析\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "# 数据可视化\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 机器学习\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载数据\n",
    "train=pd.read_csv('files/data/python59-data/train.csv')\n",
    "test=pd.read_csv('files/data/python59-data/test.csv')\n",
    "combine=[train,test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看训练集的数据字段\n",
    "train.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- PassengerId => 乘客ID\n",
    "- Survived => 是否存活\n",
    "- Pclass => 乘客等级(1/2/3等舱位)\n",
    "- Name => 乘客姓名\n",
    "- Sex => 性别\n",
    "- Age => 年龄\n",
    "- SibSp => 堂兄弟/妹个数\n",
    "- Parch => 父母与小孩个数\n",
    "- Ticket => 船票信息\n",
    "- Fare => 票价\n",
    "- Cabin => 客舱\n",
    "- Embarked => 登船港口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <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",
       "      <th>0</th>\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",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>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",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>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",
       "      <th>4</th>\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据类型\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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: 83.6+ KB\n",
      "________________________________________\n",
      "<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": [
    "# 查看训练集和测试集数据的信息\n",
    "train.info()\n",
    "print('_'*40)\n",
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数值型数据的描述信息\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于数据分析的假设\n",
    "我们根据迄今所做的数据探索得出以下假设：\n",
    "\n",
    "在采取适当行动之前，我们可能会进一步验证这些假设。\n",
    "\n",
    "关联性假设\n",
    "1.我们想知道每个特征与生存的关系。\n",
    "\n",
    "2.我们希望在项目的早期就这样做，并将这些相关性与项目后面的建模相关性进行匹配。\n",
    "\n",
    "## 数据完整性假设\n",
    "1. 我们可能想要完整的年龄特征，因为它肯定与生存相关。\n",
    "\n",
    "2. 我们可能想要完整的登船港口数据，因为它也可能与生存或另一些重要的特性相关。\n",
    "\n",
    "## 数据修正假设\n",
    "1. 在我们的分析中，票面特征可能会被丢弃，因为它包含了高比率的重复(22%)，并且可能不会存在票和生存之间的关系。\n",
    "\n",
    "2. 在训练和测试数据集中，客舱特征可能会被删除，因为它高度不完整或包含许多空值。\n",
    "\n",
    "3. 乘客id可以从训练数据集上删除，因为它对生存分析没有帮助。\n",
    "\n",
    "4. 乘客名字特征是相对不标准的，可能不会直接影响生存，所以可能会丢弃。\n",
    "\n",
    "## 创建新特征假设\n",
    "1. 我们可能想要创建一个新的功能，叫做基于Parch和SibSp的家庭信息，以获得在船上家庭成员的总数。\n",
    "\n",
    "2. 我们可能希望将名称特征进行提取作为一个新特征。\n",
    "\n",
    "3. 我们可能想要为年龄层创造新的特征。将一个连续的数字特征变成一个有序的范围特征。\n",
    "\n",
    "4. 如果它有助于我们的分析，我们可能还想创建一个票价范围的特征。\n",
    "\n",
    "## 分类假设\n",
    "我们还可以根据前面探索的数据描述增加我们的假设。\n",
    "\n",
    "1. 女性更可能存活。\n",
    "\n",
    "2. 儿童(年龄小于多少）更可能存活下来。\n",
    "\n",
    "3. 上层阶级的乘客(Pclass=1)更有可能幸存下来。\n",
    "\n",
    "\n",
    "\n",
    "有了这些假设，我们继续探索数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算每个等级舱位的平均存活率\n",
    "train[['Pclass','Survived']].groupby(['Pclass'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "        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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算不同性别乘客的平均存活率\n",
    "train[['Sex','Survived']].groupby(['Sex'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算共同乘船时不同个数堂兄弟、揭秘额的乘客存活率\n",
    "train[['SibSp','Survived']].groupby(['SibSp'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  查看不同数量父母和孩子一起乘船的乘客存活率\n",
    "train[['Parch','Survived']].groupby(['Parch'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面可以得出：\n",
    "\n",
    "1. Pclass在Pclass=1中观察到显著相关(>0.5)，超过50%的人存活 (分类假设3）。我们决定在我们的模型中包含这个特征。\n",
    "\n",
    "2. 性别对生存影响很大，女性74%都生存下来（分类假设1）\n",
    "\n",
    "3. SibSp和Parch这些特征对某些值没有相关性。最好从这些特征派生出一个或一组新特征。（创建新特征假设1）\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过数据可视化分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以通过可视化分析数据来继续确认我们的一些假设。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相关数值特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c404b3a208>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 死亡和生存乘客分别对应的年龄分布：\n",
    "g=sns.FacetGrid(train,col='Survived')\n",
    "g.map(plt.hist,'Age',bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过上图观察出：\n",
    "\n",
    "1. 婴儿(年龄<=4)生存率高。\n",
    "\n",
    "2. 最年长的乘客(年龄= 80)幸存。\n",
    "\n",
    "3. 大量15-25岁的儿童没有存活。\n",
    "\n",
    "4. 大多数乘客的年龄在15-35岁之间。\n",
    "\n",
    "决策：\n",
    "\n",
    "这个简单的分析确认了一些我们的假设，为后面的工作铺垫好了：\n",
    "\n",
    "在我们的模型训练中，我们应该考虑年龄（分类假设2）。\n",
    "\n",
    "我们应该填充年龄特征的空缺值（数据完整性假设1）。\n",
    "\n",
    "我们应该将年龄分段（创建新特征假设3）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相关的数字和分类特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Grid.add_legend of <seaborn.axisgrid.FacetGrid object at 0x000002C406EED0B8>>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v4m6/fwccvdBBLAST+ex8HvglgCSv7n4535LkIxMXTPK7SW7sHv/kjh5BklO6XsItST7XlR2e5IauB3FrkuWzCbKqvlhVD3WzXwaWDbiJpcDmcdu7tYvzuCRXjavjB5Oc3k3fl+StSb4AvDnJDeOWOzjJjm2sS7Iiye8n+R/jljk9yQe66VeOez3+eseXS5L/muTuJJ8Fjh2wTj8jyVLgF6rqS9W7nOCHgZNmu12NLNvvImq/Xd2+XFUPDGNbrTGZz1CS3YAXAbclORw4Bzi+qo4Ezpxklcur6te6xzcCZ3TlbwVe2JW/pCt7LfD+qjoKWMG4hjju+dd2jWPi7dXThH4G8PcDVvdDwEVJrk9yTpKn9Lnev1bV86rqXcAeSZ7alb8MuHTCspcBvzNu/mXA2iSHdtPHdq/Ho8ArusT7NnpfAicAkw71JfmNKV6nL06y+AE89rXe3JVpkbH99qW19rtL222hA2jQnklu7qY/D1wE/B5wWVVtA6iq70yy3hFJ3gHsDewFXNOV/yOwOsmlwOVd2ZeAc5Iso/clsmnixqrqZYMGnuQ36H0ZPG+Q9arqmq4hr6T3BXhTkiP6WHXtuOlLgZcC76bXuB8Tf1VtTXJvkmOATcAz6L02rwOeBdyY3u7rPYEtwLOBdVW1tavbWuDpk8R+PXBUn1WdbP949bmu2mD7Xbztd5dmMh/cD7pfmD+R3qd0ui/91cBJVXVLN5R1HEBVvTbJs4EXAzcnOaqqPpZkfVd2TZLXVNVnJjznWnoNZqL3VdWHJxYm+RXgQuBFVfV/+qjnY3RfcB8DPtYNzf068CCPHd153ITVvj9uei3wiSSX9zb3s19w3TIvBf4J+FRVVffarqmqsyfU5yT6SLTdF+B5kzz0L1X13Allm3nsEOYy4P7pnkNNsf0u3va7a6sqbwPcgEcmKTscuBt4cje/T3d/LvCmbnobsB+wO3AtsLorf9q47dxE71foU4F0ZecDr59lzAcB9wDPnWa51cBxk5QfDzy+m34ivWHGXwMOBO4Dfh74d/QO1Dm9W+4+YN8J27kR+Ajw5nFl64AV3fSTgHuB64Gju7LD6P3S32/Hawv8e3r7Af8ZeHL3mn4e+OAQ3t8bgWPo9dL/Hvithf7MeRvezfa7uNvvzt7nxX6zZz4EVXVHkncCn03yKL1GffqExf4UWE/vA3wbvUYF8N7uAJkA1wG30Dva9JVJfgR8m97RqLPxVnqN5i+7oa7tVbVigPWfBXwwyXZ6v+QvrKobAbrhxVvpNdibptnOWuC9wCGTPVhVDyW5Ezisqm7oyu5M8t+Bf0jyc8CPgNdV1ZeTnEtvSPMB4KvAlEfdDuD36X0p7kkvmQ+6f1KNsf0unvab3kF4/wV4fJLN9Op67my324Idvx4lkqym1+NYt8ChSBqQ7XfX5tHskiQ1zmSu8f6W3r4ySe2x/e7CHGaXJKlx9swlSWqcyVySpMbN61/TVq5cWVdfffV8PqWkyc9sNzDbr7Qg+mq/89oz37Zt23w+naQhsv1Ko8thdkmSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGtd3Mk+yJMlNSa7q5g9Jsj7JpiRrk+wxd2FKkqSpDNIzPxPYOG7+PcB5VbUceAg4Y5iBSZKk/vSVzJMsA14MXNjNBzgeuKxbZA1w0lwEKEmSdq7fnvn5wJuBH3fzTwYerqrt3fxm4IAhxyZJkvowbTJP8tvAlqr6yvjiSRad9MLoSVYl2ZBkw9atW2cYpqSFYPuV2tBPz/xY4CVJ7gMuoTe8fj6wd5IdV11bBtw/2cpVdUFVraiqFWNjY0MIWdJ8sf1KbZg2mVfV2VW1rKoOBl4OfKaqXgFcD5zcLXYacMWcRSlJkqY0m/+ZvwX4oyT30NuHftFwQpIkSYPYbfpFfqqq1gHruul7gaOHH5IkSRqEZ4CTJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGjdtMk/yuCQ3JLklyR1J3taVH5JkfZJNSdYm2WPuw5UkSRP10zP/N+D4qjoSOApYmeQY4D3AeVW1HHgIOGPuwpQkSVOZNplXzyPd7O7drYDjgcu68jXASXMSoSRJ2qm+9pknWZLkZmALcC3wNeDhqtreLbIZOGBuQpQkSTvTVzKvqker6ihgGXA0cOhki022bpJVSTYk2bB169aZRypp3tl+pTYMdDR7VT0MrAOOAfZOslv30DLg/inWuaCqVlTVirGxsdnEKmme2X6lNvRzNPtYkr276T2BFwAbgeuBk7vFTgOumKsgJUnS1HabfhGWAmuSLKGX/C+tqquS3AlckuQdwE3ARXMYpyRJmsK0ybyqbgWeOUn5vfT2n0uSpAXkGeAkSWqcyVySpMaZzCVJapzJXJKkxpnMJUlqnMlckqTGmcwlSWqcyVySpMaZzCVJapzJXJKkxpnMJUlqnMlckqTGmcwlSWqcyVySpMaZzCVJapzJXJKkxpnMJUlq3LTJPMmBSa5PsjHJHUnO7Mr3SXJtkk3d/ZPmPlxJkjRRPz3z7cAbq+pQ4BjgdUkOA84Crquq5cB13bwkSZpn0ybzqnqgqr7aTX8P2AgcAJwIrOkWWwOcNFdBSpKkqQ20zzzJwcAzgfXA/lX1APQSPrDfsIOTJEnT6zuZJ9kL+CTw+qr67gDrrUqyIcmGrVu3ziRGSQvE9iu1oa9knmR3eon8o1V1eVf8YJKl3eNLgS2TrVtVF1TViqpaMTY2NoyYJc0T26/Uhn6OZg9wEbCxqt437qErgdO66dOAK4YfniRJms5ufSxzLPAq4LYkN3dlfwK8G7g0yRnAN4BT5iZESZK0M9Mm86r6ApApHn7+cMORJEmD8gxwkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5pIkNc5kLklS40zmkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5pIkNc5kLklS40zmkiQ1zmQuSVLjTOaSJDXOZC5JUuNM5pIkNW7aZJ7k4iRbktw+rmyfJNcm2dTdP2luw5QkSVPZrY9lVgMfBD48ruws4LqqeneSs7r5tww/PJ137d1TPvaGE54+j5FIkkbVtD3zqvoc8J0JxScCa7rpNcBJQ45LkiT1aab7zPevqgcAuvv9hheSJEkaRD/D7LOSZBWwCuCggw6a66cbWXMxXO4QvObaTNuvn83B7ew1A1837dxMe+YPJlkK0N1vmWrBqrqgqlZU1YqxsbEZPp2khWD7ldow0575lcBpwLu7+yuGFpEkjSh7zxpV/fw17ePAl4BnJNmc5Ax6SfyEJJuAE7p5SZK0AKbtmVfVqVM89PwhxyJJkmZgzg+A02jxwCRp4Uw3TC/NlKdzlSSpcfbMR8Bc/Fq3ByD9rLk+gM12p4Viz1ySpMaZzCVJapzD7ANyGE2SNGrsmUuS1DiTuSRJjTOZS5LUOJO5JEmN8wA4/YRnh9N88GIlMzPb1832vbjZM5ckqXEmc0mSGucwu2Ztpv+9n8nQ3nw+lxbGQg7Dt3weiYWM3V0nC8+euSRJjbNnrr54MRi1ws/V4Bb6AjT23GdvVj3zJCuT3JXkniRnDSsoSZLUvxkn8yRLgA8BLwIOA05NctiwApMkSf2ZzTD70cA9VXUvQJJLgBOBO4cRmDRMM/2PrQfcSR5c14LZDLMfAHxz3PzmrkySJM2jVNXMVkxOAV5YVa/p5l8FHF1V/23CcquAVd3sM4C7ptn0vsC2GQU1ehZLXRZLPWDXrMu2qlo5kyew/S6KuiyWesCuWZe+2u9skvlzgHOr6oXd/NkAVfWuGW3wp9vdUFUrZrONUbFY6rJY6gHWZa6NYkwztVjqsljqAdZlZ2YzzH4jsDzJIUn2AF4OXDmcsCRJUr9mfABcVW1P8gfANcAS4OKqumNokUmSpL7M6qQxVfVp4NNDimWHC4a8vYW0WOqyWOoB1mWujWJMM7VY6rJY6gHWZUoz3mcuSZJGg+dmlySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcylySpcSZzSZIaZzKXJKlxJnNJkhpnMpckqXEmc0mSGmcyH1CSR5PcnOT2JJ9I8vidLHtukjfNZ3xTxHFiklu7uDcked4Uy61LcvAk5c/oHrs5ycYkQ7vaT5JPJ9l7CNsZymudZGWSu5Lck+Ss2W5Po8X2u+jb78VJtiS5fbbbao3JfHA/qKqjquoI4IfAaxc6oD5cBxxZVUcB/y9w4YDr/wVwXlfvQ4EPDLJykiVTPVZVv1VVDw8Yz5zo4vwQ8CLgMODUJIctbFQaMtvvIm2/ndXAyoUOYiGYzGfn88AvASR5dffr+ZYkH5m4YJLfTXJj9/gnd/QIkpzS9RJuSfK5ruzwJDd0v6RvTbJ8NkFW1SP102vdPgEY9Lq3S4HN47Z3Wxfn6Uk+OK6OVyU5rpt+JMnbk6wH/iTJpeOWOy7J33XT9yXZN8l7kvx/45Y5N8kbu+k/7l67W5O8bdwy53S96P8NPGPAOk3maOCeqrq3qn4IXAKcOITtajTZfhdX+6WqPgd8Zxjbas1uCx1Aq5LsRq8Hd3WSw4FzgGOraluSfSZZ5fKq+l/duu8AzqD3C/mtwAur6lvjhqteC7y/qj6aZA/gZ34ZJ1nL5A3gfVX14UmW/0/Au4D9gBcPWN3zgM8k+SLwD8Df9PFr/AnA7VX11u61ujfJE6rq+8DLgLUTlr8EOB/4y27+pcDKJL8JLKeXaANcmeTXge8DLweeSe9z/FXgKxODSPIK4I8nie+eqjp5QtkBwDfHzW8Gnj1NPdUg2++ibL+7NJP54PZMcnM3/XngIuD3gMuqahtAVU32y/CI7ktgb2Av4Jqu/B+B1d0v38u7si8B5yRZRu9LZNPEjVXVywYJuqo+BXyqa0h/DrxggHX/Jsk19IavTgR+L8mR06z2KPDJbv3tSa4G/mOSy+h9Gb15wnPclGS/JE8BxoCHquobSf4Q+E3gpm7Rveh9OTwR+FRV/QtAkiuniP2jwEf7rGom20Sf66oNtt/F2353aSbzwf2g23f1E0nC9F/6q4GTquqWJKcDxwFU1WuTPJteA7k5yVFV9bFueOvFwDVJXlNVn5nwnAP9st+hqj6X5GlJ9t3x5dWPqrofuBi4OL2DS44AtvPYXTWPGzf9r1X16Lj5tcDr6A2B3VhV35vkaS4DTgZ+kd4vfegl2HdV1V+PXzDJ6+kj0Q74y34zcOC4+WXA/dM9h5pi+1287XfXVlXeBrgBj0xSdjhwN/Dkbn6f7v5c4E3d9DZ6Q2S7A9cCq7vyp43bzk3AUcBTgXRl5wOvn2XMvzRue78KfGvH/ITl1gEHT1K+Eti9m/5F4IHu/nnAF+l9IRwIfBc4brLXid5Q433AJ4CXjiu/D9h33Ov4xe61XNqV/SawHtirmz+gex1/FbgV2JPer/xNO17rWbxOuwH3AocAewC3AIcv9GfO2/Butt/F237HxXQwvV0EC/55m8+bPfMhqKo7krwT+GySR+k16tMnLPan9D7U/wzcRu8DDPDe7gCZ0Dtq9RbgLOCVSX4EfBt4+yxD/M/Aq7vt/QB4WXWf+j79JvD+JP/azf9xVX07yYPA17v63E5vv9ekqurRJFfRe11Om2KZO5I8EfhWVT3Qlf1DkkOBL/U6UDwCvLKqvtr1bm6m95p+foD6TBXj9iR/QG8IdQlwcVXdMdvtarTZfhdH+wVI8nF6oyb7JtkM/FlVXTSMbY+6DPaZ0GKWZB1welXdt8ChSBqQ7XfX5l/TJElqnMlc460GRukEEJL6txrb7y7LYXZJkhpnz1ySpMbN69HsK1eurKuvvno+n1LS5CfDGZjtV1oQfbXfee2Zb9vW9zkOJI0Y2680uhxmlySpcSZzSZIaZzKXJKlxJnNJkhrnudnnwHnX3j3tMm844enzEIkkaVdgz1ySpMaZzCVJalzfyTzJkiQ3dZfBI8khSdYn2ZRkbZI95i5MSZI0lUF65mcCG8fNvwc4r6qWAw8BZwwzMEmS1J++knmSZcCLgQu7+QDHA5d1i6wBTpqLACVJ0s712zM/H3g/XbpDAAALvElEQVQz8ONu/snAw1W1vZvfDBww5NgkSVIfpk3mSX4b2FJVXxlfPMmik15LNcmqJBuSbNi6desMw5S0EGy/Uhv66ZkfC7wkyX3AJfSG188H9k6y43/qy4D7J1u5qi6oqhVVtWJsbGwIIUuaL7ZfqQ3TJvOqOruqllXVwcDLgc9U1SuA64GTu8VOA66YsyglSdKUZnMGuLcAlyR5B3ATcNFwQhpd/ZzZTZKk+TZQMq+qdcC6bvpe4OjhhyRJkgbhGeAkSWqcyVySpMaZzCVJapzJXJKkxpnMJUlqnMlckqTGzeZ/5pqFfv6z/oYTnj4PkUiSWmfPXJKkxpnMJUlqnMlckqTGmcwlSWqcyVySpMaZzCVJapzJXJKkxpnMJUlqnMlckqTGeQa4EdbPWeLAM8VJ0q5u2p55kscluSHJLUnuSPK2rvyQJOuTbEqyNskecx+uJEmaqJ9h9n8Djq+qI4GjgJVJjgHeA5xXVcuBh4Az5i5MSZI0lWmTefU80s3u3t0KOB64rCtfA5w0JxFKkqSd6usAuCRLktwMbAGuBb4GPFxV27tFNgMHzE2IkiRpZ/pK5lX1aFUdBSwDjgYOnWyxydZNsirJhiQbtm7dOvNIJc0726/UhoH+mlZVDwPrgGOAvZPsOBp+GXD/FOtcUFUrqmrF2NjYbGKVNM9sv1Ib+jmafSzJ3t30nsALgI3A9cDJ3WKnAVfMVZCSJGlq/fzPfCmwJskSesn/0qq6KsmdwCVJ3gHcBFw0h3FKkqQpTJvMq+pW4JmTlN9Lb/+5JElaQJ7OVZKkxpnMJUlqnMlckqTGmcwlSWqcyVySpMaZzCVJapzJXJKkxpnMJUlqnMlckqTGmcwlSWqcyVySpMaZzCVJapzJXJKkxpnMJUlqnMlckqTGmcwlSWqcyVySpMZNm8yTHJjk+iQbk9yR5MyufJ8k1ybZ1N0/ae7DlSRJE/XTM98OvLGqDgWOAV6X5DDgLOC6qloOXNfNS5KkeTZtMq+qB6rqq93094CNwAHAicCabrE1wElzFaQkSZraQPvMkxwMPBNYD+xfVQ9AL+ED+w07OEmSNL3d+l0wyV7AJ4HXV9V3k/S73ipgFcBBBx00kxg1jfOuvXvaZd5wwtPnIRItNrZfqQ199cyT7E4vkX+0qi7vih9MsrR7fCmwZbJ1q+qCqlpRVSvGxsaGEbOkeWL7ldowbc88vS74RcDGqnrfuIeuBE4D3t3dXzEnEWre2MOXpDb1M8x+LPAq4LYkN3dlf0IviV+a5AzgG8ApcxOiJEnamWmTeVV9AZhqB/nzhxuOJEkalGeAkySpcSZzSZIa1/df09S2fg5ukyS1yZ65JEmNM5lLktQ4k7kkSY0zmUuS1DiTuSRJjTOZS5LUOJO5JEmNM5lLktQ4k7kkSY0zmUuS1DiTuSRJjTOZS5LUuJG90Eq/FwZ5wwlPn+NIJEkabdP2zJNcnGRLktvHle2T5Nokm7r7J81tmJIkaSr9DLOvBlZOKDsLuK6qlgPXdfOSJGkBTDvMXlWfS3LwhOITgeO66TXAOuAtQ4xrqPoZsne4XpLUqpkeALd/VT0A0N3vN7yQJEnSIOb8ALgkq4BVAAcddNBcP53mWL8HJvbD0ZDRtyu23519xqf7zE7XPvzMa67MtGf+YJKlAN39lqkWrKoLqmpFVa0YGxub4dNJWgi2X6kNM03mVwKnddOnAVcMJxxJkjSoaYfZk3yc3sFu+ybZDPwZ8G7g0iRnAN8ATpnLICW1w6HmqfnaaK70czT7qVM89PwhxyJJkmZgZM8AJ2lxmuve6TAP0pRa4bnZJUlqnMlckqTGOcwuSX1yCF+jyp65JEmNs2cuSSPCv65ppuyZS5LUOJO5JEmNa36YfVgHpHhgy/zz0rSSNBz2zCVJapzJXJKkxpnMJUlqnMlckqTGmcwlSWqcyVySpMaZzCVJalzz/zOXRlG/5y3wf/Q/y1OaTm2258PYlV+7xW5WPfMkK5PcleSeJGcNKyhJktS/GffMkywBPgScAGwGbkxyZVXdOazgpFE8M5+9m4U1ip+JVrTes3fUZmqz6ZkfDdxTVfdW1Q+BS4AThxOWJEnq12yS+QHAN8fNb+7KJEnSPJrNAXCZpKx+ZqFkFbCqm30kyV3TbHdfYNss4holi6Uui6UeMIS6/NGQAhnCtvqty9VVtXImT2D7XRR1GVo9hvnZn6Gd1mUE4hvEUNtvqn4m//YlyXOAc6vqhd382QBV9a4ZbfCn291QVStms41RsVjqsljqAdZlro1iTDO1WOqyWOoB1mVnZjPMfiOwPMkhSfYAXg5cOZywJElSv2Y8zF5V25P8AXANsAS4uKruGFpkkiSpL7M6aUxVfRr49JBi2eGCIW9vIS2WuiyWeoB1mWujGNNMLZa6LJZ6gHWZ0oz3mUuSpNHgudklSWrcyCTzlk8Nm+TAJNcn2ZjkjiRnduX7JLk2yabu/kkLHWu/kixJclOSq7r5Q5Ks7+qytjvoceQl2TvJZUn+qXt/ntPi+5LkDd1n6/YkH0/yuFF6T2y/o8X2O1rmo/2ORDIfd2rYFwGHAacmOWxhoxrIduCNVXUocAzwui7+s4Drqmo5cF0334ozgY3j5t8DnNfV5SHgjAWJanDvp/c/zV8GjqRXp6belyQHAH8IrKiqI+gdcPpyRuQ9sf2OJNvviJi39ltVC34DngNcM27+bODshY5rFvW5gt456+8ClnZlS4G7Fjq2PuNfRq+RHA9cRe8EQduA3SZ7v0b1BvwC8HW6Y0PGlTf1vvDTsy3uQ++g1auAF47Ke2L7Ha2b7Xe0bvPVfkeiZ84iOjVskoOBZwLrgf2r6gGA7n6/hYtsIOcDbwZ+3M0/GXi4qrZ38628P08FtgJ/0w05XpjkCTT2vlTVt4D/CXwDeAD4v8BXGJ33xPY7Wmy/I2S+2u+oJPO+Tg076pLsBXwSeH1VfXeh45mJJL8NbKmqr4wvnmTRFt6f3YBfBf6qqp4JfJ8RH5KbTLdP8ETgEOApwBPoDWlPtFDvSaufj8ew/Y4c2+8ARiWZbwYOHDe/DLh/gWKZkSS70/si+GhVXd4VP5hkaff4UmDLQsU3gGOBlyS5j96V8I6n90t/7yQ7zkvQyvuzGdhcVeu7+cvofTm09r68APh6VW2tqh8BlwPPZXTeE9vv6LD9jp55ab+jksybPjVskgAXARur6n3jHroSOK2bPo3evriRVlVnV9WyqjqY3vvwmap6BXA9cHK3WCt1+TbwzSTP6IqeD9xJe+/LN4Bjkjy++6ztqMeovCe23xFh+x1J89N+F/rggHEHCfwWcDfwNeCchY5nwNifR2+I5Fbg5u72W/T2VV0HbOru91noWAes13HAVd30U4EbgHuATwA/v9Dx9VmHo4AN3Xvzt8CTWnxfgLcB/wTcDnwE+PlRek9sv6N3s/2Ozm0+2q9ngJMkqXGjMswuSZJmyGQuSVLjTOaSJDXOZC5JUuNM5pIkNc5kLgCS/KckleSXFzoWSYOx/cpkrh1OBb5A70QTktpi+93Fmcy145zUx9K7BN/Lu7KfS/KX3TV4r0ry6SQnd489K8lnk3wlyTU7Tq0oaf7ZfgUmc/WcRO+awXcD30nyq8DvAAcD/wF4Db1L9O04h/UHgJOr6lnAxcA7FyJoSYDtV/SuSiOdSu9iDNC7OMOpwO7AJ6rqx8C3k1zfPf4M4Ajg2t5phllC77J+khaG7Vcm811dkifTu7LSEUmKXuMu4FNTrQLcUVXPmacQJU3B9qsdHGbXycCHq+rfV9XBVXUg8HVgG/Cfu31v+9O7aAPAXcBYkp8M2yU5fCECl2T7VY/JXKfys7/iPwk8hd71hG8H/hpYD/zfqvohvS+Q9yS5hd4Vpp47f+FKGsf2KwCvmqapJdmrqh7phvJuAI6t3jWGJY042++uxX3m2pmrkuwN7AH8uV8EUlNsv7sQe+aSJDXOfeaSJDXOZC5JUuNM5pIkNc5kLklS40zmkiQ1zmQuSVLj/n/VLroVTmndQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 506.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 下面这个图展示了不同舱位的生存/死亡乘客年龄和人数分布：\n",
    "grid=sns.FacetGrid(train,col='Survived',row='Pclass',size=2.2,aspect=1.6)\n",
    "grid.map(plt.hist,'Age',alpha=.5,bins=20)\n",
    "grid.add_legend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察上图情况：\n",
    "\n",
    "1. 大多数乘客都是Pclass=3，但大多数乘客都没有生还。确认我们的分类假设2。\n",
    "\n",
    "2. Pclass=2和Pclass=3的婴儿乘客大多幸存。进一步证实了我们的分类假设2。\n",
    "\n",
    "3. Pclass=1中的大多数乘客都幸存了下来。确认我们的分类假设3。\n",
    "\n",
    "4. Pclass在乘客的年龄分布上有所不同。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策：\n",
    "\n",
    "考虑Pclass加入模型训练。    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 关联分类特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以将分类特征与我们的解决方案目标联系起来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:703: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:708: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c4079a9be0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=sns.FacetGrid(train,row='Embarked',size=2.2,aspect=1.6)\n",
    "grid.map(sns.pointplot,'Pclass','Survived','Sex',palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 类别特征和数字特征的相关性分析\n",
    "\n",
    "我们还可能希望将类别特征和数字特征关联起来。\n",
    "\n",
    "我们可以考虑登船港口特征(类别特征)，性别(类别特征)，船票费用(数字特征)，与是否幸存(数值类别特征)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:703: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2c4077db240>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=sns.FacetGrid(train,row='Embarked',col='Survived',size=2.2,aspect=1.6)\n",
    "grid.map(sns.barplot,'Sex','Fare',alpha=.5,ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察出：\n",
    "\n",
    "1. 支付更高票价的乘客更好的生存。确认我们创建新特征假设4，要新建票价范围特征的假设。\n",
    "\n",
    "2. 登船港口与存活率有关。确认我们的关联性假设1和数据完整性假设2。\n",
    "\n",
    "决策："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}