titanic_survival_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习（入门）探索数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 研究项目：《泰坦尼克号》生还探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、序\n",
    "       1912年的一天，当时世界上体积最庞大、内部设施最豪华的客运轮船，有“永不沉没”美誉的泰坦尼克号，在她的处女航中撞冰山沉入大西洋底3700米处，船上1500多人丧生。一百多年后，人们对这一场旷世海难依然充满好奇，从电影作品到考古探索，越来越多的故事与猜想。那么，这艘在上世纪初无与伦比的豪华巨轮，怎么说翻就翻了？在翻船前，除了杰克与露丝的爱情，泰坦尼克号还经历了什么？\n",
    "       那么到底是什么影响了乘客的生还，透过生还数据，我们能得到什么样的结果？接下来，我会通过数据分析告诉你哪些特征会影响生存率。\n",
    "### 2、数据集来源\n",
    "       著名的数据分析竞赛网站Kaggle上，举行了很多数据分析比赛，其中比较著名的就有 泰坦尼克号乘客生还预测 。\n",
    "       Kaggle提供的数据集中，包括泰坦尼克号上 2224 名乘客和船员中 891 名的人口学数据和乘客基本信息。\n",
    "### 3、数据字典解释\n",
    "|属性\t       |定义\t             |值说明|\n",
    "|:-|:---|:---|\n",
    "|**PassengerId**|乘客编号            |\n",
    "|**Survived**\t|存活情况            |1-存活，0-死亡|\n",
    "|**Pclass**\t    |舱位等级\t     |1-上层，2-中间层，3-底层|\n",
    "|**Name**\t    |乘客姓名\t|\n",
    "|**Sex**\t    |性别\t|\n",
    "|**Age**\t    |年龄\t|\n",
    "|**SibSp**\t    |同乘的兄弟姐妹/配偶数 |\n",
    "|**Parch**\t    |同乘的父母/小孩数 |\n",
    "|**Ticket**\t    |船票编号\t|\n",
    "|**Fare**\t    |船票价格\t|\n",
    "|**Cabin**\t    |客舱号\t|\n",
    "|**Embarked**\t|登船港口 |C：Cherbourg（瑟堡），Q：Queenstown（皇后镇），S：Southampton（南安普顿）|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question Phase 提出问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- 探索舱室等级对存活率的影响\n",
    "- 探索性别对存活率的影响\n",
    "- 探索年龄对存活率的影响\n",
    "- 探索乘客间是兄弟姐妹/配偶关系的对存活率的影响\n",
    "- 探索乘客间是父母/小孩关系的对存活率的影响\n",
    "- 探索票价对存活率的影响\n",
    "- 探索舱室等级与性别的综合因素对存活率的影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrangling Phase 数据采集与数据清理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 导入需要的资源库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 提供漂亮的可视化工具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 采集数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <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",
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       "      <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",
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      "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": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入'titanic-data.csv'数据\n",
    "titanic_df = pd.read_csv(\"./titanic-data.csv\")\n",
    "# 显示样本数据的前五条信息\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从泰坦尼克号的数据样本中，我们可以看到存在<font color=#DC143C>NAN</font>的数据（Age、Cabin、Embarked），因此为了使将要分析的变量保持一致的数据量，我们需要进行数据清洗，生成一个新的数据集，再对新的数据集进行分析探索。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 清理数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 714 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    714 non-null int64\n",
      "Survived       714 non-null int64\n",
      "Pclass         714 non-null int64\n",
      "Name           714 non-null object\n",
      "Sex            714 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          714 non-null int64\n",
      "Parch          714 non-null int64\n",
      "Ticket         714 non-null object\n",
      "Fare           714 non-null float64\n",
      "Cabin          185 non-null object\n",
      "Embarked       712 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 72.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# 过滤分析过程中缺失的数据\n",
    "titanic_df.dropna(subset = ['Age'], inplace = True)\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Expore Phase 数据探索 提炼问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义常量\n",
    "SURVIVED = 'Survived'\n",
    "DENSITY = 'Density'\n",
    "SHOW_KIND_BAR = 'bar'\n",
    "SHOW_KIND_PIE = 'pie'\n",
    "HEADER_SURVIVED = 'survived'\n",
    "HEADER_TOTAL = 'total'\n",
    "HEADER_RATE = 'rate'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_result(title, grouped_survied_factors, grouped_each_factor_counts, survived_rate):\n",
    "    \"\"\"\n",
    "    数据集横向合并后输出\n",
    "    title：feature title\n",
    "    grouped_survied_factors：存活数\n",
    "    grouped_each_factor_counts：总数\n",
    "    survived_rate：成活率\n",
    "    \"\"\"\n",
    "    print('Profile feature \\\"'+ title + '\\\" survival rate')\n",
    "    print('-------------------------------------------')\n",
    "    # 横向拼接并输\n",
    "    result = pd.concat([grouped_survied_factors, grouped_each_factor_counts, survived_rate], axis = 1)\n",
    "    result.columns = [HEADER_SURVIVED, HEADER_TOTAL, HEADER_RATE]\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prediction_with_one_factor(survived, factor, title, xlabel, ylabel):\n",
    "    \"\"\"\n",
    "    基于单个特征因素的预测\n",
    "    survived：存活\n",
    "    factor：特征\n",
    "    title：图形标题\n",
    "    xlabel：x轴标签\n",
    "    ylabel：y轴标签\n",
    "    \"\"\"\n",
    "    df_grouped = titanic_df[[survived, factor]].groupby(factor)\n",
    "    # 统计每组生存者的总数\n",
    "    grouped_survied_factors = df_grouped.sum()\n",
    "    # 统计分组总有人数\n",
    "    grouped_each_factor_counts = df_grouped.count()\n",
    "    # 计算存活率\n",
    "    survived_rate = grouped_survied_factors / grouped_each_factor_counts\n",
    "    \n",
    "    # 横向拼接并输出\n",
    "    print_result(factor, grouped_survied_factors, grouped_each_factor_counts, survived_rate)\n",
    "    \n",
    "    # 绘制直方图\n",
    "    survived_rate.plot(kind = SHOW_KIND_BAR)\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prediction_with_interval_factor(survived, factor, factor_group, bins, blabels, title, xlabel, ylabel):\n",
    "    \"\"\"\n",
    "    基于区间因素的预测\n",
    "    survived：存活\n",
    "    factor：特征\n",
    "    factor_group：特征分组的组名\n",
    "    bins：分组区间\n",
    "    blabels：分组区间标签\n",
    "    title：图形标题\n",
    "    xlabel：x轴标签\n",
    "    ylabel：y轴标签\n",
    "    \"\"\"\n",
    "    # 根据给定的区间进行分组\n",
    "    titanic_df[factor_group] = pd.cut(titanic_df[factor], bins, labels = blabels)\n",
    "\n",
    "    factor_df = pd.concat([titanic_df[survived], titanic_df[factor_group]], axis = 1)\n",
    "\n",
    "    df_grouped = factor_df.groupby(factor_group)\n",
    "    # 统计每组生存者的总数\n",
    "    grouped_survied_factors = df_grouped.sum()\n",
    "    # 统计分组总有人数\n",
    "    grouped_each_factor_counts = df_grouped.count()\n",
    "\n",
    "    # 计算存活率\n",
    "    survived_rate = grouped_survied_factors / grouped_each_factor_counts\n",
    "\n",
    "    # 横向拼接并输出\n",
    "    print_result(factor, grouped_survied_factors, grouped_each_factor_counts, survived_rate)\n",
    "    \n",
    "    # 绘制直方图与饼图\n",
    "    survived_rate.plot(kind = SHOW_KIND_BAR)\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    \n",
    "    survived_rate.plot(kind = SHOW_KIND_PIE, subplots = True)\n",
    "    plt.ylabel('')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prediction_with_double_factor(survived, factor1, factor2, title, xlabel, ylabel):\n",
    "    \"\"\"\n",
    "    基于双重因素的预测\n",
    "    survived：存活\n",
    "    factor1：特征1\n",
    "    factor2：特征2\n",
    "    title：图形标题\n",
    "    xlabel：x轴标签\n",
    "    ylabel：y轴标签\n",
    "    \"\"\"\n",
    "    series_grouped_factor = titanic_df.groupby([factor1, factor2])[survived]\n",
    "    \n",
    "    grouped_survied_factors = series_grouped_factor.sum()\n",
    "    grouped_each_factor_counts = series_grouped_factor.count()\n",
    "\n",
    "    survived_rate = grouped_survied_factors / grouped_each_factor_counts\n",
    "\n",
    "    # 横向拼接并输出\n",
    "    print_result(factor1 + ' + ' + factor2, grouped_survied_factors, grouped_each_factor_counts, survived_rate)\n",
    "    \n",
    "    # 绘制直方图\n",
    "    survived_rate.unstack().plot(kind = SHOW_KIND_BAR)\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、探索舱室等级对存活率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile feature \"Pclass\" survival rate\n",
      "-------------------------------------------\n",
      "        survived  total      rate\n",
      "Pclass                           \n",
      "1            122    186  0.655914\n",
      "2             83    173  0.479769\n",
      "3             85    355  0.239437\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a42ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_with_one_factor(SURVIVED, 'Pclass', 'Pclass Survival Rate', 'factor(Pclass)', DENSITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图形解读：x轴代表舱位等级，y轴代表存活率系数\n",
    "- 数据解读：一级舱存活率：65.59%，二级舱存活率：47.97%，三级舱存活率：23.94%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、探索性别对存活率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile feature \"Sex\" survival rate\n",
      "-------------------------------------------\n",
      "        survived  total      rate\n",
      "Sex                              \n",
      "female       197    261  0.754789\n",
      "male          93    453  0.205298\n"
     ]
    },
    {
     "data": {
      "image/png": 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oSJIahoIkqfGf3PEhlc2jZ8QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a16d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_with_one_factor(SURVIVED, 'Sex', 'Sex Survival Rate', 'Sex', DENSITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图形解读：x轴代表性别 female（女士）、male（男士），y轴代表存活率系数\n",
    "- 数据解读：女士存活率：75.47%，男士存活率：20.52%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、探索年龄对存活率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile feature \"Age\" survival rate\n",
      "-------------------------------------------\n",
      "          survived  total      rate\n",
      "AgeLevel                           \n",
      "child           45     77  0.584416\n",
      "young          162    420  0.385714\n",
      "midlife         78    195  0.400000\n",
      "aged             5     22  0.227273\n"
     ]
    },
    {
     "data": {
      "image/png": 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RDB95MsmR188j4vvFVlW/JE0GPseegxaFu7mwcj5SsN7qtSTdET8CXA8sL7ac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\nMDOzjEPBzMwy/x91bGbCpjEzOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11689ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XwBYobxIuBbC6ZExJldxIrLlwWbOr8r1B+T9QyvsnQFDvfM2uzYnv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\nUwEua8YYUwEua8YYU4H/B3weeY5OdXElAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a473dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "child：少年（0-14）\n",
    "young：青年（15-35）\n",
    "midlife：中年（36-60）\n",
    "aged：老年（61-90）\n",
    "\"\"\"\n",
    "bins = [0, 14, 35, 60, 90]\n",
    "\n",
    "labels = ['child', 'young', 'midlife', 'aged']\n",
    "prediction_with_interval_factor(SURVIVED, 'Age', 'AgeLevel', bins, labels, 'Age Survival Rate', 'Age', DENSITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图形解读：柱状图x轴代表各年龄段，child（少年）、young（青年）、midlife（中年）、aged（老年），y轴代表存活率系数\n",
    "- 数据解读：child存活率：58.44%，young存活率：38.57%，midlife存活率：40%，aged存活率：22.72%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、探索乘客间是兄弟姐妹/配偶关系的对存活率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile feature \"SibSp\" survival rate\n",
      "-------------------------------------------\n",
      "       survived  total      rate\n",
      "SibSp                           \n",
      "0           175    471  0.371550\n",
      "1            97    183  0.530055\n",
      "2            11     25  0.440000\n",
      "3             4     12  0.333333\n",
      "4             3     18  0.166667\n",
      "5             0      5  0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163c22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_with_one_factor(SURVIVED, 'SibSp', 'SibSp Survival Rate', 'SibSp Count', DENSITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图形解读：x轴代表乘客间有兄弟姐妹或配偶关系的数量，y轴代表存活率系数\n",
    "- 数据解读：关系数量0个的存活率：37.15%，1个的存活率：53%，2个的存活率：44%，3个的存活率：33.33%，4个存活率：16.66%，5个的存活率：0%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、探索乘客间是父母/小孩关系的对存活率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile feature \"Parch\" survival rate\n",
      "-------------------------------------------\n",
      "       survived  total      rate\n",
      "Parch                           \n",
      "0           186    521  0.357006\n",
      "1            61    110  0.554545\n",
      "2            39     68  0.573529\n",
      "3             3      5  0.600000\n",
      "4             0      4  0.000000\n",
      "5             1      5  0.200000\n",
      "6             0      1  0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6e56d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_with_one_factor(SURVIVED, 'Parch', 'Parch Survival Rate', 'Parch', DENSITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图形解读：x轴代表乘客中父母携带小孩的数量，y轴代表存活率系数\n",
    "- 数据解读：小孩数量0个的存活率：35.70%，1个的存活率：55.45%，2个的存活率：57.35%，3个的存活率：60%，4个存活率：0%，5个的存活率：20%，6个的存活率：0%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6、探索票价对存活率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile feature \"Fare\" survival rate\n",
      "-------------------------------------------\n",
      "           survived  total      rate\n",
      "FareLevel                           \n",
      "0~40            187    555  0.336937\n",
      "40-100           66    104  0.634615\n",
      "100-200          22     30  0.733333\n",
      "200-300          11     15  0.733333\n",
      "300~550           3      3  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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YSaMlbQ5MAeaWbXMb8L/S/Q8jaU56ogv1m5lZD6qp95GknwK7AtcBH4iIp9JVcyQtqPSa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a639048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Tl5iY6Gm+78MPP4y/4YYbKgFuuOGGymXLlnUDWLx4cfyUKVMqTSYTZ5111u6amhpLUVHR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\nhxLUqoBsvYPoCCGZkP2lOf8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168b51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0, 40, 100, 200, 300, 550]\n",
    "labels = ['0~40', '40-100', '100-200', '200-300', '300~550']\n",
    "prediction_with_interval_factor(SURVIVED, 'Fare', 'FareLevel', bins, labels, 'Fare Survival Rate', 'Fare', DENSITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图形解读：柱状图x轴代表各区间票价，y轴代表存活率系数\n",
    "- 数据解读：0-40的存活率：33.69%，40-100的存活率：63.46%，100-200个的存活率：73.33%，200-300的存活率：73.33%，300-550存活率：100%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7、探索舱室等级与性别的综合因素对存活率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Profile feature \"Pclass + Sex\" survival rate\n",
      "-------------------------------------------\n",
      "               survived  total      rate\n",
      "Pclass Sex                              \n",
      "1      female        82     85  0.964706\n",
      "       male          40    101  0.396040\n",
      "2      female        68     74  0.918919\n",
      "       male          15     99  0.151515\n",
      "3      female        47    102  0.460784\n",
      "       male          38    253  0.150198\n"
     ]
    },
    {
     "data": {
      "image/png": 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USfMkLZR0ep71mJk1taVLl3L11VezcOFCXnnlFcaPH88f//hHbrnlFn7wgx/Qs2dPpk+f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\ngpmZZRwKZmaWcSiYmVnmfwE6AWm7fFShwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1168b5668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction_with_double_factor(SURVIVED, 'Pclass', 'Sex', 'Pclass + Sex Survival Rate', 'Pclass + Sex', DENSITY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 图形解读：x轴代表不同等级的舱室中男女存活数据，y轴代表存活率系数\n",
    "- 数据解读：1级舱室中男女存活率分别为：39.6%、96.47%，2级舱室中男女存活率分别为：15.15%、91.89%，3级舱室中男女存活率分别为：15.01%、46.07%"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Drawing Conclusions Phase ( Make Predictions) 总结与预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 泰坦尼克号上有2224名乘客和船员，乘客1300多人，船员800多人，我们对891条乘客的样本清洗后得到了714条特征分析数据，分析数据占了样本数据的87%，占了总乘客数的近60%。从特征数据分析得出，小孩、女士、高等级舱室的存活率较高，如果891名样本数据是随机抽选，根据中心极限定理，该样本的数据足够大，那么我们的分析结果就具有代表性，但如果不是随机选取，由于数据有缺失且不完整，那么我们的分析结果就可能不太靠谱了。\n",
    "- 另外船上还有800多名船员和工作人员，工作人员在处理逃生时发挥的作用不容小觑，但是我们没有对这部分数据进行分析，因此乘客的生存率结论不能完全准确。\n",
    "- 很多故事和细节已经永远地沉没在大海中，我们面对的只是冷冰冰的数据。但是数据遮掩不了船上男士们的英勇，正是男士们的担当和牺牲，让船上大部分女士和超过半数的儿童获救了。\n",
    "- 人类历史上，曾经发生过很多灾难，值得人类自豪的是，危难来临之际，很多人并没有忘记人生价值和尊严。即使面对死亡，他们仍然坚持自己的理想和信念，勇敢向前，捍卫了人性的光辉。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Communication Phase 交流研究成果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 舱室等级越高，存活率越高\n",
    "- 女士的存活率明显高于男士，正常情况下女士的逃生能力低于男士，却获得了高存活率，因此可以推测女士在逃生时得到了优先照顾\n",
    "- 青少年的存活率较中老年明显高，因此可以推测青少年在逃生时得到了优先照顾\n",
    "- 1个亲戚的存活率最高，超过2个亲戚的情况存活率不断下降，可能逃生时亲戚越多顾虑越多，需要照顾的人越多，存活率反而不如独自一个人高\n",
    "- 小孩数量0-3个的存活率较高，多于3个的存活率降低，可以推测带小孩的给予了优先逃生照顾，但是小孩太多也影响了存活率，逃生时的精力分散可能造成了直接影响，但这只是猜测，毕竟样本数据有限\n",
    "- 票价越高的存活率越高，票价高的应该在高等舱室，这与舱室等级的探索结论相吻合\n",
    "- 无论在什么等级的舱室，女士都比男士存活率高，舱室等级高的男女存活率优于等级低的舱室，可以推测舱室等于对存活率的影响较大，也可以推测男士很有担当，将逃生希望优先给予了女士"
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