competitions/getting-started/titanic/titanic-data-science-solutions.ipynb

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    "# 《泰坦尼克号》数据科学解决方案\n",
    "\n",
    "## 翻译相关\n",
    "原文地址: <https://www.kaggle.com/startupsci/titanic-data-science-solutions?scriptVersionId=1145136>  \n",
    "开源组织: [ApacheCN](http://www.apachecn.org)  \n",
    "贡献者: [@那伊抹微笑](https://github.com/wangyangting), [@李铭哲](https://github.com/limingzhe), [@刘海飞](https://github.com/WindZQ), [@王德红](https://github.com/VPrincekin)  [@成飘飘](https://github.com/chengpiaopiao)  \n",
    "最近更新: 2018-01-03\n",
    "\n",
    "---\n",
    "\n",
    "### 我已经发布了一个新的 Python 包 [Speedml](https://speedml.com), 它将该 notebook 中的使用的技术编译成一个 intuitive（直观的），powerful（功能强大的）且 productive（高效的）API.\n",
    "\n",
    "### Speedml 帮助我在 Kaggle 排行榜上从最低的 80% 跳到最高的 20%, 迭代的次数很少.\n",
    "\n",
    "### 还有一件事...Speedml 实现了这一点, 代码行数减少了近 70%!\n",
    "\n",
    "### 下载并且运行代码 [Speedml 版本的泰坦尼克号解决方案](https://github.com/Speedml/notebooks/blob/master/titanic/titanic-solution-using-speedml.ipynb).\n",
    "\n",
    "---\n",
    "\n",
    "该 notebook 是 [Data Science Solutions](https://startupsci.com) 书籍的一个手册. 该 notebook 引导我们通过一个典型的工作流程来解决像 Kaggle 这样类似的网站的数据科学竞赛.\n",
    "\n",
    "有几个优秀的 notebooks 可以用来研究数据科学竞赛作品.\n",
    "然而许多手册将会跳过一些关于如何开发解决方案的解释, 因为这些 notebooks 是专门为这些专家开发的.\n",
    "该 notebook 的目标是遵循一步一步的工作流程, 解释我们在解决方案开发过程中所做的每一个决策的每个步骤和理由.\n",
    "\n",
    "## 工作流阶段\n",
    "\n",
    "1. 问题或问题的定义.\n",
    "2. 获取 training（训练）和 testing（测试）数据.\n",
    "3. Wrangle（整理）, prepare（准备）, cleanse（清洗）数据\n",
    "4. Analyze（分析）, identify patterns 以及探索数据.\n",
    "5. Model（模型）, predict（预测）以及解决问题.\n",
    "6. Visualize（可视化）, report（报告）和提出解决问题的步骤以及最终解决方案.\n",
    "7. 提供或提交结果.\n",
    "\n",
    "该工作流指出了，每个阶段如何遵循另一个阶段的常见顺序.\n",
    "但是也有例外的场景.\n",
    "\n",
    "- 我们可能结合多个工作流阶段. 我们可以通过可视化数据进行分析.\n",
    "- 比 indicated（说明）更早的进行一个阶段. 我们可能在 wrangling（整理）过程的前后来分析数据.\n",
    "- 在我们的工作流程中多次执行一个阶段. 可视化阶段可能被使用多次.\n",
    "- Drop a stage altogether. We may not need supply stage to productize or service enable our dataset for a competition.\n",
    "\n",
    "\n",
    "## 问题和问题定义\n",
    "\n",
    "像 Kaggle 这样的竞赛网站, 它们会定义要解决或质疑的问题, 同时提供用于训练数据科学模型和根据测试数据集测试模型结果的数据集,（即, 训练集 和 测试集）.\n",
    "针对《泰坦尼克号生存竞赛》的问题或定义在 [这里是 Kaggle 描述](https://www.kaggle.com/c/titanic) 中有描述.\n",
    "\n",
    "> 从泰坦尼克号的灾难中幸存下来或没有幸存的乘客的样本训练集（train.csv）中，如果测试数据集（test.csv）中的这些乘客幸存下来，我们的模型是否可以基于给定的测试数据集（test.csv）来确定。\n",
    "\n",
    "我们也可能希望对我们问题的领域有所了解.\n",
    "这在 [Kaggle 竞赛描述](https://www.kaggle.com/c/titanic) 页面有详细的描述.\n",
    "以下是要注意的事项.\n",
    "\n",
    "- 1912年4月15日, 在首航期间, 泰坦尼克号撞上一座冰山后沉没, 2224 名乘客和机组人员中有 1502 人遇难. 生成率解释为 32%.\n",
    "- 还难导致生命损失的原因之一是没有足够的救生艇给乘客和船员.\n",
    "- 尽管幸存下来的运气有一些因素, 但一些人比其他人更有可能幸存下来，比如妇女, 儿童和上层阶级.\n",
    "\n",
    "## 工作流目标\n",
    "\n",
    "数据科学解决方案工作流程有以下七个主要的目标.\n",
    "\n",
    "**Classifying（分类）.** 我们可能想对我们的样本进行分类或加以类别. 我们也可能想要了解不同类别与解决方案目标的含义或相关性.\n",
    "\n",
    "**Correlating（相关）.** 可以根据训练数据集中的可用特征来处理这个问题. 数据集中的哪些特征对我们的解决方案目标有重大贡献？从统计学上讲, 特征和解决方案的目标中有一个[相关](https://en.wikiversity.org/wiki/Correlation)？随着特征值的改变, 解决方案的状态也会随之改变, 反之亦然？这可以针对给定数据集中的数字和分类特征进行测试. 我们也可能想要确定以后的目标和工作流程阶段的生存以外的特征之间的相关性. 关联某些特征可能有助于创建, 完善或纠正特征。\n",
    "\n",
    "**Converting（转换）.** 对于建模阶段, 需要准备数据. 根据模型算法的选择, 可能需要将所有特征转换为数值等价值. 所以例如将文本分类值转换为数字的值.\n",
    "\n",
    "**Completing（完整）.** 数据准备也可能要求我们估计一个特征中的任何缺失值. 当没有缺失值时，模型算法可能效果最好.\n",
    "\n",
    "**Correcting（校正）.** 我们还可以分析给定的训练数据集以找出错误或者可能在特征内不准确的值, 并尝试对这些值进行校正或排除包含错误的样本. 一种方法是检测样本或特征中的任何异常值. 如果对分析没有贡献, 或者可能会显着扭曲结果, 我们也可能完全丢弃一个特征.\n",
    "\n",
    "**Creating（创建）.** 我们可以根据现有特征或一组特征来创建新特征, 以便新特征遵循 correlation（相关）, conversion（转换）, completeness（完整）的目标.\n",
    "\n",
    "**Charting（绘图）.** 如何根据数据的性质和解决方案的目标来选择正确的可视化图表工具以及绘图."
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    "## 重构的发布日期 2017年1月29日\n",
    "\n",
    "We are significantly refactoring the notebook based on (a) comments received by readers, (b) issues in porting notebook from Jupyter kernel (2.7) to Kaggle kernel (3.5), and (c) review of few more best practice kernels.\n",
    "\n",
    "### 用户评论\n",
    "\n",
    "- Combine training and test data for certain operations like converting titles across dataset to numerical values. (thanks @Sharan Naribole)\n",
    "- Correct observation - nearly 30% of the passengers had siblings and/or spouses aboard. (thanks @Reinhard)\n",
    "- Correctly interpreting logistic regresssion coefficients. (thanks @Reinhard)\n",
    "\n",
    "### 移植问题\n",
    "\n",
    "- Specify plot dimensions, bring legend into plot.\n",
    "\n",
    "\n",
    "### 最佳实践\n",
    "\n",
    "- 在项目早期进行特征相关分析.\n",
    "- 为了可读性, 使用多个图而不是覆盖图."
   ]
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   "source": [
    "# 数据分析和整理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# 可视化\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\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"
   ]
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   "source": [
    "## 获取数据\n",
    "\n",
    "Python 的 Pandas 包帮助我们处理我们的数据集.\n",
    "我们首先将训练和测试数据集收集到 Pandas DataFrame 中.\n",
    "我们还将这些数据集组合在一起, 在两个数据集上运行某些操作."
   ]
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   "source": [
    "train_df = pd.read_csv('../input/train.csv')\n",
    "test_df = pd.read_csv('../input/test.csv')\n",
    "combine = [train_df, test_df]"
   ]
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    "## 通过 describing（描述）数据进行分析\n",
    "\n",
    "在我们的项目早期, Pandas 还帮助描述回答数据集中的以下问题.\n",
    "\n",
    "**数据集中哪些特征是可用的?**\n",
    "\n",
    "注意: 直接操作或分析这些特征的名称.\n",
    "这些特征名称在 [Kaggle 数据页面](https://www.kaggle.com/c/titanic/data) 页面上有描述."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "_uuid": "6ca47bb664dde8eeb7fd6db2194ffbc58d7b4e9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cd19a6f6-347f-be19-607b-dca950590b37",
    "_uuid": "6d633da86183125117bd25785e185329444fa106"
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    "**哪些特征是 categorical（分类的）?**\n",
    "\n",
    "这些值将样本分成几组相似的样本.\n",
    "在分类特征中的值是 nominal（标称的）, ordinal（顺序的）或 ratio（比例的）还是 interval based（基于区间的）值？\n",
    "除此之外, 这有助于我们选择合适的图表进行可视化.\n",
    "\n",
    "- Categorical（分类的）: Survived, Sex, and Embarked. Ordinal（顺序的）: Pclass.\n",
    "\n",
    "**哪些特征是 numerical（数值的）?**\n",
    "\n",
    "哪些特征是数值的？\n",
    "这些值随样本而变化.\n",
    "在数值特征中的值是 discrete（离散的）和 continuous（连续的） 还是 timeseries based（基于时间序列的）？\n",
    "\n",
    "- Continous（连续的）: Age, Fare. Discrete（离散的）: SibSp, Parch."
   ]
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       "      <td>PC 17599</td>\n",
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       "   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",
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       "                                                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",
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       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
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       "   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",
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   ],
   "source": [
    "# 预览数据\n",
    "train_df.head()"
   ]
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   "source": [
    "**哪些特征是混合的数据类型?**\n",
    "\n",
    "相同特征中的 numerical（数值的）, alphanumeric（字母数值的）.\n",
    "这些是校正目标的候选特征.\n",
    "\n",
    "- Ticket 是numerical（数值的）和 alphanumeric（字母数值的）数据类型的混合类型. Cabin 是 alphanumeric（字母数值的）.\n",
    "\n",
    "**哪些特征也许包含错误或拼写错误?**\n",
    "\n",
    "对于一个大型的数据集来说, 这是很难审查的, 但是从较小的数据集中查看一些样本可能会直接告诉我们, 哪些特征可能需要校正.\n",
    "\n",
    "- Name 特征也许包含错误或拼写错误, 因为有几种方法可以用来描述名称, 包括头衔，圆括号和用于替代或短名称的引号."
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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>886</th>\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",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <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",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "      <th>889</th>\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",
       "      <th>890</th>\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",
       "</table>\n",
       "</div>"
      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8bfe9610-689a-29b2-26ee-f67cd4719079",
    "_uuid": "2980c677698d5b219f3eca1faa748d4ef1c06b3f"
   },
   "source": [
    "**哪些特征包含 blank（空格）, null（无效的）或 empty values（空值）?**\n",
    "\n",
    "这些将需要校正.\n",
    "\n",
    "- Cabin > Age > Embarked features contain a number of null values in that order for the training dataset.\n",
    "- Cabin > Age are incomplete in case of test dataset.\n",
    "\n",
    "**各个特征的数据类型是什么样的?**\n",
    "\n",
    "在转换的目标时可以帮助我们.\n",
    "\n",
    "- 7 个特征是 integer 或 floats. 6 个在测试数据集中.\n",
    "- 5 个特征是 strings (object)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "9b805f69-665a-2b2e-f31d-50d87d52865d",
    "_uuid": "0e026227df676ac169811999565a86288fa129b1"
   },
   "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": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "859102e1-10df-d451-2649-2d4571e5f082",
    "_uuid": "241d3667c3850d6d5ed83ffab6991f185a44cdec"
   },
   "source": [
    "**样本中数值特征值的分布是什么?**\n",
    "\n",
    "这有助于我们确定, 除了其他早期的思考, 在实际问题领域的训练数据集是如何具有代表性的.\n",
    "\n",
    "- 总样本是 891 或者在泰坦尼克号（2,224）上实际旅客的 40%.\n",
    "- Survived（生存）是一个具有 0 或 1 值的分类特征.\n",
    "- 大约 38% 样本幸存了下来, 然而实际的幸存率是 32%.\n",
    "- 大多数旅客 (> 75%) 没有和父母或孩子一起旅行.\n",
    "- 近 30% 的旅客有兄弟姐妹 和/或 配偶.\n",
    "- 少数旅客 Fares（票价）差异显著 (<1%), 最高达 $512.\n",
    "- 很少有年长的旅客 (<1%) 在年龄范围 65-80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "58e387fe-86e4-e068-8307-70e37fe3f37b",
    "_uuid": "c4bd85c7847593602a09d809337af4616d8c9e02"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()\n",
    "# Review survived rate using `percentiles=[.61, .62]` knowing our problem description mentions 38% survival rate.\n",
    "# Review Parch distribution using `percentiles=[.75, .8]`\n",
    "# SibSp distribution `[.68, .69]`\n",
    "# Age and Fare `[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5462bc60-258c-76bf-0a73-9adc00a2f493",
    "_uuid": "93ac7caf79ef8a43bf2f88e1b15e70b9521fea04"
   },
   "source": [
    "**分类特征的分布是什么样的?**\n",
    "\n",
    "- Names（名称）特征在数据集中是唯一的 (count=unique=891)\n",
    "- Sex（性别）变量有两个可能的值, 男性为 65% (top=male, freq=577/count=891).\n",
    "- Cabin（房间号）值在样本中有重复. 或者几个旅客共享一个客舱.\n",
    "- Embarked（出发港）有 3 个可能的值. 大多数乘客使用 S 港口(top=S)\n",
    "- Ticket（船票号码）特征有很高 (22%) 的重复值 (unique=681)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "8066b378-1964-92e8-1352-dcac934c6af3",
    "_uuid": "eccc6479282828bd97b4d1f391ff09eded517ce1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Mitchell, Mr. Henry Michael</td>\n",
       "      <td>male</td>\n",
       "      <td>1601</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Name   Sex Ticket        Cabin Embarked\n",
       "count                           891   891    891          204      889\n",
       "unique                          891     2    681          147        3\n",
       "top     Mitchell, Mr. Henry Michael  male   1601  C23 C25 C27        S\n",
       "freq                              1   577      7            4      644"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2cb22b88-937d-6f14-8b06-ea3361357889",
    "_uuid": "e7b4b885268793a3f7f818a28e86aa97bdc84a69"
   },
   "source": [
    "### 基于数据分析的假设\n",
    "\n",
    "到目前为止, 基于数据分析, 我们得出以下假设.\n",
    "在采取适当的行动之前, 我们可能会进一步验证这些假设.\n",
    "\n",
    "**Correlating（相关）.**\n",
    "\n",
    "我们想知道每个特征与生存相关的程度.\n",
    "我们希望在项目早期做到这一点, 并将这些快速相关性与项目后期的模型相关性相匹配.\n",
    "\n",
    "**Completing（完整）.**\n",
    "\n",
    "1. 我们可能想要去补全丢失的 Age（年龄）特征，因为它肯定与生存相关.\n",
    "2. 我们也想要去补全丢失的 Embarked（出发港）特征, 因为它也可能与生存或者其它重要的特征相关联.\n",
    "\n",
    "**Correcting（校正）.**\n",
    "\n",
    "1. Ticket（船票号码）特征可能会从我们的分析中删除, 因为它包含了很高的重复比例 (22%), 并且票号和生存之间可能没有关联.\n",
    "2. Cabin（房间号）特征可能因为高度不完整而丢失, 或者在 训练和测试数据集中都包含许多 null 值.\n",
    "3. PassengerId（旅客ID）可能会从训练数据集中删除, 因为它对生存来说没有贡献.\n",
    "4. Name（名称）特征是比较不规范的, 可能不直接影响生产, 所以也许会删除.\n",
    "\n",
    "**Creating（创建）.**\n",
    "\n",
    "1. 我们可能希望创建一个名为 Family 的基于 Parch 和 SibSp 的新特征，以获取船上家庭成员的总数.\n",
    "2. 我们可能想要设计 Name 功能以将 Title 抽取为新特征.\n",
    "3. 我们可能要为 Age（年龄）段创建新的特征. 这将一个连续的数字特征转变为一个顺序的分类特征.\n",
    "4. 如果它有助于我们的分析, 我们也可能想要创建 Fare（票价）范围的特征。\n",
    "\n",
    "**Classifying（分类）.**\n",
    "\n",
    "根据前面提到的问题描述, 我们也可以增加我们的假设.\n",
    "\n",
    "1. Women (Sex=female) 更有可能幸存下来.\n",
    "2. Children (Age<?) 更有可能幸存下来. \n",
    "3. 上层阶级的旅客 (Pclass=1) 更有可能幸存下来."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6db63a30-1d86-266e-2799-dded03c45816",
    "_uuid": "ee691f202dcb1cafc9cae26049edb3b95f386e00"
   },
   "source": [
    "## 通过旋转特征进行分析\n",
    "\n",
    "为了确认我们的一些观察和假设, 我们可以快速分析我们的特征之间的相互关系.\n",
    "我们只能在这个阶段为没有任何空值的特征做到这一点.\n",
    "对于 Sex（性别），顺序的（Pclass）或离散的（SibSp，Parch）类型的特征, 这也是有意义的.\n",
    "\n",
    "- **Pclass** 我们观察到 Pclass = 1 和 Survived（分类＃3）之间的显着相关性（> 0.5）. 我们决定在我们的模型中包含这个特征.\n",
    "- **Sex** 在 Sex=female（性别=女性）的问题定义中确认了74％（分类＃1）的幸存率非常高的观察意见.\n",
    "- **SibSp and Parch** 这些特征对于某些值具有零相关性. 从这些单独的特征（创建＃1）派生一个特征或一组特征可能是最好的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "0964832a-a4be-2d6f-a89e-63526389cee9",
    "_uuid": "62fb816bd0d5612f6b12cce8ed46036acb75527b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "68908ba6-bfe9-5b31-cfde-6987fc0fbe9a",
    "_uuid": "2cfacba29530bf3e52fedf89c5bdd12eb9a924c3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "01c06927-c5a6-342a-5aa8-2e486ec3fd7c",
    "_uuid": "c0f9f9a3d89294ba3073f040d12a9625d26af1f1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
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       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "e686f98b-a8c9-68f8-36a4-d4598638bbd5",
    "_uuid": "3c6193e348b7616bfa8624ca9562a7ad5749906d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0d43550e-9eff-3859-3568-8856570eff76",
    "_uuid": "0a396ef5d708292e88f66829bd4d3f3d48465e36"
   },
   "source": [
    "## 通过可视化数据进行分析\n",
    "\n",
    "现在我们可以继续使用可视化分析数据来确认我们的一些假设.\n",
    "\n",
    "### 关联数值的特征\n",
    "\n",
    "让我们从理解数值的特征和解决方案目标（生存）之间的相关性开始.\n",
    "\n",
    "柱状图可用于分析连续的数字变量，如 Age（年龄），其中条带或范围将有助于识别有用的模式.\n",
    "直方图可以使用自动定义的 bins 或等分范围的 bins 来说明样本的分布.\n",
    "这有助于我们回答有关特定频段的问题（婴儿有更好的幸存率吗？）\n",
    "\n",
    "请注意，直方图可视化中的 x 轴表示样本或旅客的数量.\n",
    "\n",
    "**Observations（观察）.**\n",
    "\n",
    "- 婴儿（4 岁以下）存活率高.\n",
    "- 最老的乘客（年龄= 80）幸存下来.\n",
    "- 大量的 15-25 岁的孩子没有幸.\n",
    "- 大多数乘客在 15-35 年龄范围内.\n",
    "\n",
    "**Decisions（决策）.**\n",
    "\n",
    "这个简单的分析证实了我们的假设, 作为后续工作流程阶段的决策.\n",
    "\n",
    "- 在我们的模型训练中, 我们应该考虑年龄（我们假设分类＃2）.\n",
    "- 完成空值的年龄功能（完成＃1）.\n",
    "- 我们应该 band（组合）年龄组（创建＃3）."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "50294eac-263a-af78-cb7e-3778eb9ad41f",
    "_uuid": "5e581f2b5be92a31c510f7db4bfceb3c9727c473"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xce233c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xce23e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "87096158-4017-9213-7225-a19aea67a800",
    "_uuid": "f3b60c4ce160be82ebd9ad8ff14fcf2578ff2b03"
   },
   "source": [
    "### 关联数字和顺序的特征\n",
    "\n",
    "我们可以结合多个特征使用一个图来确定其相关性.\n",
    "这可以通过具有数字值的数字和分类特征来完成。\n",
    "\n",
    "**Observations（观察）.**\n",
    "\n",
    "- Pclass=3 拥有最多的乘客，但大多数没有生存. 确认我们的分类假设 ＃2.\n",
    "- Pclass=2 和 Pclass = 3 的婴儿乘客大多存活. 进一步限定了我们的分类假设 ＃2.\n",
    "- Pclass=1 的大多数乘客幸存下来。 确认我们的分类假设 ＃3。\n",
    "- Pclass 在乘客的年龄分布方面有所不同.\n",
    "\n",
    "**Decisions（决策）.**\n",
    "\n",
    "- 考虑 Pclass 用于模型训练."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "916fdc6b-0190-9267-1ea9-907a3d87330d",
    "_uuid": "c1c736a54c925c1d1db4133b30ebe144aabc295f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A7kmyAqC93bqzBavqnKpaVVWrpqamFvjnJA2ZOS9NmIWOAFwKnAy8r729pG8R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd5eceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
    "grid = sns.FacetGrid(train_df, 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": {
    "_cell_guid": "36f5a7c0-c55c-f76f-fdf8-945a32a68cb0",
    "_uuid": "5cdb1388a846ad8b19a2c649da034f4f45cae31b"
   },
   "source": [
    "### 关联分类特征\n",
    "\n",
    "现在我们可以将分类特征与我们的解决方案目标关联起来.\n",
    "\n",
    "**Observations（观察）.**\n",
    "\n",
    "- Female（女性）旅客的幸存率比 male（男性）好得多. 确认分类（＃1）。\n",
    "- Embarked= C 的例外, 其中男性的成活率较高. 这可能是 Pclass 和 Embarked 之间的相关性, 反过来, Pclass 和 Survived 之间, 不一定是Embarked和 Survived之间的相关性。\n",
    "- 与 C 和 Q 港口的 Pclass = 2 相比, Pclass = 3 时男性的生存率更高. 完成（＃2）。\n",
    "- 出发港口的 Pclass=3 和男性乘客的生存率不同. 相关（＃1）。\n",
    "\n",
    "**Decisions（决策）.**\n",
    "\n",
    "- 增加 Sex 特征以用于模型训练.\n",
    "- 补全丢失值并添加 Embarked 特征以用于模型训练."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "db57aabd-0e26-9ff9-9ebd-56d401cdf6e8",
    "_uuid": "cb86b6c046ba3b91dfc8811b08caefd515b7f1bb",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xd6625f8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xUcGSnTEmKrSba3Yikg/simAIXYFDEdx/JNl3j5xDqjo9gvtvM9pNsos0EclW\n1axIxxEJ9t2j87u3NXYaa4yJCpbsjDFRwZLdmfNkpAOIIPvuptWza3bGmKhgR3bGmKhgya6ZRORZ\nETkoIusiHUtLE5HeIrJARHJEZL2I3BbpmFqKiMSLyGcistr97r+MdEymfnYa20wicjFQCPxVVYdH\nOp6WJCLpQLqqrhCRFGA58EVV3RDh0MLOraidpKqFIhIDLAZuU9WlEQ7N1MGO7JpJVT/CqdgSdVR1\nv6qucN+fAHKIkorS6ih0P8a4LztyaMUs2Zkzwu0MNwb4NLKRtBwR8YrIKuAgMF9Vo+a7t0WW7Eyz\niUgy8BrwI1U9Hul4Woqq+lV1NE4l7fEiElWXMdoaS3amWdzrVa8BL6jq65GOJxJU9SiwEKftp2ml\nLNmZJnMv0j8D5Kjqo5GOpyWJSJqIdHLfJwBTgI2RjcrUx5JdM4nIS8AnQKaI7BWRmyMdUwuaAPwX\nMFlEVrmvyyMdVAtJBxaIyBqcfivzVfXNCMdk6mG3nhhjooId2RljooIlO2NMVLBkZ4yJCpbsjDFR\nwZKdMSYqWLIzxkQFS3bGmKhgyc4YExUs2RljooIlO2NMVLBkZ4yJCpbs2igR8Qc9fL9KRO5sxLqT\nRKRZD62LyEIRyWrius3ev7udGBGZKSJbRGSd2xPic83drmmffJEOwDRZiVs4ssWJiDcS+63Fr3Cq\njwxX1TIR6Q5MjHBMppWyI7t2RkR2isivReQTEckWkbEi8o6IbBORW4MW7SAi/xCRDSLyFxHxuOv/\n2V3vlI5Z7nbvFZHFwDVB0z0i8ryIPOB+nubue4WIvOpWMUZEpovIRnf9L5+B75kIfAv4gaqWAajq\nAVWd09xtm/bJkl3blVDjNPbaoHl7VPV8YBEwG7gaOA+4P2iZ8cCPgRHAAE4moLtVNQsYCUwUkZFB\n65Sq6oWq+rL72Qe8AGxW1XtEpCtwDzBFVccC2cDtIhIPPAV8AbgI6FHbFxKRzBrfKfjVqcbiA4Hd\n0VQG3jSPnca2XfWdxs5zf64Fkt3OXydEpDQoaXymqtuhugDphcBc4KsiMgPndyMdGAascdd5pcZ+\nngDmqOqD7ufz3OWXOEWMicUpbDoE2KGqW9z9/R2YUTNoVd0EROTU3LR/luzapzL3ZyDofdXnqr/z\nmlVbVUT6AT8BzlHVIyIyG4gPWqaoxjofA5eIyCOqWgoITsXe64IXEpHRtezvNCKSyekJtcokt9dD\nla1AHxFJcZO5MfWy09joNV5E+rnX6q7FafLcASehHXMv9jc0svkM8B/gVRHxAUuBCSIyEJzraiIy\nGKc3Qz8RGeCud11tG1PVTap38HTCAAAgAElEQVQ6uo7X0RrLFrv7nyUise7+0kXkxsb/UZhoYMmu\n7ap5zW5mI9f/BJgJrAN2AP9Q1dXASmA98CywpKGNuI12VgB/Aw4DNwEvub0ZlgJD3KO+GcC/3QGK\nXY2MtS73APnABhFZB7zhfjbmNNaDwhgTFezIzhgTFSzZGWOigiU7Y0xUsGRnjIkKluyMMVGh3SS7\n6dOnK86Nq/ayVzS9TIjaTbI7dOhQpEMwxrRi7SbZGWNMfezZWNNkxaUVrN9+GH9AyTyrM51T4hte\nyZgICVuyE5FngSuAg6o6vJb5AjwGXA4UAzep6gp33tdxHgUCeEBVnw9XnKbxVJU5729m7gdbKC3z\nA+DzClPGn8WMLw4nxtdaansac1I4T2NnA9Prmf85YJD7mgH8GUBEUoH7gHNxaq7dJyKdwxhnswQC\nSu6hQnIPFRIIRMf14lff38Lf39pYnegAKv3K25/sZNYrqyIXmDH1CNuRnap+JCJ961nkKuCv6jyc\nu1REOolIOjAJp0xQAYCIzMdJmi+FK9ammv/pLl55bzMHCooB6NElkWunZDJlfJ8IR3bmqCrllQHK\nK/yUV/g5XlTOq+9vrnP5hSv2cu3UwfTqltKCURrTsEhes8sA9gR93utOq2t6q/LGh9t4Zt66U6bl\nHS7msVdWUlxWwZUXDahjzaYLBNRJOkHJp6zCT0VlgDL3s/MKnh+gvPLU6VXrVL2vuc2q9Src6Y21\nYuNBS3am1YlkspNapmk900/fgFNRdwZAnz4tdzRVVFLBC2/n1Dn/+X9voEuHeETkZMKp8FNRefJ9\nvYmqskbCcd9X+hufeCKh6kjXmNYkksluL9A76HMvINedPqnG9IW1bUBVnwSeBMjKymqxC2YrNh2k\ntNxf5/zyigAz/5rdUuE0i9cjxMZ4iYvxEhvjIcZ38n1sjNd9eaqXAef0vb7Lk/MWbefgkWJumD6U\nvukdWuibGFO/SCa7ecD3ReRlnMGIY6q6X0TeAX4dNCgxDbgrUkHWprSsMizb9Xk9xLmJJSbGW/0+\n1ndq8omrSkK+4GlOoooNXq9GooqN8RLj85yyvtfb+DGquFgv8z7aXu8yS9fl8en6PC4clcF10zLp\n3d1Oa01khfPWk5dwjtC6ishenBHWGABV/QtOOe/LcXoJFAPfcOcViMivgGXupu6vGqxoLQb2rtno\n6nTXXDqI9C5JpyScqqRzSsIJOqLyemo7g299vnHF2ZSV+3n3010E134dm5nGxWN68dqCLew5UIgq\nLFq1jyWr9zFxbC+umzaE9K5JkQvcRLV2U6k4KytLs7Nb7tTxZ39awtpttT+iNnpQGr+69YIWiyVS\n8g4XsWLTQSr9AYb370r/jI4A+APKopV7efHdTew/dLJHj8cjXJrVm69NzaRbamKkwm5v2sb/kK2A\nJbsmOnK8lF88tZTtucdOmd4/oyO/+NZ59jQB4PcHWLB8Dy+9u4mDR0qqp/u8wrRzz+KrUwbTpWNC\nBCNsFyzZhciSXTP4/QE+25DHH15eSXFpJakd4nj2nmlNug7WnlVUBnjvM+eexMPHSqunx/g8fO6C\nvlw9eZD959B0luxCZP8qm8Hr9XD+iJ50So4DID7WZ4muFk5S68eTd03hW18cTqcU58+rojLAvI+2\n861fv8fsN9dzvKg8wpGa9swKAZwBVdef7DpU/WJjvFx50QCmnXsW/1myg7kfbOVEcTll5X5eW7CV\n/3y8kysv7s8XJw4kOSEm0uGadsZOY03EFJdW8K/F2/nHwm0UlVRUT09KiOFLkwbwhQv7kxhvSa8B\ndhobIkt2JuIKSyp448OtzPtoOyVB9zCmJMZy9eSBXD6hH/GxdhJSB0t2IbJkZ1qN40XlvL5gC28u\n2UFZ0BMqnVLiuGbyIKaf35fYGCsfVYMluxBZsjOtzpETpbz2wVb+8/EOKoIKEXTpGM+1UwYzZfxZ\nxPhsIMhlyS5EluxMq3X4WAlz3tvMu5/uotJ/8ve0W2oiX5symMlZvW3025JdyCzZmVbvYEExr7y3\nmfeW7T6lQGp61ySum5bJxWN6tZlH7cIgar94Y1myM23G/kNFvDx/EwuX7zml6krv7slcf9kQLhjR\nE08LJr2fP/ExBwuK6ZaayK++HbHHAy3ZhSjqzwFM25HeNYn/uW4sj//vZC4afbKe654DhTz812x+\n9PuFLF23n5b6D/xgQTG5h4o4aPX72gRLdqbN6d09hTv+K4v/+8klnD8ivXr6jtzjPPjcZ9z+2Ecs\n33igxZKeaRss2Zk2q296B35203h+/6OJZA3tXj19656j/OKppfz08cWs3pIfwQhNaxLWZCci00Vk\nk4hsFZE7a5n/exFZ5b42i8jRoHn+oHnzwhmnadsG9u7Efbecx29/cBGjB6VVT8/ZWcA9f/mYn/1p\nCeu3H45ghKY1CGfxTi/wR2AqTqn1ZSIyT1U3VC2jqv8TtPwPgDFBmyhR1dHhis+0P0P6pvKrWy9g\n7bZDvPD2xuoEt3bbIe7842LGZnbjhulDGNyn1XbmNGEUzmdwxgNbVXU7gFt+/SpgQx3LX4dTzdiY\nZhkxoCsPfXcCqzbn88LbG9m0+wjg9A5Zsekg44f14IbpQ6qLjZroEM5kV1tLxHNrW1BEzgL6AR8E\nTY4XkWygEpipqm+EK1DT/ogIYzK7MXpwGtk5B/j72xvZvs8ptPrZhjw+25DHhJE9ue6yTM7qYU2B\nokE4k13ILRGBrwFzVTW4ZVcfVc0Vkf7AByKyVlW3nbKDCLVSNG2HiHDOsB5kDe3O0nX7eeHtjezK\nOwHAkjW5fLw2l4tH9+K6yzLJSEuOcLQmnMKZ7OpqlVibrwHfC56gqrnuz+0ishDnet62GstEpJWi\naXtEhPNH9OTcs9NZvHofL76ziX35TlOgD1fuZdHqfVwyrhdfm5pJjy7WFKg9Cudo7DJgkIj0E5FY\nnIR22qiqiGQCnYFPgqZ1FpE4931XYAJ1X+szJmQej3DxmF788X8v4X+uG0OPLk7B1UBAeX/ZHm6d\n+T5/nLua/KCeGaZ9CNuRnapWisj3gXcAL/Csqq4XkfuBbFWtSnzXAS/rqXeADgWeEJEATkKeGTyK\na0xzeb0eJmf14eIxvXh/2W5enr+ZQ0dL8AeUtz/ZyXuf7Wb6+WdxzaWDSe1g/THag3qfjRWRE9R9\nnQ1VbTVXdu3ZWNMcFZV+3l26iznvb6bgeFn19NgYL5+f0I+vXDKQjm6vkSrffug9cg8V0bNrEk/c\nNaWlQ65iz8aGKKRCAO7RWB7wN5w/3BuAFFX9TXjDC50lO3MmlFX4eevjHcz9YAvHCk82AIqP9fKF\ni/rzpUkDOV5Uzpz3NrNg+R5UIdbn4Sc3juP8ET0jEbIluxCFmuw+VdVzG5oWSZbszJlUUlbJm4u3\n8/qCrRQG9ceIj/XiD+gpRUWr3PT5YXxl8qCWDBMs2YUs1AEKv4jcICJeEfGIyA2Av8G1jGmjEuJ8\nXHPpYJ65ZyrXXzaExHjn8nZpub/WRAfw17dybGCjFQs12V0PfBU44L6ucacZ064lxsdw3bRMnr57\nKpdf0LfeZQMB5cOVe1smMNNoIY3GqupOnEe9jIlKKYmxTM7qzX8+3lnvcscKy+qdbyInpCM7ERks\nIu+LyDr380gRuSe8oRnTunRPTWqwEnLPrnZDcmsV6mnsU8BdQAWAqq7BuUnYmKjRKSWOC4KKhdaU\nGO/j4jG9WjAi0xihJrtEVf2sxrTKWpc0ph379pdG0qdHymnTY3we/vfGLJISYiIQlQlFqMnukIgM\nwL3BWESuBvaHLSpjWqlOKXE88sOLufXLI4lzG3YnJcTwx/+dfEq1ZNP6hJrsvgc8AQwRkX3Aj4Bb\nwxaVMa1YfJyPz0/oR5eOzmNkHZNiSbdrda1eqM/G7lLVKSKSBHhU9UQ4gzLGmDMt1GS3Q0TeBl7h\n1AKbxpgIWr58eTefz/c0MBxroFUlAKyrrKy8Zdy4cQerJoaa7DKBL+Cczj4jIm/iVCpZfObjNMaE\nyufzPd2jR4+haWlpRzwej9V0BAKBgOTn5w/Ly8t7GriyanqoNxWXAHOAOSLSGXgM+BCndFPUe2Dh\nLPKLDpOW1IV7Jv0w0uGY6DLcEt2pPB6PpqWlHcvLyxsePD3kenYiMhG4FvgcTmHOr57ZENuu/KLD\n7C882PCCxpx5Hkt0p3P/TE45rQ/1CYodOCOwi4DhqvpVVX0thPUa6ht7k4jkB/WHvSVo3tdFZIv7\n+noocRpj6rfjyJ74F9e80eP5la/2XLpnRYeA1l7UoDEeeOCBbv379z/7yiuv7HcGQjzN7bff3vPe\ne+9t9n09oR7ZjVLV443ZcCh9Y12vqOr3a6ybitNWMQvn3r7l7rpHGhODMcZRGfAz65Nn+i7du7JL\n1bR/b/6A9JRuJXde9L2t6Sndyutbvz7PPPNM2ltvvbVlyJAhTd5GS6g32YnIHW6BzgdF5LRDZVWt\n7wJVY/vGBrsMmK+qBe6684HpwEshrGuMqWH2yjkZwYmuyv4TBxMe+ujxgb//3H0bvJ7GX4K//vrr\n++zduzfuyiuvHPilL32pYPv27fE5OTkJfr9f7r777twbb7zx6KxZs7rMmzevUyAQkE2bNiV873vf\nyysvL/e88sorXWJjYwPvvvvulu7du/sfeeSRrs8991xaRUWF9O3bt2zu3Lk7UlJSTjn0XL9+fdyt\nt97ap6CgwBcfHx94+umnd40ZM6Y0lFgbOo3NcX9mA8tredWntr6xGbUs9xURWSMic0WkqhtZqOua\nCHtg4Sxu+/d9PLBwVqRDMXUoLi/xfLhjabe65ucV5ics3buySR3DX3zxxd3dunWr+PDDDzcXFRV5\nL7nkkuPr1q3LWbRo0aZ77rmn1/Hjxz0AmzdvTnjttde2L1u2LOehhx7KSExMDOTk5GzIysoqeuKJ\nJ7oA3HDDDUfWrVuXs2nTpg2ZmZkls2bN6lpzf7fccstZf/rTn3avX78+57e//e3e73znOyH3UK33\nyE5V/+W+XaOqKxvxZwCh9Y39F/CSqpaJyK3A88DkENe1vrGtgA3OtH7bjuxKKPOX13tgk5O/JXlC\nn6xjzdnPwoULO7zzzjudZs2a1QOgrKxMtm7dGgtwwQUXnOjcuXOgc+fOgeTkZP8111xzFGDEiBHF\na9asSQRYvnx5wr333ptx4sQJb1FRkXfixImnxHPs2DHPypUrk6+55poBVdPKy8tDrtQc6jW7R0Uk\nHXgV5/669SGs02DfWFU9HPTxKeDhoHUn1Vh3Yc0dWN9YYxoW641pcBQixuNr9r8fVWXu3LlbR40a\ndUpRv8WLFyfFxsZWb9/j8RAfH69V7ysrKwVgxowZ/ebOnbv1/PPPL5k1a1aXDz/88JSKC36/n5SU\nlMqNGzc2qdNgSKOxqnoJTvLJB54UkbUh1LNrsG+sm0CrXMnJ0+Z3gGlu/9jOwDR3mjGmkQam9i3p\nFN+h3sGDc3uNOdrc/VxyySXHH3nkke6BgJNblyxZktCY9YuLiz19+vSpKCsrk5dffjm15vzU1NRA\nr169yp999tnOAIFAgE8++STkfYT8eImq5qnqLJwCAKuAextYvhKo6hubA8yp6hsrIlV3Nf9QRNaL\nyGrgh8BN7roFwK9wEuYy4P6qwQpjWotuqYn07JpEt9TESIdSL6/Hy1fOvnxfXfNH9zj7yJC0gcXN\n3c/MmTNzKysrZciQIcMGDRp09j333NOo6+x33nln7vjx44dedNFFgwcNGlTroMNLL720/bnnnuua\nmZk5bNCgQWe/9tprnULdfqjdxYbi3FB8NXAYeBl4TVVbzcWaSHQX25i/jTdy3mbF/nUAxHlj+fmk\n2xjctX+LxhFJt/37PvYXHiQ9uRuPff6XkQ4n6qxevXrXqFGjDoWy7NtbFnZ5bcN/Mo6VnogB8Hl8\nOqFPVv63sq7bG+uNbXeXgVavXt111KhRfas+h3rN7jmc2z6mqWpuQwtHg8/2ruLRj58i+KbMMn85\n933wCD+eMIOsjFERjM6Y000fNOnwlAEXHd6YvzWpzF/uGZTat7hDfErUdAlsMNm5NwdvU9XHWiCe\nNqHCX8GT2S9Q293nfg3wZPaLjO5xNj5vyE/jGdMifB4vw7tnFkU6jkho8JqdqvqBLu4ggwFW5+Vw\nvKywzvlHS4+z5sDGFozIGNOQkIt3AktEZB5Q/b+Cqj4alqhauWOlDT859+r6NyksL2Js+nCS46yK\nrTGRFmqyy3VfHuD0biNRJj2l4WeStxXs4vFPZ+MRD5ldBzCu5wiyeo6gZ4ceLRChMaamUOvZ2TBb\nkKFpA+nVIZ29x2vvOeTzeKkMONd9AxogJ38LOflb+Pvq10lP7sa4jJFk9RxBZtcBNOV5RNM6WB3D\n5nvzzTdTHnnkke4LFizYGu59hZTsRGQBtTyupaqTz3hEbYCI8KPzb+b+hX847dpdx/gUfj7xNjwe\nD8v3rWV57ho2Hd5O1S0++wsP8uam93hz03skxSYypsfZZGWMZHSPs0mMbdQ9mCbC2uKjctv3HYtf\ntGpfp4rKgGdov9TC84enH2+o8Xd7Eepp7E+C3scDXyHK+8b26ZTBI9N/zvxti3kj523K/RWkxCbx\nyGU/p0O8c6bfq0M6Vw2dxvGyQlbmrmN57lpW522gpNK5X7KovJjFu5exePcyvOJhaNogxvUcwbiM\nkfRITovk1zPtTKU/wO/+vrzvkjW51ZVP/vnRNjLSkkvuvfncrT3TkptcnmnTpk2x06dPHzR+/PjC\nFStWJA8dOrT4m9/85qH7778/4/Dhw77Zs2dvB7j99tv7lJaWeuLj4wOzZ8/eUfOxsuPHj3tuvvnm\nPjWrpjT9W58q1NPYmhVOlojIh2cqiLaqY3wHrj77chbt/JT9hQdJjk2qTnTBOsQlM7HfeUzsdx4V\n/go25G+pPurLL3YeDPFrgHUHN7Hu4CaeXzWXXh3SncTXcySDu/TD47FeKqbpnnpjbUZwoquyL78w\n4ZdPLx34pzsmb/B6m/47tmfPnvhXXnll+7hx43aNHDly6AsvvNAlOzt744svvtjpwQcfTJ8zZ86O\nzz77bGNMTAxvvPFGyh133NHrnXfe2Ra8jZ/97Gfpl1xyyfFXX31156FDh7xZWVlDr7zyyuMdOnRo\nfoVRQj+NDX5OzYNTVNOutDdBjDeGUT2GMarHML4x9qvsOZZLdu4alu9bw9aCXah7tWDv8f3sPb6f\nf258l5S4ZMamD2dczxGM6jGMhJj4CH8L05YUlVR43s/eU2eJp9xDRQlL1uR2vHhMryZXPcnIyCgb\nP358CcDgwYNLJk+efNzj8TB27NjiBx54oGdBQYH32muv7bdz5854EdGKiorTzp3rqpoyduzYkOrV\nNSTU09jlnLxmVwnsBG4+EwFEMxGhT6cM+nTK4MvDPsfRkmOs2L+O7Ny1rM3LoczvnFmcKCvkw51L\n+XDnUnweH2d3G8S4niPJ6jmSrkmnPS9tzCm27j2aUFbur/ewbf32w8nNSXZ1VTXxer34/X756U9/\nmjFx4sQT8+fP37Zp06bYyZMnZ9bcRl1VU86UhioVnwPsUdV+7uev41yv20loFYdNI3RK6Mjk/hOY\n3H8C5ZXlrDu4meW5a1ieu5aCEufSRWWgktV5OazOy+HZFa9wVscMxmU4p7sDUs/CI3a6a04V6/M0\nXOLJ5w3rs7HHjx/39urVqxzgiSeeOK0oJ5ysmjJ79uzdHo+HJUuWJEyYMKHkTMXQ0JHdE8AUABG5\nGHgI+AEwGqeO3NVnKhBzqlhfLGN7Dmdsz+HcosqOI3uqE9/2I7url9t1bB+7ju3j9Q1v0zG+A+PS\nhzMuYyQjug8h3hcXwW9gWovBfTqXdE6JKz9yoqzOp6AuGJl+xgYCavPTn/4075Zbbuk3a9asHhdd\ndFGtd+XPnDkzd8aMGX2GDBkyTFWlV69eZWfylpSGkp03qLTStcCTblex10Rk1ZkKwtRPROif2of+\nqX24ZvgVFBQfZXmuM8Cx9uAmKvwVgPNkxwc7PuaDHR8T441hRLdMxvUcybieI0hNDLkSjmlnvF4P\nX5uaue/Pr6+ptfvXuCHdjgzr16XJJZ4yMzPLt2zZUl3Q97XXXttZ27ydO3euq5r+2GOP5QJcccUV\nJ6644ooTAMnJyfriiy/uamocDWkw2YmIz61NdyluCfQQ10VEpuM01PYCT6vqzBrzbwduwbkOmA98\nU1V3ufP8wFp30d2qeiUGgNTETkwdeBFTB15EaWUZaw9sZPm+NSzfv676UbYKfwUr9q9jxf51PLUc\n+nXuXX2dr1/n3ohEx71VxnH5hH4FAVV55b3NGUdPlDklnrwevXhMRv53rx61N9LxtYSGEtZLwIci\ncggowekbi4gMBOq9mBliK8WVQJaqFovId4Df4BxBApSo6ujGfqFoE++L45yMUZyTMYqABthesJvs\n3NUs37eWXcdO1mvccWQPO47sYe76f5Oa0Imx7uNrw7tlEuuzGg+Nsf/EQV7f8BZ5hfkAHCo+wrJ9\nqzmnlZf1uuLC/oenn9/38Ibth5PKKvyewX06F3dMjrMSTwCq+qCIvA+kA+/qyUqfHpxrd/VpsJWi\nqi4IWn4pcGPjwjfBPOJhYJe+DOzSl6+NuIr8osPVp7vrDm7G7z7CVlBylPe2LeK9bYuI88YyosdQ\nsnqOYGz6cDolNKnJVNTYdXQv933wKMUVJ6+bVwQq+O3iv3DjqC9z5ZCpEYyuYT6vh5GD0qKyxFOD\np6KqurSWaZtD2HZt7RDPrWf5m4G3gj7Hi0g2zinuTFV9I4R9miBpSV2YPmgS0wdNoqSilNV5G1ie\nu5YV+9dxwn3MrcxfTva+1WTvWw3AoNS+jMtwrvP16Zhhp7s1PLvilVMSXbAX17zBhD5ZdEns3MJR\nmVCEs7pkSO0QAUTkRpwblScGTe6jqrki0h/4QETWquq2Guu1ilaKaUldTvnZGiXExHNe77Gc13ss\ngUCAzYd3VI/uBhc02FKwky0FO3l57TzSElOdAY6MEQxLG0SMN6Z6ub3H9vNGzjvVp3IFJUfJyd/C\n0LRBLf7dGiMQCFBaWea+Simpfu9+rjj1c2nQ52NlJ9h0aFvd29YAi3ct46qh01rwGxEIBALi8Xja\nXVn15ggEAgKccstNSD0omkJEzgd+oaqXuZ/vAlDVh2osNwX4P2BiXT0tRGQ28Kaqzq1rf5HoQdFe\n5BXmOwMcuWvJyd+Cv5YKzPG+OEb1GMa4niPoEJfM7z9+uvqm5yqC8IPzbuLCs8afkbgCGqCsspzS\nyjJKaiSe0srSoPdllFSc/FxSWUpZZVn18iVBy5a7I9fhcsXgS/nvMS13R9bq1av/1aNHj2FpaWnH\nLOE5AoGA5Ofnd8zLy9swatSo6oHNcB7ZVbdSBPbhtFK8PngBERmDcy/f9OBE57ZPLHabZ3cFJuAM\nXpgw6JGcxuczL+XzmZdSVF7Mqrz1LN+3lpV56ykqd+5IKK0s49O9K/l0b9290hXlyWUv0qtDevU6\n1YmmlsRzMnmVuvPKTklgZZVhuZG+yYJLd9WlR0rLFnCorKy8JS8v7+m8vLzhNKJbYDsXANZVVlbe\nEjwxbEd2ACJyOfAHnFtPnnUHPO4HslV1noi8B4wAqs6jdqvqlSJyAU4SDOD8Bf5BVZ+pb192ZHfm\n+QN+Nh3aRrZ71NeWyhl5xEOCL454XzzxMXHE+5xXgi+++n28L474mPhT59WzrM/r49GPn2LpnhW1\n7jPBF8+fv/Drli7VZRdVQxTWZNeSLNmFX+7xPN7IeYeFO08bs2oWETktuSTExBPnqzvxJMTUSFpB\niSvBF4fP4wvL4MrR0uP8csHv2Xc875TpMR4fP54wg7E9R5zxfTbAkl2ILNmZRtl7bD+3v31/vcuM\nSR9Ov869nKOqUxJSPAm1HDnFeGPa1KhvaUUpC3cu5e+rX6fcX0FiTAIzp95Jj5Q6C4uEU9v5g4sw\n6/VnGqVXx3Qyuw6oc1SyU3wHfjJhxikjt+1NfEw80wdN4q3NC9hfeJCOcSmRSnSmEeyCpmm0747/\nb1ITTn/WNs4by4/Ov7ldJzrTdlmyM42WntKN31x2N18bcSUxHufkICkmkUem/5xh3QZHODpjamfJ\nzjRJh7hkvjzsc3RNTK3+3C251jJlxrQKluyMMVHBkp0xJipYsjPGRAVLdsaYqGDJzhgTFeymYmOa\nqC2U9jInWbIzponumfTDSIdgGsFOY40xUcGSnTEmKoQ12YnIdBHZJCJbReTOWubHicgr7vxPRaRv\n0Ly73OmbROSycMZpjGn/wpbsglopfg4YBlwnIsNqLHYzcERVBwK/Bx521x2GU9n4bGA68Cd3e8YY\n0yThPLKrbqWoquVAVSvFYFcBz7vv5wKXilPY7CrgZVUtU9UdwFZ3e8YY0yThTHa1tVLMqGsZVa3E\nabzdJcR1jTEmZOFMdqG0UqxrmZDaMIrIDBHJFpHs/Pz8JoRojIkW4Ux2e4HeQZ97Abl1LSMiPqAj\nUBDiuqjqk6qapapZaWkt29XJONKSupCe3M1urDWtXkRbKQLzgK8DnwBXAx+oqorIPOBFEXkU6AkM\nAj4LY6ymiezGWtNWhC3ZqWqliHwfeIeTrRTXB7dSBJ4B/iYiW3GO6L7mrrteROYAG4BK4HuqWn/D\nTmOMqYd1FzOmbbPuYiGyJyiMMVHBkp0xJipYsjPGRIV2c81ORPKBXREMoStwKIL7jyT77pFzSFWn\nR3D/bUa7SXaRJiLZqpoV6Tgiwb57dH73tsZOY40xUcGSnTEmKliyO3OejHQAEWTf3bR6ds3OGBMV\n7MjOGBMVLNk1k4g8KyIHRWRdpGNpaSLSW0QWiEiOiKwXkdsiHVNLEZF4EflMRFa73/2XkY7J1M9O\nY5tJRC4GCoG/qurwSMfTkkQkHUhX1RUikgIsB76oqhsiHFrYuRW1k1S1UERigMXAbaq6NMKhmTrY\nkV0zqepHOBVboo6q7mDfh3cAABs7SURBVFfVFe77E0AOUVJRWh2F7scY92VHDq2YJTtzRrid4cYA\nn0Y2kpYjIl4RWQUcBOaratR897bIkp1pNhFJBl4DfqSqxyMdT0tRVb+qjsappD1eRKLqMkZbY8nO\nNIt7veo14AVVfT3S8USCqh4FFuK0/TStlCU702TuRfpngBxVfTTS8bQkEUkTkU7u+wRgCrAxslGZ\n+liyayYReQmnh0amiOwVkZsjHVMLmgD8FzBZRFa5r8sjHVQLSQcWiMganH4r81X1zQjHZOpht54Y\nY6KCHdkZY6KCJTtjTFSwZGeMiQqW7IwxUcGSnTEmKliyM8ZEBUt2xpioYMnOGBMVLNkZY6KCJTtj\nTFSwZGeMiQqW7NooEfEHPXy/SkTubMS6k0SkWQ+ti8hCEclq4rrN3r+7nVgR+YOIbBORrSLypoj0\nae52Tfvki3QApslK3MKRLU5EvJHYby1+DaQAg1XVLyLfAP4pIuNUNRDh2EwrY0d27YyI7BSRX4vI\nJyKSLSJjReQd9+jn1qBFO4jIP0Rkg4j8RUQ87vp/dtc7pWOWu917RWQxcE3QdI+IPC8iD7ifp7n7\nXiEir7pVjBGR6SKy0V3/y2fgeyYC3wD+R1X9AKr6HE7zoynN3b5pfyzZtV0JNU5jrw2at0dVzwcW\nAbOBq4HzgPuDlhkP/BgYAQzgZAK6W1WzgJHARBEZGbROqapeqKovu599wAvAZlW9R0S6AvcAU1R1\nLJAN3C4i8cBTwBeAi4AetX0hEcms8Z2CX51qLD4Q2F1LGfhsYFidf2omatlpbNtV32nsPPfnWiDZ\n7fx1QkRKg5LGZ6q6HaoLkF4IzAW+KiIzcH430nESxxp3nVdq7OcJYI6qPuh+Ps9dfolTxJhYnMKm\nQ4AdqrrF3d/fgRk1g1bVTUCop+ZC7d28JMT1TZSxZNc+lbk/A0Hvqz5X/Z3XTBQqIv2AnwDnqOoR\nEZnN/7d35+FR1VcDx79nwpKwJSSEXbbIvggYRQWUKiC2VVq7qLWttrbWbnZ9W7vbxdYur+/T1mql\n2kWrWLda7CJllSCCIPuuCaAQhIRAFrLPnPePexMnIcncLHdmkjmf55mHmbvMnIFw8rv3/u45kBy2\nzdlG+2wA3iUi/6uqlTiJZoWq3hS+kYhMb+LzziEi4zk3odaZ5/Z6qPMGMFJE+rrJvM5MnKRtTAN2\nGJu4LhaR0e65uhtwmjz3w0loxSIyCLgmwns8AvwbeFpEugEbgdkicj4459VEZBxOb4bRIpLl7ndT\nU2+mqgdUdXozjzONtj0L/AW4r+6CiYh8HKgEXm7tX4bp+mxk13mluD1L67yoqp6nn+AcXt6Lc85u\nHfB3VQ2JyDZgD5CHh6ShqveJSCrwGHAzcCuwVER6upt8V1UPuofG/xKRQpzE2hFtB78F/BI44Da9\nKQAuVes1YJpgPShMlyAig4EXgQdUdUms4zHxx5KdMSYh2Dk7Y0xCsGRnjEkIluyMMQnBkp0xJiF0\nmWS3aNEixZm4ag97JNLDeNRlkl1hYWGsQzDGxLEuk+yMMaYldgdFOwVDQfJLTwAwtO8gkgLxUurN\nfyENcbz0JMFQkKF9B9EtyX6cTPzy7adTRP4IvBc4qarn3BokTlmMXwPvBsqBW1V1q7vuFpxSQQA/\nUdW/+BVnW6kqK3LX8dzeFymqcG7bzEjpz/WTrmF+1hzcqh9d1vojr/K33f/kRFkBAKk9+/Le8fO5\ndsJ8AmIHDCb++PlT+WdgUQvrrwHGuo/bgQcBRCQd+AEwC6fm2g9EpL+PcbbJP/b/l4dfe7I+0QGc\nqjjNH157ghcOrIxhZP5bk7eB32z8U32iAyiuKuXxnX/n8R1/j2FkxjTPt5Gdqq4TkVEtbLIYeNS9\naXujiKSJyBBgHk6ZoCIAEVmBkzSX+hVra52tLueZPf9qdv1Tu19g0sCxpHRLbnabzqo2WMtj259t\ndv2/Dq7m3eOuJKNX3P1+MgkulidZhgFvhb0+6i5rbnnc2PH2XqqDNc2urw7W8O0VP49iRPEjpCG2\nHNvJ1WOviHUoxjQQy5MrTZ3U0haWn/sGIre7/RK2FBQUNLWJLyprq6P2WZ3R5vwdDQ7vjYkHsRzZ\nHQXOC3s9HMh3l89rtHxtU2/glvJZApCdnR21CZbnp4+MuM28UZfSt2fvKEQTXZW1VazIzWlxm51v\n7+NzL3yHC4dOZUHWXKYNmkggYBctTGzFMtktA74gIk/iXIwoVtXjIrIc+GnYRYmFOEUa48aItGFM\nHTSBXSf2N7l++uBJfG7Wx6McVfRU1Fax/sirTa4TBEUJaYjNx3aw+dgOMnulc1XWHN41+jL6p6RG\nOVpjHL7Vs3ObuMwDBgAncK6wdgdQ1d+7U0/ux7n4UA58QlW3uPt+Evi2+1b3uC3yWpSdna1btmzp\n6K/RrJLKUn6W8ztyi440WD42fRTfvPzz9OvZJ2qxRFtFTSW/evmhc5L98H5D+PKlt7G34HVW5Obw\nVnF+g/VJEuDCYdNYkDWXqYMm2BSVjtG15zh1oC5TvDPayQ6ck/Hbj++t/09/weCJTBs8MSH+E6sq\newteZ2v+LoKhIJMGjuPCoVPrJ1WrKgdP5bEiN4dX3tpKTaMLOoN6D+CqrDnMG30pacn9YvEVugpL\ndh5ZsjO+K6s6y7ojm1iRm8OxkrcbrEuSABcNm878rDlMGTQ+IX5RdDBLdh5ZsjNRo6rsL3yDFbnr\n2fTWVmpCtQ3WD+6TyVVj5jBv9CWk2mjPK0t2HlmyMzFRWlXGS4c3sTI3p/7e4jpJgSRmDZvO/Ky5\nTB44rsvfetdO9pfjkSU7E1Oqyj73gsamo9upbTTaG9J3IPPHzOWK0Zd06Ys+7WDJziNLdiZulFSV\nsfbQK6zKXc/xspMN1nULdGPW8OksyJrLxMyxNtp7h/1FeGTJzsQdVWXPyYOszM1h07HtBEPBBuuH\n9R3sXMkddQl9Yjhx+ydrf0PB2VNk9s7gu/PujFUYluw8sgJkJu6ICFMGjWfKoPEUV5aw9tBGVuat\nr6+ycqz0bR7d/gxLdz7PJefNZEHWXMYPyIr6aK/g7KlzRqAmflmyM3EtNbkfiycu5NoJ89l94gAr\nc9ez+dh2ghqiJlRLzpFXyTnyKsP7DWF+1hwuHzWLPj263m16pv0s2ZlOISABprmTts9UFLPm0Cus\nylvPybOnADhacpw/b3uax3c+z6XuaG9cxhg7t2fqWbIznU5aSirvn7SIxRMXsuvEflbk5rDl2E5C\nGqImWMO6w5tYd3gT56UOZUHWXOaOvJjePXrFOmwTY5bsTKcVkAAXDJ7EBYMncbqimDWHNrAqdz0F\n5UUAvFWczx+3/o2/7niOy87LZn7WHMZmjLbRXoKyZGe6hP4pqVw/6RreN+FqdpzYy4rc9WzN30VI\nQ1QHa1h7+BXWHn6FkanDmO+O9nr1SIl12CaKLNmZLiUQCDBjyBRmDJlCUfkZVh/awKq89ZwqPw3A\nkeJjPLL1Sf664zlmj8hmftZcstJH2mgvAViyM11Weq80Pjj53Vw/cRHb397Ditwcth7fjapSFaxm\n9aENrD60gVFpw+tHeyndu17fEOPwNdmJyCKcdolJwMOqem+j9f8HvMt92QsYqKpp7rogsMtd96aq\nXudnrKbrCgQCzBw6lZlDp1JYXsSavA2sztvAqQpntHf4zFEefm0pj+14jjkjLmJB1hzGeKhGbToX\nP/vGJgG/AxbglFrfLCLLVHVv3Taq+pWw7b8IzAh7iwpVne5XfCYxDeiVzoemvJfrJ13DtuN7WJmb\nw7bje1CUqtoqVuWtZ1Xeesb0H8H8rLnMGZFNso32ugQ/R3YXA2+oah6AW359MbC3me1vwqlmbIzv\nkgJJZA+bRvawaRSeLWJV3susPvQypyuKAcg7/SZLtjzOo9ufYc7Ii1mQNZfR/Z2WKWXVZ/nvG+so\ndK/6nqks4fDpo4zqPzxm38dE5mdZ9g8Ci1T1U+7rjwGzVPULTWw7EtgIDFfVoLusFtgO1AL3qurz\nLX2e3Rtr2isYCvJa/i5W5a1n+/G9aKOmdlnpI7n0vAtZ/vra+uktdZIkwBcv+SSXjbgwmiGD3Rvr\nmZ8jO88tEYEbgWfqEp1rhKrmi8gYYLWI7FLV3AYfIHI7cDvAiBEjOiJmk8CSAklcPHw6Fw+fzsmz\np1idt57VeRs4U1kCQG7RkXN6jtQJaogHX32UaYMmxLQ4gWmenzWwm2uV2JQbgaXhC1Q13/0zD6eV\n4ozGO6nqElXNVtXszMzMjojZGAAG9s7gxqmLeeDan/K12bdzweCJEfepClaT00zXNRN7fia7zcBY\nERktIj1wEtqyxhuJyHigP/BK2LL+ItLTfT4AmE3z5/qM8U23QBKzhs/gO1fcydcuuz3i9gXuvbom\n/vh2GKuqtSLyBWA5ztSTP6rqHhH5EbBFVesS303Ak9rw5OFE4CERCeEk5HvDr+IaEwtZGZGno/RP\nSYtCJKYtfJ1np6r/Bv7daNn3G72+u4n9NgBT/YzNmNYa0CudqYPGs+vEgSbXJ0kSc0deFOWojFct\nHsaKSKmIlDT3iFaQxsSL22beSGrPvk2u+8TMD5OWkhrliIxXLY7sVLUvgHvo+TbwGM5V1puBpv/F\njenChvYbzM8W3sUL+1ey/I2XCGmInkk9+ObczzJl0IRYh2da4PUCxdWq+oCqlqpqiao+CHzAz8CM\niVcDeqXziZkfZlDvAQCkp6RZousEvCa7oIjcLCJJIhIQkZuBYMS9jDEmTnhNdh8BPgyccB8fcpcZ\nY0yn4OlqrKoexrmv1RhjOiVPIzsRGSciq0Rkt/t6moh819/QjDGm43g9jP0D8C2gBkBVd+LcEWGM\nMZ2C12TXS1Ub3/RX29HBGGOMX7wmu0IRycKtWuKWbzruW1TGGNPBvN4u9nlgCTBBRI4Bh3AmFhtj\nTKfgNdkdUdX5ItIbCKhqqZ9BGWNMR/N6GHtIRJYAlwBlPsZjjDG+8JrsxgMrcQ5nD4nI/SIyx7+w\njIl/mb0zGNJnIJm9M2IdivGg1T0oRKQ/TnvEm1U1yZeo2sB6UJgEZT0oPPJcqVhErhCRB4CtQDLO\n7WOR9lkkIgdE5A0RuauJ9beKSIGIbHcfnwpbd4uIvO4+bvEapzHGNMXTBQoROYTT6esp4H9U9ayH\nfSL2jXX9rXHHMRFJx2mrmI0z3eU1d9/TXuI1xpjGvF6NvUBVW1uss7V9Y8NdDaxQ1SJ33xXAIho1\n5THGGK9aTHYi8g1V/QVwj4icc3JPVe9sYfdhwFthr48Cs5rY7gMicjlwEPiKqr7VzL7DWorVGGNa\nEmlkt8/9sy1n/r30jX0BWKqqVSJyB/AX4EqP+1rfWGOMZ5HKsr/gPt2pqtta+d4R+8aqanjfuT8A\nPw/bd16jfdc2Ed8SnDs7yM7Obt1lZWNMQvF6NfY+EdkvIj8Wkcke94nYN1ZEhoS9vI53RpLLgYVu\n/9j+wEJ3mTHGtInX4p3vEpHBONNNlohIP5yrqD9pYR8vfWPvFJHrcCqoFAG3uvsWiciPcRImwI/q\nLlYYY0xbtGVS8VTgG8ANqtrDl6jawCYVmwRlk4o98lqpeKKI3O1WKr4f2IBzHs0YYzoFr/Ps/oQz\nx22hquZH2tgYY+JNxGTn3gmRq6q/jkI8xhjji4iHsaoaBDLcK6rGGNMpeS7eCbwsIsuA+vtiVfU+\nX6IyxpgO5jXZ5buPANDXv3CMMcYfXufZ/dDvQIwxxk9eSzytoYl7U1X1yg6PyBhjfOD1MPbrYc+T\ngQ9gfWONMZ2I18PY1xotellEXvIhHmOM8YXXw9j0sJcBnArCg32JyBhjfOD1MPY13jlnVwscBm7z\nIyBjjPFDpErFFwFvqepo9/UtOOfrDuOtvLoxxsSFSHdQPARUA7il03+GU024GLdopjHGdAaRkl1S\nWB25G4Alqvqsqn4POD/Sm3topfhVEdkrIjtFZJWIjAxbFwxrsbis8b7GGNMaEZOdiNQd6l4FrA5b\nF+kQuK6V4jXAJOAmEZnUaLNtQLaqTgOeAX4Rtq5CVae7j+sixGmMMS2KlOyWAi+JyD+ACiAHQETO\nxzmUbUl9K0VVrQbqWinWU9U1qlruvtyI1cgzxvgkUsOde0RkFTAE+K++U9Y4AHwxwnt7baVY5zbg\nP2Gvk0VkC87V33tV9fkIn2eMMc2KOPVEVTc2seygh/f21A4RQEQ+ijN374qwxSNUNV9ExgCrRWSX\nquY22s9aKRpjPPHaXawtIrZSBBCR+cB3gOtUtapueV1FZFXNw2mjOKPxvqq6RFWzVTU7MzOzY6M3\nxnQpfiY7L60UZ+BMb7lOVU+GLe8vIj3d5wOA2di8PmNMO3i9g6LVPLZS/CXQB3haRADedK+8TgQe\nEpEQTkK+V1Ut2Rlj2qzVrRTjlbVSNAnKWil65OdhrDHGxA1LdsaYhGDJzhiTEHy7QGFMV/e9hzZw\nsqicgem9+PFnLot1OCYCS3bGtNHJonLyC89G3tDEBTuMNcYkBEt2xrRBWXk1FVVOz6naYCjG0Rgv\nLNkZ0wqqylMrD3LLj5ZzutS5u/Hk6Qp++PBGSsurYxydaYklO2Na4YX1eTz2n31U1zQczW3Zd4If\nP7KJrjJJvyuyZGeMRzW1IZ5e9Xqz6/cdLmLn64VRjMi0hl2NNSZMZXUthWcq6h8FZyrrn+cXlHGm\ntKrF/bcdPMkF46wCTzyyZGcSRk1tkFPFlRQ0SGYVDZJbaXlNrMM0PrFkZ7qEYDBEUUlVwyRW3DCh\nRRqVtSQgkNY3meKyKoKh5s/LzRg3sM2fYfxlya6damqDHMovAWD00H5075YU44iipzYY4nB+CbWh\nEKOG9CO5hz8/TqGQUlxW1eKIrKi0ilALSSiStD49GZCWzIC0FAakpZDp/ln3yOiXTFJSgGU5ufzh\n+d1NvsfEUelMGzugzTEYf/ma7ERkEfBrnHp2D6vqvY3W9wQeBS4ETgE3qOphd923cPpSBIE7VXW5\nn7G2lqry97W5PLvmdUrOOlMOUvv04INXjmXx5Vm49fm6rOUbj/DE8v0UlVQC0Du5G9fOzeLGheNJ\nCnj/7qpKaXlNk0ms7vmp4sp2zWXrk9K9ySRW9zwjNZke3b39krp2zhgqq4L8beWBBldksycO4is3\nzezy/+6dmW/17NxWigeBBTgl2jcDN4UX4RSRzwHTVPUOEbkReL+q3uC2XFyK06FsKLASGKeqweY+\nL9r17JYu388T/z3Q5LqPLprADQvGRy2WaPv3hkM8+OzOJte9d/ZoPnP9tPrX5ZU1LY7ICosrqapu\n9p81opSeSU7ySm16RJaZlkJyz47/nV5WXs3nfrGa06VVDOyfwiPfXdjhn+GRZVeP/BzZ1bdSBBCR\nulaK4RWHFwN3u8+fAe4X51fjYuBJtyfFIRF5w32/V3yM17OSs9U8vbr5KQhPrjzAxFHp9EruHsWo\noqOmNshf/tV80eh/vnyIw2+XUHK2msIzFZRX1rb5s7p3CzSbwOqe907uFpPRVJ9ePUjp2Y3TpVV0\nS7IZXJ2Bn8nOSyvF+m3cMu7FQIa7fGOjfYf5F2rrbDtwkpra5g+ramuV7/x+QxQjii+7c09F3CYp\nIGSkNn+OLDMthX69e9hhoekwfiY7L60Um9vGUxvGWLVSrKlt+2FXokjvl9zMiMxJcGl9k1t1bs+Y\n9vIz2XlppVi3zVER6QakAkUe90VVlwBLwDln12GRRzBuRP+I27xn9mj69e4RhWiiq6Kqln+8lNt0\nA2DXTz97GVPP7/oTawem92rwp4lvfia7+laKwDGcVoofabTNMuAWnHNxHwRWq6qKyDLgCRG5D+cC\nxVjgVR9jbZURg/uRPXEQW/adaHL9rMmDuSPsJH1XU1Zew8rNbza5bvzI/kzJSozpF1aws3Px7cyq\nqtYCda0U9wFP1bVSFJHr3M0eATLcCxBfBe5y990DPIVzMeNF4PMtXYmNha99ZCaTx2Scs3xq1gC+\nfNPMGEQUPZ+5fiqzJg8+Z/n5w1P59q0X23k2E5eslWI7qCq7806x42ABANPHZTJ5TEbC/Gc/+OZp\ntuw7QW0wxJSsAUwfm0nAzsNFm/2Fe2TJzpjOzZKdRzZByBiTECzZGWMSQpc5jBWRAuBIDEMYACRq\n5Ub77rFTqKqLYvj5nUaXSXaxJiJbVDU71nHEgn33xPzunY0dxhpjEoIlO2NMQrBk13GWxDqAGLLv\nbuKenbMzxiQEG9kZYxKCJbt2EpE/ishJEWm6MUEXJiLnicgaEdknIntE5EuxjilaRCRZRF4VkR3u\nd/9hrGMyLbPD2HYSkcuBMuBRVZ0S63iiSUSGAENUdauI9AVeA94XXnq/q3IravdW1TIR6Q6sB76k\nqhsj7GpixEZ27aSq63Bq8CUcVT2uqlvd56U41W3ipqK0n9RR5r7s7j5s5BDHLNmZDiEio4AZwKbY\nRhI9IpIkItuBk8AKVU2Y794ZWbIz7SYifYBngS+rakms44kWVQ2q6nScStoXi0hCncbobCzZmXZx\nz1c9Czyuqs/FOp5YUNUzwFrA7lGNY5bsTJu5J+kfAfap6n2xjieaRCRTRNLc5ynAfGB/bKMyLbFk\n104ishSnh8Z4ETkqIrfFOqYomg18DLhSRLa7j3fHOqgoGQKsEZGdOP1WVqjqP2Mck2mBTT0xxiQE\nG9kZYxKCJTtjTEKwZGeMSQiW7IwxCcGSnTEmIViyM/VEJOhOH9ktIk+LSK8Wtr1bRL4ezfiMaQ9L\ndiZchapOd6u3VAN3xDogYzqKJTvTnBzgfAAR+biI7HRrtz3WeEMR+bSIbHbXP1s3IhSRD7mjxB0i\nss5dNtmtA7fdfc+xUf1WJmHZpGJTT0TKVLWPiHTDud/1RWAd8BwwW1ULRSRdVYtE5G6gTFV/JSIZ\nqnrKfY+fACdU9bcisgtYpKrHRCRNVc+IyG+Bjar6uIj0AJJUtSImX9gkFBvZmXApbsmiLcCbOPe9\nXgk8o6qFAKraVO2+KSKS4ya3m4HJ7vKXgT+LyKeBJHfZK8C3ReSbwEhLdCZausU6ABNXKtySRfXc\nm/0jDf//jFOheIeI3ArMA1DVO0RkFvAeYLuITFfVJ0Rkk7tsuYh8SlVXd/D3MOYcNrIzkawCPiwi\nGQAikt7ENn2B4265p5vrFopIlqpuUtXvA4XAeSIyBshT1d8Ay4Bpvn8DY7CRnYlAVfeIyD3ASyIS\nBLYBtzba7Hs4FYqPALtwkh/AL90LEIKTNHcAdwEfFZEa4G3gR75/CWOwCxTGmARhh7HGmIRgyc4Y\nkxAs2RljEoIlO2NMQrBkZ4xJCJbsjDEJwZKdMSYhWLIzxiSE/wcoLOyFxwlyhAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd84a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, 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": {
    "_cell_guid": "6b3f73f4-4600-c1ce-34e0-bd7d9eeb074a",
    "_uuid": "6e031e6217b278363f1bd659b2e591bcb30562bd"
   },
   "source": [
    "### 关联分类和数值的特征\n",
    "\n",
    "我们也可能想要关联分类特征（非数值的）和数值的特征.\n",
    "我们可以考虑将 Embarked（类别非数字）, Sex（类别非数字）, Fare（数字连续）与生存（分类数字）相关联.\n",
    "\n",
    "**Observations（观察）.**\n",
    "\n",
    "- Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.\n",
    "- Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).\n",
    "\n",
    "- 更高的票价付费旅客有更好的生存. 证实我们对创造（＃4）票价范围的假设.\n",
    "- 搭乘港口与生存率相关. 确认关联（＃1）和完成（＃2）.\n",
    "\n",
    "**Decisions（决策）.**\n",
    "\n",
    "- 考虑关联Fare(票价)特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "a21f66ac-c30d-f429-cc64-1da5460d16a9",
    "_uuid": "af9099be9d7fe9f358ccdcf704991abde0880409"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xd5cb198>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OTHIccCfwmhZjkDRc5rU0JlorAKrqUmB9JwYPbGu7ktpjXkvjwzsBSpLUQRYAkiR1kAWA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd7e61d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, 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": {
    "_cell_guid": "cfac6291-33cc-506e-e548-6cad9408623d",
    "_uuid": "53c762e996de3aefbe4e7e70671a43930189d9b9"
   },
   "source": [
    "## 整理数据\n",
    "\n",
    "我们收集了关于我们的数据集和解决方案要求的一些假设和决策.\n",
    "到目前为止, 我们没有必要改变一个单个的特征或值来达到目标.\n",
    "让我们现在执行我们的决定和假设来 correcting(校正), creating（创建）和 completing（完整）目标.\n",
    "\n",
    "### 通过删除特征进行校正\n",
    "\n",
    "这是一个很好的开始执行目标. 通过丢弃特征, 我们正在处理更少的数据点. 加快我们的 notebook, 并简化分析.\n",
    "\n",
    "根据我们的假设和决策, 我们要放弃 Cabin（房间号）（更正＃2）和 Ticket（票号）（更正＃1）的特征.\n",
    "\n",
    "请注意, 如果适用, 我们将对训练和测试数据集进行操作, 以保持一致."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "da057efe-88f0-bf49-917b-bb2fec418ed9",
    "_uuid": "6c7b899f23e0c2cb0b2b05447eece4f0aab769f5",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('After', (891, 10), (418, 9), (891, 10), (418, 9))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6b3a1216-64b6-7fe2-50bc-e89cc964a41c",
    "_uuid": "762ea4993c2d5c7b9667521badc4bb0802f35a24"
   },
   "source": [
    "### 从现在的提取以创建性特征\n",
    "\n",
    "我们想要分析一下, Name 特征是否可以被设计来提取 titles（头衔）和测试titles与survival（生存）之间的相关性, 然后再删除Name 和 PassengerId 特征.\n",
    "\n",
    "在下面的代码中, 我们使用正则表达式提取 Title 特征.  正则表达式`(\\w+\\.)`匹配 Name 特征中以点号字符结尾的第一个单词.\n",
    "`expand = False` 标志返回一个 DataFrame.\n",
    "\n",
    "**Observations（观察）.**\n",
    "\n",
    "当我们绘制出 Title, Age 和 Survived 的图时, 我们可以发现以下观察.\n",
    "\n",
    "- 大多数 titles band 年龄组准确. 例如: Master Title的Age平均为5岁。\n",
    "- Title 中的生存年龄段略有不同.\n",
    "- 某些 Title 大多存活(Mme, Lady, Sir)或某些Title没有存活(Don, Rev, Jonkheer)\n",
    "\n",
    "**Decision（决策）.**\n",
    "\n",
    "- 我们决定保留模型训练的新 Title 特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "df7f0cd4-992c-4a79-fb19-bf6f0c024d4b",
    "_uuid": "6e1e16b53b683ba5c4e0b7f22ff97209d12f8e0c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "908c08a6-3395-19a5-0cd7-13341054012a",
    "_uuid": "a780a39e03cc679742db03ad2531cff65492cc20"
   },
   "source": [
    "我们可以用更常见的Title来替换很多Title, 或者将它们分类为 `Rare`(稀有)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "553f56d7-002a-ee63-21a4-c0efad10cfe9",
    "_uuid": "a77aab132073d20a9050d63a57aab0b77931cf98",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6d46be9a-812a-f334-73b9-56ed912c9eca",
    "_uuid": "570174039502747cfc590e3a23b2b2407649c9df"
   },
   "source": [
    "我们可以将 titles（头衔）转换为顺序的."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "67444ebc-4d11-bac1-74a6-059133b6e2e8",
    "_uuid": "12d48fc3ce08e6daff750ecdf741852f7ada87fd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</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>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</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>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</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>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</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>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f27bb974-a3d7-07a1-f7e4-876f6da87e62",
    "_uuid": "ebbb6df8eeb4898e25cf2e375a5d5540906ba554"
   },
   "source": [
    "现在我们可以放心地从训练和测试数据集中删除 Name 特征.\n",
    "我们也不需要训练数据集中的 PassengerId 特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "9d61dded-5ff0-5018-7580-aecb4ea17506",
    "_uuid": "3699ea6473ef7e16779b28b319ccff0da2615e2f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2c8e84bb-196d-bd4a-4df9-f5213561b5d3",
    "_uuid": "d55d78bc8cab25fa58194dbc46610e2395d1d3e0"
   },
   "source": [
    "### 转换分类的特征\n",
    "\n",
    "现在我们可以将包含字符串的特征转换为数字值.\n",
    "这是大多数模型算法所要求的.\n",
    "这样做也将帮助我们实现特征完成目标.\n",
    "让我们开始将 Sex（性别）特征转换为名为 Gender（性别）的新特征, 其中 female=1, male=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "c20c1df2-157c-e5a0-3e24-15a828095c96",
    "_uuid": "f61f46627bdef1b318c019c852f2e370f6bfd9e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d72cb29e-5034-1597-b459-83a9640d3d3a",
    "_uuid": "2799e6b10611997bdeca13d86fcacced318853c9"
   },
   "source": [
    "### 完整化数值字连续特征\n",
    "\n",
    "现在我们应该开始估计和完成缺少或空值的特征.\n",
    "我们将首先为 Age（年龄）特征执行此操作.\n",
    "\n",
    "我们可以考虑三种方法来完整化一个数值连续的特征.\n",
    "\n",
    "1.简单的方法是在平均值和 [标准偏差](https://en.wikipedia.org/wiki/Standard_deviation) 之间生成随机数.\n",
    "\n",
    "2.更准确地猜测缺失值的方法是使用其他相关特征. 在我们的例子中, 我们注意到 Age（年龄）, Sex（性别）和 Pclass 之间的相关性.猜测年龄值，使用Pclass和Gender特征组合中Age的[中位数](https://en.wikipedia.org/wiki/Median). 因此, Pclass=1 和 Gender=0，Pclass=1 和 Gender=1 的年龄中位数等等...\n",
    "\n",
    "3.结合方法 1 和 2. 因此. 不要根据中位数来猜测年龄值, 而应根据 Pclass 和 Sex 组合, 使用平均数和标准差之间的随机数.\n",
    "\n",
    "方法 1 和 3 将在我们的模型中引入随机噪声. 多次执行的结果可能会有所不同. 我们更喜欢方法 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "c311c43d-6554-3b52-8ef8-533ca08b2f68",
    "_uuid": "3348a1cbf0c7e33e5b576f13a4aa881b103fd628",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xece2e10>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4sLHbJ58B9p22/QG8B/g6cDPwceCh07gvFhijOT8Bk/k+GdM4ct47AUqS1KBJeQtAkiSN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\nNej/B+1qWsqKF3O5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xece2748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n",
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a4f166f9-f5f9-1819-66c3-d89dd5b0d8ff",
    "_uuid": "d3ef5e2578429c297e6f7eefa7c769a7cd59c564"
   },
   "source": [
    "让我们开始准备一个空数组, 以包含基于 Pclass x Gender 组合以猜测 Age 值."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "9299523c-dcf1-fb00-e52f-e2fb860a3920",
    "_uuid": "f28a31b2520fa6cb7684868464f96c8ed4f5897b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ec9fed37-16b1-5518-4fa8-0a7f579dbc82",
    "_uuid": "4e6dab5115d513ce035864f6693a87c26897e47b"
   },
   "source": [
    "现在我们迭代 Sex（0 或 1）和 Pclass（1, 2, 3）来计算 6 个组合的 Age 的猜测值."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "a4015dfa-a0ab-65bc-0cbe-efecf1eb2569",
    "_uuid": "30fe7bd956f87da84afb5bc9bed792cc6dd0831f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "\n",
    "            # age_mean = guess_df.mean()\n",
    "            # age_std = guess_df.std()\n",
    "            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "\n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dbe0a8bf-40bc-c581-e10e-76f07b3b71d4",
    "_uuid": "ed080ed0742604812ae933aa2dcd9ffe0f752abf"
   },
   "source": [
    "让我们创建年龄段并确定与 Survived 的相关性."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "725d1c84-6323-9d70-5812-baf9994d3aa1",
    "_uuid": "572a2e9a8a268159849d89ed7ec5902a305b0d14"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ba4be3a0-e524-9c57-fbec-c8ecc5cde5c6",
    "_uuid": "ef3552778390d811d6105a2dd0c2b81e77407283"
   },
   "source": [
    "让我们使用年龄段的顺序值来替换 Aage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "797b986d-2c45-a9ee-e5b5-088de817c8b2",
    "_uuid": "f7661c3d74c059e1d3664fb502690cb9bfd27339"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age']\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "004568b6-dd9a-ff89-43d5-13d4e9370b1d",
    "_uuid": "76893d49eff6e15e05569448e030358486870409"
   },
   "source": [
    "我们删除 AgeBand 特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "875e55d4-51b0-5061-b72c-8a23946133a3",
    "_uuid": "0726fd8a5151812ae337d9ee80bd4fd1c08e592a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1c237b76-d7ac-098f-0156-480a838a64a9",
    "_uuid": "9c0f835b1ce116035e125c9f33b6b529bafe5f77"
   },
   "source": [
    "### 结合现有特征创建新特征\n",
    "\n",
    "我们可以为 Parch 和 SibSp 结合的 FamilySize 创建一个新的特征.\n",
    "这将使我们能够从我们的数据集中删除 Parch 和 SibSp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "7e6c04ed-cfaa-3139-4378-574fd095d6ba",
    "_uuid": "d532b86557317521f6cdfab2b05a366e8272738c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "842188e6-acf8-2476-ccec-9e3451e4fa86",
    "_uuid": "fe2ce46b514c097ac6d9b55b01ec5cca1e3cceed"
   },
   "source": [
    "我们可以创建另一个名为 IsAlone 特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "5c778c69-a9ae-1b6b-44fe-a0898d07be7a",
    "_uuid": "3251ed4ebd08403d7dab1b7a1ce7088f63f1f1b9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e6b87c09-e7b2-f098-5b04-4360080d26bc",
    "_uuid": "0eea444a8e105e416b6786fbc98b4ed6855c50f0"
   },
   "source": [
    "让我们放弃 Parch, SibSp 和 FamilySize 特征, 转而使用 IsAlone 特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "74ee56a6-7357-f3bc-b605-6c41f8aa6566",
    "_uuid": "56fadcd70a1cd2ac2110809c2279fd0b60204174"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f890b730-b1fe-919e-fb07-352fbd7edd44",
    "_uuid": "512eac83ac2ba3117b275e48518596352a4ca0ff"
   },
   "source": [
    "我们还可以创建一个结合 Pclass 和 Age 的人造特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "305402aa-1ea1-c245-c367-056eef8fe453",
    "_uuid": "d8ec9d2b6d57f680a248275d032a1735f2ee5f1a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "\n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "13292c1b-020d-d9aa-525c-941331bb996a",
    "_uuid": "9ffa98cb5aec630439e16831382f8456e0db4ce7"
   },
   "source": [
    "### 完整化分类特征\n",
    "\n",
    "Embarked（出发港）特征有 S, Q, C 三个基于出发港口的值.\n",
    "我们的训练集有两个丢失值.\n",
    "我们简单的使用最常发生的情况来填充它."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "bf351113-9b7f-ef56-7211-e8dd00665b18",
    "_uuid": "9c05cd517bc2727bfba3b88fc283f9b2f59cb51f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "51c21fcc-f066-cd80-18c8-3d140be6cbae",
    "_uuid": "f820e03ca066443667a164be135a2d2217958d75"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f6acf7b2-0db3-e583-de50-7e14b495de34",
    "_uuid": "b683a829d3a58ef016d38ba2146931c87241e4ec"
   },
   "source": [
    "### 转换分类特征为数值的\n",
    "\n",
    "我们现在可以通过创建一个新的数字港特征来转换 EmbarkedFill 特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "89a91d76-2cc0-9bbb-c5c5-3c9ecae33c66",
    "_uuid": "c9378a196d37cf745a6989f7de3bfec5dab80b35"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         1      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        1          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        1          6"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e3dfc817-e1c1-a274-a111-62c1c814cecf",
    "_uuid": "b6f1a6648c4cfcce63bba886403bae239413b912"
   },
   "source": [
    "### 快速完整化兵转换数值的特征\n",
    "\n",
    "现在，我们可以在测试数据集使用模式下为单个缺失值完整化票价特征, 以获取此特征最常出现的值. 我们用一行代码来完成.\n",
    "\n",
    "请注意, 我们并没有创建中间用的新特征, 也没有对相关性进行任何进一步的分析以猜测丢失的特征, 因为我们只替换单个值. 完成目标达到了模型算法对非空值操作的期望要求.\n",
    "\n",
    "我们可能还想把票价四舍五入到小数点后两位, 因为它代表货币."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "3600cb86-cf5f-d87b-1b33-638dc8db1564",
    "_uuid": "b697a634d61798fe5b64d128b330eaae7e219eb2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "\n",
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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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2   7.8292         2      1        1          6\n",
       "1          893       3    1    2   7.0000         0      3        0          6\n",
       "2          894       2    0    3   9.6875         2      1        1          6\n",
       "3          895       3    0    1   8.6625         0      1        1          3\n",
       "4          896       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4b816bc7-d1fb-c02b-ed1d-ee34b819497d",
    "_uuid": "8ae1f2214210644f0670e9d16e4ab19a5c1e9f36"
   },
   "source": [
    "我们创建 FareBand 特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "0e9018b1-ced5-9999-8ce1-258a0952cbf2",
    "_uuid": "098b3896b60d1385b2297c16c405d33778e44993"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d65901a5-3684-6869-e904-5f1a7cce8a6d",
    "_uuid": "c6d38425e429ad5f7370174aee7db5fc28b9d0c1"
   },
   "source": [
    "将 Fare 特征转换为基于 FareBand 的顺序值."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "385f217a-4e00-76dc-1570-1de4eec0c29c",
    "_uuid": "bc6ba15392d78699e3ed46cf539d1251d3d64ec2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "27272bb9-3c64-4f9a-4a3b-54f02e1c8289",
    "_uuid": "daa1ef2745944d7029c5c1d4659c1e393f19013b"
   },
   "source": [
    "并且测试数据集也一样."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "d2334d33-4fe5-964d-beac-6aa620066e15",
    "_uuid": "550dccae1471b9c2f6d627ae48d376ca59a322c1"
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     1         2      1        1          6\n",
       "3          895       3    0    1     1         0      1        1          3\n",
       "4          896       3    1    1     1         0      3        0          3\n",
       "5          897       3    0    0     1         0      1        1          0\n",
       "6          898       3    1    1     0         2      2        1          3\n",
       "7          899       2    0    1     2         0      1        0          2\n",
       "8          900       3    1    1     0         1      3        1          3\n",
       "9          901       3    0    1     2         0      1        0          3"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "69783c08-c8cc-a6ca-2a9a-5e75581c6d31",
    "_uuid": "d5bf81957541c7ce9dcb2f25210f1bb9fe2f0602"
   },
   "source": [
    "## 模型, 预测和解决方案\n",
    "\n",
    "现在我们准备训练模型并通过训练得到的模型预测结果。有60多种用于预测的模型可供选择。我们必须了解问题的类型和解决方案的要求，将模型数量缩小到少数几个。我们的问题是分类和回归问题，因为需要确定输出（生存与否）与其他变量或特征（性别，年龄，港口...）之间的关系。此外，我们的问题应该属于监督学习，因为我们用已知类别的数据集来训练我们的模型。有了监督学习、分类和回归这两个标准，我们可以将模型选择的范围缩小到几个。这些包括：\n",
    "- Logistic回归\n",
    "- KNN或K—近邻\n",
    "- 支持向量机\n",
    "- 朴素贝叶斯分类器\n",
    "- 决策树\n",
    "- 随机森林\n",
    "- 感知器\n",
    "- 人工神经网络\n",
    "- 相关向量机\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "0acf54f9-6cf5-24b5-72d9-29b30052823a",
    "_uuid": "2ba5475a0389b56ceb1ddcb5b1ed927107e80fe8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "579bc004-926a-bcfe-e9bb-c8df83356876",
    "_uuid": "dcd0657cf810fe62e145a86ba7cdc5c1f7370e7a"
   },
   "source": [
    "Logistic回归形式简单，易于建模，适合用于早期的工作流程。Logistics回归使用线性回归模型的预测结果去逼近真实标记的对数几率，形式为参数化的Logistics分布。参考维基百科[Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).\n",
    "\n",
    "注意模型产生的“置信度评分”是基于训练集的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "0edd9322-db0b-9c37-172d-a3a4f8dec229",
    "_uuid": "f0b92b7d35145c43a11ef16d4be0d257c616fb37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.359999999999999"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3af439ae-1f04-9236-cdc2-ec8170a0d4ee",
    "_uuid": "461f8f4d266fb785bd3f29fa0aa9fd47353a4053"
   },
   "source": [
    "我们可以使用Logistic回归来验证我们之前对特征的创建所做的假设。这可以通过计算决策函数中的特征的系数来完成。\n",
    "\n",
    "系数为正说明该特征增加了结果的对数几率（因而增加了概率），系数为负说明该特征降低了结果的对数几率（从而降低了概率）\n",
    "\n",
    "- Sex特征有最高的正系数，意味着当Sex从男（0）变成女（1）时，Survived = 1的概率增加最多。\n",
    "- 相反地，随着Pclass特征的增加，Survived = 1的概率减少的最多。\n",
    "- Age * Class是一个很好的人造特征，因为它与Survived具有次高的负相关性。\n",
    "- Title特征有第二高的正相关系数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "e545d5aa-4767-7a41-5799-a4c5e529ce72",
    "_uuid": "d13fa4e9617cc61c8801689fd4d9e5470510d7ad"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.201527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.398234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.287164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.261762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.129140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.085150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.311199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.749006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.201527\n",
       "5      Title     0.398234\n",
       "2        Age     0.287164\n",
       "4   Embarked     0.261762\n",
       "6    IsAlone     0.129140\n",
       "3       Fare    -0.085150\n",
       "7  Age*Class    -0.311199\n",
       "0     Pclass    -0.749006"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ac041064-1693-8584-156b-66674117e4d0",
    "_uuid": "07a0a0f3a820c9d4ca0472d1b9ec05fa822d3479"
   },
   "source": [
    "接下来，我们使用支持向量机（SVM）模型。支持向量机是一个监督学习模型，它使用相关学习算法来分析数据，可以用于分类和回归问题。在二元分类的情况下，SVM算法建立一个模型，去找两类训练样本“正中间”的划分超平面，因为该划分超平面对训练样本局部扰动的“容忍性”最好。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).\n",
    "\n",
    "注意SVM模型生成的“置信度评分”高于Logistics回归模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "7a63bf04-a410-9c81-5310-bdef7963298f",
    "_uuid": "32a425989ee9cf681fad51c867ce3c76126f5b05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.840000000000003"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "172a6286-d495-5ac4-1a9c-5b77b74ca6d2",
    "_uuid": "0075b5fb532a249c701efa7ef84b2f52c9f29776"
   },
   "source": [
    "在模式识别中，k-近邻算法（简称k-NN）是一种用于分类和回归的无参数方法。测试样本找出训练集中与其最靠近的k个训练样本，选择这k个样本中出现最多的类别标记作为预测结果（k是一个正整数，通常很小）。如果k = 1，则该对象的类别和最近邻样本的类别一致。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\n",
    "\n",
    "KNN的“置信度评分”比Logistics回归好，但比SVM差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "ca14ae53-f05e-eb73-201c-064d7c3ed610",
    "_uuid": "f65598719a7e411ec09f6665d98f86e3e26a1f85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.739999999999995"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "810f723d-2313-8dfd-e3e2-26673b9caa90",
    "_uuid": "c1e80aa85d47f1076aa3d0628a37d903b1959ad4"
   },
   "source": [
    "在机器学习中，朴素贝叶斯分类器是一个基于所有特征互相独立的贝叶斯理论的简单概率分类器。朴素贝叶斯分类器具有高度可扩展性，在学习过程中需要大量的线性特征作为参数。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).\n",
    "\n",
    "该模型生成的“置信度评分”是目前模型中最低的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "50378071-7043-ed8d-a782-70c947520dae",
    "_uuid": "db060b792effa86c5483bf02786b8a69bb043fd5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.280000000000001"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1e286e19-b714-385a-fcfa-8cf5ec19956a",
    "_uuid": "c5f397f24dda3a6181708bee43314f6f316d1328"
   },
   "source": [
    "感知器是用于二元分类器的监督学习的算法（可以决定包含一个向量的输入是否属于某个类别）。它是一种线性分类器，即一种分类算法，通过一个线性预测函数将一组权重与特征向量组合来进行预测。该算法允许在线学习，因为它在一次迭代中只处理一个训练集中的元素。 参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Perceptron)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "ccc22a86-b7cb-c2dd-74bd-53b218d6ed0d",
    "_uuid": "4ae3698170341015098b1fcc4b716bbee4b01f54"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "78.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "a4d56857-9432-55bb-14c0-52ebeb64d198",
    "_uuid": "a7667ed1e8c4f753d0c8a04111beee9526f3e0e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.120000000000005"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear SVC\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "dc98ed72-3aeb-861f-804d-b6e3d178bf4b",
    "_uuid": "4d0cc9dd1855a0e8c2206a7ae53f2aa5c35b87b3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80.019999999999996"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bae7f8d7-9da0-f4fd-bdb1-d97e719a18d7",
    "_uuid": "5e191ae0e5c2fad6c4601d792cbc3d7b71097822"
   },
   "source": [
    "该模型使用决策树作为预测模型，将特征（树的分支）映射到决策结果（树的叶结点）。目标变量是有限的一组值的树称为分类树; 在这些树结构中，叶结点对应于决策结果，其他每个结点对应于一个属性测试，每个结点包含的样本集合根据属性测试的结果被划分到子结点中。目标变量可以取连续值（通常是实数）的决策树称为回归树。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).\n",
    "\n",
    "该模型的“置信度评分”是目前模型中最高的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "dd85f2b7-ace2-0306-b4ec-79c68cd3fea0",
    "_uuid": "acc5910f9900f1404b6fcdba93dd28fdda0766b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.760000000000005"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "85693668-0cd5-4319-7768-eddb62d2b7d0",
    "_uuid": "0c37a62dd5b0c6e9a6f644d45a92eb3851bc2991"
   },
   "source": [
    "随机森林是最流行的模型之一。随机森林或随机决策树森林是一种用于分类，回归或其他任务的集成学习模型，它通过在训练时构造大量的决策树（n_estimators = 100），再使用某种策略将这些“个体学习器”结合起来。参考维基百科。[Wikipedia](https://en.wikipedia.org/wiki/Random_forest).\n",
    "\n",
    "该模型的“置信度评分”是目前模型中最高的。我们决定使用这个模型的输出（Y_pred）来作为竞赛结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "f0694a8e-b618-8ed9-6f0d-8c6fba2c4567",
    "_uuid": "a3a92337489f7f75e233d7a5d645cdbf791071e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.760000000000005"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f6c9eef8-83dd-581c-2d8e-ce932fe3a44d",
    "_uuid": "24d40c30b491d0d109908035ed86f0929860dd5e"
   },
   "source": [
    "### 模型评估\n",
    "\n",
    "现在, 我们可以对所有模型进行评估, 为我们的问题选择最好的模型。\n",
    "虽然决策树和随机森林评分相同, 但我们选择使用随机森林，因为随机森林会校正决策树“过拟合”的缺点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "1f3cebe0-31af-70b2-1ce4-0fd406bcdfc6",
    "_uuid": "79536b2878fa12ceaf3648bfd5bb5de63b903709"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>80.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>80.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>72.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  86.76\n",
       "8               Decision Tree  86.76\n",
       "1                         KNN  84.74\n",
       "0     Support Vector Machines  83.84\n",
       "2         Logistic Regression  80.36\n",
       "6  Stochastic Gradient Decent  80.02\n",
       "7                  Linear SVC  79.12\n",
       "5                  Perceptron  78.00\n",
       "4                 Naive Bayes  72.28"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "28854d36-051f-3ef0-5535-fa5ba6a9bef7",
    "_uuid": "a2cda3bdd06c9b6a0cb2c02ca276c049865108fb",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "# submission.to_csv('../output/submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fcfc8d9f-e955-cf70-5843-1fb764c54699",
    "_uuid": "b8e1264e98af00d119e07a776643e6ce08b59666"
   },
   "source": [
    "我们提交给竞赛网站 Kaggle 的比赛结果在 6,082 个参赛作品中获得 3883 名.\n",
    "当竞赛正在进行时，这个结果是具有指导意义的.\n",
    "这个结果只占提交数据集的一部分.\n",
    "对我们的第一次尝试是不错的.\n",
    "欢迎任何提高我们的分数的建议."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "aeec9210-f9d8-cd7c-c4cf-a87376d5f693",
    "_uuid": "b4758c6c3b2f72da72397cf2f82bc0b92bec3c5b"
   },
   "source": [
    "## 参考文献\n",
    "\n",
    "该手册是基于完成解决《泰坦尼克号》竞赛和其它来源的伟大工作而创建的.\n",
    "\n",
    "- [泰坦尼克号之旅](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)\n",
    "- [ Pandas 入门指南: Kaggle 的泰坦尼克号竞赛](https://www.kaggle.com/c/titanic/details/getting-started-with-random-forests)\n",
    "- [泰坦尼克号的最佳处理分类器](https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier)"
   ]
  }
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