titanic_survival_exploration.ipynb

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    "# 机器学习工程师纳米学位\n",
    "## 入门\n",
    "## 项目 0: 预测泰坦尼克号乘客生还率\n",
    "\n",
    "1912年，泰坦尼克号在第一次航行中就与冰山相撞沉没，导致了大部分乘客和船员身亡。在这个入门项目中，我们将探索部分泰坦尼克号旅客名单，来确定哪些特征可以最好地预测一个人是否会生还。为了完成这个项目，你将需要实现几个基于条件的预测并回答下面的问题。我们将根据代码的完成度和对问题的解答来对你提交的项目的进行评估。 \n",
    "\n",
    "> **提示**：这样的文字将会指导你如何使用 iPython Notebook 来完成项目。"
   ]
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   "source": [
    "点击[这里](https://github.com/udacity/machine-learning/blob/master/projects/titanic_survival_exploration/Titanic_Survival_Exploration.ipynb)查看本文件的英文版本。"
   ]
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   "source": [
    "# 开始\n",
    "\n",
    "当我们开始处理泰坦尼克号乘客数据时，会先导入我们需要的功能模块以及将数据加载到 `pandas` DataFrame。运行下面区域中的代码加载数据，并使用 `.head()` 函数显示前几项乘客数据。 \n",
    "\n",
    "> **提示**：你可以通过单击代码区域，然后使用键盘快捷键 **Shift+Enter** 或 **Shift+ Return** 来运行代码。或者在选择代码后使用**播放**（run cell）按钮执行代码。像这样的 MarkDown 文本可以通过双击编辑，并使用这些相同的快捷键保存。[Markdown](http://daringfireball.net/projects/markdown/syntax) 允许你编写易读的纯文本并且可以转换为 HTML。"
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <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",
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       "      <td>53.1000</td>\n",
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       "      <td>Allen, Mr. William Henry</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",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# RMS Titanic data visualization code \n",
    "# 数据可视化代码\n",
    "from titanic_visualizations import survival_stats\n",
    "from IPython.display import display\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the dataset \n",
    "# 加载数据集\n",
    "in_file = 'titanic_data.csv'\n",
    "full_data = pd.read_csv(in_file)\n",
    "\n",
    "# Print the first few entries of the RMS Titanic data \n",
    "# 显示数据列表中的前几项乘客数据\n",
    "display(full_data.head())"
   ]
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   "source": [
    "从泰坦尼克号的数据样本中，我们可以看到船上每位旅客的特征\n",
    "\n",
    "- **Survived**：是否存活（0代表否，1代表是）\n",
    "- **Pclass**：社会阶级（1代表上层阶级，2代表中层阶级，3代表底层阶级）\n",
    "- **Name**：船上乘客的名字\n",
    "- **Sex**：船上乘客的性别\n",
    "- **Age**:船上乘客的年龄（可能存在 `NaN`）\n",
    "- **SibSp**：乘客在船上的兄弟姐妹和配偶的数量\n",
    "- **Parch**：乘客在船上的父母以及小孩的数量\n",
    "- **Ticket**：乘客船票的编号\n",
    "- **Fare**：乘客为船票支付的费用\n",
    "- **Cabin**：乘客所在船舱的编号（可能存在 `NaN`）\n",
    "- **Embarked**：乘客上船的港口（C 代表从 Cherbourg 登船，Q 代表从 Queenstown 登船，S 代表从 Southampton 登船）\n",
    "\n",
    "因为我们感兴趣的是每个乘客或船员是否在事故中活了下来。可以将 **Survived** 这一特征从这个数据集移除，并且用一个单独的变量 `outcomes` 来存储。它也做为我们要预测的目标。\n",
    "\n",
    "运行该代码，从数据集中移除 **Survived** 这个特征，并将它存储在变量 `outcomes` 中。"
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       "      <th>Pclass</th>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
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   "source": [
    "# Store the 'Survived' feature in a new variable and remove it from the dataset \n",
    "# 从数据集中移除 'Survived' 这个特征，并将它存储在一个新的变量中。\n",
    "outcomes = full_data['Survived']\n",
    "data = full_data.drop('Survived', axis = 1)\n",
    "\n",
    "# Show the new dataset with 'Survived' removed\n",
    "# 显示已移除 'Survived' 特征的数据集\n",
    "display(data.head())"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个例子展示了如何将泰坦尼克号的 **Survived** 数据从 DataFrame 移除。注意到 `data`（乘客数据）和 `outcomes` （是否存活）现在已经匹配好。这意味着对于任何乘客的 `data.loc[i]` 都有对应的存活的结果 `outcome[i]`。\n",
    "\n",
    "为了验证我们预测的结果，我们需要一个标准来给我们的预测打分。因为我们最感兴趣的是我们预测的**准确率**，既正确预测乘客存活的比例。运行下面的代码来创建我们的 `accuracy_score` 函数以对前五名乘客的预测来做测试。\n",
    "\n",
    "**思考题**：从第六个乘客算起，如果我们预测他们全部都存活，你觉得我们预测的准确率是多少？ 60%"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 60.00%.\n"
     ]
    }
   ],
   "source": [
    "def accuracy_score(truth, pred):\n",
    "    \"\"\" Returns accuracy score for input truth and predictions. \"\"\"\n",
    "    \n",
    "    # Ensure that the number of predictions matches number of outcomes\n",
    "    # 确保预测的数量与结果的数量一致\n",
    "    if len(truth) == len(pred): \n",
    "        \n",
    "        # Calculate and return the accuracy as a percent\n",
    "        # 计算预测准确率（百分比）\n",
    "        return \"Predictions have an accuracy of {:.2f}%.\".format((truth == pred).mean()*100)\n",
    "    \n",
    "    else:\n",
    "        return \"Number of predictions does not match number of outcomes!\"\n",
    "    \n",
    "# Test the 'accuracy_score' function\n",
    "# 测试 'accuracy_score' 函数\n",
    "predictions = pd.Series(np.ones(5, dtype = int))\n",
    "print accuracy_score(outcomes[:5], predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **提示**：如果你保存 iPython Notebook，代码运行的输出也将被保存。但是，一旦你重新打开项目，你的工作区将会被重置。请确保每次都从上次离开的地方运行代码来重新生成变量和函数。\n",
    "\n",
    "# 预测\n",
    "\n",
    "如果我们要预测泰坦尼克号上的乘客是否存活，但是我们又对他们一无所知，那么最好的预测就是船上的人无一幸免。这是因为，我们可以假定当船沉没的时候大多数乘客都遇难了。下面的 `predictions_0` 函数就预测船上的乘客全部遇难。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "collapsed": false
   },
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   "source": [
    "def predictions_0(data):\n",
    "    \"\"\" Model with no features. Always predicts a passenger did not survive. \"\"\"\n",
    "\n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        # Predict the survival of 'passenger'\n",
    "        # 预测 'passenger' 的生还率\n",
    "        predictions.append(0)\n",
    "    \n",
    "    # Return our predictions\n",
    "    # 返回预测结果\n",
    "    return pd.Series(predictions)\n",
    "\n",
    "# Make the predictions\n",
    "# 进行预测\n",
    "predictions = predictions_0(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题1\n",
    "\n",
    "对比真实的泰坦尼克号的数据，如果我们做一个所有乘客都没有存活的预测，你认为这个预测的准确率能达到多少？\n",
    "\n",
    "**提示**：运行下面的代码来查看预测的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 61.62%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:** *Predictions have an accuracy of 61.62%.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "我们可以使用 `survival_stats` 函数来看看 **Sex** 这一特征对乘客的存活率有多大影响。这个函数定义在名为 `titanic_visualizations.py` 的 Python 脚本文件中，我们的项目提供了这个文件。传递给函数的前两个参数分别是泰坦尼克号的乘客数据和乘客的 生还结果。第三个参数表明我们会依据哪个特征来绘制图形。\n",
    "\n",
    "运行下面的代码绘制出依据乘客性别计算存活率的柱形图。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    {
     "data": {
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h7TXaZptt2HfffTnppJPo168fe+65JxdccAHz5s2rqj3IF3V96lOfWqi8oaGB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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107da4f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察泰坦尼克号上乘客存活的数据统计，我们可以发现大部分男性乘客在船沉没的时候都遇难了。相反的，大部分女性乘客都在事故中**生还**。让我们在先前推断的基础上继续创建：如果乘客是男性，那么我们就预测他们遇难；如果乘客是女性，那么我们预测他们在事故中活了下来。\n",
    "\n",
    "将下面的代码补充完整，让函数可以进行正确预测。  \n",
    "\n",
    "**提示**：您可以用访问 dictionary（字典）的方法来访问船上乘客的每个特征对应的值。例如， `passenger['Sex']` 返回乘客的性别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def predictions_1(data):\n",
    "    \"\"\" Model with one feature: \n",
    "            - Predict a passenger survived if they are female. \"\"\"\n",
    "    \n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        # Remove the 'pass' statement below \n",
    "        # 移除下方的 'pass' 声明\n",
    "        # and write your prediction conditions here\n",
    "        # 输入你自己的预测条件\n",
    "        if passenger['Sex'] == 'male':\n",
    "            predictions.append(0);\n",
    "        else:\n",
    "            predictions.append(1)\n",
    "    \n",
    "    # Return our predictions\n",
    "    # 返回预测结果\n",
    "    return pd.Series(predictions)\n",
    "\n",
    "# Make the predictions\n",
    "# 进行预测\n",
    "predictions = predictions_1(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题2\n",
    "当我们预测船上女性乘客全部存活，而剩下的人全部遇难，那么我们预测的准确率会达到多少？\n",
    "\n",
    "**提示**：运行下面的代码来查看我们预测的准确率。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 78.68%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答**: *Predictions have an accuracy of 78.68%.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "仅仅使用乘客性别（Sex）这一特征，我们预测的准确性就有了明显的提高。现在再看一下使用额外的特征能否更进一步提升我们的预测准确度。例如，综合考虑所有在泰坦尼克号上的男性乘客：我们是否找到这些乘客中的一个子集，他们的存活概率较高。让我们再次使用 `survival_stats` 函数来看看每位男性乘客的年龄（Age）。这一次，我们将使用第四个参数来限定柱形图中只有男性乘客。\n",
    "\n",
    "运行下面这段代码，把男性基于年龄的生存结果绘制出来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jjjqKCy+8kEceeYRu3bo1Ws/70UcfNdheO9NFse23354NN9yQKVOmMGLECKZP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a9ccf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Age', [\"Sex == 'male'\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "仔细观察泰坦尼克号存活的数据统计，在船沉没的时候，大部分小于10岁的男孩都活着，而大多数10岁以上的男性都随着船的沉没而**遇难**。让我们继续在先前预测的基础上构建：如果乘客是女性，那么我们就预测她们全部存活；如果乘客是男性并且小于10岁，我们也会预测他们全部存活；所有其它我们就预测他们都没有幸存。  \n",
    "\n",
    "将下面缺失的代码补充完整，让我们的函数可以实现预测。  \n",
    "**提示**: 您可以用之前 `predictions_1` 的代码作为开始来修改代码，实现新的预测函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def predictions_2(data):\n",
    "    \"\"\" Model with two features: \n",
    "            - Predict a passenger survived if they are female.\n",
    "            - Predict a passenger survived if they are male and younger than 10. \"\"\"\n",
    "    \n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        # Remove the 'pass' statement below \n",
    "        # 移除下方的 'pass' 声明\n",
    "        # and write your prediction conditions here\n",
    "        # 输入你自己的预测条件\n",
    "        if passenger['Sex'] == 'female' or passenger['Age'] < 10:\n",
    "            predictions.append(1);\n",
    "        else:\n",
    "            predictions.append(0);\n",
    "    \n",
    "    # Return our predictions\n",
    "    # 返回预测结果\n",
    "    return pd.Series(predictions)\n",
    "\n",
    "# Make the predictions\n",
    "# 进行预测\n",
    "predictions = predictions_2(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题3\n",
    "\n",
    "当预测所有女性以及小于10岁的男性都存活的时候，预测的准确率会达到多少？\n",
    "\n",
    "**提示：**运行下面的代码来查看预测的准确率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 79.35%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答**: *Predictions have an accuracy of 79.35%.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "***\n",
    "添加年龄（Age）特征与性别（Sex）的结合比单独使用性别（Sex）也提高了不少准确度。现在该你来做预测了：找到一系列的特征和条件来对数据进行划分，使得预测结果提高到80%以上。这可能需要多个特性和多个层次的条件语句才会成功。你可以在不同的条件下多次使用相同的特征。**Pclass**，**Sex**，**Age**，**SibSp** 和 **Parch** 是建议尝试使用的特征。   \n",
    "\n",
    "使用 `survival_stats` 函数来观测泰坦尼克号上乘客存活的数据统计。  \n",
    "**提示:** 要使用多个过滤条件，把每一个条件放在一个列表里作为最后一个参数传递进去。例如: `[\"Sex == 'male'\", \"Age < 18\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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MmTMBP2rsnKOgoKDcY1auXBnzdoYMGcKCBQvYsmUL+fn5tG3blp49e1b5uIED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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b428210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survival_stats(data, outcomes, 'Age', [\"Sex == 'female'\",\"Pclass == 3\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当查看和研究了图形化的泰坦尼克号上乘客的数据统计后，请补全下面这段代码中缺失的部分，使得函数可以返回你的预测。   \n",
    "在到达最终的预测模型前请确保记录你尝试过的各种特征和条件。   \n",
    "**提示:** 您可以用之前 `predictions_2` 的代码作为开始来修改代码，实现新的预测函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def predictions_3(data):\n",
    "    \"\"\" Model with multiple features. Makes a prediction with an accuracy of at least 80%. \"\"\"\n",
    "    \n",
    "    predictions = []\n",
    "    for _, passenger in data.iterrows():\n",
    "        \n",
    "        # Remove the 'pass' statement below \n",
    "        # and write your prediction conditions here\n",
    "        if passenger['Sex'] == 'female' or passenger['Age'] < 10:\n",
    "            if passenger['Age'] > 40 and passenger['Age'] < 50 and  passenger['Pclass'] == 3:\n",
    "                predictions.append(0);\n",
    "            else:\n",
    "                predictions.append(1);\n",
    "        else:\n",
    "            predictions.append(0);\n",
    "    \n",
    "    # Return our predictions\n",
    "    return pd.Series(predictions)\n",
    "\n",
    "# Make the predictions\n",
    "predictions = predictions_3(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论\n",
    "\n",
    "请描述你实现80%准确度的预测模型所经历的步骤。您观察过哪些特征？某些特性是否比其他特征更有帮助？你用了什么条件来预测生还结果？你最终的预测的准确率是多少？\n",
    "**提示:**运行下面的代码来查看你的预测准确度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions have an accuracy of 80.25%.\n"
     ]
    }
   ],
   "source": [
    "print accuracy_score(outcomes, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答**: *Predictions have an accuracy of 80.25%.\n",
    "主要是Age、Pclass特征，Age大于一定值的特征不明显，Pclass更明显；再将Pclass和Age、Sex一起做对比，便有了结果*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 结论\n",
    "\n",
    "经过了数次对数据的探索和分类，你创建了一个预测泰坦尼克号乘客存活率的有用的算法。在这个项目中你手动地实现了一个简单的机器学习模型——决策树（decision tree）。决策树每次按照一个特征把数据分割成越来越小的群组（被称为 *nodes*）。每次数据的一个子集被分出来，如果分割结果的子集中的数据比之前更同质（包含近似的标签），我们的预测也就更加准确。电脑来帮助我们做这件事会比手动做更彻底，更精确。[这个链接](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)提供了另一个使用决策树做机器学习入门的例子。  \n",
    "\n",
    "决策树是许多**监督学习**算法中的一种。在监督学习中，我们关心的是使用数据的特征并根据数据的结果标签进行预测或建模。也就是说，每一组数据都有一个真正的结果值，不论是像泰坦尼克号生存数据集一样的标签，或者是连续的房价预测。\n",
    "\n",
    "### 问题5\n",
    "\n",
    "想象一个真实世界中应用监督学习的场景，你期望预测的结果是什么？举出两个在这个场景中能够帮助你进行预测的数据集中的特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**回答**: *推荐喜欢听的歌。根据听歌的种类、时间进行预测推荐*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **注意**: 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出**File -> Download as -> HTML (.html)** 把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。"
   ]
  },
  {
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    "---\n",
    "翻译：毛礼建 ｜ 校译：黄强 ｜ 审译：曹晨巍"
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