开胃小菜-铁达尼机器学习.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gkzHOCwtnLtP"
   },
   "source": [
    "## 运行环境\n",
    "\n",
    "请在 Colab 环境中使用该文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4aLdKvqh8J-r",
    "outputId": "41cee679-90fa-4042-a3e3-8446a8714771"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The nltk version is 3.7.\n",
      "The scikit-learn version is 1.1.1.\n",
      "1.4.4\n",
      "1.21.5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "import sklearn\n",
    "\n",
    "print('The nltk version is {}.'.format(nltk.__version__))\n",
    "print('The scikit-learn version is {}.'.format(sklearn.__version__))\n",
    "print(pd.__version__)\n",
    "print(np.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QRStgzSaVfSe"
   },
   "source": [
    "## 泰坦尼克号问题之背景\n",
    "\n",
    "* 就是那个大家都熟悉的『Jack and Rose』的故事，豪华游艇倒了，大家都惊恐逃生，可是救生艇的数量有限，无法人人都有，副船长发话了『lady and kid first！』，所以**是否获救其实并非随机，而是基于一些背景有rank先后的**。\n",
    "\n",
    "* 训练和测试数据是一些乘客的个人信息以及存活状况，要尝试根据它生成合适的模型并**预测其他人的存活状况**。\n",
    "\n",
    "* 对，这是一个**二分类**问题，是我们之前讨论的**logistic regression**所能处理的范畴。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2G04DVG6V9Qr"
   },
   "source": [
    "接触过Kaggle的同学们可能知道这个问题，也可能知道RandomForest和SVM等等算法，甚至还对多个模型做过融合，取得过非常好的结果，那maybe这篇文章并不是针对你的，你可以自行略过。\n",
    "\n",
    "我们因为之前只介绍了Logistic Regression这一种分类算法。所以本次的问题解决过程和优化思路，都集中在这种算法上。其余的方法可能我们之后的文章里会提到。\n",
    "\n",
    "说点个人的观点。不一定正确。  \n",
    "\n",
    "**『解决一个问题的方法和思路不止一种』**  \n",
    "**『没有所谓的机器学习算法优劣，也没有绝对高性能的机器学习算法，只有在特定的场景、数据和特征下更合适的机器学习算法。』**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "izla2wGoWF0E"
   },
   "source": [
    "## 怎么做？\n",
    "\n",
    "手把手教程马上就来，先来两条我看到的，觉得很重要的经验。\n",
    "\n",
    "1. 印象中Andrew Ng老师似乎在coursera上说过，应用机器学习，千万不要一上来就试图做到完美，**先撸一个baseline的model出来，再进行后续的分析步骤，一步步提高**，所谓后续步骤可能包括『分析model现在的状态(欠/过拟合)，分析我们使用的feature的作用大小，进行feature selection，以及我们模型下的bad case和产生的原因』等等。\n",
    "\n",
    "2. Kaggle上的大神们，也分享过一些experience，说几条我记得的哈：\n",
    "* **『对数据的认识太重要了！』**\n",
    "* **『数据中的特殊点/离群点的分析和处理太重要了！』**\n",
    "* **『特征工程(feature engineering)太重要了！在很多Kaggle的场景下，甚至比model本身还要重要』**\n",
    "* **『要做模型融合(model ensemble)！』**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qJGNMZtYWWPt"
   },
   "source": [
    "## 初探数据\n",
    "\n",
    "先看看我们的数据，长什么样吧。在Data下我们train.csv和test.csv两个文件，分别存着官方给的训练和测试数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "zDJmf1qeW3Hd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-04-11 19:02:10--  https://raw.githubusercontent.com/WangLaoShi/Kaggle_Titanic/master/train.csv\r\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... ::, 0.0.0.0\r\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|::|:443... failed: Connection refused.\r\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|0.0.0.0|:443... failed: Connection refused.\r\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/WangLaoShi/Kaggle_Titanic/master/train.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "hzGvgitN9Eob"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-04-11 19:02:10--  https://raw.githubusercontent.com/WangLaoShi/Kaggle_Titanic/master/test.csv\r\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... ::, 0.0.0.0\r\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|::|:443... failed: Connection refused.\r\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|0.0.0.0|:443... failed: Connection refused.\r\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/WangLaoShi/Kaggle_Titanic/master/test.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EM8KkHn8XJKl"
   },
   "source": [
    "pandas是常用的python数据处理包，把csv文件读入成dataframe各式，我们在ipython notebook中，看到data_train如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 991
    },
    "id": "MV5zZN7iVIef",
    "outputId": "d952d454-ebba-4743-b957-c6f55f1f5eeb"
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/content/train.csv'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
      "Input \u001B[0;32mIn [6]\u001B[0m, in \u001B[0;36m<cell line: 5>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m \u001B[38;5;66;03m# 科学计算\u001B[39;00m\n\u001B[1;32m      3\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mpandas\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m Series,DataFrame\n\u001B[0;32m----> 5\u001B[0m data_train \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mread_csv\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m/content/train.csv\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m      6\u001B[0m data_train\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001B[0m, in \u001B[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m    305\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m>\u001B[39m num_allow_args:\n\u001B[1;32m    306\u001B[0m     warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[1;32m    307\u001B[0m         msg\u001B[38;5;241m.\u001B[39mformat(arguments\u001B[38;5;241m=\u001B[39marguments),\n\u001B[1;32m    308\u001B[0m         \u001B[38;5;167;01mFutureWarning\u001B[39;00m,\n\u001B[1;32m    309\u001B[0m         stacklevel\u001B[38;5;241m=\u001B[39mstacklevel,\n\u001B[1;32m    310\u001B[0m     )\n\u001B[0;32m--> 311\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py:678\u001B[0m, in \u001B[0;36mread_csv\u001B[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001B[0m\n\u001B[1;32m    663\u001B[0m kwds_defaults \u001B[38;5;241m=\u001B[39m _refine_defaults_read(\n\u001B[1;32m    664\u001B[0m     dialect,\n\u001B[1;32m    665\u001B[0m     delimiter,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    674\u001B[0m     defaults\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdelimiter\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m,\u001B[39m\u001B[38;5;124m\"\u001B[39m},\n\u001B[1;32m    675\u001B[0m )\n\u001B[1;32m    676\u001B[0m kwds\u001B[38;5;241m.\u001B[39mupdate(kwds_defaults)\n\u001B[0;32m--> 678\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_read\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py:575\u001B[0m, in \u001B[0;36m_read\u001B[0;34m(filepath_or_buffer, kwds)\u001B[0m\n\u001B[1;32m    572\u001B[0m _validate_names(kwds\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnames\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m))\n\u001B[1;32m    574\u001B[0m \u001B[38;5;66;03m# Create the parser.\u001B[39;00m\n\u001B[0;32m--> 575\u001B[0m parser \u001B[38;5;241m=\u001B[39m \u001B[43mTextFileReader\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfilepath_or_buffer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwds\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    577\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m chunksize \u001B[38;5;129;01mor\u001B[39;00m iterator:\n\u001B[1;32m    578\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m parser\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py:932\u001B[0m, in \u001B[0;36mTextFileReader.__init__\u001B[0;34m(self, f, engine, **kwds)\u001B[0m\n\u001B[1;32m    929\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moptions[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhas_index_names\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;241m=\u001B[39m kwds[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhas_index_names\u001B[39m\u001B[38;5;124m\"\u001B[39m]\n\u001B[1;32m    931\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles: IOHandles \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m--> 932\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_make_engine\u001B[49m\u001B[43m(\u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mengine\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1216\u001B[0m, in \u001B[0;36mTextFileReader._make_engine\u001B[0;34m(self, f, engine)\u001B[0m\n\u001B[1;32m   1212\u001B[0m     mode \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mrb\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m   1213\u001B[0m \u001B[38;5;66;03m# error: No overload variant of \"get_handle\" matches argument types\u001B[39;00m\n\u001B[1;32m   1214\u001B[0m \u001B[38;5;66;03m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001B[39;00m\n\u001B[1;32m   1215\u001B[0m \u001B[38;5;66;03m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001B[39;00m\n\u001B[0;32m-> 1216\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles \u001B[38;5;241m=\u001B[39m \u001B[43mget_handle\u001B[49m\u001B[43m(\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# type: ignore[call-overload]\u001B[39;49;00m\n\u001B[1;32m   1217\u001B[0m \u001B[43m    \u001B[49m\u001B[43mf\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1218\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1219\u001B[0m \u001B[43m    \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mencoding\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1220\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcompression\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mcompression\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1221\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmemory_map\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mmemory_map\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1222\u001B[0m \u001B[43m    \u001B[49m\u001B[43mis_text\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mis_text\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1223\u001B[0m \u001B[43m    \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mencoding_errors\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstrict\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1224\u001B[0m \u001B[43m    \u001B[49m\u001B[43mstorage_options\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mstorage_options\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1225\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1226\u001B[0m \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m   1227\u001B[0m f \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mhandles\u001B[38;5;241m.\u001B[39mhandle\n",
      "File \u001B[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py:786\u001B[0m, in \u001B[0;36mget_handle\u001B[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001B[0m\n\u001B[1;32m    781\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(handle, \u001B[38;5;28mstr\u001B[39m):\n\u001B[1;32m    782\u001B[0m     \u001B[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001B[39;00m\n\u001B[1;32m    783\u001B[0m     \u001B[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001B[39;00m\n\u001B[1;32m    784\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mencoding \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m ioargs\u001B[38;5;241m.\u001B[39mmode:\n\u001B[1;32m    785\u001B[0m         \u001B[38;5;66;03m# Encoding\u001B[39;00m\n\u001B[0;32m--> 786\u001B[0m         handle \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mopen\u001B[39;49m\u001B[43m(\u001B[49m\n\u001B[1;32m    787\u001B[0m \u001B[43m            \u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    788\u001B[0m \u001B[43m            \u001B[49m\u001B[43mioargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmode\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    789\u001B[0m \u001B[43m            \u001B[49m\u001B[43mencoding\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mioargs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mencoding\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    790\u001B[0m \u001B[43m            \u001B[49m\u001B[43merrors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43merrors\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    791\u001B[0m \u001B[43m            \u001B[49m\u001B[43mnewline\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    792\u001B[0m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    793\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    794\u001B[0m         \u001B[38;5;66;03m# Binary mode\u001B[39;00m\n\u001B[1;32m    795\u001B[0m         handle \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mopen\u001B[39m(handle, ioargs\u001B[38;5;241m.\u001B[39mmode)\n",
      "\u001B[0;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: '/content/train.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd # 数据分析\n",
    "import numpy as np # 科学计算\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "data_train = pd.read_csv(\"/content/train.csv\")\n",
    "data_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_badeDgHXQ0j"
   },
   "source": [
    "这就是典型的dataframe格式，如果你没接触过这种格式，完全没有关系，你就把它想象成Excel里面的列好了。  \n",
    "我们看到，总共有12列，其中Survived字段表示的是该乘客是否获救，其余都是乘客的个人信息，包括：\n",
    "\n",
    "* PassengerId => 乘客ID\n",
    "* Pclass => 乘客等级(1/2/3等舱位)\n",
    "* Name => 乘客姓名\n",
    "* Sex => 性别\n",
    "* Age => 年龄\n",
    "* SibSp => 堂兄弟/妹个数\n",
    "* Parch => 父母与小孩个数\n",
    "* Ticket => 船票信息\n",
    "* Fare => 票价\n",
    "* Cabin => 客舱\n",
    "* Embarked => 登船港口\n",
    "\n",
    "逐条往下看，要看完这么多条，眼睛都有一种要瞎的赶脚。好吧，我们让dataframe自己告诉我们一些信息，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "heStiT7tXTB7",
    "outputId": "5c3c2477-e3a6-47bc-846e-e6816ad54fc5"
   },
   "outputs": [],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K3ukhd2EXxj-"
   },
   "source": [
    "上面的数据说啥了？它告诉我们，训练数据中总共有891名乘客，但是很不幸，我们有些属性的数据不全，比如说：\n",
    "\n",
    "* Age（年龄）属性只有714名乘客有记录\n",
    "* Cabin（客舱）更是只有204名乘客是已知的\n",
    "\n",
    "似乎信息略少啊，想再瞄一眼具体数据数值情况呢？恩，我们用下列的方法，得到数值型数据的一些分布(因为有些属性，比如姓名，是文本型；而另外一些属性，比如登船港口，是类目型。这些我们用下面的函数是看不到的)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "XLyUr15wX4lY",
    "outputId": "9f88693e-29de-4007-d9df-8f0d9f2e0d3c"
   },
   "outputs": [],
   "source": [
    "data_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A-sJu8qPX_CV"
   },
   "source": [
    "我们从上面看到更进一步的什么信息呢？  \n",
    " mean字段告诉我们，\n",
    " * 大概0.383838的人最后获救了，\n",
    " * 2/3等舱的人数比1等舱要多，\n",
    " * 平均乘客年龄大概是29.7岁(计算这个时候会略掉无记录的)等等…"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6D9hNDr3YDVy"
   },
   "source": [
    "## 数据初步分析\n",
    "\n",
    "每个乘客都这么多属性，那我们咋知道哪些属性更有用，而又应该怎么用它们啊？说实话这会儿我也不知道，但我们记得前面提到过\n",
    "\n",
    "* **『对数据的认识太重要了！』**\n",
    "* **『对数据的认识太重要了！』**\n",
    "* **『对数据的认识太重要了！』**\n",
    "\n",
    "重要的事情说三遍，恩，说完了。仅仅最上面的对数据了解，依旧无法给我们提供想法和思路。我们再深入一点来看看我们的数据，看看**每个/多个属性和最后的Survived之间有着什么样的关系**呢。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zlN9ofD_YLty"
   },
   "source": [
    "### 乘客各属性分布\n",
    "\n",
    "脑容量太有限了…数值看花眼了。我们还是统计统计，画些图来看看属性和结果之间的关系好了，代码如下："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1IyjYfMEZUPr"
   },
   "source": [
    "引入中文，解决乱码问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e-nDIZQgZYYk"
   },
   "outputs": [],
   "source": [
    "!wget -O /usr/share/fonts/truetype/liberation/simhei.ttf \"https://www.wfonts.com/download/data/2014/06/01/simhei/chinese.simhei.ttf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qCefusheZeOL"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "zhfont = mpl.font_manager.FontProperties(fname='/usr/share/fonts/truetype/liberation/simhei.ttf')\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "id": "8S-GZxeaYRqF",
    "outputId": "bfa6d3b3-bb7c-43a1-a8ad-6211e6e0c31a"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "plt.subplot2grid((2,3),(0,0))             # 在一张大图里分列几个小图\n",
    "data_train.Survived.value_counts().plot(kind='bar')# 柱状图 \n",
    "plt.title(u\"获救情况 (1为获救)\", fontproperties=zhfont) # 标题\n",
    "plt.ylabel(u\"人数\", fontproperties=zhfont)  \n",
    "\n",
    "plt.subplot2grid((2,3),(0,1))\n",
    "data_train.Pclass.value_counts().plot(kind=\"bar\")\n",
    "plt.ylabel(u\"人数\", fontproperties=zhfont)\n",
    "plt.title(u\"乘客等级分布\", fontproperties=zhfont)\n",
    "\n",
    "plt.subplot2grid((2,3),(0,2))\n",
    "plt.scatter(data_train.Survived, data_train.Age)\n",
    "plt.ylabel(u\"年龄\", fontproperties=zhfont)                         # 设定纵坐标名称\n",
    "# plt.grid(b=True, which='major', axis='y') \n",
    "plt.title(u\"按年龄看获救分布 (1为获救)\", fontproperties=zhfont)\n",
    "\n",
    "\n",
    "plt.subplot2grid((2,3),(1,0), colspan=2)\n",
    "data_train.Age[data_train.Pclass == 1].plot(kind='kde')   \n",
    "data_train.Age[data_train.Pclass == 2].plot(kind='kde')\n",
    "data_train.Age[data_train.Pclass == 3].plot(kind='kde')\n",
    "plt.xlabel(u\"年龄\", fontproperties=zhfont)# plots an axis lable\n",
    "plt.ylabel(u\"密度\", fontproperties=zhfont) \n",
    "plt.title(u\"各等级的乘客年龄分布\", fontproperties=zhfont)\n",
    "plt.legend((u'头等舱', u'2等舱',u'3等舱'),loc='best', prop=zhfont) # sets our legend for our graph.\n",
    "\n",
    "\n",
    "plt.subplot2grid((2,3),(1,2))\n",
    "data_train.Embarked.value_counts().plot(kind='bar')\n",
    "plt.title(u\"各登船口岸上船人数\", fontproperties=zhfont)\n",
    "plt.ylabel(u\"人数\", fontproperties=zhfont)\n",
    "\n",
    "fig.tight_layout() # 调整整体空白\n",
    "plt.subplots_adjust(wspace = 1, hspace = 1 )# 调整子图间距\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PRR8_7vObzTN"
   },
   "source": [
    "图还是比数字好看多了。所以我们在图上可以看出来，\n",
    "\n",
    "* 被救的人300多点，不到半数；\n",
    "* 3等舱乘客灰常多；\n",
    "* 遇难和获救的人年龄似乎跨度都很广；\n",
    "* 3个不同的舱年龄总体趋势似乎也一致，\n",
    "* 2/3等舱乘客20岁多点的人最多，\n",
    "* 1等舱40岁左右的最多；\n",
    "* 登船港口人数按照S、C、Q递减，而且S远多于另外俩港口。\n",
    "\n",
    "这个时候我们可能会有一些想法了：\n",
    "\n",
    "* 不同舱位/乘客等级可能和财富/地位有关系，最后获救概率可能会不一样\n",
    "* 年龄对获救概率也一定是有影响的，毕竟前面说了，副船长还说『小孩和女士先走』呢\n",
    "* 和登船港口是不是有关系呢？也许登船港口不同，人的出身地位不同？\n",
    "\n",
    "口说无凭，空想无益。老老实实再来统计统计，看看这些属性值的统计分布吧。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ThRuiiL2b1j3"
   },
   "source": [
    "### 属性与获救结果的关联统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "id": "eq5ZkIXhb4Im",
    "outputId": "fd5c33c0-d147-4d95-b16f-e03f38747ddb"
   },
   "outputs": [],
   "source": [
    "# 看看各乘客等级的获救情况\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_0 = data_train.Pclass[data_train.Survived == 0].value_counts()\n",
    "Survived_1 = data_train.Pclass[data_train.Survived == 1].value_counts()\n",
    "df = pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"各乘客等级的获救情况\", fontproperties=zhfont)\n",
    "plt.xlabel(u\"乘客等级\", fontproperties=zhfont) \n",
    "plt.ylabel(u\"人数\", fontproperties=zhfont) \n",
    "plt.legend((u'获救', u'未获救'),loc='best', prop=zhfont) # sets our legend for our graph.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nv7Bxq7FdVRE"
   },
   "source": [
    "明显等级为1的乘客，获救的概率高很多。恩，这个一定是影响最后获救结果的一个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "id": "y1zkbqDFdWI5",
    "outputId": "67c53733-c5ac-4c84-9abe-775977b86573"
   },
   "outputs": [],
   "source": [
    "# 看看各性别的获救情况\n",
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_m = data_train.Survived[data_train.Sex == 'male'].value_counts()\n",
    "print(\"男性存活---> 0 未存活 1 存活\")\n",
    "print(Survived_m)\n",
    "Survived_f = data_train.Survived[data_train.Sex == 'female'].value_counts()\n",
    "print(\"女性存活---> 0 未存活 1 存活\")\n",
    "print(Survived_f)\n",
    "df=pd.DataFrame({u'男性':Survived_m, u'女性':Survived_f})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"按性别看获救情况\", fontproperties=zhfont)\n",
    "plt.xlabel(u\"性别\", fontproperties=zhfont) \n",
    "plt.ylabel(u\"人数\", fontproperties=zhfont)\n",
    "plt.legend((u'男性', u'女性'),loc='best', prop=zhfont) # sets our legend for our graph.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "92sHxh7oeoDi"
   },
   "source": [
    "歪果盆友果然很尊重lady，lady first践行得不错。\n",
    "\n",
    "\n",
    "性别无疑也要作为重要特征加入最后的模型之中。\n",
    "\n",
    "再来个详细版的好了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 463
    },
    "id": "9o_nePu8eozN",
    "outputId": "542936cf-f518-4b00-b04e-d3ec2aa7b888"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[0;32mIn [7]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m# 然后我们再来看看各种舱级别情况下各性别的获救情况\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m fig\u001B[38;5;241m=\u001B[39m\u001B[43mplt\u001B[49m\u001B[38;5;241m.\u001B[39mfigure()\n\u001B[1;32m      3\u001B[0m fig\u001B[38;5;241m.\u001B[39mset(alpha\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.65\u001B[39m) \u001B[38;5;66;03m# 设置图像透明度，无所谓\u001B[39;00m\n\u001B[1;32m      4\u001B[0m plt\u001B[38;5;241m.\u001B[39mtitle(\u001B[38;5;124mu\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m根据舱等级和性别的获救情况\u001B[39m\u001B[38;5;124m\"\u001B[39m, fontproperties\u001B[38;5;241m=\u001B[39mzhfont)\n",
      "\u001B[0;31mNameError\u001B[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "# 然后我们再来看看各种舱级别情况下各性别的获救情况\n",
    "fig=plt.figure()\n",
    "fig.set(alpha=0.65) # 设置图像透明度，无所谓\n",
    "plt.title(u\"根据舱等级和性别的获救情况\", fontproperties=zhfont)\n",
    "\n",
    "ax1=fig.add_subplot(221)\n",
    "data_train.Survived[data_train.Sex == 'female'][data_train.Pclass != 3].value_counts().plot(kind='bar', label=\"female highclass\", color='#FA2479')\n",
    "ax1.set_xticklabels([u\"获救\", u\"未获救\"], rotation=90, fontproperties=zhfont)\n",
    "ax1.legend([u\"女性/高级舱\"], loc='best', prop=zhfont)\n",
    "\n",
    "ax2=fig.add_subplot(222)\n",
    "data_train.Survived[data_train.Sex == 'female'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='female, low class', color='pink')\n",
    "ax2.set_xticklabels([u\"未获救\", u\"获救\"], rotation=90, fontproperties=zhfont)\n",
    "plt.legend([u\"女性/低级舱\"], loc='best', prop=zhfont)\n",
    "\n",
    "ax3=fig.add_subplot(223)\n",
    "data_train.Survived[data_train.Sex == 'male'][data_train.Pclass != 3].value_counts().plot(kind='bar', label='male, high class',color='lightblue')\n",
    "ax3.set_xticklabels([u\"未获救\", u\"获救\"], rotation=90, fontproperties=zhfont)\n",
    "plt.legend([u\"男性/高级舱\"], loc='best', prop=zhfont)\n",
    "\n",
    "ax4=fig.add_subplot(224)\n",
    "data_train.Survived[data_train.Sex == 'male'][data_train.Pclass == 3].value_counts().plot(kind='bar', label='male low class', color='steelblue')\n",
    "ax4.set_xticklabels([u\"未获救\", u\"获救\"], rotation=90, fontproperties=zhfont)\n",
    "plt.legend([u\"男性/低级舱\"], loc='best', prop=zhfont)\n",
    "\n",
    "fig.tight_layout() # 调整整体空白\n",
    "plt.subplots_adjust(wspace = 2, hspace = 1 )# 调整子图间距\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oP0mADsifeOg"
   },
   "source": [
    "恩，坚定了之前的判断。\n",
    "\n",
    "我们看看各登船港口的获救情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 482
    },
    "id": "cmmlHLgGfe6x",
    "outputId": "a09a6a45-4562-45a6-fe6a-f9e251c7be69"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[0;32mIn [8]\u001B[0m, in \u001B[0;36m<cell line: 1>\u001B[0;34m()\u001B[0m\n\u001B[0;32m----> 1\u001B[0m fig \u001B[38;5;241m=\u001B[39m \u001B[43mplt\u001B[49m\u001B[38;5;241m.\u001B[39mfigure()\n\u001B[1;32m      2\u001B[0m fig\u001B[38;5;241m.\u001B[39mset(alpha\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.2\u001B[39m)  \u001B[38;5;66;03m# 设定图表颜色alpha参数\u001B[39;00m\n\u001B[1;32m      4\u001B[0m Survived_0 \u001B[38;5;241m=\u001B[39m data_train\u001B[38;5;241m.\u001B[39mEmbarked[data_train\u001B[38;5;241m.\u001B[39mSurvived \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m.\u001B[39mvalue_counts()\n",
      "\u001B[0;31mNameError\u001B[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_0 = data_train.Embarked[data_train.Survived == 0].value_counts()\n",
    "Survived_1 = data_train.Embarked[data_train.Survived == 1].value_counts()\n",
    "df = pd.DataFrame({u'获救':Survived_1, u'未获救':Survived_0})\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"各登录港口乘客的获救情况\", fontproperties=zhfont)\n",
    "plt.xlabel(u\"登录港口\", fontproperties=zhfont) \n",
    "plt.ylabel(u\"人数\", fontproperties=zhfont) \n",
    "plt.legend((u'获救', u'未获救'),loc='best', prop=zhfont) # sets our legend for our graph.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5EV6L8nEf1iR"
   },
   "source": [
    "下面我们来看看 堂兄弟/妹，孩子/父母有几人，对是否获救的影响。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8vEEg7I2f2bY",
    "outputId": "689bd492-8299-4b6b-b76d-b2780ee1ed84"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[0;32mIn [9]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m# 堂兄弟/妹影响\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m g \u001B[38;5;241m=\u001B[39m \u001B[43mdata_train\u001B[49m\u001B[38;5;241m.\u001B[39mgroupby([\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mSibSp\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mSurvived\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[1;32m      3\u001B[0m df \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mDataFrame(g\u001B[38;5;241m.\u001B[39mcount()[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mPassengerId\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[1;32m      4\u001B[0m \u001B[38;5;28mprint\u001B[39m(df)\n",
      "\u001B[0;31mNameError\u001B[0m: name 'data_train' is not defined"
     ]
    }
   ],
   "source": [
    "# 堂兄弟/妹影响\n",
    "g = data_train.groupby(['SibSp','Survived'])\n",
    "df = pd.DataFrame(g.count()['PassengerId'])\n",
    "print(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PxUUxBvwgNuj",
    "outputId": "982fa960-2884-4b8a-f613-e32e50ee3be6"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[0;32mIn [10]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[0;34m()\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m# 父母/孩子影响\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m g \u001B[38;5;241m=\u001B[39m \u001B[43mdata_train\u001B[49m\u001B[38;5;241m.\u001B[39mgroupby([\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mParch\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mSurvived\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[1;32m      3\u001B[0m df \u001B[38;5;241m=\u001B[39m pd\u001B[38;5;241m.\u001B[39mDataFrame(g\u001B[38;5;241m.\u001B[39mcount()[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mPassengerId\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[1;32m      4\u001B[0m \u001B[38;5;28mprint\u001B[39m(df)\n",
      "\u001B[0;31mNameError\u001B[0m: name 'data_train' is not defined"
     ]
    }
   ],
   "source": [
    "# 父母/孩子影响\n",
    "g = data_train.groupby(['Parch','Survived'])\n",
    "df = pd.DataFrame(g.count()['PassengerId'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oBoThhMkgiKu"
   },
   "source": [
    "好吧，没看出特别特别明显的规律(为自己的智商感到捉急…)，先作为备选特征，放一放。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H0PScFZ3KyUl"
   },
   "source": [
    "ticket是船票编号，应该是unique的，和最后的结果没有太大的关系，先不纳入考虑的特征范畴\n",
    "\n",
    "cabin只有204个乘客有值，我们先看看它的一个分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F4Fsq-KNLUhL"
   },
   "source": [
    "![XEilcb](https://oss.images.shujudaka.com/uPic/XEilcb.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M5S8-IZFggcC",
    "outputId": "e198f570-2624-4db4-cc9b-f1d485d5947b"
   },
   "outputs": [],
   "source": [
    "data_train.Cabin.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G-2vdDJXgxcY"
   },
   "source": [
    "这三三两两的…如此不集中…我们猜一下，也许，前面的ABCDE是指的甲板位置、然后编号是房间号？\n",
    "\n",
    "关键是Cabin这鬼属性，应该算作类目型的，本来缺失值就多，还如此不集中，注定是个棘手货…\n",
    "\n",
    "第一感觉，如果直接按照类目特征处理的话，太散了，估计每个因子化后的特征都拿不到什么权重。\n",
    "\n",
    "加上有那么多缺失值，要不我们先把Cabin缺失与否作为条件(虽然这部分信息缺失可能并非未登记，maybe只是丢失了而已，\n",
    "\n",
    "所以这样做未必妥当)，先在有无Cabin信息这个粗粒度上看看Survived的情况好了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KFpUfI0nOJIE",
    "outputId": "bea1915b-1670-4ab9-976e-4cf89397a51d"
   },
   "outputs": [],
   "source": [
    "data_train.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AP4Vx8rLODdV",
    "outputId": "48a63841-9a14-4d3a-bc5d-4bb9686e66dc"
   },
   "outputs": [],
   "source": [
    "data_train.Survived[pd.notnull(data_train.Cabin)].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 565
    },
    "id": "SlYGy01og34U",
    "outputId": "3c714a71-9fed-4812-dc2f-9aa83853a413"
   },
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "fig.set(alpha=0.2)  # 设定图表颜色alpha参数\n",
    "\n",
    "Survived_cabin = data_train.Survived[pd.notnull(data_train.Cabin)].value_counts()\n",
    "\n",
    "Survived_nocabin = data_train.Survived[pd.isnull(data_train.Cabin)].value_counts()\n",
    "\n",
    "df = pd.DataFrame({u'有':Survived_cabin, u'无':Survived_nocabin}).transpose()\n",
    "\n",
    "print(df)\n",
    "\n",
    "df.plot(kind='bar', stacked=True)\n",
    "plt.title(u\"按Cabin有无看获救情况\", fontproperties=zhfont)\n",
    "plt.xlabel(u\"Cabin有无\", fontproperties=zhfont) \n",
    "plt.ylabel(u\"人数\", fontproperties=zhfont)\n",
    "plt.legend((u'有', u'无'),loc='best', prop=zhfont) # sets our legend for our graph.\n",
    "plt.xticks([0,1],[u\"未获救\", u\"获救\"], fontproperties=zhfont)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PDKyqkTSiBRC"
   },
   "source": [
    "有Cabin记录的似乎获救概率稍高一些，先这么着放一放吧。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DpLAMIyViCXp"
   },
   "source": [
    "## 简单数据预处理\n",
    "\n",
    "对了，我这里说的数据预处理，其实就包括了很多Kaggler津津乐道的feature engineering过程，灰常灰常有必要！\n",
    "\n",
    "**『特征工程(feature engineering)太重要了！』**  \n",
    "**『特征工程(feature engineering)太重要了！』**  \n",
    "**『特征工程(feature engineering)太重要了！』**\n",
    "\n",
    "恩，重要的事情说三遍。\n",
    "\n",
    "先从最突出的数据属性开始吧，对，Cabin和Age，有丢失数据实在是对下一步工作影响太大。\n",
    "\n",
    "先说Cabin，暂时我们就按照刚才说的，按Cabin有无数据，将这个属性处理成Yes和No两种类型吧。\n",
    "\n",
    "再说Age：\n",
    "\n",
    "通常遇到缺值的情况，我们会有几种常见的处理方式\n",
    "\n",
    "* 如果**缺值的样本占总数比例极高**，我们可能就直接**舍弃**了，作为特征加入的话，可能反倒带入noise，影响最后的结果了\n",
    "* 如果缺值的样本适中，而该属性**非连续值特征属性**(比如说类目属性)，那就把NaN作为一个新类别，加到类别特征中\n",
    "* 如果缺值的样本适中，而该属性为**连续值特征属性**，有时候我们会考虑给定一个step(比如这里的age，我们可以考虑每隔2/3岁为一个步长)，然后把它**离散化**，之后把NaN作为一个type加到属性类目中。\n",
    "* 有些情况下，缺失的值个数并不是特别多，那我们也可以试着根据已有的值，拟合一下数据，补充上。\n",
    "\n",
    "本例中，后两种处理方式应该都是可行的，我们先试试拟合补全吧(虽然说没有特别多的背景可供我们拟合，这不一定是一个多么好的选择)\n",
    "\n",
    "我们这里用scikit-learn中的RandomForest来拟合一下缺失的年龄数据(注：RandomForest 是一个用在原始数据中做不同采样，建立多棵 DecisionTree，再进行 average 等等来降低过拟合现象，提高结果的机器学习算法)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "s_7LGwOYjpNP",
    "outputId": "67499182-02de-4907-f5af-443a0ddab406"
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "_sQVy1J9jiar",
    "outputId": "25473932-e37c-4e87-cb3a-8f4292d598d3"
   },
   "outputs": [],
   "source": [
    "# 把已有的数值型特征取出来丢进Random Forest Regressor中\n",
    "data_train = pd.read_csv(\"/content/train.csv\")\n",
    "# data_train\n",
    "age_df = data_train[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "age_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LklIVCSsj9Xb",
    "outputId": "a77d3031-6936-46f6-d848-49ff0889e93d"
   },
   "outputs": [],
   "source": [
    "age_df.Age.notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E2WO04W_j_5Q",
    "outputId": "c3289cbb-f092-4906-efa9-d13ffbd02af0"
   },
   "outputs": [],
   "source": [
    "age_df.Age.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FoeJ5625j4nI"
   },
   "outputs": [],
   "source": [
    "# 乘客分成已知年龄和未知年龄两部分\n",
    "known_age = age_df[age_df.Age.notnull()]\n",
    "unknown_age = age_df[age_df.Age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "VolqPgcckD2G",
    "outputId": "3c3ea485-dff5-4cc6-9432-e1197ca759ab"
   },
   "outputs": [],
   "source": [
    "known_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "hZ8ONuZ9kFiw",
    "outputId": "1599647e-5bca-4d6a-99e0-00da24d18dd4"
   },
   "outputs": [],
   "source": [
    "unknown_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-hgEYPRmkTFg",
    "outputId": "123254f6-a120-4564-a393-58bc581e1d66"
   },
   "outputs": [],
   "source": [
    "# y即目标年龄\n",
    "y = known_age.iloc[:, 0]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "VZgaVqTMk6-j",
    "outputId": "d3bd3f39-26f1-4705-f2fe-2c31e20894be"
   },
   "outputs": [],
   "source": [
    "# X即特征属性值\n",
    "X = known_age.iloc[:, 1:]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "FyD6kVHtSH-B",
    "outputId": "21c1873c-06f1-489c-d325-7a515f86e2c3"
   },
   "outputs": [],
   "source": [
    "unknown_age.iloc[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "m9Lf59wCiTiI"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    " \n",
    "## 使用 RandomForestClassifier 填补缺失的年龄属性\n",
    "def set_missing_ages(df):\n",
    "    \n",
    "    # 把已有的数值型特征取出来丢进Random Forest Regressor中\n",
    "    age_df = df[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "\n",
    "    # 乘客分成已知年龄和未知年龄两部分\n",
    "    known_age = age_df[age_df.Age.notnull()]\n",
    "    unknown_age = age_df[age_df.Age.isnull()]\n",
    "\n",
    "    # y即目标年龄\n",
    "    y = known_age.iloc[:, 0]\n",
    "\n",
    "    # X即特征属性值\n",
    "    X = known_age.iloc[:, 1:]\n",
    "\n",
    "    # fit到RandomForestRegressor之中\n",
    "    rfr = RandomForestRegressor(random_state=0, n_estimators=2000, n_jobs=-1)\n",
    "    rfr.fit(X, y)\n",
    "    \n",
    "    # 用得到的模型进行未知年龄结果预测\n",
    "    predictedAges = rfr.predict(unknown_age.iloc[:, 1::])\n",
    "    \n",
    "    # 用得到的预测结果填补原缺失数据\n",
    "    df.loc[ (df.Age.isnull()), 'Age' ] = predictedAges \n",
    "    \n",
    "    return df, rfr\n",
    "\n",
    "def set_Cabin_type(df):\n",
    "    df.loc[ (df.Cabin.notnull()), 'Cabin' ] = \"Yes\"\n",
    "    df.loc[ (df.Cabin.isnull()), 'Cabin' ] = \"No\"\n",
    "    return df\n",
    "\n",
    "data_train, rfr = set_missing_ages(data_train)\n",
    "data_train = set_Cabin_type(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "F5o6od9MzHpi",
    "outputId": "1429f666-70fa-4ac9-fae0-a998f76dd6ce"
   },
   "outputs": [
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      ],
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       "RandomForestRegressor(n_estimators=2000, n_jobs=-1, random_state=0)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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    },
    "id": "lfjQjo56zGQb",
    "outputId": "e0ebecf7-b3d7-4f1b-da91-133dbe260a76"
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       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>886</th>\n",
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       "      <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>16.185117</td>\n",
       "      <td>1</td>\n",
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       "    <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",
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       "      <td>26.000000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>No</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>891 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  16.185117   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0        1      0         A/5 21171   7.2500    No        S  \n",
       "1        1      0          PC 17599  71.2833   Yes        C  \n",
       "2        0      0  STON/O2. 3101282   7.9250    No        S  \n",
       "3        1      0            113803  53.1000   Yes        S  \n",
       "4        0      0            373450   8.0500    No        S  \n",
       "..     ...    ...               ...      ...   ...      ...  \n",
       "886      0      0            211536  13.0000    No        S  \n",
       "887      0      0            112053  30.0000   Yes        S  \n",
       "888      1      2        W./C. 6607  23.4500    No        S  \n",
       "889      0      0            111369  30.0000   Yes        C  \n",
       "890      0      0            370376   7.7500    No        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dRKpMixE45jb"
   },
   "source": [
    "恩。目的达到，OK了。\n",
    "\n",
    "因为逻辑回归建模时，需要输入的特征都是数值型特征，我们通常会先对类目型的特征因子化。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eOqhrSw8SzTE"
   },
   "source": [
    "## 什么叫做因子化/one-hot编码？\n",
    "\n",
    "举个例子：\n",
    "\n",
    "### 以Embarked为例\n",
    "\n",
    "原本一个属性维度，因为其取值可以是[‘S’,’C’,’Q‘]，而将其平展开为’Embarked_C’,’Embarked_S’, ‘Embarked_Q’三个属性\n",
    "\n",
    "原本Embarked取值为S的，在此处的”Embarked_S”下取值为1，在’Embarked_C’, ‘Embarked_Q’下取值为0\n",
    "\n",
    "原本Embarked取值为C的，在此处的”Embarked_C”下取值为1，在’Embarked_S’, ‘Embarked_Q’下取值为0\n",
    "\n",
    "原本Embarked取值为Q的，在此处的”Embarked_Q”下取值为1，在’Embarked_C’, ‘Embarked_S’下取值为0\n",
    "\n",
    "\n",
    "\n",
    "我们使用pandas的”get_dummies”来完成这个工作，并拼接在原来的”data_train”之上，如下所示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i3HG0JFBTR6U"
   },
   "source": [
    "### 以Cabin为例\n",
    "\n",
    "原本一个属性维度，因为其取值可以是[‘yes’,‘no’]，而将其平展开为’Cabin_yes’,'Cabin_no’两个属性\n",
    "\n",
    "原本Cabin取值为yes的，在此处的\"Cabin_yes\"下取值为1，在\"Cabin_no\"下取值为0\n",
    "原本Cabin取值为no的，在此处的\"Cabin_yes\"下取值为0，在\"Cabin_no\"下取值为1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tuAMDdY0TlTH"
   },
   "source": [
    "![tE5x3b](https://oss.images.shujudaka.com/uPic/tE5x3b.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "l2SZ5Vw14-Pd",
    "outputId": "32f46a01-5d09-4da7-83b4-1b5ea7ff161f"
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 16 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b6b4ad7-5533-4baa-b8a9-31a405e1e034')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-9b6b4ad7-5533-4baa-b8a9-31a405e1e034 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-9b6b4ad7-5533-4baa-b8a9-31a405e1e034');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     PassengerId  Survived        Age  SibSp  Parch     Fare  Cabin_No  \\\n",
       "0              1         0  22.000000      1      0   7.2500         1   \n",
       "1              2         1  38.000000      1      0  71.2833         0   \n",
       "2              3         1  26.000000      0      0   7.9250         1   \n",
       "3              4         1  35.000000      1      0  53.1000         0   \n",
       "4              5         0  35.000000      0      0   8.0500         1   \n",
       "..           ...       ...        ...    ...    ...      ...       ...   \n",
       "886          887         0  27.000000      0      0  13.0000         1   \n",
       "887          888         1  19.000000      0      0  30.0000         0   \n",
       "888          889         0  16.185117      1      2  23.4500         1   \n",
       "889          890         1  26.000000      0      0  30.0000         0   \n",
       "890          891         0  32.000000      0      0   7.7500         1   \n",
       "\n",
       "     Cabin_Yes  Embarked_C  Embarked_Q  Embarked_S  Sex_female  Sex_male  \\\n",
       "0            0           0           0           1           0         1   \n",
       "1            1           1           0           0           1         0   \n",
       "2            0           0           0           1           1         0   \n",
       "3            1           0           0           1           1         0   \n",
       "4            0           0           0           1           0         1   \n",
       "..         ...         ...         ...         ...         ...       ...   \n",
       "886          0           0           0           1           0         1   \n",
       "887          1           0           0           1           1         0   \n",
       "888          0           0           0           1           1         0   \n",
       "889          1           1           0           0           0         1   \n",
       "890          0           0           1           0           0         1   \n",
       "\n",
       "     Pclass_1  Pclass_2  Pclass_3  \n",
       "0           0         0         1  \n",
       "1           1         0         0  \n",
       "2           0         0         1  \n",
       "3           1         0         0  \n",
       "4           0         0         1  \n",
       "..        ...       ...       ...  \n",
       "886         0         1         0  \n",
       "887         1         0         0  \n",
       "888         0         0         1  \n",
       "889         1         0         0  \n",
       "890         0         0         1  \n",
       "\n",
       "[891 rows x 16 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因为逻辑回归建模时，需要输入的特征都是数值型特征\n",
    "# 我们先对类目型的特征离散/因子化\n",
    "# 以Cabin为例，原本一个属性维度，因为其取值可以是['yes','no']，\n",
    "# 而将其平展开为'Cabin_yes','Cabin_no'两个属性\n",
    "# 原本Cabin取值为yes的，在此处的'Cabin_yes'下取值为1，在'Cabin_no'下取值为0\n",
    "# 原本Cabin取值为no的，在此处的'Cabin_yes'下取值为0，在'Cabin_no'下取值为1\n",
    "# 我们使用pandas的get_dummies来完成这个工作，并拼接在原来的data_train之上，如下所示\n",
    "\n",
    "dummies_Cabin = pd.get_dummies(data_train['Cabin'], prefix= 'Cabin')\n",
    "\n",
    "dummies_Embarked = pd.get_dummies(data_train['Embarked'], prefix= 'Embarked')\n",
    "\n",
    "dummies_Sex = pd.get_dummies(data_train['Sex'], prefix= 'Sex')\n",
    "\n",
    "dummies_Pclass = pd.get_dummies(data_train['Pclass'], prefix= 'Pclass')\n",
    "\n",
    "df = pd.concat([data_train, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)\n",
    "df.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FJNyAoAz5NGK"
   },
   "source": [
    "我们很成功地把这些类目属性全都转成0，1的数值属性了。\n",
    "\n",
    "这样，看起来，是不是我们需要的属性值都有了，且它们都是数值型属性呢。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rn5ikgA-5xKk",
    "outputId": "7030fe52-329e-4113-f526-e1a605f10abb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.647214\n",
       "std       13.629887\n",
       "min        0.420000\n",
       "25%       21.000000\n",
       "50%       29.000000\n",
       "75%       36.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "v51PcLeMUqnH",
    "outputId": "8a9b81d7-0338-4136-8f4f-d9e4099ad487"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "OY0AlnL-rPmG"
   },
   "outputs": [],
   "source": [
    "!rm -rf PythonUtils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8XIikOHEWoRj",
    "outputId": "970879ba-cfe1-4d4a-f199-3da840851709"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'PythonUtils'...\n",
      "remote: Enumerating objects: 10920, done.\u001B[K\n",
      "remote: Counting objects: 100% (159/159), done.\u001B[K\n",
      "remote: Compressing objects: 100% (113/113), done.\u001B[K\n",
      "remote: Total 10920 (delta 37), reused 148 (delta 26), pack-reused 10761\u001B[K\n",
      "Receiving objects: 100% (10920/10920), 130.80 MiB | 17.78 MiB/s, done.\n",
      "Resolving deltas: 100% (165/165), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/WangLaoShi/PythonUtils.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "gpQC36jOWubx"
   },
   "outputs": [],
   "source": [
    "from PythonUtils.pandas_util.feature_analytis import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "PmxGg4rvadR0",
    "outputId": "1a59fa47-fd09-4e1e-f3f7-1d469612ea13"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "描述性统计信息,你也可以自己使用下面的方法来做探索\n",
      "\n",
      "编号     函数           描述\n",
      "1       count()        非空观测数量\n",
      "2       sum()          所有值之和\n",
      "3       mean()         所有值的平均值\n",
      "4       median()       所有值的中位数\n",
      "5       mode()         值的模值\n",
      "6       std()          值的标准偏差\n",
      "7       min()          所有值中的最小值\n",
      "8       max()          所有值中的最大值\n",
      "9       abs()          绝对值\n",
      "10      prod()         数组元素的乘积\n",
      "11      cumsum()       累计总和\n",
      "12      cumprod()      累计乘积\n",
      "    \n",
      "---描述性信息统计---\n",
      "count    891.000000\n",
      "mean      29.647214\n",
      "std       13.629887\n",
      "min        0.420000\n",
      "25%       21.000000\n",
      "50%       29.000000\n",
      "75%       36.000000\n",
      "max       80.000000\n",
      "Name: Age, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "kstest方法：KS检验，\n",
      "参数分别是：待检验的数据，检验方法（这里设置成norm正态分布），均值与标准差\n",
      "\n",
      "结果返回两个值：statistic → D值，pvalue → P值\n",
      "\n",
      "p 值大于0.05，为正态分布\n",
      "H0:样本符合  \n",
      "H1:样本不符合 \n",
      "如何 p>0.05 接受H0 ,反之 \n",
      "    \n",
      "KstestResult(statistic=0.07253879395740581, pvalue=0.0001599578330893097, statistic_location=36.0, statistic_sign=1)\n",
      "\n",
      "变量极差\n",
      "Max(80.000000)-Min(0.420000) = 79.580000\n",
      "---频率分布情况---\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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erdbWVqWnp19ybMRHPrZs2aLXX39dBw4c6LLu9OnTSk1N7RQ8JCk7O1unT5/udr6lS5eqoqIidN/n8yk/P1/Tp0/vsfhwBQIB1dbWqri4WG63OyZz9ieJ2t/oyl1hj/UkGf1gfFDLDibJH3R1WtdQWRKTbcVqnmhcqr9Lsdl7byXq8zQSTu/R6f1J9NgbF9+5CEdE4ePEiRNauHChamtrlZaWFnFh3fF4PPJ4PF2Wu93umO/4vpizP0m0/vwd4f+RDT0m6OryuHB6DmdbsZqnN7rr71Js9h4rifY8jYbTe3R6fxI9RjtfuCI64fTQoUM6c+aMvvjFLyolJUUpKSmqr6/XunXrlJKSouzsbF24cEEtLS2dHtfc3KycnJxINgUAABwqoiMf06ZN05EjRzotmzt3rkaOHKmHHnpI+fn5crvdqqurU2lpqSSpsbFRTU1N8nq9sasaAAAkrIjCx+DBgzV69OhOyy677DJlZWWFls+bN08VFRUaMmSI0tPTtWDBAnm93m6vdAEAAJ89UV1qeylr1qxRUlKSSktL5ff7VVJSovXr18d6MwAAIEH1Ony88sorne6npaWpqqpKVVVVvZ0aAAA4EN/tAgAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAqpR4FwD0FyOW7Ix3CXETTu9vr57Zq3k8yUZPTIioLAAOxZEPAABgVUThY8OGDRozZozS09OVnp4ur9erF198MbS+vb1d5eXlysrK0qBBg1RaWqrm5uaYFw0AABJXROHjqquu0urVq3Xo0CEdPHhQU6dO1R133KHf/e53kqTFixdrx44d2rp1q+rr63Xy5EnNmjWrTwoHAACJKaJzPm6//fZO9x999FFt2LBB+/bt01VXXaVNmzappqZGU6dOlSRVV1dr1KhR2rdvnyZNmhS7qgEAQMKK+oTTjo4Obd26VefPn5fX69WhQ4cUCARUVFQUGjNy5EgVFBRo7969nxo+/H6//H5/6L7P55MkBQIBBQKBaMvr5OI8sZqvv0nU/jzJJvyxSabTvx8XTt+RbCseLtXfpdjsvbfbuthboj1PI5Gov4vhcnp/Ej3GYt5wuIwxEb0yHTlyRF6vV+3t7Ro0aJBqamr05S9/WTU1NZo7d26nICFJEyZM0JQpU/T44493O19lZaVWrFjRZXlNTY0GDhwYSWkAACBO2traNHv2bLW2tio9Pf2SYyM+8nHttdfq8OHDam1t1c9+9jOVlZWpvr4+6mKXLl2qioqK0H2fz6f8/HxNnz69x+LDFQgEVFtbq+LiYrnd7pjM2Z8kan+jK3eFPdaTZPSD8UEtO5gkf9DVh1XFR7T9NVSW9Dgmkp9zX27rYo+J9jyNRKL+LobL6f1J9NgbF9+5CEfE4SM1NVWf//znJUnjxo3TgQMH9NRTT+nuu+/WhQsX1NLSoszMzND45uZm5eTkfOp8Ho9HHo+ny3K32x3zHd8Xc/YnidafvyPyEOEPuqJ6XKKItL9w9nesfl6x2laiPU+j4fQend6fRI/RzheuXn/ORzAYlN/v17hx4+R2u1VXVxda19jYqKamJnm93t5uBgAAOERERz6WLl2qGTNmqKCgQGfPnlVNTY1eeeUV7dq1SxkZGZo3b54qKio0ZMgQpaena8GCBfJ6vVzpAgAAQiIKH2fOnNE3v/lNnTp1ShkZGRozZox27dql4uJiSdKaNWuUlJSk0tJS+f1+lZSUaP369X1SOAAASEwRhY9NmzZdcn1aWpqqqqpUVVXVq6IAAIBz8d0uAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKwifAAAAKsIHwAAwCrCBwAAsIrwAQAArCJ8AAAAqwgfAADAKsIHAACwivABAACsInwAAACrCB8AAMAqwgcAALCK8AEAAKxKiXcBAKI3YsnOeJcAABHjyAcAALCK8AEAAKwifAAAAKsIHwAAwKrP3Amn4Zyg9/bqmRYqSVz8DAEAvcGRDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVqXEuwAAcIIRS3bKk2z0xARpdOUu+TtcXca8vXpmHCoD+h+OfAAAAKsiCh+rVq3SzTffrMGDB2vYsGG688471djY2GlMe3u7ysvLlZWVpUGDBqm0tFTNzc0xLRoAACSuiMJHfX29ysvLtW/fPtXW1ioQCGj69Ok6f/58aMzixYu1Y8cObd26VfX19Tp58qRmzZoV88IBAEBiiuicj5deeqnT/c2bN2vYsGE6dOiQ/vZv/1atra3atGmTampqNHXqVElSdXW1Ro0apX379mnSpEld5vT7/fL7/aH7Pp9PkhQIBBQIBCJuqDsX5wkEAvIkm7DHJ4qP92dDrH6G4cwTGptkOv3rNInQX2/36cXeEu33K1yeZNPjfkz03m2/1sQDPfZ+3nC4jDFRv9odO3ZM11xzjY4cOaLRo0dr9+7dmjZtmj788ENlZmaGxg0fPlyLFi3S4sWLu8xRWVmpFStWdFleU1OjgQMHRlsaAACwqK2tTbNnz1Zra6vS09MvOTbqq12CwaAWLVqkyZMna/To0ZKk06dPKzU1tVPwkKTs7GydPn2623mWLl2qioqK0H2fz6f8/HxNnz69x+LDFQgEVFtbq+LiYt306O4exzdUlsRku7Z8vD+3293n2xtduavHMeH8DMOZ5yJPktEPxge17GCS/MGuVxEkukTor7f79GKPtp6nto2u3NXjfky015ZPsv1aEw/0GL2L71yEI+rwUV5eroaGBr366qvRTiFJ8ng88ng8XZa73e6Y73i3293t5W/djUtEffEz606sfobhzNPlMUFXVI9LFP25v1jtU1vPU9s+3vun7Uen9O3Uffhx9BjdfOGK6lLb+fPn64UXXtCePXt01VVXhZbn5OTowoULamlp6TS+ublZOTk50WwKAAA4TEThwxij+fPna9u2bdq9e7cKCws7rR83bpzcbrfq6upCyxobG9XU1CSv1xubigEAQEKL6G2X8vJy1dTU6Oc//7kGDx4cOo8jIyNDAwYMUEZGhubNm6eKigoNGTJE6enpWrBggbxeb7dXugBAd0Ys2dnjGD4tFEhcEYWPDRs2SJJuu+22Tsurq6t17733SpLWrFmjpKQklZaWyu/3q6SkROvXr49JsQAAIPFFFD7CuSo3LS1NVVVVqqqqirooAADgXHy3CwAAsIrwAQAArCJ8AAAAq6L+kDEA6O/CuWomHFxZA8QWRz4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVhA8AAGBVSrwLgDPF6qvM0X+wTwHECkc+AACAVYQPAABgFeEDAABYRfgAAABWET4AAIBVXO0SpXDO/H979UwLlQAAkFg48gEAAKwifAAAAKsIHwAAwCrCBwAAsIoTTh1kdOUu+Ttcn7qeE2DhJHzcO5C4OPIBAACsInwAAACrCB8AAMAqwgcAALCKE07jjE9KBQB81nDkAwAAWEX4AAAAVhE+AACAVYQPAABgFSecArCqp0/iBeB8HPkAAABWRRw+fvnLX+r2229XXl6eXC6Xtm/f3mm9MUYPP/ywcnNzNWDAABUVFeno0aOxqhcAACS4iMPH+fPndeONN6qqqqrb9U888YTWrVunjRs3av/+/brssstUUlKi9vb2XhcLAAASX8TnfMyYMUMzZszodp0xRmvXrtX3v/993XHHHZKkn/zkJ8rOztb27dt1zz339K5aAACQ8GJ6wunx48d1+vRpFRUVhZZlZGRo4sSJ2rt3b7fhw+/3y+/3h+77fD5JUiAQUCAQiEldF+cJBALyJJuwx19Kf5rn4npP0qXnitXPM5yaY+1ibz31mKic3p+U2D2G+7vcU4+x+h2Ml4+/ljoVPfZ+3nC4jDFRvxK4XC5t27ZNd955pyTp17/+tSZPnqyTJ08qNzc3NO6uu+6Sy+XSs88+22WOyspKrVixosvympoaDRw4MNrSAACARW1tbZo9e7ZaW1uVnp5+ybFxv9R26dKlqqioCN33+XzKz8/X9OnTeyw+XIFAQLW1tSouLtZNj+7ucXxDZUmPY0ZX7uo381zsb9nBJPmDn34JYzj1hCOcmmPNk2T0g/HBHntMVE7vT0rsHsP9Xe6px1j9DsbLx19L3W53vMvpE/QYvYvvXIQjpuEjJydHktTc3NzpyEdzc7PGjh3b7WM8Ho88Hk+X5W63O+Y73u12h/X5AuFst7/NI0n+oOuS88Xq5xnPz2joqcdE5/T+pMTsMdLf5U/r0Sl/zPri9bm/ocfo5gtXTD/no7CwUDk5Oaqrqwst8/l82r9/v7xebyw3BQAAElTERz7OnTunY8eOhe4fP35chw8f1pAhQ1RQUKBFixbpkUce0TXXXKPCwkItW7ZMeXl5ofNCAADAZ1vE4ePgwYOaMmVK6P7F8zXKysq0efNmPfjggzp//rweeOABtbS06NZbb9VLL72ktLS02FUNAA41YsnOsMa9vXpmH1cC9J2Iw8dtt92mS10g43K5tHLlSq1cubJXhQEAAGfiu10AAIBVhA8AAGAV4QMAAFgV9w8ZQ896OgHNk2z0xARLxQBwlHBOcOXkVsQaRz4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFVc7YJOwv1oZ+CzpD/+XvS3q1T6Wz3o3zjyAQAArCJ8AAAAqwgfAADAKsIHAACwihNOu9EfTy4DkPh4bQE+wpEPAABgFeEDAABYRfgAAABWET4AAIBVnHAKALikiyfKepKNnpggja7cJX+HK85VIZFx5AMAAFhF+AAAAFYRPgAAgFWEDwAAYBUnnAIArAjnE17fXj3TQiWIN458AAAAqwgfAADAKsIHAACwivABAACsInwAAACruNqlD4VzZrdN/a0eAPik/nZFTH+rxyk48gEAAKwifAAAAKsIHwAAwCrCBwAAsIoTTgHAoTjJ/NK6+/l4ko2emCCNrtwlf4crDlV9NnDkAwAAWEX4AAAAVhE+AACAVYQPAABgFSecAgASSiJ+6qjNmnva1sWTauOJIx8AAMCqPgsfVVVVGjFihNLS0jRx4kS99tprfbUpAACQQPokfDz77LOqqKjQ8uXL9frrr+vGG29USUmJzpw50xebAwAACaRPzvl48skndf/992vu3LmSpI0bN2rnzp368Y9/rCVLlnQa6/f75ff7Q/dbW1slSR988IECgUBM6gkEAmpra9P777+vlP89H5M5+5OUoFFbW1ApgSR1BJ35oThO79Hp/Un06ASJ1N/777/f45ju/h5E02O024pmnnD0tK2LPb7//vtyu90x2aYknT17VpJkjOl5sIkxv99vkpOTzbZt2zot/+Y3v2m++tWvdhm/fPlyI4kbN27cuHHj5oDbiRMneswKMT/y8d5776mjo0PZ2dmdlmdnZ+uPf/xjl/FLly5VRUVF6H4wGNQHH3ygrKwsuVyxSdY+n0/5+fk6ceKE0tPTYzJnf+L0/iTn9+j0/iR6dAKn9yfRY28YY3T27Fnl5eX1ODbul9p6PB55PJ5OyzIzM/tkW+np6Y59MknO709yfo9O70+iRydwen8SPUYrIyMjrHExP+F06NChSk5OVnNzc6flzc3NysnJifXmAABAgol5+EhNTdW4ceNUV1cXWhYMBlVXVyev1xvrzQEAgATTJ2+7VFRUqKysTOPHj9eECRO0du1anT9/PnT1i20ej0fLly/v8vaOUzi9P8n5PTq9P4kencDp/Un0aIvLmHCuiYncv//7v+uHP/yhTp8+rbFjx2rdunWaOHFiX2wKAAAkkD4LHwAAAN3hu10AAIBVhA8AAGAV4QMAAFhF+AAAAFY5PnxUVVVpxIgRSktL08SJE/Xaa6/Fu6So/fKXv9Ttt9+uvLw8uVwubd++vdN6Y4wefvhh5ebmasCAASoqKtLRo0fjU2wUVq1apZtvvlmDBw/WsGHDdOedd6qxsbHTmPb2dpWXlysrK0uDBg1SaWlplw+06882bNigMWPGhD5Z0Ov16sUXXwytT/T+Pmn16tVyuVxatGhRaFmi91hZWSmXy9XpNnLkyND6RO/vonfffVff+MY3lJWVpQEDBuiGG27QwYMHQ+sT/fVmxIgRXfajy+VSeXm5pMTfjx0dHVq2bJkKCws1YMAAXX311frBD37Q6Uvf4roPe/k9cv3ali1bTGpqqvnxj39sfve735n777/fZGZmmubm5niXFpX//u//Nt/73vfM888/byR1+fK+1atXm4yMDLN9+3bzm9/8xnz1q181hYWF5q9//Wt8Co5QSUmJqa6uNg0NDebw4cPmy1/+sikoKDDnzp0LjfnWt75l8vPzTV1dnTl48KCZNGmSueWWW+JYdWR+8YtfmJ07d5o333zTNDY2mu9+97vG7XabhoYGY0zi9/dxr732mhkxYoQZM2aMWbhwYWh5ove4fPlyc/3115tTp06Fbn/5y19C6xO9P2OM+eCDD8zw4cPNvffea/bv32/eeusts2vXLnPs2LHQmER/vTlz5kynfVhbW2skmT179hhjEn8/PvrooyYrK8u88MIL5vjx42br1q1m0KBB5qmnngqNiec+dHT4mDBhgikvLw/d7+joMHl5eWbVqlVxrCo2Phk+gsGgycnJMT/84Q9Dy1paWozH4zH/9V//FYcKe+/MmTNGkqmvrzfGfNSP2+02W7duDY35wx/+YCSZvXv3xqvMXrv88svNf/7nfzqqv7Nnz5prrrnG1NbWmi996Uuh8OGEHpcvX25uvPHGbtc5oT9jjHnooYfMrbfe+qnrnfh6s3DhQnP11VebYDDoiP04c+ZMc99993VaNmvWLDNnzhxjTPz3oWPfdrlw4YIOHTqkoqKi0LKkpCQVFRVp7969caysbxw/flynT5/u1G9GRoYmTpyYsP22trZKkoYMGSJJOnTokAKBQKceR44cqYKCgoTssaOjQ1u2bNH58+fl9Xod1V95eblmzpzZqRfJOfvw6NGjysvL0+c+9znNmTNHTU1NkpzT3y9+8QuNHz9eX/va1zRs2DDddNNN+tGPfhRa77TXmwsXLuiZZ57RfffdJ5fL5Yj9eMstt6iurk5vvvmmJOk3v/mNXn31Vc2YMUNS/Pdh3L/Vtq+899576ujoUHZ2dqfl2dnZ+uMf/xinqvrO6dOnJanbfi+uSyTBYFCLFi3S5MmTNXr0aEkf9ZiamtrlW48TrccjR47I6/Wqvb1dgwYN0rZt23Tdddfp8OHDjuhvy5Ytev3113XgwIEu65ywDydOnKjNmzfr2muv1alTp7RixQr9zd/8jRoaGhzRnyS99dZb2rBhgyoqKvTd735XBw4c0D/+4z8qNTVVZWVljnu92b59u1paWnTvvfdKcsbzdMmSJfL5fBo5cqSSk5PV0dGhRx99VHPmzJEU/78Zjg0fSGzl5eVqaGjQq6++Gu9SYu7aa6/V4cOH1draqp/97GcqKytTfX19vMuKiRMnTmjhwoWqra1VWlpavMvpExf/z1GSxowZo4kTJ2r48OF67rnnNGDAgDhWFjvBYFDjx4/XY489Jkm66aab1NDQoI0bN6qsrCzO1cXepk2bNGPGDOXl5cW7lJh57rnn9NOf/lQ1NTW6/vrrdfjwYS1atEh5eXn9Yh869m2XoUOHKjk5ucvZyc3NzcrJyYlTVX3nYk9O6Hf+/Pl64YUXtGfPHl111VWh5Tk5Obpw4YJaWlo6jU+0HlNTU/X5z39e48aN06pVq3TjjTfqqaeeckR/hw4d0pkzZ/TFL35RKSkpSklJUX19vdatW6eUlBRlZ2cnfI+flJmZqS984Qs6duyYI/ahJOXm5uq6667rtGzUqFGht5ec9Hrzzjvv6OWXX9Y//MM/hJY5YT/+y7/8i5YsWaJ77rlHN9xwg/7+7/9eixcv1qpVqyTFfx86NnykpqZq3LhxqqurCy0LBoOqq6uT1+uNY2V9o7CwUDk5OZ369fl82r9/f8L0a4zR/PnztW3bNu3evVuFhYWd1o8bN05ut7tTj42NjWpqakqYHrsTDAbl9/sd0d+0adN05MgRHT58OHQbP3685syZE/rvRO/xk86dO6c//elPys3NdcQ+lKTJkyd3ucz9zTff1PDhwyU54/Xmourqag0bNkwzZ84MLXPCfmxra1NSUuc/8cnJyQoGg5L6wT7s81Na42jLli3G4/GYzZs3m9///vfmgQceMJmZmeb06dPxLi0qZ8+eNW+88YZ54403jCTz5JNPmjfeeMO88847xpiPLpvKzMw0P//5z81vf/tbc8cddyTUpW/f/va3TUZGhnnllVc6XQLX1tYWGvOtb33LFBQUmN27d5uDBw8ar9drvF5vHKuOzJIlS0x9fb05fvy4+e1vf2uWLFliXC6X+Z//+R9jTOL3152PX+1iTOL3+E//9E/mlVdeMcePHze/+tWvTFFRkRk6dKg5c+aMMSbx+zPmo8ukU1JSzKOPPmqOHj1qfvrTn5qBAweaZ555JjQm0V9vjPnoCsiCggLz0EMPdVmX6PuxrKzMXHnllaFLbZ9//nkzdOhQ8+CDD4bGxHMfOjp8GGPMv/3bv5mCggKTmppqJkyYYPbt2xfvkqK2Z88eI6nLrayszBjz0aVTy5YtM9nZ2cbj8Zhp06aZxsbG+BYdge56k2Sqq6tDY/7617+a73znO+byyy83AwcONH/3d39nTp06Fb+iI3TfffeZ4cOHm9TUVHPFFVeYadOmhYKHMYnfX3c+GT4Svce7777b5ObmmtTUVHPllVeau+++u9PnXyR6fxft2LHDjB492ng8HjNy5Ejz9NNPd1qf6K83xhiza9cuI6nbuhN9P/p8PrNw4UJTUFBg0tLSzOc+9znzve99z/j9/tCYeO5DlzEf+7gzAACAPubYcz4AAED/RPgAAABWET4AAIBVhA8AAGAV4QMAAFhF+AAAAFYRPgAAgFWEDwAAYBXhAwAAWEX4AAAAVhE+AACAVf8Pf2QPO5X79h4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---分组区间---\n",
      "0    [16.336, 24.294)\n",
      "1     [32.252, 40.21)\n",
      "2    [24.294, 32.252)\n",
      "3     [32.252, 40.21)\n",
      "4     [32.252, 40.21)\n",
      "Name: Age, dtype: category\n",
      "Categories (10, interval[float64, left]): [[0.42, 8.378) < [8.378, 16.336) < [16.336, 24.294) <\n",
      "                                           [24.294, 32.252) ... [48.168, 56.126) <\n",
      "                                           [56.126, 64.084) < [64.084, 72.042) < [72.042, 80.08)] \n",
      "------\n",
      "[0.42, 8.378)        59\n",
      "[8.378, 16.336)      55\n",
      "[16.336, 24.294)    208\n",
      "[24.294, 32.252)    240\n",
      "[32.252, 40.21)     163\n",
      "[40.21, 48.168)      79\n",
      "[48.168, 56.126)     50\n",
      "[56.126, 64.084)     26\n",
      "[64.084, 72.042)      9\n",
      "[72.042, 80.08)       2\n",
      "Name: Age, dtype: int64\n",
      "0    22.0\n",
      "1    38.0\n",
      "2    26.0\n",
      "3    35.0\n",
      "4    35.0\n",
      "Name: Age, dtype: float64\n",
      "---输出频*表---\n",
      "                   频数        频率      累计频率     频率%    累计频率%\n",
      "[0.42, 8.378)      59  0.066218  0.066218   6.62%    6.62%\n",
      "[8.378, 16.336)    55  0.061728  0.127946   6.17%   12.79%\n",
      "[16.336, 24.294)  208  0.233446  0.361392  23.34%   36.14%\n",
      "[24.294, 32.252)  240  0.269360  0.630752  26.94%   63.08%\n",
      "[32.252, 40.21)   163  0.182941  0.813692  18.29%   81.37%\n",
      "[40.21, 48.168)    79  0.088664  0.902357   8.87%   90.24%\n",
      "[48.168, 56.126)   50  0.056117  0.958474   5.61%   95.85%\n",
      "[56.126, 64.084)   26  0.029181  0.987654   2.92%   98.77%\n",
      "[64.084, 72.042)    9  0.010101  0.997755   1.01%   99.78%\n",
      "[72.042, 80.08)     2  0.002245  1.000000   0.22%  100.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---箱线图---\n",
      "\n",
      "简单直观的异常值检测方法:箱形图（箱线图）\n",
      "箱形图中，从上到下依次有 6 个数据节点，\n",
      "分别是上界、上四分位、均值、中位数、下四分位、下界。\n",
      "而那些超过上界的值就会被标记为离群点，也就是异常数据。\n",
      "    \n",
      "0      22.000000\n",
      "1      38.000000\n",
      "2      26.000000\n",
      "3      35.000000\n",
      "4      35.000000\n",
      "         ...    \n",
      "886    27.000000\n",
      "887    19.000000\n",
      "888    16.185117\n",
      "889    26.000000\n",
      "890    32.000000\n",
      "Name: Age, Length: 891, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_distribution(df['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "HNSrgel_HVUV",
    "outputId": "afd8ecf2-5bab-4df3-98d7-29a5b5f2250c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "描述性统计信息,你也可以自己使用下面的方法来做探索\n",
      "\n",
      "编号     函数           描述\n",
      "1       count()        非空观测数量\n",
      "2       sum()          所有值之和\n",
      "3       mean()         所有值的平均值\n",
      "4       median()       所有值的中位数\n",
      "5       mode()         值的模值\n",
      "6       std()          值的标准偏差\n",
      "7       min()          所有值中的最小值\n",
      "8       max()          所有值中的最大值\n",
      "9       abs()          绝对值\n",
      "10      prod()         数组元素的乘积\n",
      "11      cumsum()       累计总和\n",
      "12      cumprod()      累计乘积\n",
      "    \n",
      "---描述性信息统计---\n",
      "count    891.000000\n",
      "mean      32.204208\n",
      "std       49.693429\n",
      "min        0.000000\n",
      "25%        7.910400\n",
      "50%       14.454200\n",
      "75%       31.000000\n",
      "max      512.329200\n",
      "Name: Fare, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "kstest方法：KS检验，\n",
      "参数分别是：待检验的数据，检验方法（这里设置成norm正态分布），均值与标准差\n",
      "\n",
      "结果返回两个值：statistic → D值，pvalue → P值\n",
      "\n",
      "p 值大于0.05，为正态分布\n",
      "H0:样本符合  \n",
      "H1:样本不符合 \n",
      "如何 p>0.05 接受H0 ,反之 \n",
      "    \n",
      "KstestResult(statistic=0.28184804098597455, pvalue=4.179692707891544e-63, statistic_location=6.4375, statistic_sign=-1)\n",
      "\n",
      "变量极差\n",
      "Max(512.329200)-Min(0.000000) = 512.329200\n",
      "---频率分布情况---\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---分组区间---\n",
      "0        [0.0, 51.233)\n",
      "1    [51.233, 102.466)\n",
      "2        [0.0, 51.233)\n",
      "3    [51.233, 102.466)\n",
      "4        [0.0, 51.233)\n",
      "Name: Fare, dtype: category\n",
      "Categories (10, interval[float64, left]): [[0.0, 51.233) < [51.233, 102.466) < [102.466, 153.699) <\n",
      "                                           [153.699, 204.932) ... [307.398, 358.63) <\n",
      "                                           [358.63, 409.863) < [409.863, 461.096) <\n",
      "                                           [461.096, 512.842)] \n",
      "------\n",
      "[0.0, 51.233)         732\n",
      "[51.233, 102.466)     106\n",
      "[102.466, 153.699)     31\n",
      "[153.699, 204.932)      2\n",
      "[204.932, 256.165)     11\n",
      "[256.165, 307.398)      6\n",
      "[307.398, 358.63)       0\n",
      "[358.63, 409.863)       0\n",
      "[409.863, 461.096)      0\n",
      "[461.096, 512.842)      3\n",
      "Name: Fare, dtype: int64\n",
      "0     7.2500\n",
      "1    71.2833\n",
      "2     7.9250\n",
      "3    53.1000\n",
      "4     8.0500\n",
      "Name: Fare, dtype: float64\n",
      "---输出频*表---\n",
      "                     频数        频率      累计频率     频率%    累计频率%\n",
      "[0.0, 51.233)       732  0.821549  0.821549  82.15%   82.15%\n",
      "[51.233, 102.466)   106  0.118967  0.940516  11.90%   94.05%\n",
      "[102.466, 153.699)   31  0.034792  0.975309   3.48%   97.53%\n",
      "[153.699, 204.932)    2  0.002245  0.977553   0.22%   97.76%\n",
      "[204.932, 256.165)   11  0.012346  0.989899   1.23%   98.99%\n",
      "[256.165, 307.398)    6  0.006734  0.996633   0.67%   99.66%\n",
      "[307.398, 358.63)     0  0.000000  0.996633   0.00%   99.66%\n",
      "[358.63, 409.863)     0  0.000000  0.996633   0.00%   99.66%\n",
      "[409.863, 461.096)    0  0.000000  0.996633   0.00%   99.66%\n",
      "[461.096, 512.842)    3  0.003367  1.000000   0.34%  100.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---箱线图---\n",
      "\n",
      "简单直观的异常值检测方法:箱形图（箱线图）\n",
      "箱形图中，从上到下依次有 6 个数据节点，\n",
      "分别是上界、上四分位、均值、中位数、下四分位、下界。\n",
      "而那些超过上界的值就会被标记为离群点，也就是异常数据。\n",
      "    \n",
      "0       7.2500\n",
      "1      71.2833\n",
      "2       7.9250\n",
      "3      53.1000\n",
      "4       8.0500\n",
      "        ...   \n",
      "886    13.0000\n",
      "887    30.0000\n",
      "888    23.4500\n",
      "889    30.0000\n",
      "890     7.7500\n",
      "Name: Fare, Length: 891, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_distribution(df['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HvleMj45UfWe"
   },
   "source": [
    "我们还得做一些处理，仔细看看Age和Fare两个属性，乘客的数值幅度变化，也忒大了吧！！\n",
    "\n",
    "如果大家了解逻辑回归与梯度下降的话，会知道，各属性值之间scale差距太大，将对收敛速度造成几万点伤害值！甚至不收敛，所以我们先用scikit-learn里面的preprocessing模块对它们做一个scaling，所谓 scaling，其实就是将一些变化幅度较大的特征化到 [-1,1] 之内。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FiZ7ZWg46Lyj"
   },
   "source": [
    "### 因子化、目标特征离散化\n",
    "\n",
    "1. fit\n",
    "\n",
    "用于计算训练数据的均值和方差， 后面就会用均值和方差来转换训练数据\n",
    "\n",
    "2. fit_transform\n",
    "\n",
    "不仅计算训练数据的均值和方差，还会基于计算出来的均值和方差来转换训练数据，从而把数据转换成标准的正态分布\n",
    "\n",
    "3. transform\n",
    "\n",
    "很显然，它只是进行转换，只是把训练数据转换成标准的正态分布\n",
    "\n",
    "### 一般使用方法：\n",
    "\n",
    "1. 先用fit\n",
    "\n",
    "`scaler = preprocessing.StandardScaler().fit(X)`\n",
    "\n",
    "这一步可以得到scaler，scaler里面存的有计算出来的均值和方差\n",
    "\n",
    "2. 再用transform\n",
    "\n",
    "`scaler.transform(X)`\n",
    "\n",
    "这一步再用scaler中的均值和方差来转换X，使X标准化\n",
    "\n",
    "3. 那么在预测的时候， 也要对数据做同样的标准化处理，即也要用上面的scaler中的均值和方差来对预测时候的特征进行标准化\n",
    "\n",
    "注意：测试数据和预测数据的标准化的方式要和训练数据标准化的方式一样， 必须用同一个scaler来进行transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "id": "r5YrH7TH5bTf",
    "outputId": "e11777a1-ba54-4c4b-e36d-1def6a201ec3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---Age Scale---\n",
      "[29.64721448]\n",
      "[185.56530827]\n",
      "---Fare Scale---\n",
      "[32.20420797]\n",
      "[2466.66531169]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-08ad3eba-1a9c-45ef-950a-7c9d4cfab761\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin_No</th>\n",
       "      <th>Cabin_Yes</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Age_scaled</th>\n",
       "      <th>Fare_scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.561377</td>\n",
       "      <td>-0.502445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.613173</td>\n",
       "      <td>0.786845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.267740</td>\n",
       "      <td>-0.488854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.392945</td>\n",
       "      <td>0.420730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.392945</td>\n",
       "      <td>-0.486337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.194330</td>\n",
       "      <td>-0.386671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.781606</td>\n",
       "      <td>-0.044381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>16.185117</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.988244</td>\n",
       "      <td>-0.176263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.267740</td>\n",
       "      <td>-0.044381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.172717</td>\n",
       "      <td>-0.492378</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 18 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08ad3eba-1a9c-45ef-950a-7c9d4cfab761')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
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       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-08ad3eba-1a9c-45ef-950a-7c9d4cfab761');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "     PassengerId  Survived        Age  SibSp  Parch     Fare  Cabin_No  \\\n",
       "0              1         0  22.000000      1      0   7.2500         1   \n",
       "1              2         1  38.000000      1      0  71.2833         0   \n",
       "2              3         1  26.000000      0      0   7.9250         1   \n",
       "3              4         1  35.000000      1      0  53.1000         0   \n",
       "4              5         0  35.000000      0      0   8.0500         1   \n",
       "..           ...       ...        ...    ...    ...      ...       ...   \n",
       "886          887         0  27.000000      0      0  13.0000         1   \n",
       "887          888         1  19.000000      0      0  30.0000         0   \n",
       "888          889         0  16.185117      1      2  23.4500         1   \n",
       "889          890         1  26.000000      0      0  30.0000         0   \n",
       "890          891         0  32.000000      0      0   7.7500         1   \n",
       "\n",
       "     Cabin_Yes  Embarked_C  Embarked_Q  Embarked_S  Sex_female  Sex_male  \\\n",
       "0            0           0           0           1           0         1   \n",
       "1            1           1           0           0           1         0   \n",
       "2            0           0           0           1           1         0   \n",
       "3            1           0           0           1           1         0   \n",
       "4            0           0           0           1           0         1   \n",
       "..         ...         ...         ...         ...         ...       ...   \n",
       "886          0           0           0           1           0         1   \n",
       "887          1           0           0           1           1         0   \n",
       "888          0           0           0           1           1         0   \n",
       "889          1           1           0           0           0         1   \n",
       "890          0           0           1           0           0         1   \n",
       "\n",
       "     Pclass_1  Pclass_2  Pclass_3  Age_scaled  Fare_scaled  \n",
       "0           0         0         1   -0.561377    -0.502445  \n",
       "1           1         0         0    0.613173     0.786845  \n",
       "2           0         0         1   -0.267740    -0.488854  \n",
       "3           1         0         0    0.392945     0.420730  \n",
       "4           0         0         1    0.392945    -0.486337  \n",
       "..        ...       ...       ...         ...          ...  \n",
       "886         0         1         0   -0.194330    -0.386671  \n",
       "887         1         0         0   -0.781606    -0.044381  \n",
       "888         0         0         1   -0.988244    -0.176263  \n",
       "889         1         0         0   -0.267740    -0.044381  \n",
       "890         0         0         1    0.172717    -0.492378  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.preprocessing as preprocessing\n",
    "scaler = preprocessing.StandardScaler()\n",
    "\n",
    "age_scale_param = scaler.fit(df[['Age']])\n",
    "df['Age_scaled'] = scaler.fit_transform(df[['Age']], age_scale_param)\n",
    "\n",
    "print('---Age Scale---')\n",
    "print(age_scale_param.mean_) # 均值\n",
    "print(age_scale_param.var_)  # 方差\n",
    "\n",
    "fare_scale_param = scaler.fit(df[['Fare']])\n",
    "df['Fare_scaled'] = scaler.fit_transform(df[['Fare']], fare_scale_param)\n",
    "\n",
    "print('---Fare Scale---')\n",
    "print(fare_scale_param.mean_) # 均值\n",
    "print(fare_scale_param.var_)  # 方差\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rD2mvai17w6t"
   },
   "source": [
    "万事俱备，只欠建模。马上就要看到成效了。\n",
    "\n",
    "我们把需要的属性值抽出来，转成scikit-learn里面LogisticRegression可以处理的格式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FgqE2Hkn7y5k"
   },
   "source": [
    "## 逻辑回归建模\n",
    "\n",
    "我们把需要的 feature 字段取出来，转成 numpy 格式，使用 scikit-learn 中的 LogisticRegression 建模。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "lPA7Pbol7yGB",
    "outputId": "816c654d-5a31-485a-ca07-5f0d6d36654d"
   },
   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(tol=1e-06)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(tol=1e-06)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(tol=1e-06)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "# 用正则取出我们要的属性值\n",
    "train_np = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "\n",
    "# y即Survival结果\n",
    "y = train_np.iloc[:, 0]\n",
    "\n",
    "# X即特征属性值\n",
    "X = train_np.iloc[:, 1:]\n",
    "\n",
    "train_df = train_np\n",
    "\n",
    "# fit到RandomForestRegressor之中\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l2', tol=1e-6)\n",
    "clf.fit(X, y)\n",
    "    \n",
    "clf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u2N-CzEC85Ym"
   },
   "source": [
    "很顺利，我们得到了一个model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IuQQVMPd9ANT"
   },
   "source": [
    "你以为把 test.csv 直接丢进 model 里就能拿到结果啊，图样图森破啊！\n",
    "\n",
    "我们的\"test_data\"也要做和\"train_data\"一样的预处理啊！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "0jFU7nrz7xtl",
    "outputId": "4b1e245c-8532-431d-c791-94d6a72b5ea1"
   },
   "outputs": [
    {
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       "      <th>Age_scaled</th>\n",
       "      <th>Fare_scaled</th>\n",
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       "</table>\n",
       "<p>418 rows × 17 columns</p>\n",
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       "\n",
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "          const element = document.querySelector('#df-686422fa-4bf4-4f17-a392-1164e489db26');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "     PassengerId        Age  SibSp  Parch      Fare  Cabin_No  Cabin_Yes  \\\n",
       "0            892  34.500000      0      0    7.8292         1          0   \n",
       "1            893  47.000000      1      0    7.0000         1          0   \n",
       "2            894  62.000000      0      0    9.6875         1          0   \n",
       "3            895  27.000000      0      0    8.6625         1          0   \n",
       "4            896  22.000000      1      1   12.2875         1          0   \n",
       "..           ...        ...    ...    ...       ...       ...        ...   \n",
       "413         1305  30.705727      0      0    8.0500         1          0   \n",
       "414         1306  39.000000      0      0  108.9000         0          1   \n",
       "415         1307  38.500000      0      0    7.2500         1          0   \n",
       "416         1308  30.705727      0      0    8.0500         1          0   \n",
       "417         1309  25.755877      1      1   22.3583         1          0   \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_S  Sex_female  Sex_male  Pclass_1  \\\n",
       "0             0           1           0           0         1         0   \n",
       "1             0           0           1           1         0         0   \n",
       "2             0           1           0           0         1         0   \n",
       "3             0           0           1           0         1         0   \n",
       "4             0           0           1           1         0         0   \n",
       "..          ...         ...         ...         ...       ...       ...   \n",
       "413           0           0           1           0         1         0   \n",
       "414           1           0           0           1         0         1   \n",
       "415           0           0           1           0         1         0   \n",
       "416           0           0           1           0         1         0   \n",
       "417           1           0           0           0         1         0   \n",
       "\n",
       "     Pclass_2  Pclass_3  Age_scaled  Fare_scaled  \n",
       "0           0         1    0.307526    -0.496637  \n",
       "1           0         1    1.256242    -0.511497  \n",
       "2           1         0    2.394702    -0.463335  \n",
       "3           0         1   -0.261704    -0.481704  \n",
       "4           0         1   -0.641190    -0.416740  \n",
       "..        ...       ...         ...          ...  \n",
       "413         0         1    0.019551    -0.492680  \n",
       "414         0         0    0.649064     1.314641  \n",
       "415         0         1    0.611115    -0.507017  \n",
       "416         0         1    0.019551    -0.492680  \n",
       "417         0         1   -0.356130    -0.236263  \n",
       "\n",
       "[418 rows x 17 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = pd.read_csv(\"/content/test.csv\")\n",
    "data_test.loc[data_test.Fare.isnull(), 'Fare' ] = 0\n",
    "# 接着我们对 test_data 做和 train_data 中一致的特征变换\n",
    "# 首先用同样的 RandomForestRegressor 模型填上丢失的年龄\n",
    "tmp_df = data_test[['Age','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "null_age = tmp_df[data_test.Age.isnull()]\n",
    "# 根据特征属性X预测年龄并补上\n",
    "X = null_age.iloc[:, 1:]\n",
    "predictedAges = rfr.predict(X)\n",
    "data_test.loc[data_test.Age.isnull(), 'Age' ] = predictedAges\n",
    "\n",
    "data_test = set_Cabin_type(data_test)\n",
    "\n",
    "dummies_Cabin = pd.get_dummies(data_test['Cabin'], prefix= 'Cabin')\n",
    "dummies_Embarked = pd.get_dummies(data_test['Embarked'], prefix= 'Embarked')\n",
    "dummies_Sex = pd.get_dummies(data_test['Sex'], prefix= 'Sex')\n",
    "dummies_Pclass = pd.get_dummies(data_test['Pclass'], prefix= 'Pclass')\n",
    "\n",
    "\n",
    "df_test = pd.concat([data_test, dummies_Cabin, dummies_Embarked, dummies_Sex, dummies_Pclass], axis=1)\n",
    "\n",
    "df_test.drop(['Pclass', 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], axis=1, inplace=True)\n",
    "\n",
    "df_test['Age_scaled'] = scaler.fit_transform(df_test[['Age']], age_scale_param)\n",
    "df_test['Fare_scaled'] = scaler.fit_transform(df_test[['Fare']], fare_scale_param)\n",
    "\n",
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9_zEXXTO9o5_"
   },
   "source": [
    "不错不错，数据很OK，差最后一步了。\n",
    "下面就做预测取结果吧！！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "0MxVWzot9tjk"
   },
   "outputs": [],
   "source": [
    "test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "predictions = clf.predict(test)\n",
    "result = pd.DataFrame({\n",
    "        'PassengerId':data_test['PassengerId'], \n",
    "        'Survived':predictions.astype(np.int32)\n",
    "        }\n",
    "    )\n",
    "result.to_csv(\"/content/logistic_regression_predictions.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZxqIbZlE98Sr"
   },
   "source": [
    "## 逻辑回归系统优化\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hLTQxnisSO5r"
   },
   "source": [
    "sklearn中Logistics Regression的coef_和intercept_的具体意义\n",
    "\n",
    "使用`sklearn`库可以很方便的实现各种基本的机器学习算法，例如今天说的逻辑斯谛回归（Logistic Regression），我在实现完之后，可能陷入代码太久，忘记基本的算法原理了，突然想不到 `coef_` 和 `intercept_` 具体是代表什么意思了，就是具体到公式中的哪个字母，虽然总体知道代表的是模型参数。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jcD8oCCqSXrw",
    "outputId": "c15170a0-fcd0-4cbd-8c3b-a384c50a5c1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4.51740321 -1.56048296]\n",
      " [-0.08918381  0.72635442]\n",
      " [ 4.60658703  0.83412854]]\n",
      "[-1.69874938  2.89695153 -1.19820215]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# 构造一些数据点\n",
    "centers = [[-5, 0], [0, 1.5], [5, -1]]\n",
    "X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)\n",
    "transformation = [[0.4, 0.2], [-0.4, 1.2]]\n",
    "X = np.dot(X, transformation)\n",
    "\n",
    "clf2 = LogisticRegression(solver='sag', max_iter=100, random_state=42, multi_class='multinomial').fit(X, y)\n",
    "\n",
    "print(clf2.coef_ )\n",
    "print(clf2.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hQQLnwlyS8hO"
   },
   "source": [
    "可以看到 `clf.coef_` 是一个3×2`(n_class, n_features)`的矩阵，`clf.intercept_`是一个1×3的矩阵（向量），那么这些到底是什么意思呢？\n",
    "\n",
    "我们来回顾一下 Logistic 回归的模型:\n",
    "$$\n",
    "\\mathrm{h}_\\theta(\\mathrm{x})=\\frac{1}{1+\\mathrm{e}^{\\left(-\\theta^{\\mathrm{T}} \\mathrm{x}\\right)}}\n",
    "$$\n",
    "其中 $\\theta$ 是模型参数, 其实 $\\theta^{\\mathrm{T}} \\mathrm{x}$ 就是一个线性表达式, 将这个表达式的结果再一次利用 Logistic 函数映射到 $0 \\sim 1$ 之间。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xY7QK3Sr-CXY"
   },
   "source": [
    "### 模型系数关联分析\n",
    "\n",
    "这才刚撸完 baseline model 啊,还得优化啊！！！\n",
    "\n",
    "看过Andrew Ng老师的machine Learning课程的同学们，知道，我们应该分析分析模型现在的状态了，是过/欠拟合？，以确定我们需要更多的特征还是更多数据，或者其他操作。\n",
    "\n",
    "我们有一条很著名的learning curves对吧。\n",
    "\n",
    "不过在现在的场景下，先不着急做这个事情，我们这个baseline系统还有些粗糙，先再挖掘挖掘。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nSjGEAW--TjE"
   },
   "source": [
    "1.  首先，Name和Ticket两个属性被我们完整舍弃了(好吧，其实是因为这俩属性，几乎每一条记录都是一个完全不同的值，我们并没有找到很直接的处理方式)。\n",
    "\n",
    "2.  然后，我们想想，年龄的拟合本身也未必是一件非常靠谱的事情，我们依据其余属性，其实并不能很好地拟合预测出未知的年龄。再一个，以我们的日常经验，小盆友和老人可能得到的照顾会多一些，这样看的话，年龄作为一个连续值，给一个固定的系数，应该和年龄是一个正相关或者负相关，似乎体现不出两头受照顾的实际情况，所以，说不定我们把年龄离散化，按区段分作类别属性会更合适一些。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fwrFaKMX-mZz"
   },
   "source": [
    "上面只是我瞎想的，who knows是不是这么回事呢，老老实实先把得到的model系数和feature关联起来看看。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
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     "height": 488
    },
    "id": "I7BKKhc4-nXd",
    "outputId": "fb3b4d52-71a9-4c64-cecd-631ee6f00119"
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>SibSp</td>\n",
       "      <td>[-0.3496926584355984]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>[-0.11594837829153005]</td>\n",
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       "      <th>10</th>\n",
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       "      <td>[0.2860850112586188]</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pclass_3</td>\n",
       "      <td>[-0.9038179850100043]</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>[-0.5340503256368928]</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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      "text/plain": [
       "        columns                    coef\n",
       "0         SibSp   [-0.3496926584355984]\n",
       "1         Parch  [-0.11594837829153005]\n",
       "2      Cabin_No   [-0.4668144866563084]\n",
       "3     Cabin_Yes   [0.46673068010949337]\n",
       "4    Embarked_C   [0.09104690422277256]\n",
       "5    Embarked_Q   [0.08401916280250503]\n",
       "6    Embarked_S   [-0.3498583736223922]\n",
       "7    Sex_female     [1.315782811475313]\n",
       "8      Sex_male    [-1.315866618022128]\n",
       "9      Pclass_1    [0.6176491672045659]\n",
       "10     Pclass_2    [0.2860850112586188]\n",
       "11     Pclass_3   [-0.9038179850100043]\n",
       "12   Age_scaled   [-0.5340503256368928]\n",
       "13  Fare_scaled    [0.0922075064668107]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"columns\":list(train_df.columns)[1:], \"coef\":list(clf.coef_.T)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zbm8mzcV-xmk"
   },
   "source": [
    "这些系数为正的特征，和最后结果是一个正相关，反之为负相关。\n",
    "\n",
    "我们先看看那些权重绝对值非常大的feature，在我们的模型上：\n",
    "\n",
    "* Sex 属性，如果是 female 会极大提高最后获救的概率，而 male 会很大程度拉低这个概率。\n",
    "* Pclass 属性，1 等舱乘客最后获救的概率会上升，而乘客等级为 3 会极大地拉低这个概率。\n",
    "* 有 Cabin 值会很大程度拉升最后获救概率(**这里似乎能看到了一点端倪，事实上从最上面的有无Cabin记录的Survived分布图上看出，即使有Cabin记录的乘客也有一部分遇难了，估计这个属性上我们挖掘还不够**)\n",
    "* Age 是一个负相关，意味着在我们的模型里，年龄越小，越有获救的优先权(**还得回原数据看看这个是否合理**）\n",
    "* 有一个登船港口S会很大程度拉低获救的概率，另外俩港口压根就没啥作用(**这个实际上非常奇怪，因为我们从之前的统计图上并没有看到S港口的获救率非常低，所以也许可以考虑把登船港口这个feature去掉试试**)。\n",
    "* 船票 Fare 有小幅度的正相关(**并不意味着这个feature作用不大，有可能是我们细化的程度还不够，举个例子，说不定我们得对它离散化，再分至各个乘客等级上？**)\n",
    "\n",
    "噢啦，观察完了，我们现在有一些想法了，但是怎么样才知道，哪些优化的方法是promising的呢？\n",
    "\n",
    "因为test.csv里面并没有Survived这个字段(这就是我们要预测的结果)，我们无法在这份数据上评定我们算法在该场景下的效果…\n",
    "\n",
    "而『每做一次调整就make a submission，然后根据结果来判定这次调整的好坏』其实是行不通的…"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2j9j9NwZ-y-C"
   },
   "source": [
    "## 交叉验证\n",
    "\n",
    "重点又来了：\n",
    "\n",
    "**『要做交叉验证(cross validation)!』**  \n",
    "**『要做交叉验证(cross validation)!』**  \n",
    "**『要做交叉验证(cross validation)!』**\n",
    "\n",
    "恩，重要的事情说三遍。我们通常情况下，这么做cross validation：\n",
    "\n",
    "把train.csv分成两部分，\n",
    "\n",
    "* 一部分用于训练我们需要的模型，\n",
    "* 另外一部分数据上看我们预测算法的效果。\n",
    "\n",
    "我们用scikit-learn的cross_validation来帮我们完成小数据集上的这个工作。\n",
    "\n",
    "先简单看看cross validation情况下的打分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gNaH2fbY-3nc",
    "outputId": "24e25684-8cf7-4687-8996-ec2f05caeb99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.81564246 0.80898876 0.79213483 0.78651685 0.81460674]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "# 简单看看打分情况\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l2', tol=1e-6)\n",
    "all_data = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "X = all_data.iloc[:,1:]\n",
    "y = all_data.iloc[:,0]\n",
    "print(model_selection.cross_val_score(clf, X, y, cv=5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dyQjo9xj_yyW"
   },
   "source": [
    "似乎比Kaggle上的结果略高哈，毕竟用的是不是同一份数据集评估的。\n",
    "\n",
    "等等，既然我们要做交叉验证，那我们干脆先把交叉验证里面的bad case拿出来看看，看看人眼审核，是否能发现什么蛛丝马迹，是我们忽略了哪些信息，使得这些乘客被判定错了。再把bad case上得到的想法和前头系数分析的合在一起，然后逐个试试。\n",
    "\n",
    "下面我们做数据分割，并且在原始数据集上瞄一眼bad case（用原始的数据来测试模型，**但是预测出来的结果不对照不代表就是错了**）："
   ]
  },
  {
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   "execution_count": 81,
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    },
    "id": "jnUsNgt9X2W3",
    "outputId": "21552c04-a455-4a7d-c97d-6443ffce1ec1"
   },
   "outputs": [
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       "     PassengerId  Survived        Age  SibSp  Parch     Fare  Cabin_No  \\\n",
       "0              1         0  22.000000      1      0   7.2500         1   \n",
       "1              2         1  38.000000      1      0  71.2833         0   \n",
       "2              3         1  26.000000      0      0   7.9250         1   \n",
       "3              4         1  35.000000      1      0  53.1000         0   \n",
       "4              5         0  35.000000      0      0   8.0500         1   \n",
       "..           ...       ...        ...    ...    ...      ...       ...   \n",
       "886          887         0  27.000000      0      0  13.0000         1   \n",
       "887          888         1  19.000000      0      0  30.0000         0   \n",
       "888          889         0  16.185117      1      2  23.4500         1   \n",
       "889          890         1  26.000000      0      0  30.0000         0   \n",
       "890          891         0  32.000000      0      0   7.7500         1   \n",
       "\n",
       "     Cabin_Yes  Embarked_C  Embarked_Q  Embarked_S  Sex_female  Sex_male  \\\n",
       "0            0           0           0           1           0         1   \n",
       "1            1           1           0           0           1         0   \n",
       "2            0           0           0           1           1         0   \n",
       "3            1           0           0           1           1         0   \n",
       "4            0           0           0           1           0         1   \n",
       "..         ...         ...         ...         ...         ...       ...   \n",
       "886          0           0           0           1           0         1   \n",
       "887          1           0           0           1           1         0   \n",
       "888          0           0           0           1           1         0   \n",
       "889          1           1           0           0           0         1   \n",
       "890          0           0           1           0           0         1   \n",
       "\n",
       "     Pclass_1  Pclass_2  Pclass_3  Age_scaled  Fare_scaled  \n",
       "0           0         0         1   -0.561377    -0.502445  \n",
       "1           1         0         0    0.613173     0.786845  \n",
       "2           0         0         1   -0.267740    -0.488854  \n",
       "3           1         0         0    0.392945     0.420730  \n",
       "4           0         0         1    0.392945    -0.486337  \n",
       "..        ...       ...       ...         ...          ...  \n",
       "886         0         1         0   -0.194330    -0.386671  \n",
       "887         1         0         0   -0.781606    -0.044381  \n",
       "888         0         0         1   -0.988244    -0.176263  \n",
       "889         1         0         0   -0.267740    -0.044381  \n",
       "890         0         0         1    0.172717    -0.492378  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # 原始数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
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    "id": "Ww72T63R_3Sc",
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       "      <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>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Arnold-Franchi, Mrs. Josef (Josefine Franchi)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>349237</td>\n",
       "      <td>17.8000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19947</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moubarek, Master. Gerios</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2661</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Miss. Erna Alexandra</td>\n",
       "      <td>female</td>\n",
       "      <td>17.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3101281</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...</td>\n",
       "      <td>female</td>\n",
       "      <td>33.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3101278</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Jussila, Miss. Katriina</td>\n",
       "      <td>female</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4136</td>\n",
       "      <td>9.8250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Mrs. Joseph (Sultana)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>205</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Cohen, Mr. Gurshon \"Gus\"</td>\n",
       "      <td>male</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 3540</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Zabour, Miss. Thamine</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2665</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>252</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Strom, Mrs. Wilhelm (Elna Matilda Persson)</td>\n",
       "      <td>female</td>\n",
       "      <td>29.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347054</td>\n",
       "      <td>10.4625</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>262</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Master. Edvin Rojj Felix</td>\n",
       "      <td>male</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Henry, Miss. Delia</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>382649</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>268</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Persson, Mr. Ernst Ulrik</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>347083</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Abbott, Mrs. Stanton (Rosa Hunt)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 2673</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>284</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dorking, Mr. Edward Arthur</td>\n",
       "      <td>male</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 10482</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Haas, Miss. Aloisia</td>\n",
       "      <td>female</td>\n",
       "      <td>24.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349236</td>\n",
       "      <td>8.8500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Saalfeld, Mr. Adolphe</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19988</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>C106</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McCoy, Mr. Bernard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>367226</td>\n",
       "      <td>23.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>313</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Lahtinen, Mrs. William (Anna Sylfven)</td>\n",
       "      <td>female</td>\n",
       "      <td>26.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250651</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>339</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahl, Mr. Karl Edwart</td>\n",
       "      <td>male</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7598</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>363</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Barbara, Mrs. (Catherine David)</td>\n",
       "      <td>female</td>\n",
       "      <td>45.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2691</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>391</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Carter, Mr. William Ernest</td>\n",
       "      <td>male</td>\n",
       "      <td>36.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113760</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Jussila, Miss. Mari Aina</td>\n",
       "      <td>female</td>\n",
       "      <td>21.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4137</td>\n",
       "      <td>9.8250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>448</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Seward, Mr. Frederic Kimber</td>\n",
       "      <td>male</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113794</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Strandberg, Miss. Ida Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7553</td>\n",
       "      <td>9.8375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>484</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Turkula, Mrs. (Hedwig)</td>\n",
       "      <td>female</td>\n",
       "      <td>63.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>490</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Coutts, Master. Eden Leslie \"Neville\"</td>\n",
       "      <td>male</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C.A. 37671</td>\n",
       "      <td>15.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Canavan, Miss. Mary</td>\n",
       "      <td>female</td>\n",
       "      <td>21.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>364846</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>504</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laitinen, Miss. Kristina Sofia</td>\n",
       "      <td>female</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4135</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>506</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Penasco y Castellana, Mr. Victor de Satode</td>\n",
       "      <td>male</td>\n",
       "      <td>18.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C65</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>565</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meanwell, Miss. (Marion Ogden)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 392087</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>571</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Harris, Mr. George</td>\n",
       "      <td>male</td>\n",
       "      <td>62.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S.W./PP 752</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>588</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Frolicher-Stehli, Mr. Maxmillian</td>\n",
       "      <td>male</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13567</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>643</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Skoog, Miss. Margit Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>347088</td>\n",
       "      <td>27.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>644</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Foo, Mr. Choong</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>648</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Simonius-Blumer, Col. Oberst Alfons</td>\n",
       "      <td>male</td>\n",
       "      <td>56.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13213</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A26</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Hegarty, Miss. Hanora \"Nora\"</td>\n",
       "      <td>female</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365226</td>\n",
       "      <td>6.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peters, Miss. Katie</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330935</td>\n",
       "      <td>8.1375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>713</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Taylor, Mr. Elmer Zebley</td>\n",
       "      <td>male</td>\n",
       "      <td>48.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19996</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>C126</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hawksford, Mr. Walter James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16988</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>D45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>762</th>\n",
       "      <td>763</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Barah, Mr. Hanna Assi</td>\n",
       "      <td>male</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2663</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>789</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Dean, Master. Bertram Vere</td>\n",
       "      <td>male</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>C.A. 2315</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>839</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Chip, Mr. Chang</td>\n",
       "      <td>male</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Marechal, Mr. Pierre</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11774</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>C47</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>853</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Boulos, Miss. Nourelain</td>\n",
       "      <td>female</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2678</td>\n",
       "      <td>15.2458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "14            15         0       3   \n",
       "49            50         0       3   \n",
       "55            56         1       1   \n",
       "65            66         1       3   \n",
       "68            69         1       3   \n",
       "85            86         1       3   \n",
       "113          114         0       3   \n",
       "140          141         0       3   \n",
       "204          205         1       3   \n",
       "240          241         0       3   \n",
       "251          252         0       3   \n",
       "261          262         1       3   \n",
       "264          265         0       3   \n",
       "267          268         1       3   \n",
       "271          272         1       3   \n",
       "279          280         1       3   \n",
       "283          284         1       3   \n",
       "293          294         0       3   \n",
       "298          299         1       1   \n",
       "301          302         1       3   \n",
       "312          313         0       2   \n",
       "338          339         1       3   \n",
       "362          363         0       3   \n",
       "390          391         1       1   \n",
       "402          403         0       3   \n",
       "447          448         1       1   \n",
       "474          475         0       3   \n",
       "483          484         1       3   \n",
       "489          490         1       3   \n",
       "501          502         0       3   \n",
       "503          504         0       3   \n",
       "505          506         0       1   \n",
       "564          565         0       3   \n",
       "570          571         1       2   \n",
       "587          588         1       1   \n",
       "642          643         0       3   \n",
       "643          644         1       3   \n",
       "647          648         1       1   \n",
       "654          655         0       3   \n",
       "680          681         0       3   \n",
       "712          713         1       1   \n",
       "740          741         1       1   \n",
       "762          763         1       3   \n",
       "788          789         1       3   \n",
       "803          804         1       3   \n",
       "838          839         1       3   \n",
       "839          840         1       1   \n",
       "852          853         0       3   \n",
       "882          883         0       3   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.00      0   \n",
       "49       Arnold-Franchi, Mrs. Josef (Josefine Franchi)  female  18.00      1   \n",
       "55                                   Woolner, Mr. Hugh    male    NaN      0   \n",
       "65                            Moubarek, Master. Gerios    male    NaN      1   \n",
       "68                     Andersson, Miss. Erna Alexandra  female  17.00      4   \n",
       "85   Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...  female  33.00      3   \n",
       "113                            Jussila, Miss. Katriina  female  20.00      1   \n",
       "140                      Boulos, Mrs. Joseph (Sultana)  female    NaN      0   \n",
       "204                           Cohen, Mr. Gurshon \"Gus\"    male  18.00      0   \n",
       "240                              Zabour, Miss. Thamine  female    NaN      1   \n",
       "251         Strom, Mrs. Wilhelm (Elna Matilda Persson)  female  29.00      1   \n",
       "261                  Asplund, Master. Edvin Rojj Felix    male   3.00      4   \n",
       "264                                 Henry, Miss. Delia  female    NaN      0   \n",
       "267                           Persson, Mr. Ernst Ulrik    male  25.00      1   \n",
       "271                       Tornquist, Mr. William Henry    male  25.00      0   \n",
       "279                   Abbott, Mrs. Stanton (Rosa Hunt)  female  35.00      1   \n",
       "283                         Dorking, Mr. Edward Arthur    male  19.00      0   \n",
       "293                                Haas, Miss. Aloisia  female  24.00      0   \n",
       "298                              Saalfeld, Mr. Adolphe    male    NaN      0   \n",
       "301                                 McCoy, Mr. Bernard    male    NaN      2   \n",
       "312              Lahtinen, Mrs. William (Anna Sylfven)  female  26.00      1   \n",
       "338                              Dahl, Mr. Karl Edwart    male  45.00      0   \n",
       "362                    Barbara, Mrs. (Catherine David)  female  45.00      0   \n",
       "390                         Carter, Mr. William Ernest    male  36.00      1   \n",
       "402                           Jussila, Miss. Mari Aina  female  21.00      1   \n",
       "447                        Seward, Mr. Frederic Kimber    male  34.00      0   \n",
       "474                        Strandberg, Miss. Ida Sofia  female  22.00      0   \n",
       "483                             Turkula, Mrs. (Hedwig)  female  63.00      0   \n",
       "489              Coutts, Master. Eden Leslie \"Neville\"    male   9.00      1   \n",
       "501                                Canavan, Miss. Mary  female  21.00      0   \n",
       "503                     Laitinen, Miss. Kristina Sofia  female  37.00      0   \n",
       "505         Penasco y Castellana, Mr. Victor de Satode    male  18.00      1   \n",
       "564                     Meanwell, Miss. (Marion Ogden)  female    NaN      0   \n",
       "570                                 Harris, Mr. George    male  62.00      0   \n",
       "587                   Frolicher-Stehli, Mr. Maxmillian    male  60.00      1   \n",
       "642                      Skoog, Miss. Margit Elizabeth  female   2.00      3   \n",
       "643                                    Foo, Mr. Choong    male    NaN      0   \n",
       "647                Simonius-Blumer, Col. Oberst Alfons    male  56.00      0   \n",
       "654                       Hegarty, Miss. Hanora \"Nora\"  female  18.00      0   \n",
       "680                                Peters, Miss. Katie  female    NaN      0   \n",
       "712                           Taylor, Mr. Elmer Zebley    male  48.00      1   \n",
       "740                        Hawksford, Mr. Walter James    male    NaN      0   \n",
       "762                              Barah, Mr. Hanna Assi    male  20.00      0   \n",
       "788                         Dean, Master. Bertram Vere    male   1.00      1   \n",
       "803                    Thomas, Master. Assad Alexander    male   0.42      0   \n",
       "838                                    Chip, Mr. Chang    male  32.00      0   \n",
       "839                               Marechal, Mr. Pierre    male    NaN      0   \n",
       "852                            Boulos, Miss. Nourelain  female   9.00      1   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.00      0   \n",
       "\n",
       "     Parch             Ticket      Fare    Cabin Embarked  \n",
       "14       0             350406    7.8542      NaN        S  \n",
       "49       0             349237   17.8000      NaN        S  \n",
       "55       0              19947   35.5000      C52        S  \n",
       "65       1               2661   15.2458      NaN        C  \n",
       "68       2            3101281    7.9250      NaN        S  \n",
       "85       0            3101278   15.8500      NaN        S  \n",
       "113      0               4136    9.8250      NaN        S  \n",
       "140      2               2678   15.2458      NaN        C  \n",
       "204      0           A/5 3540    8.0500      NaN        S  \n",
       "240      0               2665   14.4542      NaN        C  \n",
       "251      1             347054   10.4625       G6        S  \n",
       "261      2             347077   31.3875      NaN        S  \n",
       "264      0             382649    7.7500      NaN        Q  \n",
       "267      0             347083    7.7750      NaN        S  \n",
       "271      0               LINE    0.0000      NaN        S  \n",
       "279      1          C.A. 2673   20.2500      NaN        S  \n",
       "283      0         A/5. 10482    8.0500      NaN        S  \n",
       "293      0             349236    8.8500      NaN        S  \n",
       "298      0              19988   30.5000     C106        S  \n",
       "301      0             367226   23.2500      NaN        Q  \n",
       "312      1             250651   26.0000      NaN        S  \n",
       "338      0               7598    8.0500      NaN        S  \n",
       "362      1               2691   14.4542      NaN        C  \n",
       "390      2             113760  120.0000  B96 B98        S  \n",
       "402      0               4137    9.8250      NaN        S  \n",
       "447      0             113794   26.5500      NaN        S  \n",
       "474      0               7553    9.8375      NaN        S  \n",
       "483      0               4134    9.5875      NaN        S  \n",
       "489      1         C.A. 37671   15.9000      NaN        S  \n",
       "501      0             364846    7.7500      NaN        Q  \n",
       "503      0               4135    9.5875      NaN        S  \n",
       "505      0           PC 17758  108.9000      C65        C  \n",
       "564      0  SOTON/O.Q. 392087    8.0500      NaN        S  \n",
       "570      0        S.W./PP 752   10.5000      NaN        S  \n",
       "587      1              13567   79.2000      B41        C  \n",
       "642      2             347088   27.9000      NaN        S  \n",
       "643      0               1601   56.4958      NaN        S  \n",
       "647      0              13213   35.5000      A26        C  \n",
       "654      0             365226    6.7500      NaN        Q  \n",
       "680      0             330935    8.1375      NaN        Q  \n",
       "712      0              19996   52.0000     C126        S  \n",
       "740      0              16988   30.0000      D45        S  \n",
       "762      0               2663    7.2292      NaN        C  \n",
       "788      2          C.A. 2315   20.5750      NaN        S  \n",
       "803      1               2625    8.5167      NaN        C  \n",
       "838      0               1601   56.4958      NaN        S  \n",
       "839      0              11774   29.7000      C47        C  \n",
       "852      1               2678   15.2458      NaN        C  \n",
       "882      0               7552   10.5167      NaN        S  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分割数据，按照 训练数据:cv数据 = 7:3的比例\n",
    "split_train, split_cv = model_selection.train_test_split(df, test_size=0.3, random_state=0)\n",
    "train_df = split_train.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "# 生成模型\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l2', tol=1e-6)\n",
    "clf.fit(train_df.iloc[:,1:], train_df.iloc[:,0]) # 训练得到一个模型。\n",
    "\n",
    "# 对cross validation数据进行预测\n",
    "cv_df = split_cv.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass_.*')\n",
    "predictions = clf.predict(cv_df.iloc[:,1:]) # 用之前的模型，来进行预测\n",
    "\n",
    "origin_data_train = pd.read_csv(\"/content/train.csv\")\n",
    "bad_cases = origin_data_train.loc[origin_data_train['PassengerId'].isin(split_cv[predictions != cv_df.iloc[:,0]]['PassengerId'].values)]\n",
    "bad_cases\n"
   ]
  },
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   "metadata": {
    "id": "2E8kvxrxAeEj"
   },
   "source": [
    "大家可以自己跑一遍试试，拿到bad cases之后，仔细看看。也会有一些猜测和想法。其中会有一部分可能会印证在系数分析部分的猜测，那这些优化的想法优先级可以放高一些。\n",
    "\n",
    "现在有了\"train_df\" 和 “vc_df” 两个数据部分，前者用于训练model，后者用于评定和选择模型。可以开始可劲折腾了。\n",
    "\n",
    "我们随便列一些可能可以做的优化操作：\n",
    "\n",
    "* Age 属性不使用现在的拟合方式，而是根据名称中的『Mr』『Mrs』『Miss』等的平均值进行填充。\n",
    "* Age 不做成一个连续值属性，而是使用一个步长进行离散化，变成离散的类目feature。\n",
    "* Cabin 再细化一些，对于有记录的Cabin属性，我们将其分为前面的字母部分(我猜是位置和船层之类的信息) 和 后面的数字部分(应该是房间号，有意思的事情是，如果你仔细看看原始数据，你会发现，这个值大的情况下，似乎获救的可能性高一些)。\n",
    "* Pclass 和 Sex 俩太重要了，我们试着用它们去组出一个组合属性来试试，这也是另外一种程度的细化。\n",
    "* 单加一个 Child 字段，Age<=12 的，设为1，其余为0(你去看看数据，确实小盆友优先程度很高啊)\n",
    "* 如果名字里面有『Mrs』，而Parch>1的，我们猜测她可能是一个母亲，应该获救的概率也会提高，因此可以多加一个Mother字段，此种情况下设为1，其余情况下设为0\n",
    "* 登船港口可以考虑先去掉试试(Q和C本来就没权重，S有点诡异)\n",
    "* 把堂兄弟/兄妹 和 Parch 还有自己 个数加在一起组一个Family_size字段(考虑到大家族可能对最后的结果有影响)\n",
    "* Name是一个我们一直没有触碰的属性，我们可以做一些简单的处理，比如说男性中带某些字眼的(‘Capt’, ‘Don’, ‘Major’, ‘Sir’)可以统一到一个Title，女性也一样。\n",
    "\n",
    "大家接着往下挖掘，可能还可以想到更多可以细挖的部分。我这里先列这些了，然后我们可以使用手头上的\"train_df\"和\"cv_df\"开始试验这些feature engineering的tricks是否有效了。\n",
    "\n",
    "试验的过程比较漫长，也需要有耐心，而且我们经常会面临很尴尬的状况，就是我们灵光一闪，想到一个feature，然后坚信它一定有效，结果试验下来，效果还不如试验之前的结果。恩，需要坚持和耐心，以及不断的挖掘。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1L2RJSQ4AfRY"
   },
   "source": [
    "我最好的结果是在『Survived~C(Pclass)+C(Title)+C(Sex)+C(Age_bucket)+C(Cabin_num_bucket)Mother+Fare+Family_Size』下取得的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rKgihwoGAlBz"
   },
   "source": [
    "## Learning curves\n",
    "\n",
    "有一个很可能发生的问题是，我们不断地做feature engineering，产生的特征越来越多，用这些特征去训练模型，会**对我们的训练集拟合得越来越好，同时也可能在逐步丧失泛化能力**，从而在待预测的数据上，表现不佳，也就是发生**过拟合**问题。\n",
    "\n",
    "从另一个角度上说，如果模型在待预测的数据上表现不佳，除掉上面说的过拟合问题，也有可能是欠拟合问题，也就是说在训练集上，其实拟合的也不是那么好。\n",
    "\n",
    "额，这个欠拟合和过拟合怎么解释呢。这么说吧：\n",
    "\n",
    "* 过拟合就像是你班那个学数学比较刻板的同学，老师讲过的题目，一字不漏全记下来了，于是老师再出一样的题目，分分钟精确出结果。but数学考试，因为总是碰到新题目，所以成绩不咋地。\n",
    "* 欠拟合就像是差生。连老师讲的练习题也记不住，于是连老师出一样题目复习的周测都做不好，考试更是可想而知了。\n",
    "\n",
    "而在机器学习的问题上，对于**过拟合**和**欠拟合**两种情形。我们优化的方式是不同的。\n",
    "\n",
    "对过拟合而言，通常以下策略对结果优化是有用的：\n",
    "\n",
    "* 做一下feature selection，挑出较好的feature的subset来做training\n",
    "* 提供更多的数据，从而弥补原始数据的bias问题，学习到的model也会更准确\n",
    "\n",
    "而对于欠拟合而言，我们通常需要更多的feature，更复杂的模型来提高准确度。\n",
    "\n",
    "著名的learning curve可以帮我们判定我们的模型现在所处的状态。我们以样本数为横坐标，训练和交叉验证集上的错误率作为纵坐标，两种状态分别如下两张图所示：过拟合(overfitting/high variace)，欠拟合(underfitting/high bias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gfeOywtxgZDN"
   },
   "source": [
    "![xuBP2q](https://oss.images.shujudaka.com/uPic/xuBP2q.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kx_32CfQApH5"
   },
   "source": [
    "我们也可以把错误率替换成准确率(得分)，得到另一种形式的learning curve(sklearn 里面是这么做的)。\n",
    "\n",
    "回到我们的问题，我们用scikit-learn里面的learning_curve来帮我们分辨我们模型的状态。\n",
    "\n",
    "举个例子，这里我们一起画一下我们最先得到的baseline model的learning curve。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 485
    },
    "id": "LIZGb_KKAv1y",
    "outputId": "0a078249-1fdf-47d3-a2e1-3258bb672ccf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8055053665368028, 0.027829684691101142)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "# 用sklearn的learning_curve得到training_score和cv_score，使用matplotlib画出learning curve\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1, \n",
    "                        train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True):\n",
    "    \"\"\"\n",
    "    画出data在某模型上的learning curve.\n",
    "    参数解释\n",
    "    ----------\n",
    "    estimator : 你用的分类器。\n",
    "    title : 表格的标题。\n",
    "    X : 输入的feature，numpy类型\n",
    "    y : 输入的target vector\n",
    "    ylim : tuple格式的(ymin, ymax), 设定图像中纵坐标的最低点和最高点\n",
    "    cv : 做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份)\n",
    "    n_jobs : 并行的的任务数(默认1)\n",
    "    \"\"\"\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, verbose=verbose)\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    if plot:\n",
    "        plt.figure()\n",
    "        plt.title(title, fontproperties=zhfont)\n",
    "        if ylim is not None:\n",
    "            plt.ylim(*ylim)\n",
    "        plt.xlabel(u\"训练样本数\", fontproperties=zhfont)\n",
    "        plt.ylabel(u\"得分\", fontproperties=zhfont)\n",
    "        plt.gca().invert_yaxis()\n",
    "        plt.grid()\n",
    "\n",
    "        plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, \n",
    "                         alpha=0.1, color=\"b\")\n",
    "        plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, \n",
    "                         alpha=0.1, color=\"r\")\n",
    "        plt.plot(train_sizes, train_scores_mean, 'o-', color=\"b\", label=u\"训练集上得分\")\n",
    "        plt.plot(train_sizes, test_scores_mean, 'o-', color=\"r\", label=u\"交叉验证集上得分\")\n",
    "\n",
    "        plt.legend(loc=\"best\",prop=zhfont)\n",
    "\n",
    "        # plt.draw()\n",
    "        # plt.show()\n",
    "        plt.gca().invert_yaxis()\n",
    "\n",
    "    midpoint = ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] - test_scores_std[-1])) / 2\n",
    "    diff = (train_scores_mean[-1] + train_scores_std[-1]) - (test_scores_mean[-1] - test_scores_std[-1])\n",
    "    return midpoint, diff\n",
    "\n",
    "plot_learning_curve(clf, u\"学习曲线\", X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nh6s92kkCUMV"
   },
   "source": [
    "在实际数据上看，我们得到的learning curve没有理论推导的那么光滑哈，但是可以大致看出来，训练集和交叉验证集上的得分曲线走势还是符合预期的。\n",
    "\n",
    "目前的曲线看来，我们的model并不处于overfitting的状态(overfitting的表现一般是训练集上得分高，而交叉验证集上要低很多，中间的gap比较大)。因此我们可以再做些feature engineering的工作，添加一些新产出的特征或者组合特征到模型中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "niIHb4IuCWDZ"
   },
   "source": [
    "## 模型融合(model ensemble)\n",
    "\n",
    "好了，终于到这一步了，我们要祭出机器学习/数据挖掘上通常最后会用到的大杀器了。恩，模型融合。\n",
    "\n",
    "『强迫症患者』打算继续喊喊口号…  \n",
    "**『模型融合(model ensemble)很重要！』**  \n",
    "**『模型融合(model ensemble)很重要！』**  \n",
    "**『模型融合(model ensemble)很重要！』**  \n",
    "重要的事情说三遍，恩，噢啦。\n",
    "\n",
    "先解释解释，一会儿再回到我们的问题上哈。  \n",
    "啥叫模型融合呢，我们还是举几个例子直观理解一下好了。\n",
    "\n",
    "大家都看过知识问答的综艺节目中，求助现场观众时候，让观众投票，最高的答案作为自己的答案的形式吧，每个人都有一个判定结果，最后我们相信答案在大多数人手里。\n",
    "\n",
    "再通俗一点举个例子。你和你班某数学大神关系好，每次作业都『模仿』他的，于是绝大多数情况下，他做对了，你也对了。突然某一天大神脑子犯糊涂，手一抖，写错了一个数，于是…恩，你也只能跟着错了。  \n",
    "我们再来看看另外一个场景，你和你班5个数学大神关系都很好，每次都把他们作业拿过来，对比一下，再『自己做』，那你想想，如果哪天某大神犯糊涂了，写错了，but另外四个写对了啊，那你肯定相信另外4人的是正确答案吧？\n",
    "\n",
    "最简单的模型融合大概就是这么个意思，比如分类问题，当我们手头上**有一堆在同一份数据集上训练得到的分类器(比如Logistic regression，SVM，KNN，random forest，神经网络)**，那我们让他们**都分别去做判定，然后对结果做投票统计，取票数最多的结果为最后结果**。\n",
    "\n",
    "问题就这么完美的解决了。\n",
    "\n",
    "模型融合可以比较好地缓解，训练过程中产生的过拟合问题，从而对于结果的准确度提升有一定的帮助。\n",
    "\n",
    "话说回来，回到我们现在的问题。你看，我们现在只讲了logistic regression，如果我们还想用这个融合思想去提高我们的结果，我们该怎么做呢？\n",
    "\n",
    "既然这个时候模型没得选，那咱们就在数据上动动手脚咯。大家想想，如果模型出现过拟合现在，一定是在我们的训练上出现拟合过度造成的对吧。\n",
    "\n",
    "那我们干脆就不要用全部的训练集，每次取训练集的一个subset，做训练，这样，我们虽然用的是同一个机器学习算法，但是得到的模型却是不一样的；同时，因为我们没有任何一份子数据集是全的，因此即使出现过拟合，也是在子训练集上出现过拟合，而不是全体数据上，这样做一个融合，可能对最后的结果有一定的帮助。对，这就是常用的Bagging。\n",
    "\n",
    "我们用scikit-learn里面的Bagging来完成上面的思路，过程非常简单。代码如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "zMoBl4kRCaJD"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "train_df = df.filter(regex='Survived|Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*|Mother|Child|Family|Title')\n",
    "train_np = train_df\n",
    "\n",
    "# y即Survival结果\n",
    "y = train_np.iloc[:, 0]\n",
    "\n",
    "# X即特征属性值\n",
    "X = train_np.iloc[:, 1:]\n",
    "\n",
    "# fit到BaggingRegressor之中\n",
    "clf = linear_model.LogisticRegression(C=1.0, penalty='l2', tol=1e-6)\n",
    "bagging_clf = BaggingRegressor(clf, n_estimators=20, max_samples=0.8, max_features=1.0, bootstrap=True, bootstrap_features=False, n_jobs=-1)\n",
    "bagging_clf.fit(X, y)\n",
    "\n",
    "test = df_test.filter(regex='Age_.*|SibSp|Parch|Fare_.*|Cabin_.*|Embarked_.*|Sex_.*|Pclass.*|Mother|Child|Family|Title')\n",
    "predictions = bagging_clf.predict(test)\n",
    "result = pd.DataFrame({'PassengerId':data_test['PassengerId'], 'Survived':predictions.astype(np.int32)})\n",
    "result.to_csv(\"/content/logistic_regression_bagging_predictions.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RAb-tkYoCuDM"
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   "source": [
    "## 总结\n",
    "\n",
    "文章稍微有点长，非常感谢各位耐心看到这里。  \n",
    "总结的部分，我就简短写几段，出现的话，很多在文中有对应的场景，大家有兴趣再回头看看。\n",
    "\n",
    "对于任何的机器学习问题，不要一上来就追求尽善尽美，**先用自己会的算法撸一个baseline的model出来，再进行后续的分析步骤，一步步提高**。\n",
    "\n",
    "在问题的结果过程中：\n",
    "\n",
    "* **『对数据的认识太重要了！』**\n",
    "* **『数据中的特殊点/离群点的分析和处理太重要了！』**\n",
    "* **『特征工程(feature engineering)太重要了！』**\n",
    "* **『模型融合(model ensemble)太重要了！』**"
   ]
  }
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