Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Предисловие\n",
    "\n",
    "Во время работы над этим блокнотом я пользовался материалами из [Titanic Data Science Solutions](https://www.kaggle.com/startupsci/titanic-data-science-solutions)\n",
    "\n",
    "Дополнительно я использую [шпаргалку](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) для работы с текстом Markdown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Стадии рабочего процесса\n",
    "\n",
    "1. Вопрос, определение проблемы;\n",
    "2. Создание данных для обучения и тестирования;\n",
    "3. Обработка и очистка данных;\n",
    "4. Анализ и выявление закономерностей в данных;\n",
    "5. Моделирование, прогнозирование, решение поставленной задачи;\n",
    "6. Визуализация, составление отчета, презентация задачи и последовательности её решения;\n",
    "7. Отправление результатов.\n",
    "\n",
    "Это общая схема рабочего процесса. Однако, могут быть нюансы, например:\n",
    "\n",
    "* Некоторые шаги могут выполняться одновременно, например, анализ данных может происходить в процессе визуализации;\n",
    "* Шаги могут повторяться несколько раз, например, визуализация может оказаться полезной многократно;\n",
    "* Какие-то шаги могут идти раньше, чем обычно, например анализ данных может происходить до обработки;\n",
    "* Какой-то шаг может быть опущен.\n",
    "\n",
    "## Определение задачи\n",
    "Постановка задачи о Титанике:\n",
    "\n",
    "> Knowing from a training set of samples listing passengers who survived or did not survive the Titanic disaster, can our model determine based on a given test dataset not containing the survival information, if these passengers in the test dataset survived or not.\n",
    "\n",
    "Или в переводе на русский язык:\n",
    "\n",
    "> Из тренировочного набора данных о выживших и погибших пассажирах во время катастрофы на Титанике, построить и обучить модель, предсказывающую выживание или гибель пассажиров в тестовом наборе данных.\n",
    "\n",
    "Суть происходящего:\n",
    "\n",
    "* 15 апреля 1912 года во время своего первого круиза Титаник затонул, столкнувшись с айсбергом. Погибли 1502 человека из 2224. Базовый уровень выживаемости 32%\n",
    "* Одной из причин гибели столь большого количества людей стала нехватка спасательных шлюпок.\n",
    "* Несмотря на присутствие фактора удачи в выживании людей, некоторые группы людей имели больше шансов на спасение, например, женщины, дети и люди из высшего класса."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Цели рабочего процесса\n",
    "\n",
    "**Классификация.** Данные для обучения модели нужно зачастую классифицировать. Также может потребоваться осознать последствия или корреляцию разных классов с нашей целью прогнозирования.\n",
    "\n",
    "**Корреляция.** Какие параметры вносят значительный вклад в целевую переменную (выживаемость)? Статистически говоря, существует ли [корреляция](https://en.wikiversity.org/wiki/Correlation) между независимыми и целевой переменными? Это работает как для численных, так и для категориальных переменных. Можно также заранее определить корреляции между независимыми переменными для последующих возможных задач.\n",
    "\n",
    "**Конвертация.** Для стадии моделирования потребуется подготовить данные. Для некоторых методов потребуется преобразовать текстовые категориальные данные в числовые.\n",
    "\n",
    "**Дополнение.** Подготовка данных также может подразумевать заполнение пропусков в данных. Модельные алгоритмы лучше работают в случаях, когда нет пропусков в данных.\n",
    "\n",
    "**Корректировка.** Также может потребоваться изучить набор данных на предмет наличия ошибок и выбросов, которые могут значительно повлиять на результаты прогнозов. \n",
    "\n",
    "**Создание.** Можем ли мы создать новые переменные, основанные на старых, такие, чтобы они помогали нам достигать целей корреляции, конвертации и дополнения данных.\n",
    "\n",
    "**Построение графиков.** Выбор правильного метода визуализации зависит от природы данных и целей нашего прогноза"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# обработка информации\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# визуализация\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Получение данных\n",
    "Загружаем данные и объединяем их в один датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('train.csv')\n",
    "test_df = pd.read_csv('test.csv')\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ через описание данных\n",
    "\n",
    "** Какие переменные вообще есть в наборе данных?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Какие переменные являются категориальными?**\n",
    "\n",
    "Категориальные переменные бывают номинальными, порядковыми (ordinal), соотношениями или интервальными. Размышления об этом позволяют строить правильные графики\n",
    "\n",
    "* Категориальные: Survived, Sex, Emarked. Порядковой при этом будет: PClass\n",
    "\n",
    "**Какие переменные численные?**\n",
    "\n",
    "Численные переменные бывают непрерывными, дискретными и временным рядом. Аналогично, понимание данных помогает принять решение о правильной визуализации.\n",
    "\n",
    "* Непрерывные: Age, Fare. Дискретные: SibSp, Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим на данные\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Какие переменные обладают смешанным типом данных?**\n",
    "\n",
    "Это важно понимать, чтобы не совершить ошибку (<span style=\"color:red\">какую?</span>). В нашем наборе данных:\n",
    "\n",
    "* Ticket является смесью численных и численно-буквенных данных. Cabin содержит численно-буквенные данные\n",
    "\n",
    "**Какие переменные могут содержать ошибки или опечатки?**\n",
    "\n",
    "Этот вопрос сложно задавать применительно к большим наборам данных, однако несколько примеров из небольшого набора могут сразу же сказать, какие переменные потребуют корректировки.\n",
    "\n",
    "* Переменная Name может содержать ошибки или опечатки, поскольку есть несколько способов указать имя, включая титулы и звания, круглые скобки, цитирование и так далее."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
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       "      <td>Montvila, Rev. Juozas</td>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
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       "      <td>19.0</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Какие переменные содержать пустые значения?**\n",
    "\n",
    "Это потребует корректировки.\n",
    "\n",
    "* Cabin > Age > Embarked содержат некоторое количество пустых значений в таком порядке тренировочного набора данных.\n",
    "* Cabin > Age неполны в тестовом наборе данных\n",
    "\n",
    "**Каковы типы данных для разных переменных?**\n",
    "\n",
    "Это поможет нам во время конвертации\n",
    "\n",
    "* Семь переменных являются целыми или числами с плавающей точкой. Шесть в случае тестового набора данных\n",
    "* Пять переменных являются строковыми (object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print(\"_\"*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Каково распределение значений численных переменных по данным?**\n",
    "\n",
    "Это поможет нам определить, насколько репрезентативен тренировочный набор даных по отношению к реальной области решения задачи.\n",
    "\n",
    "* Общий объем выборки 891 или 40% общего количества пассажиров на Титанике (2224)\n",
    "* Survived - категориальная переменная со значениями 0 и 1\n",
    "* Около 38% из выборки выжили, реальная выживаемость 32%\n",
    "* Большинство пассажиров (>75%) путешествовали без родителей или детей *(где это он разглядел?)*. \n",
    "* Примерно 30% пассажиров ехали с родственниками или супругами\n",
    "* Стоимость билетов значительно различалась, несколько пассажиров (<1%) заплатили целых $512.\n",
    "* Несколько очень пожилых пассажиров (<1%) в возрасте 65-80 лет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Каково распределение категориальных переменных?**\n",
    "\n",
    "* Имена уникальны по всему объему выборки\n",
    "* 65% мужчин\n",
    "* В переменной Cabin (каюта) есть несколько дубликатов. Очевидно некоторые пассажиры путешествовали в одной каюте.\n",
    "* Переменная Embarked имеет три возможных пункта назначения для пассажиров, большинство ехали в S.\n",
    "* У переменной Ticket большое количество дубликатов 22% (unique=681)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Garside, Miss. Ethel</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name   Sex  Ticket    Cabin Embarked\n",
       "count                    891   891     891      204      889\n",
       "unique                   891     2     681      147        3\n",
       "top     Garside, Miss. Ethel  male  347082  B96 B98        S\n",
       "freq                       1   577       7        4      644"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Предположения построенные на анализе данных**\n",
    "\n",
    "Из проведенного выше анализа уже можно сделать некоторые предположения, которые впоследствии можно будет скорректировать прежде чем предпринимать активные действия.\n",
    "\n",
    "**Корреляция**\n",
    "Нам бы хотелось знать, насколько каждый из независимых признаков коррелирует с Survival. Мы хотим выяснить это на ранней стадии проекта и затем сравнивать эти корреляции со полученными моделями в дальнейшем.\n",
    "\n",
    "**Дополнение**\n",
    "1. Возможно нам потребуется дополнить Age, поскольку она совершенно точно связана с Survival\n",
    "2. Также возможно нам потребуется дополнить Embarked, поскольку она может коррелировать с выживаемостью.\n",
    "\n",
    "**Исправление**\n",
    "1. Переменная Ticket (билет) может быть выброшена из нашего анализа, поскольку она содержит большое количество дубликатов (22%) и вероятно не будет корреляции между номером билета и выживаемостью.\n",
    "2. Переменная Cabin (каюта)　также может быть выброшена из рассмотрения, поскольку она содержит множество нулевых значений как в тренировочном, так и в тестовом наборе данных.\n",
    "3. PassengerId может быть отброшена, поскольку нет вклада в выживаемость\n",
    "4. Переменная Name не дает напрямую вклада в выживаемость, поэтому может быть отброшена.\n",
    "\n",
    "**Создание**\n",
    "1. Нам может потребоваться создать новую переменную под названием Family, основанную на Parch и SibSp, чтобы получить общее количество членов семей на борту.\n",
    "2. Нам потребуется обработать переменную Name, чтобы достать значение статуса пассажира (Title) в качестве новой переменной\n",
    "3. Возможно потребуется создать порядковую категориальную переменную из Age.\n",
    "4. Также стоит создать переменную Fare по диапазонам стоимости билетов.\n",
    "\n",
    "**Классификация**\n",
    "\n",
    "Также нужно добавить к нашим предположениям, основываясь на описании задачи\n",
    "1. Женщины (Sex=female) выживуют с большей вероятностью.\n",
    "2. Дети (Age<?) также выживут с большей вероятностью.\n",
    "3. Пассажиры первого класса (Pclass=1) выживут с большей вероятностью."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ через группировку по признакам\n",
    "Чтобы проверить некоторые из наших наблюдений и предположений, мы можем быстро проанализировать связи между признаками, сгруппировав одни относительно других.  На этой стадии мы можем провернуть такую процедуру для признаков, у которых нет пустых значений. Также есть смысл делать это только для прищнаков, которые являются категориальными (Sex), порядковыми (Pclass) или дискретными (SibSP, Parch). \n",
    "\n",
    "* __Pclass.__ Можно наблюдать большую корреляцию (>0.5) между Pclass=1 и Survived. Решаем оставить этот признак в нашей модели.\n",
    "* __Sex.__ Действительно, женщины имеют высокую выживаемость (74%)\n",
    "* __SibSP и Parch.__ Для этих переменных потребуется создать дополнительную переменную или набор переменных (создание №1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
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      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass','Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Sex','Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['SibSp','Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Parch','Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Анализ через визуализацию данных\n",
    "__Сравниваем численные признаки__\n",
    "Начнем с выяснения отношений между численными признаками и нашей целевой переменной (Survival)\n",
    "\n",
    "Гистограмма в этом случае очень полезна, позволяет сравнить непрерывные переменные типа возраста. Таким образом мы можем ответить на вопросы, касающиеся некоторых групп возрастов, например, более ли вероятно выживание для маленьких детей.\n",
    "\n",
    "Обратите внимание, что ось Oy отражает количество элементов в выборке по определенному возрасту или пассажиров.\n",
    "\n",
    "__Наблюдения__\n",
    "\n",
    "* Младенцы (Age<=4) имеют высокий уровень выживаемости\n",
    "* Самые пожилые пассажиры (Age=80) выжили.\n",
    "* Большая часть пассажиров 15-25 лет не выжили.\n",
    "* Большая часть пассажиров приходится на диапазон 15-35 лет.\n",
    "\n",
    "__Решения__\n",
    "\n",
    "Этот простой анализ подтверждает наши предположения для дальнейших стадий работы.\n",
    "\n",
    "* Мы должны включить возраст (Age) в нашу модель (предположение классификации №2)\n",
    "* Придется дополнить переменную Age, закрыв нулевые значения (дополнение №1)\n",
    "* Нужно будет создать группы по возрастам (создание №3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x5709e01940>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V6DAv24B3VtWLgROAt3UxrQKuq6ojgOu65VE6F7hrh+ULgA918T0CnDOSqJ5yIb0v5L0I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5709e017f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Сопоставление численных и порядковых переменных__\n",
    "\n",
    "Мы можем использовать несколько переменных для определния связей на одном графике. Это может быть сделано для численных и категориальных признаков, которые имеют числовые выражения.\n",
    "\n",
    "__Наблюдения__\n",
    "\n",
    "* Pclass=3 содержит наибольшее количество пассажиров, однако большинство не выжили. \n",
    "* Пассажиры-младенцы из Pclass=2 и Pclass=3 большей частью выжили.\n",
    "* Большинство пассажиров из Pclass=1 выжили. Подтверждает наше предположение по классификации №3\n",
    "* Pclass имеет вариантивность в отношении распределения возрастов пассажиров.\n",
    "\n",
    "__Решения__\n",
    "\n",
    "* Добавить Pclass в нашу модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A7kmyAqC93bqzBavqnKpaVVWrpqamFvjnJA2ZOS9NmIWOAFwKnAy8r729pG8R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5709e01ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Сопоставление категориальных переменных__\n",
    "\n",
    "__Наблюдения__\n",
    "\n",
    "* Женщины выживают гораздо чаще мужчин.\n",
    "* Существует исключение с Embarked=C, где мужчины имеют больший уровень выживаемости. Это может быть корреляцией между Pclass и Embarked, а впоследствии между Pclass и целевой переменной, а не прямой связью между Embarked и Survived\n",
    "* Порты отправления имеют переменные уровни выживаемости для Pclass=3 и среди пассажиров-мужчин.\n",
    "\n",
    "__Решения__\n",
    "\n",
    "* Добавить пол в прогностическую модель.\n",
    "* Дополнить и добавить переменную Embarked в прогностическую модель."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x570a594cc0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dSvEZ4O8isg3niO4b7robRORVYCNQAXxfVc9cATNjTNSxsuzGtG1WeSBE9gSF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\nNQ21rM7Pa3ebPW8d5LPPf5NLR85kSc5CZg2bSiB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TWOPzrPRU+vT29kvq+gUTqKkN8qc1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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x570a594240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Сопоставление категориальных и численных переменных__\n",
    "\n",
    "Мы можем также захотеть сопоставить категориальные переменные (с нечисловыми значениями) и численные переменные. Можно рассмотреть связь между Embarked (категориальная нечисловая переменная), Sex (категориальная нечисловая), Fare (стоимость, числовая непрерывная переменная) с Survived\n",
    "\n",
    "__Наблюдения__\n",
    "\n",
    "* Пассажиры, купившие более дорогие билеты имели больше шансов на выживание. Подтверждает наше предположение сделать стратификацию по диапазонам стоимости билетов.\n",
    "* Порт отправления связан с уровнем выживания.\n",
    "\n",
    "__Решения__\n",
    "\n",
    "* Сделать группы по стоимости билетов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x570a5ea9b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OTHIccCfwmhZjkDRc5rU0JlorAKrqUmB9JwYPbGu7ktpjXkvjwzsBSpLUQRYAkiR1kAWA\nJEkdZAEgSVIHWQBIktRBFgCSJHWQBYAkSR1kASBJUgdZAEiS1EEWAJIkdZAFgCRJHWQBIElSB1kA\nSJLUQRYAkiR1kAWAJEkdZAEgSVIHjaQASHJIkluSrEzynlHEIGm4zGtpYZnzAiDJhsCngUOB3YCj\nkuw213FIGh7zWlp4RtEDsDewsqpuq6pHgdOBw0YQh6ThMa+lBWYUBcD2wF1906uaeZIWLvNaWmAW\njWCbmWRePaVRshRY2kz+JMktrUY1954D3DfqIKby5lEHMFrjuI/Or6pDWghjyrw2p+eHjuc0jN9+\nmnFOj6IAWAU8v296B+DuiY2q6iTgpLkKaq4lWV5VS0Ydh9bPfTQtU+a1Oa35wP30hFGcArgS2CXJ\nzkk2Bl4LnDuCOCQNj3ktLTBz3gNQVWuSvBW4ANgQ+HxVrZjrOCQNj3ktLTyjOAVAVX0d+Pootj2P\njG1X6BhxH02Dee2/lwXC/dRI1VOuv5MkSWPOWwFLktRBFgAzlOTtSW5KclpL6/9Akne1sW7NTJID\nkpw36jjUDnO6e7qe0yO5BmBM/Gfg0Kq6fdSBSBoKc1qdYg/ADCT5LPAC4Nwk703y+SRXJrkmyWFN\nm2OTfDXJ15LcnuStSY5v2nw3ydZNuz9ulr0uydlJNptkey9Mcn6Sq5L8c5IXze07Hh9Jdkpyc5KT\nk9yY5LQkByW5LMmtSfZuhm83++rbSXadZD2bT7bftTCZ0wuXOT0LVeUwgwG4g94dpT4CvK6ZtxXw\nfWBz4FhgJfBMYDHwIPDmpt2JwDub8W361nkC8LZm/APAu5rxi4BdmvF9gG+O+v0v1AHYCVgDvJhe\nAXwV8Hl6d7I7DPgqsCWwqGl/EHB2M34AcF4zPul+H/X7c5jVvw1zegEO5vTMB08BzN7BwKv6zu1t\nAuzYjF9cVQ8DDyd5EPhaM/8G4CXN+O5JTqD3D24Ler+jflySLYCXA19KHr/b6jPaeCMdcntV3QCQ\nZAVwUVVVkhvoHUyeBSxLsgu929luNMk61rffb2o7eLXOnF54zOkZsACYvQCvrqon3dc8yT7Az/tm\nre2bXssTn/2pwOFVdV2SY+lVpP02AB6oqj2GG3anTbVfPkTvQP/7SXYCLplkHZPud40Fc3rhMadn\nwGsAZu8C4G1pSvkkL5vm8s8E7kmyEXD0xBer6iHg9iSvadafJC+dZcx6es8CftiMH7ueNrPd75q/\nzOnxY05PwgJg9j5Erzvp+iQ3NtPT8RfA5cCFwM3raXM0cFyS64AV+Jz1tn0c+Kskl9G7re1kZrvf\nNX+Z0+PHnJ6EdwKUJKmD7AGQJKmDLAAkSeogCwBJkjrIAkCSpA6yAJAkqYMsACRJ6iALAEmSOsgC\nQJKkDrIAkCSpgywAJEnqIAsASZI6yAJAkqQOsgAYUJLHklzbN7xnGssekOS8WW7/kiRLZrjsrLff\nrGejJB9NcmuSG5NckeTQSdp9oHkO+sT5myU5LckNzfKXJtlitnE16/7LJAcNYT3D+qx2TnJ581md\nkWTj2a5Tw2dem9fTXM9bk6xMUkmeM9v1jdqiUQewgPysqvYYxYaTrO/xlXPtQ8B2wO5V9fMkzwV+\naxrLvwP4UVW9GCDJrsAvBl04yaKqWjPZa1X1/mnEMRc+BpxYVacn+SxwHPCZEcekpzKvzevpuAw4\nD7hkxHEMhT0As5TkjiQfSfKdJMuT7JnkgiT/kuTNfU23TPKVJN9L8tkkGzTLf6ZZbkWSD05Y7/uT\nXAq8pm/+BkmWJTmhmT642fbVSb60rvJOckiSm5vl/2AI73Mz4I+Bt1XVzwGq6kdVdeY0VrMd8MN1\nE1V1S3PA2al5/va6bb0ryQea8Uuaz/dbwHubz2XdZ7dZkruabzCnJjkiyaFJzuxb1wFJvtaMz9Vn\nFeAVwFnNrGXA4bNdr+aOeW1eT6aqrqmqO4axrvnAAmBwm+bJXYVH9r12V1XtB/wzcCpwBLAv8Jd9\nbfYG/hR4MfBCnvgH+d6qWgK8BPitJC/pW+aRqvr1qjq9mV4EnAZ8v6rel14X1PuAg6pqT2A5cHyS\nTYD/Afwe8BvA8yZ7Q0l2nfCe+oetJjT/ZeDOqnpowM9rMp8H3t0k6wlJdhlwua2q6req6oPAdTzx\n7eT3gAuqqv/bxoXAvkk2b6aPBM6Y489qG+CBvm81q4DtB3yvmlvmtXk96Gc1djwFMLin6yo8t/l7\nA7BFVT0MPJzkkb5/RFdU1W0ASb4I/Dq9b4h/mGQpvX2xHbAbcH2zzBkTtvM54Myq+nAzvW/T/rLe\nl042Br4DvAi4vapubbb3P4GlE4OuqluAOev+rKprk7wAOBg4CLgyyX7Az6ZY9IwJ40cCFwOvBf5u\nwjbWJDkf+L0kZwG/C/wZvYPLXH1WmWReDbis5pZ5PUsdyuuxYwEwHD9v/q7tG183ve4znvgfQCXZ\nGXgX8GtVdX+SU4FN+tr8dMIy3wZ+O8l/q6pH6P1Hc2FVHdXfKMkek2zvKdI7VzfxYLTOAVX1QN/0\nSmDHJM9sDoQzUlU/Ab4MfDnJWuB3mhj6e6M2mbBY/+dwLvBXSbYG9gK+OclmzgDeAvwrcGVVPZze\n0WGuPqv7gK3yxLnNHYC7p9qG5h3zekAdyeux4ymAubN3eleGb0Cv0r0U2JJeEjyY3oU3T7nydoJT\ngK8DX0qyCPgusH+SX4bHz539CnAzsHOSFzbLHTXZyppzdXusZ3hgQtt/a7b/yTRXtCfZLsnrBv0A\nkuyf5NnN+Mb0KvcfAD8Ctk2yTZJnAK9c3zqaA80VwN8C51XVY5M0uwTYk965zXXJPZefVdH7JnNE\nM+sY4Jz1fzJawMzrjuT1OLIAGNzEc4Ufneby3wE+CtwI3A58paquA64BVtA7j3bZVCupqk8AVwP/\nAPwf4Fjgi0mup5cML2q+RSwF/jG9C2B+MM1Y1+d9wGrge+ld3PPVZnpQLwS+leQGeu97OXB2c67v\nL4HL6V1he/MU6zkDeB3rqdybg8d59A685zXzVjO3n9W76Z2LXEnvmoBThrReDZd5bV4PLMnbk6yi\n16t3fZKTh7HeUUnvy4o0POld6XtHVZ064lAkDYl5PX7sAZAkqYO8CFBtuAQY+/NnUsdcgnk9VjwF\nIElSB3kKQJKkDloQBcAhhxxS9H7T6eDgMLthXjCnHRyGNszYgigA7rvvvlGHIGmIzGlp9BZEASBJ\nkobLAkCSpA6yAJAkqYO8D4Ckee1z37h21CGMlTcd3NmH32mCVnsAkvxJkhVJbkzyxSSbNA/OuDzJ\nrUnOWPcACkkLg3ktjYfWCoAk2wNvB5ZU1e7AhvSe8/wx4MSq2gW4HziurRgkDZd5LY2Ptq8BWETv\naVuLgM2Ae4BXAGc1ry8DDm85BknDZV5LY6C1AqCqfgj8NXAnvQPEg8BVwANVtaZptgrYvq0YJA2X\neS2NjzZPATwbOAzYGfglYHN6z3GeaNI7GSVZmmR5kuWrV0/n0dSS2jKbvDanpfmlzVMABwG3V9Xq\nqvoF8GXg5cBWTdchwA7A3ZMtXFUnVdWSqlqyePHiFsOUNA0zzmtzWppf2iwA7gT2TbJZkgAHAt8D\nLgaOaNocA5zTYgyShsu8lsZEm9cAXE7voqCrgRuabZ0EvBs4PslKYBvglLZikDRc5rU0Plq9EVBV\n/Vfgv06YfRuwd5vbldQe81oaD94KWJKkDrIAkCSpgywAJEnqIAsASZI6yAJAkqQO8nHAkqRZ8ZHN\nwzcXj222B0CSpA6yAJAkqYMsACRJ6iALAEmSOsgCQJKkDrIAkCSpgywAJEnqIAsASZI6yAJAkqQO\narUASLJVkrOS3JzkpiT7Jdk6yYVJbm3+PrvNGCQNl3ktjYe2ewD+Fji/ql4EvBS4CXgPcFFV7QJc\n1ExLWjjMa2kMtFYAJNkS+E3gFICqerSqHgAOA5Y1zZYBh7cVg6ThMq+l8dFmD8ALgNXAF5Jck+Tk\nJJsDz62qewCav9u2GIOk4TKvpTHRZgGwCNgT+ExVvQz4KdPoFkyyNMnyJMtXr17dVoySpmfGeW1O\nS/NLmwXAKmBVVV3eTJ9F78DxoyTbATR/fzzZwlV1UlUtqaolixcvbjFMSdMw47w2p6X5pbUCoKru\nBe5Ksmsz60Dge8C5wDHNvGOAc9qKQdJwmdfS+FjU8vrfBpyWZGPgNuAN9IqOM5McB9wJvKblGCQN\nl3ktjYFWC4CquhZYMslLB7a5XUntMa+l8eCdACVJ6iALAEmSOsgCQJKkDrIAkCSpgywAJEnqIAsA\nSZI6yAJAkqQOGqgASM/rkry/md4xyd7thiZJktoyaA/A3wH7AUc10w8Dn24lIkmS1LpB7wS4T1Xt\nmeQagKq6v7kNqCRJWoAG7QH4RZINgQJIshhY21pUkiSpVYMWAJ8EvgJsm+TDwKXAR1qLSpIktWqg\nUwBVdVqSq+g97CPA4VV1U6uRSZKk1kxZACTZALi+qnYHbm4/JEmS1LYpTwFU1VrguiQ7zkE8kiRp\nDgz6K4DtgBVJrgB+um5mVb2qlagkSVKrBi0APjjTDTS/HlgO/LCqXplkZ+B0YGvgauD1VfXoTNcv\naW6Z09J4GOhXAFX1rcmGAbfxDqD/gsGPASdW1S7A/cBx0wtZ0oiZ09IYGPRWwPsmuTLJT5I8muSx\nJA8NsNwOwO8CJzfTAV4BnNU0WQYcPrPQJc01c1oaH4PeB+BT9G4DfCuwKfDGZt5U/gb4M564adA2\nwANVtaaZXgVsP3C0kkbNnJbGxKDXAFBVK5NsWFWPAV9I8u2na5/klcCPq+qqJAesmz3Zqtez/FJg\nKcCOO/oDBGnUzGlpfrvqqqu2XbRo0cnA7jz5C/5a4MY1a9a8ca+99vrxupmDFgD/1tz7/9okHwfu\nATafYpn9gVcl+R1gE2BLet8etkqyqPnGsANw92QLV9VJwEkAS5YsmfSAImlOmdPSPLZo0aKTn/e8\n5/3q4sWL799ggw0ez7G1a9dm9erVu917770nA4//em/QUwCvb9q+ld7PAJ8PvPrpFqiqP6+qHapq\nJ+C1wDer6mjgYuCIptkxwDkDxiBphMxpad7bffHixQ/1/+cPsMEGG9TixYsfpNcz8Lin7QFIsmNV\n3VlVP2hmPcIsfhLYeDdwepITgGuAU2a5vsd97hvXDmtVarzp4D1GHYLmv9ZyWtK0bDDxP/++F4oJ\nX/qnOgXwVWBPgCRnV9XTfutfn6q6BLikGb8N2Hsm65E0P5jT0sI31SmA/gt8XtBmIJIkae5MVQDU\nesYlSdL8snbt2rWT/TKHZv7a/nlTFQAvTfJQkoeBlzTjDyV5eJAbAUmSpDlz4+rVq581sQhofgXw\nLODG/vlPew1AVW3YQoCSJGnI1qxZ88Z777335HvvvXe99wHobz/wjYAkSdL81dzkZ+Cn9A56HwBJ\nkjRGLAAkSeogCwBJkjrIAkCSpA6yAJAkqYMsACRJ6iALAEmSOsgCQJKkDrIAkCSpgywAJEnqIAsA\nSZI6qLUCIMnzk1yc5KYkK5K8o5m/dZILk9za/H12WzFIGi7zWhofbfYArAH+tKp+FdgXeEuS3YD3\nABdV1S7ARc20pIXBvJbGRGsFQFXdU1VXN+MPAzcB2wOHAcuaZsuAw9uKQdJwmdfS+JiTawCS7AS8\nDLgceG5V3QO9gwmw7VzEIGm4zGtpYWu9AEiyBXA28M6qemgayy1NsjzJ8tWrV7cXoKRpm0lem9PS\n/NJqAZBkI3oHidOq6svN7B8l2a55fTvgx5MtW1UnVdWSqlqyePHiNsOUNA0zzWtzWppf2vwVQIBT\ngJuq6hN9L50LHNOMHwOc01YMkobLvJbGx6IW170/8HrghiTXNvP+C/BR4MwkxwF3Aq9pMQZJw2Ve\nS2OitQKgqi4Fsp6XD2xru5LaY15L46PNHgBpUp/7xrVTN9LA3nTwHqMOQdIC5K2AJUnqIAsASZI6\nyAJAkqQOsgCQJKmDLAAkSeogCwBJkjrIAkCSpA6yAJAkqYMsACRJ6iALAEmSOsgCQJKkDrIAkCSp\ngywAJEnqIAsASZI6yAJAkqQOGkkBkOSQJLckWZnkPaOIQdJwmdfSwjLnBUCSDYFPA4cCuwFHJdlt\nruOQNDzmtbTwjKIHYG9gZVXdVlWPAqcDh40gDknDY15LC8woCoDtgbv6plc18yQtXOa1tMAsGsE2\nM8m8ekqjZCmwtJn8SZJbWo3aebuxAAAWHUlEQVRq7j0HuG/UQUzlzaMOYLTGcR+dX1WHtBDGlHlt\nTs8PHc9pGL/9NOOcHkUBsAp4ft/0DsDdExtV1UnASXMV1FxLsryqlow6Dq2f+2hapsxrc1rzgfvp\nCaM4BXAlsEuSnZNsDLwWOHcEcUgaHvNaWmDmvAegqtYkeStwAbAh8PmqWjHXcUgaHvNaWnhGcQqA\nqvo68PVRbHseGduu0DHiPpoG89p/LwuE+6mRqqdcfydJksactwKWJKmDLABmKMnbk9yU5LSW1v+B\nJO9qY92amSQHJDlv1HGoHeZ093Q9p0dyDcCY+M/AoVV1+6gDkTQU5rQ6xR6AGUjyWeAFwLlJ3pvk\n80muTHJNksOaNscm+WqSryW5PclbkxzftPlukq2bdn/cLHtdkrOTbDbJ9l6Y5PwkVyX55yQvmtt3\nPD6S7JTk5iQnJ7kxyWlJDkpyWZJbk+zdDN9u9tW3k+w6yXo2n2y/a2Eypxcuc3oWqsphBgNwB707\nSn0EeF0zbyvg+8DmwLHASuCZwGLgQeDNTbsTgXc249v0rfME4G3N+AeAdzXjFwG7NOP7AN8c9ftf\nqAOwE7AGeDG9Avgq4PP07mR3GPBVYEtgUdP+IODsZvwA4LxmfNL9Pur35zCrfxvm9AIczOmZD54C\nmL2DgVf1ndvbBNixGb+4qh4GHk7yIPC1Zv4NwEua8d2TnEDvH9wW9H5H/bgkWwAvB76UPH631We0\n8UY65PaqugEgyQrgoqqqJDfQO5g8C1iWZBd6t7PdaJJ1rG+/39R28GqdOb3wmNMzYAEwewFeXVVP\nuq95kn2An/fNWts3vZYnPvtTgcOr6rokx9KrSPttADxQVXsMN+xOm2q/fIjegf73k+wEXDLJOibd\n7xoL5vTCY07PgNcAzN4FwNvSlPJJXjbN5Z8J3JNkI+DoiS9W1UPA7Ule06w/SV46y5j19J4F/LAZ\nP3Y9bWa73zV/mdPjx5yehAXA7H2IXnfS9UlubKan4y+Ay4ELgZvX0+Zo4Lgk1wEr8Dnrbfs48FdJ\nLqN3W9vJzHa/a/4yp8ePOT0J7wQoSVIH2QMgSVIHWQBIktRBFgCSJHWQBYAkSR1kASBJUgdZAEiS\n1EEWAJIkdZAFgCRJHWQBIElSB1kASJLUQRYAkiR1kAWAJEkdZAEwoCSPJbm2b3jPNJY9IMl5s9z+\nJUmWzHDZWW+/Wc/GSf4myb8kWZnkvCQ7rqftqUkOmGT+c5vlrkvyvSRfn21cfes+OcluQ1jPsUk+\nNYT17JXkhuaz+uS6x4xqfjCnzekZrOfDSe5K8pPZrms+WDTqABaQn1XVHqPYcJL1Pb5yrn2E3rPO\nf6WqHkvyBuCcJHtV1doB1/GXwIVV9bcASV4ynQCSbFhVj032WlW9cTrrmgOfAZYC3wW+DhwC/O+R\nRqR+5rQ5PV1fAz4F3DrqQIbBHoBZSnJHko8k+U6S5Un2THJBU1G/ua/plkm+0lTIn02yQbP8Z5rl\nViT54IT1vj/JpcBr+uZvkGRZkhOa6YObbV+d5EtJtmjmH5Lk5mb5PxjC+9wMeAPwJ+uStaq+APwE\nOGgaq9oOWLVuoqqub9b/pG80ST6V5NhmvP+z+LMkV/S12ynJunVckmRJkv+U5ON9bY5N8t+b8dcl\nuaL5xve5dQfiJG9I8v0k3wL2n9aHM4kk2wFbVtV3qvfM7b8HDp/tetU+c9qcXp+q+m5V3TOMdc0H\nFgCD2zRP7i48su+1u6pqP+CfgVOBI4B96VXG6+wN/CnwYuCFPJHA762qJcBLgN+aUD0/UlW/XlWn\nN9OLgNOA71fV+5I8B3gfcFBV7QksB45PsgnwP4DfA34DeN5kbyjJrhPeU/+w1YTmvwzcWVUPTZi/\nHJhOF92ngVOSXJzkvUl+acDl1n0WfwVsnOQFzfwjgTMntD2LJx8gjwTOSPKrzfj+zTe/x4Cjm/+s\nP0jvIPHv1/d+kvz2ej6rb0/SfHv6DorN+PYDvlfNDXPanJ5OTo8dTwEM7um6C89t/t4AbFFVDwMP\nJ3mkL+muqKrbAJJ8Efh1ev+o/zDJUnr7Yjt6/1Cvb5Y5Y8J2PgecWVUfbqb3bdpflt7p5Y2B7wAv\nAm6vqlub7f1Pel3RT1JVtwCDdoEGqPXMH1hVXdAk+iHAocA1SXYfYNH+z+JM4A+Bj9JL/v4DN1W1\nOsltSfal11W3K3AZ8BZgL+DK5vPaFPgxsA9wSVWtBkhyBvArk8R+MdP7vJ6yigGX1dwwp83p6eT0\n2LEAGI6fN3/X9o2vm173GU9MtEqyM/Au4Neq6v4kpwKb9LX56YRlvg38dpL/VlWP0EvUC6vqqP5G\nSfaYZHtPkWRXnnpAWueAqnqgb3ol8O+SPLM5GK6zJ72D3sCq6l+B/wX8r6aL8DeBH/HkHqlNJizW\n/1mcAXwpyZd7q6vJzsedQe+AcjPwlaqq9I4Qy6rqz/sbJjmcwT6v3wZOnOSlf6uql0+YtwrYoW96\nB+DuqbahecOcnoaO5PTY8RTA3Nk7yc7pnSc8ErgU2JJeEjyY5Ln0quencwq9i8m+lGQRvYvL9k/y\ny9A7p5fkV+glyM5JXtgsd9RkK6uqW6pqj/UMD0xo+1NgGfCJvnNsfwQ8Qq8SH0iSV6R37pEkz6TX\ndXon8ANgtyTPSPIs4MD1raOq/oVeV99fsP6D3ZfpnXM/qq/NRcARSbZttr91kn8HXA4ckGSbJBvR\nd352wnYvXs9n9ZQDRXOe8OEk+zYHqT8Cznm6z0YLjjlNd3J6HNkDMLhNk1zbN31+VQ38syF63Xgf\npXe+8J/oVbBrk1wDrABuY4Ckq6pPNMn0D8DRwLHAF5M8o2nyvqr6ftMF+Y9J7qN3YBqkS24qfw78\nf8AtSTYFVgP7NRe5DWov4FNJ1tArQE+uqisBkpxJr6v0VuCaKdZzRhPLzpO92Hz7+h6wW1Vd0cz7\nXpL3Ad9oDtq/AN5SVd9N8gF6++ge4GpgGFdp/yd65483pXf1v78AmF/MaXN6WtK7EPH/BTZLsore\ne/3AbNc7KpnefpZ6kjwPOB/4u6o6aZLXTwVOrapL5jg0STNgTnePPQCakaq6lw5fPCONG3O6e7wG\nQG35KnDHqIOQNDTm9JjxFIAkSR1kD4AkSR20IAqAQw45pOj9ptPBwWF2w7xgTjs4DG2YsQVRANx3\n332jDkHSEJnT0ugtiAJAkiQNlwWAJEkdZAEgSVIHWQBIktRBrRYASf4kyYokNyb5YpJNmodnXJ7k\n1iRnJNm4zRgkDZd5LY2H1gqAJNsDbweWVNXu9B7E8FrgY8CJVbULcD9wXFsxSBou81oaH22fAlhE\n74lbi4DN6D2V6RU88azpZfQe7yhp4TCvpTHQWgFQVT8E/prec6HvAR4ErgIeqKo1TbNVwPZtxSBp\nuMxraXy0eQrg2cBh9J7t/EvA5sChkzSd9E5GSZYmWZ5k+erVq9sKU9I0zCavzWlpfmnzFMBBwO1V\ntbqqfgF8GXg5sFXTdQiwA3D3ZAtX1UlVtaSqlixevLjFMCVNw4zz2pyW5pc2C4A7gX2TbJYkwIHA\n94CLgSOaNscA57QYg6ThMq+lMdHmNQCX07so6GrghmZbJwHvBo5PshLYBjilrRgkDZd5LY2PVM3q\nYUJzYsmSJbV8+fJRhyGNg4w6ADCnpSGacU57J0BJkjrIAkCSpA6yAJAkqYMsACRJ6iALAEmSOsgC\nQJKkDrIAkCSpgywAJEnqIAsASZI6yAJAkqQOsgCQJKmDLAAkSeogCwBJkjrIAkCSpA6yAJAkqYMs\nACRJ6qBWC4AkWyU5K8nNSW5Ksl+SrZNcmOTW5u+z24xB0nCZ19J4aLsH4G+B86vqRcBLgZuA9wAX\nVdUuwEXNtKSFw7yWxkBrBUCSLYHfBE4BqKpHq+oB4DBgWdNsGXB4WzFIGi7zWhofbfYAvABYDXwh\nyTVJTk6yOfDcqroHoPm7bYsxSBou81oaE20WAIuAPYHPVNXLgJ8yjW7BJEuTLE+yfPXq1W3FKGl6\nZpzX5rQ0v7RZAKwCVlXV5c30WfQOHD9Ksh1A8/fHky1cVSdV1ZKqWrJ48eIWw5Q0DTPOa3Naml9a\nKwCq6l7griS7NrMOBL4HnAsc08w7BjinrRgkDZd5LY2PRS2v/23AaUk2Bm4D3kCv6DgzyXHAncBr\nWo5B0nCZ19IYaLUAqKprgSWTvHRgm9uV1B7zWhoP3glQkqQOsgCQJKmDLAAkSeogCwBJkjrIAkCS\npA6yAJAkqYMGKgDS87ok72+md0yyd7uhSZKktgzaA/B3wH7AUc30w8CnW4lIkiS1btAbAe1TVXsm\nuQagqu5v7gImSZIWoEF7AH6RZEOgAJIsBta2FpUkSWrVoAXAJ4GvANsm+TBwKfCR1qKSJEmtGugU\nQFWdluQqevf6DnB4Vd3UamSSJKk1UxYASTYArq+q3YGb2w9JkiS1bcpTAFW1FrguyY5zEI8kSZoD\ng/4KYDtgRZIrgJ+um1lVr2olKkmS1KpBC4APznQDza8HlgM/rKpXJtkZOB3YGrgaeH1VPTrT9Uua\nW+a0NB4G+hVAVX1rsmHAbbwD6L9g8GPAiVW1C3A/cNz0QpY0Yua0NAYGvRXwvkmuTPKTJI8meSzJ\nQwMstwPwu8DJzXSAVwBnNU2WAYfPLHRJc82clsbHoPcB+BS92wDfCmwKvLGZN5W/Af6MJ24atA3w\nQFWtaaZXAdsPHK2kUTOnpTEx8NMAq2olsGFVPVZVXwAOeLr2SV4J/LiqruqfPdmq17P80iTLkyxf\nvXr1oGFKaok5LY2XQS8C/Lfm3v/XJvk4cA+w+RTL7A+8KsnvAJsAW9L79rBVkkXNN4YdgLsnW7iq\nTgJOAliyZMmkBxRJc8qclsbIoD0Ar2/avpXezwCfD7z66Raoqj+vqh2qaifgtcA3q+po4GLgiKbZ\nMcA5M4hb0hwzp6Xx8rQFwLqb/1TVD6rqkap6qKo+WFXHN6cEZuLdwPFJVtI7f3jKDNcjaX4wp6UF\naKpTAF8F9gRIcnZVPe23/vWpqkuAS5rx24C9Z7IeSfODOS0tfFOdAui/wOcFbQYiSZLmzlQFQK1n\nXJIkLWBTnQJ4aXPDnwCb9t38J0BV1ZatRidJklrxtAVAVW04V4FIkqS5M/CNgCRJ0viwAJAkqYMs\nACRJ6iALAEmSOsgCQJKkDrIAkCSpgywAJEnqIAsASZI6yAJAkqQOsgCQJKmDLAAkSeogCwBJkjqo\ntQIgyfOTXJzkpiQrkryjmb91kguT3Nr8fXZbMUgaLvNaGh9t9gCsAf60qn4V2Bd4S5LdgPcAF1XV\nLsBFzbSkhcG8lsZEawVAVd1TVVc34w8DNwHbA4cBy5pmy4DD24pB0nCZ19L4mJNrAJLsBLwMuBx4\nblXdA72DCbDtXMQgabjMa2lha70ASLIFcDbwzqp6aBrLLU2yPMny1atXtxegpGmbSV6b09L80moB\nkGQjegeJ06rqy83sHyXZrnl9O+DHky1bVSdV1ZKqWrJ48eI2w5Q0DTPNa3Naml/a/BVAgFOAm6rq\nE30vnQsc04wfA5zTVgyShsu8lsbHohbXvT/weuCGJNc28/4L8FHgzCTHAXcCr2kxBknDZV5LY6K1\nAqCqLgWynpcPbGu7ktpjXkvjwzsBSpLUQRYAkiR1kAWAJEkd1OZFgHPuc9+4dupGmpY3HbzHqENQ\nx5nXw2VOa52xKgAkSXPPIm345qJQ8xSAJEkdZA+A5pzfFobLLl1JM2EPgCRJHWQBIElSB1kASJLU\nQRYAkiR1kAWAJEkdZAEgSVIHWQBIktRBFgCSJHWQBYAkSR00kgIgySFJbkmyMsl7RhGDpOEyr6WF\nZc4LgCQbAp8GDgV2A45KsttcxyFpeMxraeEZRQ/A3sDKqrqtqh4FTgcOG0EckobHvJYWmFEUANsD\nd/VNr2rmSVq4zGtpgRnF0wAzybx6SqNkKbC0mfxJkltajWruPQe4b9RBTOXNow5gtMZxH51fVYe0\nEMaUeW1Ozw8dz2kYv/0045weRQGwCnh+3/QOwN0TG1XVScBJcxXUXEuyvKqWjDoOrZ/7aFqmzGtz\nWvOB++kJozgFcCWwS5Kdk2wMvBY4dwRxSBoe81paYOa8B6Cq1iR5K3ABsCHw+apaMddxSBoe81pa\neEZxCoCq+jrw9VFsex4Z267QMeI+mgbz2n8vC4T7qZGqp1x/J0mSxpy3ApYkqYMsAGYoyduT3JTk\ntJbW/4Ek72pj3ZqZJAckOW/Ucagd5nT3dD2nR3INwJj4z8ChVXX7qAORNBTmtDrFHoAZSPJZ4AXA\nuUnem+TzSa5Mck2Sw5o2xyb5apKvJbk9yVuTHN+0+W6SrZt2f9wse12Ss5NsNsn2Xpjk/CRXJfnn\nJC+a23c8PpLslOTmJCcnuTHJaUkOSnJZkluT7N0M32721beT7DrJejafbL9rYTKnFy5zehaqymEG\nA3AHvTtKfQR4XTNvK+D7wObAscBK4JnAYuBB4M1NuxOBdzbj2/St8wTgbc34B4B3NeMXAbs04/sA\n3xz1+1+oA7ATsAZ4Mb0C+Crg8/TuZHcY8FVgS2BR0/4g4Oxm/ADgvGZ80v0+6vfnMKt/G+b0AhzM\n6ZkPngKYvYOBV/Wd29sE2LEZv7iqHgYeTvIg8LVm/g3AS5rx3ZOcQO8f3Bb0fkf9uCRbAC8HvpQ8\nfrfVZ7TxRjrk9qq6ASDJCuCiqqokN9A7mDwLWJZkF3q3s91oknWsb7/f1Hbwap05vfCY0zNgATB7\nAV5dVU+6r3mSfYCf981a2ze9lic++1OBw6vquiTH0qtI+20APFBVeww37E6bar98iN6B/veT7ARc\nMsk6Jt3vGgvm9MJjTs+A1wDM3gXA29KU8kleNs3lnwnck2Qj4OiJL1bVQ8DtSV7TrD9JXjrLmPX0\nngX8sBk/dj1tZrvfNX+Z0+PHnJ6EBcDsfYhed9L1SW5spqfjL4DLgQuBm9fT5mjguCTXASvwOett\n+zjwV0kuo3db28nMdr9r/jKnx485PQnvBChJUgfZAyBJUgdZAEiS1EEWAJIkdZAFgCRJHWQBIElS\nB1kAaGDNPdJXJLk+ybXNjVEkLVDmdLd5J0ANJMl+wCuBPavq50meA2w84rAkzZA5LXsANKjtgPuq\n6ucAVXVfVd2dZK8k32qeanZBku2SLGqeqHUAQJK/SvLhUQYv6SnM6Y7zRkAaSPMAk0uBzYD/HzgD\n+DbwLeCwqlqd5EjgP1TVf0zy/wBnAW+ndxeufarq0dFEL2kic1qeAtBAquonSfYCfgP4bXoHixOA\n3YELm9tnbwjc07RfkeQf6D0tbT8PFNL8Yk7LAkADq6rH6D1F65LmMZtvAVZU1X7rWeTFwAPAc+cm\nQknTYU53m9cAaCBJdm2epb3OHvSek724uZiIJBs13YQk+QNgG+A3gU8m2WquY5a0fua0vAZAA2m6\nCv87sBWwBlgJLAV2AD5J73Gbi4C/Ab5C71zigVV1V5K3A3tV1TGjiF3SU5nTsgCQJKmDPAUgSVIH\nWQBIktRBFgCSJHWQBYAkSR1kASBJUgdZAEiS1EEWAJIkdZAFgCRJHfR/AXXBtREJhqndAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x570a6f9e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex','Fare', alpha=0.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обработка данных\n",
    "\n",
    "Собралось несколько предположений и решений относительно наших наборов данных и требований к решению. Пока нам не требовалось видоизменять какие-либо переменные, чтобы прийти к нашим выводам. Теперь будем реализовывать наши решения и предположения по корректированию, созданию и дополнению признаков.\n",
    "\n",
    "__Исправление путем удаления признаков.__\n",
    "\n",
    "Это будет хорошим способом начать. Удаляя признаки мы будем иметь дело с меньшим количеством единиц данных. Это ускорит наш ноутбук и облегчит анализ.\n",
    "\n",
    "Основываясь на предположениях и решениях мы хотим избавиться от Cabin (каюта) и Ticket (билет).\n",
    "\n",
    "Обратить внимание, что там, где это возможно, мы выполняем операции одновременно на тренировочном и тестовом наборах данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: (891, 12) (418, 11) (891, 12) (418, 11)\n",
      "After: (891, 10) (418, 9) (891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "print('Before:', train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket','Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket','Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "print('After:', train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Создание новых признаков через извлечение данных из существующих__\n",
    "\n",
    "Проанализируем признак Name на предмет возможности извлечения титулов и протестируем, есть ли связь между званиями (титулами) и выживаемостью, перед тем как отбросить переменные Name и PassengerId.\n",
    "\n",
    "В следующем ниже коде мы извлекаем признак Title, используя регулярные выражения. Шаблон RegEx `(\\w+\\.)` достает первое слово, которое заканчивается точкой внутри переменной Name. Флаг `expand=False` возвращает объект DataFrame\n",
    "\n",
    "__Наблюдения__\n",
    "\n",
    "Когда мы строим на грфике Title, Age и Survived, мы обнаруживаем следующее.\n",
    "* Большинство званий группируются по возрасту довольно хорошо. Например, титул Master имеет средний возраст в 5 лет.\n",
    "* Некоторые звания в основном выживают (Mme, Lady, Sir) или нет (Don, Rev, Jonkheer).\n",
    "\n",
    "__Решение__\n",
    "\n",
    "* Сохраняем переменную Title для прогностической модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заменим некоторые титулы повсеместно используемыми аналогами или классифицируем их как редкие (`Rare`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Capt', 'Col', 'Countess',\\\n",
    "            'Don','Dr','Jonkheer','Lady','Major','Rev','Sir','Dona'], 'Rare')\n",
    "    \n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms','Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title','Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно сконвертировать категориальные переменные в порядковые"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\":3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "    \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь можно безопасно избавить от переменной Name из тренировочного и тестового набора данных. Также нам не потребуется PassengerId из тренировочного набора данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 9) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name','PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "print(train_df.shape, test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Конвертация категориального признака__\n",
    "\n",
    "Теперь можно конвертировать переменные, которые содержат строки в числовые переменные. Это требуется большинством алгоритмов. Это также позволит нам проще заполнять пропуски в данных.\n",
    "\n",
    "Начнем с конвертации признака Sex в признак, где female=1, а male=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map({\"female\": 1, \"male\": 0}).astype(int)\n",
    "    \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Дополнение числового непрерывного признака__\n",
    "\n",
    "Теперь нужно начать оценивать и дополнять признаки пропущенными или нулевыми значениями. Сначала сделаем это с Age.\n",
    "\n",
    "Рассмотрим три метода заполнения непрерывного численного признака.\n",
    "1. Простой способ создавать случайные числа между средним и среднеквадратическими отклонениями.\n",
    "2. Более точный способ угадать пропущенные значения - использовать другие связанные признаки. В нашем случае мы замечаем связи между Age, Gender и Pclass.  Поэтому можно угадывать значения Age, используя медианные значения для Age по наборам разных комбинаций Pclass и Gender. Таким образом, выбираем медианный возраст для первого класса и женщин, первого класса и мужчин и так далее.\n",
    "3. Сочетать методы 1 и 2. Вместо прогнозирования возрастов, в зависимости от медианного возраста, использовать случайные числа между средним и среднеквадратическим отклонением для разных наборов Pclass и Gender\n",
    "\n",
    "Методы 1 и 3 добавят в нашу модель случайный шум. Это может привести к разным непредсказуемым последствиям. Мы предпочтем метод 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x570b8e0518>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pVwC3dz9XjjvWRYzpBcD67vfHAV8G7gA+BTx03PH1Ef/hwMZun3wG2Hfa9gfw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\nNej/B+1qWsqKF3O5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x570b8e0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist,'Age', alpha=0.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Начнем с создания пустого массива, который впоследствии будет содержать значения Age, спрогнозированные на основании комбинаций Pclass и Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь пройдемся по разным сочетаниям Sex и Pclass и вычислим предполагаемые значения Age для шести комбинаций"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                              (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "            age_guess = guess_df.median()\n",
    "            guess_ages[i,j] = int(age_guess/0.5 + 0.5) * 0.5\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[(dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1), \\\n",
    "                       'Age'] = guess_ages[i,j]\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь создадим несколько групп возрастов AgeBand и определим корреляцию этого созданного признака с Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand','Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заменим переменную Age порядковым признаков, основываясь на группировке, сделанной выше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[dataset['Age']<=16,'Age'] = 0\n",
    "    dataset.loc[(dataset['Age']>16) & (dataset['Age']<=32),'Age'] = 1\n",
    "    dataset.loc[(dataset['Age']>32) & (dataset['Age']<=48),'Age'] = 2\n",
    "    dataset.loc[(dataset['Age']>48) & (dataset['Age']<=64),'Age'] = 3\n",
    "    dataset.loc[dataset['Age']>64,'Age'] = 4\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь можно удалить переменную AgeBand за ненадобностью"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Создание нового признака, комбинируя существующие признаки__\n",
    "\n",
    "Мы можем создать новую переменную FamilySize (размер семьи), которая будет комбинировать переменные Parch (родитель/ребенок) и SibSp (родственник/супруг). Это позволит выбросить впоследствии Parch и SibSp из наших наборов данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize','Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы можем создать переменную IsAlone (в одиночестве) и посмотреть, как она коррелирует с выживаемостью."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "    \n",
    "train_df[['IsAlone','Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбросим переменные Parch, SibSp и FamilySize, оставив только IsAlone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch','SibSp','FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch','SibSp','FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также можно создать искусственный признак, комбинируя Pclass и Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "    \n",
    "train_df.loc[:, ['Age*Class','Age','Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Дополняем категориальную переменную__\n",
    "\n",
    "Переменная Embarked принимает значения S, Q, C в зависимости от порта назначения. Наш тренировочный набор данных имеет в этой колонке два пропущенных значения. Мы просто заполним эти пропуски при помощи самого частого порта назначения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked','Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Конвертация категориального признака в численный__\n",
    "\n",
    "Здесь всё достаточно очевидно, мы заменяем буквенные значения категории числами."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         1      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        1          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        1          6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map({'S':0, 'C':1,'Q':2}).astype(int)\n",
    "    \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Быстрое заполнение и конвертация численного признака__\n",
    "\n",
    "Теперь можно дополнить единственное пропущенное значение переменной Fare, используя медиану по колонке. \n",
    "\n",
    "Обрати внимание, что мы не создаем дополнительных промежуточных переменных и не проводим сложного анализа, чтобы дополнить всего лишь одно пропущенное значение. Наша процедура заполнения пропусков в данном случае имеет тлько цель закрыть нулевые значения, чтобы моделирующий алгоритм мог работать.\n",
    "\n",
    "Также можно округлить стоимость билета до двух знаков после запятой, поскольку она представляет стоимость."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2   7.8292         2      1        1          6\n",
       "1          893       3    1    2   7.0000         0      3        0          6\n",
       "2          894       2    0    3   9.6875         2      1        1          6\n",
       "3          895       3    0    1   8.6625         0      1        1          3\n",
       "4          896       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим группы в признаке Fare, создав новый признак FareBand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'],4)\n",
    "train_df[['FareBand','Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сконвертируем признак Fare в порядковый категориальный признак, основываясь на границах, которые нам поставил FareBand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[dataset['Fare']<=7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare']>7.91) & (dataset['Fare']<=14.454),'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare']>14.454) & (dataset['Fare']<=31),'Fare'] = 2\n",
    "    dataset.loc[dataset['Fare']>31,'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "    \n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогично с тестовым набором данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     1         2      1        1          6\n",
       "3          895       3    0    1     1         0      1        1          3\n",
       "4          896       3    1    1     1         0      3        0          3\n",
       "5          897       3    0    0     1         0      1        1          0\n",
       "6          898       3    1    1     0         2      2        1          3\n",
       "7          899       2    0    1     2         0      1        0          2\n",
       "8          900       3    1    1     0         1      3        1          3\n",
       "9          901       3    0    1     2         0      1        0          3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Моделируем, прогнозируем, решаем задачу.\n",
    "\n",
    "Теперь мы в состоянии обучать наши модели и прогнозировать требуемое решение. Существуе более 60 моделирующих алгоритмов, из которых мы вольны выбирать. Необходимо понимать тип задачи и требования к решению, чтобы сузить выбор до нескольких моделей, которые мы сможем впоследствии оценивать. Наша задача относится к проблемам классификации и регрессии. Мы хотим определить связь между выходной переменной (Survived) и независимыми переменными. Также мы используем вид машинного обучения, который называется \"обучение с учителем\", поскольку мы обучаем нашу модель при помощи тренировочного набора данных. Учитывая эти два критерия - обучение с учителем и классификация и регрессия, мы можем сократить количество вариантов до следующих:\n",
    "\n",
    "* Логистическая регрессия (Logistic Regression)\n",
    "* К ближайших соседей (KNN, k-Nearest Neighbors)\n",
    "* Метод опорных векторов (SVM, Support Vector Machines)\n",
    "* Классификатор наивного Байеса (NB, Naive Bayes classifier)\n",
    "* Дерево решений (Decision Tree)\n",
    "* Случайный лес (Random Forest)\n",
    "* Перцептрон (Perceptron)\n",
    "* Искусственная нейронная сеть (Artificial neural network)\n",
    "* Метод значимых векторов (?) (RVM, Relevance Vector Machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Логистическая регрессия - полезная для применения в начале работы модель. Логистическая регрессия измеряет отношения между категориальной зависимой переменной и одной или несколькими независимыми переменными через оценку вероятностей, используя логистическую функцию. \n",
    "\n",
    "Обрати внимание на качество модели, созданной нашим тренировочным набором данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.260000000000005"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Логистическая регрессия\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train)*100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы можем использовать модель логистической регрессии для оценки наших предположений и решений во время создания переменных и заполнения пропусков. Эту оценку можно провести, рассчитав коэффициенты при независимых переменных в функции решения.\n",
    "\n",
    "Положительные коэффициенты увеличивают логарифмические шансы (log-odds) отклика функции (таким образом, увеличивая вероятность), негативные коэффициенты уменьшают лог-шансы отклика (таким образом, уменьшая вероятность)\n",
    "\n",
    "* Пол (Sex) имеент наивысший положительный коэффициент, то есть по мере увеличения переменной Sex (мужчины - 0, женщины - 1), вероятность того, что Survived = 1 увеличивается наибольшим образом.\n",
    "* Наоборот, по мере увеличения Pclass, вероятность Survived = 1 уменьшается наибыстрейшим образом.\n",
    "* Звание (Title) также подобрано хорошо, имея второй по величине положительный эффект на вероятности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.200978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.414362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.281026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.262084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.022062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.085959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.371647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-1.083515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.200978\n",
       "5      Title     0.414362\n",
       "4   Embarked     0.281026\n",
       "6    IsAlone     0.262084\n",
       "3       Fare    -0.022062\n",
       "7  Age*Class    -0.085959\n",
       "2        Age    -0.371647\n",
       "0     Pclass    -1.083515"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns=['Feature']\n",
    "coeff_df['Correlation'] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дальше используем метод опорных векторов, который по набору тренировочных данных, где каждый элемент отмечен принадлежностью к одной из **двух категорий**, создает модель, которая назначает каждому новому элементу из тестового набора данных первую или вторую категорию. По сути это невероятностный двоичный линейный классификатор. Подробнее можно посмотреть [Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Метод опорных векторов (Support Vector Machines)\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train)*100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Среди алгоритмов, распознающих категории, метод \"К ближайших соседей\" является непараметрическим. Элемент классифицируется по большинству голосов его соседей, ему назначается класс наиболее часто встречающийся среди К его ближайших соседей (К - положительное целое число, обычно небольшое). Если К=1, то объекту просто назначается класс его ближайшего соседа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.060000000000002"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Метод к ближайших соседей (KNN, k-Nearest Neighbors)\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train)*100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Классификаторы наивного Байеса - это семейство простых вероятностных классификаторов, основанных на применении теоремы Байеса с предположением (наивным) о независимости между признаками. Классификаторы наивного Байеса хорошо масштабируются. Ссылка на [Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.879999999999995"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Гауссовый наивный Байес (Gaussian Naive Bayes)\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train)*100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Перцептрон это алгоритм обучения с учителем бинарных классификаторов (функций, которые могут определить, может ли принадлежать объект, описываемый некоторым вектором тому или иному классу). Это тип линейного классификатора, т.е. алгоритм классификации, который делает свои прогнозы на основе линейной комбинации весов и признаков. Опять же ссылка на [Wikipedia](https://en.wikipedia.org/wiki/Perceptron)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "78.790000000000006"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Перцептрон (Perceptron)\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train)*100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.459999999999994"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Линейный классификатор на опорных векторах (Linear SVC)\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train)*100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "38.380000000000003"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Стохастический градиентный спуск (Stochastic Gradient Descent)\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train)*100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Деревья решений представлены в виде иерархических структур, где конечные точки (листья) представляют собой решения об отнесении элемента к некоторому классу, а промежуточные точки и ветви представляют собой решения относительно независимых признаков, направляющие классификатор к тому или иному \"листку\".\n",
    "\n",
    "Дерево решений, где целевая переменная является непрерывной называются регрессионными деревьями. Подробнее в [Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.640000000000001"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Дерево решений (Decision Tree)\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train)*100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Случайные леса это ансабли из деревьев принятия решений, количество деревьев прописано в параметрах модели. При классификации возвращает самый частый класс, возвращаемый деревьями, при регрессии возвращает среднее значение, возвращаемое деревьями, входящими в ансамбль. Опять же, ссылка в [Wikipedia](https://en.wikipedia.org/wiki/Random_forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.640000000000001"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Случайный лес (Random Forest)\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "acc_random_forest = round(random_forest.score(X_train,Y_train)*100,2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Оценка моделей**\n",
    "\n",
    "Теперь можно отранжировать модели и выбрать те, которые работают наилучшим образом. Несмотря на то, что и деревья принятия решений и случайные леса показали себя одинаково хорошо, выберем метод случайных лесов, поскольку он лучше справляется с проблемой переобучения на тренировочных данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Models</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>81.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>76.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Descent</td>\n",
       "      <td>38.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Models  Score\n",
       "3                Random Forest  86.64\n",
       "8                Decision Tree  86.64\n",
       "1                          KNN  84.06\n",
       "0      Support Vector Machines  83.50\n",
       "2          Logistic Regression  81.26\n",
       "7                   Linear SVC  79.46\n",
       "5                   Perceptron  78.79\n",
       "4                  Naive Bayes  76.88\n",
       "6  Stochastic Gradient Descent  38.38"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Models':['Support Vector Machines', 'KNN','Logistic Regression', \n",
    "              'Random Forest','Naive Bayes','Perceptron',\n",
    "              'Stochastic Gradient Descent','Linear SVC', 'Decision Tree'],\n",
    "    'Score':[acc_svc, acc_knn, acc_log, acc_random_forest, acc_gaussian,\n",
    "            acc_perceptron, acc_sgd, acc_linear_svc, acc_decision_tree]\n",
    "})\n",
    "models.sort_values(by='Score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "    \"PAssengerId\": test_df[\"PassengerId\"],\n",
    "    \"Survived\": Y_pred\n",
    "})\n",
    "submission.to_csv('submission.csv')"
   ]
  }
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