{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/4dsolutions/clarusway_data_analysis/blob/main/DAwPy_S12_(Recap-EDA%20on%20Titanic%20Dataset)/DAwPy-S12%20(Recap-EDA%20on%20Titanic%20Dataset).ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a><br/>\n",
    "[![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/4dsolutions/clarusway_data_analysis/blob/main/DAwPy_S12_%28Recap-EDA%20on%20Titanic%20Dataset%29/DAwPy-S12%20%28Recap-EDA%20on%20Titanic%20Dataset%29.ipynb)\n",
    "\n",
    "________\n",
    "\n",
    "<p style=\"text-align: center;\"><img src=\"https://docs.google.com/uc?id=1lY0Uj5R04yMY3-ZppPWxqCr5pvBLYPnV\" class=\"img-fluid\" \n",
    "alt=\"CLRSWY\"></p>\n",
    "\n",
    "## <p style=\"background-color:#FDFEFE; font-family:newtimeroman; color:#9d4f8c; font-size:100%; text-align:center; border-radius:10px 10px;\">WAY TO REINVENT YOURSELF</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=https://i.ibb.co/6gCsHd6/1200px-Pandas-logo-svg.png width=\"700\" height=\"200\">\n",
    "\n",
    "## <p style=\"background-color:#FDFEFE; font-family:newtimeroman; color:#060108; font-size:200%; text-align:center; border-radius:10px 10px;\">Data Analysis with Python</p>\n",
    "\n",
    "## <p style=\"background-color:#FDFEFE; font-family:newtimeroman; color:#060108; font-size:150%; text-align:center; border-radius:10px 10px;\">Session - 12 </p>\n",
    "\n",
    "## <p style=\"background-color:#FDFEFE; font-family:newtimeroman; color:#4d77cf; font-size:200%; text-align:center; border-radius:10px 10px;\">Recap</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"toc\"></a>\n",
    "\n",
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Content</p>\n",
    "\n",
    "* [IMPORTING LIBRARIES NEEDED IN THIS NOTEBOOK](#0)\n",
    "* [EXPLORATORY DATA ANALYSIS (EDA) on TITANIC DATASET](#1)\n",
    "* [READING & UNDERSTANDING THE DATA](#2)\n",
    "* [THE DETAILED EXAMINATION OF DATA](#3)    \n",
    "* [SOME FEATURE ENGINEERING](#4)\n",
    "* [DROPPING UNNECESSARY FEATURES](#5)\n",
    "* [DUMMY OPERATION](#6)\n",
    "* [THE END OF THE SESSION - 07](#7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Importing Libraries Needed in This Notebook</p>\n",
    "\n",
    "<a id=\"0\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>\n",
    "\n",
    "Once you've installed NumPy & Pandas you can import them as a library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "warnings.warn(\"this will not show\")\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10,6)\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "\n",
    "# Set it None to display all rows in the dataframe\n",
    "# pd.set_option('display.max_rows', None)\n",
    "\n",
    "# Set it to None to display all columns in the dataframe\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "print(\"%s\" % (\"hello\", ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              python|  world   |world\n"
     ]
    }
   ],
   "source": [
    "print(\"{arg3:>20}|{arg2:^10}|{arg2:5}\".format(arg1=\"hello\", arg2=\"world\", arg3=\"python\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/5095eabce4b06cb305058603/5095eabce4b02d37bef4c24c/1352002236895/100_anniversary_titanic_sinking_by_esai8mellows-d4xbme8.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Exploratory Data Analysis (EDA) on Titanic Dataset</p>\n",
    "\n",
    "<a id=\"1\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>\n",
    "\n",
    "# Aim\n",
    "\n",
    "Applying Exploratory Data Analysis (EDA) and preparing the data to implement the Machine Learning Algorithms\n",
    "1. Analyzing the features according to survival status (target feature)\n",
    "2. Preparing data to create a model that will predict the survival status of people (So the \"survive\" feature is the target feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Reading & Understanding Data</p>\n",
    "\n",
    "<a id=\"2\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's read the data from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# \"C:/Users/tuseb.000/Desktop/Cohorts/DE-DS Cohort-01/DAwPY/titanic_train.csv\"\n",
    "\n",
    "df = pd.read_csv(\"titanic.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
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       "      <td>female</td>\n",
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       "      <td>373450</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.600</td>\n",
       "      <td>C2</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>14.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 12 columns</p>\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",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male 22.000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                               Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                             Allen, Mr. William Henry    male 35.000      0   \n",
       "..                                                 ...     ...    ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female 22.000      1   \n",
       "152                                   Meo, Mr. Alfonzo    male 55.500      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male 40.500      0   \n",
       "154                              Olsen, Mr. Ole Martin    male    NaN      0   \n",
       "155                        Williams, Mr. Charles Duane    male 51.000      0   \n",
       "\n",
       "     Parch            Ticket   Fare Cabin Embarked  \n",
       "0        0         A/5 21171  7.250   NaN        S  \n",
       "1        0          PC 17599 71.283   C85        C  \n",
       "2        0  STON/O2. 3101282  7.925   NaN        S  \n",
       "3        0            113803 53.100  C123        S  \n",
       "4        0            373450  8.050   NaN        S  \n",
       "..     ...               ...    ...   ...      ...  \n",
       "151      0            113776 66.600    C2        S  \n",
       "152      0        A.5. 11206  8.050   NaN        S  \n",
       "153      2          A/5. 851 14.500   NaN        S  \n",
       "154      0         Fa 265302  7.312   NaN        S  \n",
       "155      1          PC 17597 61.379   NaN        C  \n",
       "\n",
       "[156 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/kirbyurner/Documents/clarusway_data_analysis/DAwPy_S12_(Recap-EDA on Titanic Dataset)'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's understand the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.283</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</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>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.050</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 22.000      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                             Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                           Allen, Mr. William Henry    male 35.000      0   \n",
       "\n",
       "   Parch            Ticket   Fare Cabin Embarked  \n",
       "0      0         A/5 21171  7.250   NaN        S  \n",
       "1      0          PC 17599 71.283   C85        C  \n",
       "2      0  STON/O2. 3101282  7.925   NaN        S  \n",
       "3      0            113803 53.100  C123        S  \n",
       "4      0            373450  8.050   NaN        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanations about data from Kaggle\n",
    "\n",
    "\n",
    "<table>\n",
    "<tbody>\n",
    "<tr><th><b>Variable</b></th><th><b>Definition</b></th><th><b>Key</b></th></tr>\n",
    "<tr>\n",
    "<td>survival</td>\n",
    "<td>Survival</td>\n",
    "<td>0 = No, 1 = Yes</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pclass</td>\n",
    "<td>Ticket class</td>\n",
    "<td>1 = 1st, 2 = 2nd, 3 = 3rd</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>sex</td>\n",
    "<td>Sex</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Age</td>\n",
    "<td>Age in years</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>sibsp</td>\n",
    "<td># of siblings / spouses aboard the Titanic</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>parch</td>\n",
    "<td># of parents / children aboard the Titanic</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>ticket</td>\n",
    "<td>Ticket number</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>fare</td>\n",
    "<td>Passenger fare</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>cabin</td>\n",
    "<td>Cabin number</td>\n",
    "<td></td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>embarked</td>\n",
    "<td>Port of Embarkation</td>\n",
    "<td>C = Cherbourg, Q = Queenstown, S = Southampton</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 156 entries, 0 to 155\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  156 non-null    int64  \n",
      " 1   Survived     156 non-null    int64  \n",
      " 2   Pclass       156 non-null    int64  \n",
      " 3   Name         156 non-null    object \n",
      " 4   Sex          156 non-null    object \n",
      " 5   Age          126 non-null    float64\n",
      " 6   SibSp        156 non-null    int64  \n",
      " 7   Parch        156 non-null    int64  \n",
      " 8   Ticket       156 non-null    object \n",
      " 9   Fare         156 non-null    float64\n",
      " 10  Cabin        31 non-null     object \n",
      " 11  Embarked     155 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 14.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             30\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          125\n",
       "Embarked         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0.000\n",
       "Survived       0.000\n",
       "Pclass         0.000\n",
       "Name           0.000\n",
       "Sex            0.000\n",
       "Age           19.231\n",
       "SibSp          0.000\n",
       "Parch          0.000\n",
       "Ticket         0.000\n",
       "Fare           0.000\n",
       "Cabin         80.128\n",
       "Embarked       0.641\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()/df.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roughly 20 percent of the Age data and 77 percent of the Cabin data are missing. \n",
    "\n",
    "Only 2 people aboard has no information about where he/she got on the ship.\n",
    "\n",
    "These two rows can be dropped.\n",
    "\n",
    "The heatmap below shows the distribution of the missing data within all data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>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>156.000</td>\n",
       "      <td>156.000</td>\n",
       "      <td>156.000</td>\n",
       "      <td>126.000</td>\n",
       "      <td>156.000</td>\n",
       "      <td>156.000</td>\n",
       "      <td>156.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>78.500</td>\n",
       "      <td>0.346</td>\n",
       "      <td>2.423</td>\n",
       "      <td>28.142</td>\n",
       "      <td>0.615</td>\n",
       "      <td>0.397</td>\n",
       "      <td>28.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>45.177</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.795</td>\n",
       "      <td>14.614</td>\n",
       "      <td>1.056</td>\n",
       "      <td>0.870</td>\n",
       "      <td>39.401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.750</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>19.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>78.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>26.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>14.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>117.250</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>30.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>156.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>71.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>263.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId  Survived  Pclass     Age   SibSp   Parch    Fare\n",
       "count      156.000   156.000 156.000 126.000 156.000 156.000 156.000\n",
       "mean        78.500     0.346   2.423  28.142   0.615   0.397  28.110\n",
       "std         45.177     0.477   0.795  14.614   1.056   0.870  39.401\n",
       "min          1.000     0.000   1.000   0.830   0.000   0.000   6.750\n",
       "25%         39.750     0.000   2.000  19.000   0.000   0.000   8.003\n",
       "50%         78.500     0.000   3.000  26.000   0.000   0.000  14.454\n",
       "75%        117.250     1.000   3.000  35.000   1.000   0.000  30.372\n",
       "max        156.000     1.000   3.000  71.000   5.000   5.000 263.000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>156</td>\n",
       "      <td>145</td>\n",
       "      <td>2651</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>155</td>\n",
       "      <td>3</td>\n",
       "      <td>S</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count unique                      top freq\n",
       "Name       156    156  Braund, Mr. Owen Harris    1\n",
       "Sex        156      2                     male  100\n",
       "Ticket     156    145                     2651    2\n",
       "Cabin       31     28              C23 C25 C27    2\n",
       "Embarked   155      3                        S  110"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = \"O\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_col = df.select_dtypes(include='object').columns\n",
    "object_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name\n",
      "----------------\n",
      "Name\n",
      "Braund, Mr. Owen Harris                               1\n",
      "Moss, Mr. Albert Johan                                1\n",
      "Petranec, Miss. Matilda                               1\n",
      "Petroff, Mr. Pastcho (\"Pentcho\")                      1\n",
      "White, Mr. Richard Frasar                             1\n",
      "                                                     ..\n",
      "Harper, Mrs. Henry Sleeper (Myna Haxtun)              1\n",
      "Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)    1\n",
      "Ostby, Mr. Engelhart Cornelius                        1\n",
      "Woolner, Mr. Hugh                                     1\n",
      "Williams, Mr. Charles Duane                           1\n",
      "Name: count, Length: 156, dtype: int64\n",
      "----------------------------------------\n",
      "Sex\n",
      "----------------\n",
      "Sex\n",
      "male      100\n",
      "female     56\n",
      "Name: count, dtype: int64\n",
      "----------------------------------------\n",
      "Ticket\n",
      "----------------\n",
      "Ticket\n",
      "2651               2\n",
      "237736             2\n",
      "35281              2\n",
      "19950              2\n",
      "CA 2144            2\n",
      "                  ..\n",
      "14311              1\n",
      "370371             1\n",
      "S.C./A.4. 23567    1\n",
      "330958             1\n",
      "PC 17597           1\n",
      "Name: count, Length: 145, dtype: int64\n",
      "----------------------------------------\n",
      "Cabin\n",
      "----------------\n",
      "Cabin\n",
      "NaN            125\n",
      "C123             2\n",
      "C23 C25 C27      2\n",
      "D26              2\n",
      "F G73            1\n",
      "F2               1\n",
      "B86              1\n",
      "D47              1\n",
      "F E69            1\n",
      "E101             1\n",
      "B58 B60          1\n",
      "C110             1\n",
      "D10 D12          1\n",
      "A5               1\n",
      "E31              1\n",
      "C83              1\n",
      "F33              1\n",
      "C85              1\n",
      "B28              1\n",
      "C52              1\n",
      "B30              1\n",
      "D33              1\n",
      "B78              1\n",
      "A6               1\n",
      "D56              1\n",
      "C103             1\n",
      "G6               1\n",
      "E46              1\n",
      "C2               1\n",
      "Name: count, dtype: int64\n",
      "----------------------------------------\n",
      "Embarked\n",
      "----------------\n",
      "Embarked\n",
      "S      110\n",
      "C       32\n",
      "Q       13\n",
      "NaN      1\n",
      "Name: count, dtype: int64\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for col in object_col:\n",
    "    print(col)\n",
    "    print(\"--\"*8)\n",
    "    print(df[col].value_counts(dropna=False))\n",
    "    print(\"--\"*20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">The Detailed Examination of Data Column by Column</p>\n",
    "\n",
    "<a id=\"3\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets examine the Age column and decide how we will handle with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df, x='Age', kde=True, bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.141507936507935"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = df.Age.mean()\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = df.Age.median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age mean:   28.1\n",
      "Age median: 26.0\n"
     ]
    }
   ],
   "source": [
    "#print('Age mean:{}\\nAge median:{}'.format(mean, median))\n",
    "print(f'Age mean:  {mean:>5.1f}\\nAge median:{median:>5}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x='Age');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x='Pclass', y='Age');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these boxplots can be interpreted that the older people preferd to be in first class, and as the class quality decreases the median age decreases. Lets find these median values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1   38.000\n",
       "2   29.000\n",
       "3   22.000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Pclass').Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o3Xff1ZYtWzR48GD9/Oc/Py1DQ0OD7rvvPoWFhemdd97R6tWrdfDgwSYFKxgoRQAAeb1ezZgxQ/Hx8Zo+fbq8Xq/dkQAANvrTn/6k2tpazZgxQ1FRUbr44os1bdo05eXlNT6mXbt2uuuuuxQWFqbBgwcrEAjof//3f+Xz+ZSUlKSLLrpI+/btkyQtXbpUU6ZMkWma2rdvny688EIdOHDgtO3u3LlTn3zyiR599FHFxMSoffv2evDBB/X222/r8OHDQdtfJloAAEiSUlJSlJKSYncMAEAI2Ldvnw4dOqR+/fo1LjNNU3V1dY0X+46NjW0ceh0W9tW5lgsvvLDx8WFhYWpoaJAk7dq1S/fdd5+++OILXX755YqLizvjJBDl5eUKBAIaMmRIk+WRkZHau3ev2rdv37o7+i+UIgAAAABNdO7cWd26ddPatWsblx07dkyVlZWKi4uTpGZPxnPgwAFNmzZNzz77rIYNGyZJeuedd7Ru3bozbtfr9Wrz5s2NU4nX1tZq7969Qb1EBMPnAAAAADQxdOhQHT9+XMuWLVNtba2+/PJLPfjgg5o+fXqLZyY9fvy4AoGAfD6fJKm0tFS/+tWvJKlx4oaTkpOTddlll+mJJ57Q8ePH5ff7NX/+fN19991BvRgtpQgAAACwUMCU6huC+yvQsssTnSYmJka5ubnavHmzvve97+nGG29UWFiYnn/++Ravq3v37po1a5aysrJ0zTXXaNq0aRozZowiIiL017/+tcljw8PDtXTpUlVUVOimm27S4MGDVVZWppdeeklRUVHnt1NfwzBbekWnEBcIBLRt2zb17t2bq/c2w4kTJzRy5EhJUlFRUWODBwAAwLnx+/36/PPP9Z3vfKfJxDWBQEDj0ker4lCVJTk6xn1Lr61c5fific/2fEvN7wZ8pwgAAACwgMfj0WsrV51xgoFgMAzD8YWotVCKAAAAAItQUkIT3ykCAAAA4GqUIgAAAACuRikCAAAAgsBh85mFrNZ4nilFAAAAQCuKiIiQJFVXV9ucxB1OPs8nn/dzwUQLAAAAQCvyeDyKjY3VwYMHJUnt2rVr8QVP8c1M01R1dbUOHjyo2NjY85rEglIEAAAAtLLOnTtLUmMxQvDExsY2Pt/nilIEAAAAtDLDMHTxxRfroosuUl1dnd1xHCsiIqJVpjmnFAEAAABB4vF4uDZRG8BECwAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNVsKUVHjhzRrFmzdO2116pfv3667777dPDgQUnS9u3blZ6erj59+mjYsGFauXKlHREBAAAAuIQtpWjKlCmqrq7Wu+++q/Xr18vj8einP/2pqqqqNGHCBI0aNUrFxcWaN2+eFixYoB07dtgREwAAAIALhFu9wZ07d2r79u3asGGDYmJiJElz587VF198oXXr1ik2NlaZmZmSpIEDByo1NVV5eXlKTk62OioAAAAAF7D8TNGOHTvUo0cPvf766xo+fLgGDx6shQsXqlOnTtq9e7cSEhKaPL5Hjx7atWuX1TEBAAAAuITlZ4qqqqr02WefKTExUYWFhfL7/Zo1a5YefPBBdezYUT6fr8njvV6vqqurW7ydQCDQWpEd7dTnKRAI8LwBAADAMZr7s63lpSgyMlKS9MgjjygqKkoxMTG6//77dfvtt2v06NHy+/1NHu/3+xUdHd3i7ZSUlLRKXqerqalpvL1jxw5FRUXZmAYAAACwnuWlqEePHmpoaFBdXV3jD+ANDQ2SpP/6r/9Sfn5+k8eXlpaqZ8+eLd5OUlKSPB7P+Qd2uBMnTjTeTk5OPu1MHQAAcI8NGzbo2Wef1eTJk5WSkmJ3HOC8BQKBZp0ssbwUpaSkqGvXrpo9e7YWLFigmpoaLV68WDfeeKNuueUWLVmyRLm5ucrMzNTWrVu1evVqPffccy3ejsfjoRQ1w6nPEc8ZAADu5ff7lZ2drYqKCmVnZ6tfv37yer12xwIsYflECxEREXrllVfk8Xg0YsQIjRgxQp07d9b8+fPVvn17LV++XGvXrtW1116rOXPmaM6cORowYIDVMQEAAFwlLy9PlZWVkqTKysrTRu8ATmb5mSJJio+P1+LFi894X1JSklasWGFxIgAAAPcqLy9Xfn6+TNOUJJmmqfz8fN10003q0qWLzemA4LPl4q0AAAAIDaZpKjs7+6zLTxYlwMkoRQAAAC5WVlam4uLi06YuDgQCKi4uVllZmU3JAOtQigAAAFysW7du6tev32mTLXk8HvXv31/dunWzKRlgHUoRAACAixmGoWnTpp11uWEYNqQCrEUpAgAAcLkuXbooIyOjsQAZhqGMjAxdeumlNicDrGHL7HOAE5imKb/fb8l2JAX9kzqv18ungQDgYpmZmSoqKlJFRYU6duyojIwMuyMBlqEUAefANE1NmTJFO3futDtKq0lMTFROTg7FCABcyuv1asaMGcrOzta0adO4cCtchVIEAAAASVJKSopSUlLsjgFYjlIEnAPDMJSTkxP04XN+v19paWmSpMLCwqB+asfwOQAA4FaUIuAcGYYhn89n2fa8Xq+l2wMAAHALZp8DAAAA4GqUIgAAAACuRikCAAAA4GqUIgAAAACuRikCAAAA4GqUIgAAAACuRikCAACAJGnDhg0aN26cNmzYYHcUwFKUIgAAAMjv92vRokU6cOCAFi1aFPQLlAOhhFIEAAAA5eXlqbKyUpJUWVmp/Px8mxMB1qEUAQAQ4hjShGArLy9Xfn6+TNOUJJmmqfz8fJWXl9ucDLAGpQgAgBDGkCYEm2mays7OPuvyk0UJcDJKEQAAIYwhTQi2srIyFRcXKxAINFkeCARUXFyssrIym5IB1qEUAQAQohjSBCt069ZN/fr1k8fjabLc4/Gof//+6tatm03JAOtQigAACEEMaYJVDMPQtGnTzrrcMAwbUgHWohQBABCCGNIEK3Xp0kUZGRmNBcgwDGVkZOjSSy+1ORlgDUoRAAAhiCFNsFpmZqY6dOggSerYsaMyMjJsTgRYh1IEAEAIYkgTrOb1ejVjxgzFx8dr+vTp8nq9dkcCLEMpAgAgRDGkCVZLSUnRa6+9ppSUFLujAJaiFAEAEMIY0gQAwUcpAgAghDGkCQCCL9zuAAAA4OulpKQwnAkAgogzRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcjVIEAAAAwNUoRQAAAABcLdzuAACAr2eapvx+vyXbkSTDMIK6Ha/XG/RtAADQEpQiAAhhpmlqypQp2rlzp91RWk1iYqJycnIoRgCAkMHwOQAAAACuxpkiAAhhhmEoJycn6MPn/H6/0tLSJEmFhYXyer1B2xbD5wAAocaWUrRmzRrNnDlTUVFRjctuvPFGPfXUU9q+fbsef/xxlZaWqn379po4caLS09PtiAkAIcEwDPl8Psu25/V6Ld0eAAB2s6UUlZSU6LbbbtOCBQuaLK+qqtKECRM0depUjRs3TsXFxZo0aZJ69eql5ORkO6ICAAAAcDhbvlNUUlKixMTE05avW7dOsbGxyszMVHh4uAYOHKjU1FTl5eXZkBIAAACAG1h+pqihoUGffPKJfD6fli1bpkAgoCFDhmjmzJnavXu3EhISmjy+R48eKigoaPF2AoFAa0V2tFOfp0AgwPMWYjg+sAqvNQCAEzX3/zPLS9GhQ4f03e9+VyNGjNCSJUt0+PBhPfjgg8rKylKnTp1OG8fu9XpVXV3d4u2UlJS0VmRHq6mpaby9Y8eOJt/zgv04PrAKrzUAgJtZXoo6duzYZDicz+dTVlaWbr/9do0ePfq0GZb8fr+io6NbvJ2kpCR5PJ7zzut0J06caLydnJzMl6tDDMcHVuG1BgBwokAg0KyTJZaXol27dumtt97SAw880Dgla21trcLCwpScnKyXX365yeNLS0vVs2fPFm/H4/FQiprh1OeI5yz0cHxgFV5rAAA3s3yihdjYWOXl5WnZsmWqr6/X/v379dRTTyktLU0jRoxQRUWFcnNzVVdXp02bNmn16tUaM2aM1TEBAAAAuITlZ4o6d+6spUuXatGiRXr++ecVFRWlm2++WVlZWYqKitLy5cs1b948LVmyRHFxcZozZ44GDBhgdcxvZJpm0C+maIVT98EJ+3MSF4cEAABAc9lynaL+/ftrxYoVZ7wvKSnprPeFEr/fr5EjR9odo1WdvJq9ExQVFfGdCAAAADSLLdcpAgAAAIBQYcuZIqc51vsOmWFt+Kk0za9+b+PDzYyGesVs+63dMQAAANDGtOGf5EOHGRYueSLsjuF6pt0BAAAA0CYxfA4AAACAq1GKAAAAALgapQgAAACAq1GKAAAAALgapQgAAAAIcRs2bNC4ceO0YcMGu6M4EqUIAAAACGF+v1+LFi3SgQMHtGjRIvn9frsjOQ6lCAAAAAhheXl5qqyslCRVVlYqPz/f5kTOQykCAAAAQlR5ebny8/Nlml9dkdE0TeXn56u8vNzmZM5CKQIAAABCkGmays7OPuvyk0UJ549SBAAAAISgsrIyFRcXKxAINFkeCARUXFyssrIym5I5D6UIAAAACEHdunVTv3795PF4miz3eDzq37+/unXrZlMy56EUAQAAACHIMAxNmzbtrMsNw7AhlTNRigAAAIAQ1aVLF2VkZDQWIMMwlJGRoUsvvdTmZM5CKQIAAABCWGZmpjp06CBJ6tixozIyMmxO5DyUIgAAACCEeb1ezZgxQ/Hx8Zo+fbq8Xq/dkRwn3O4AAAAAAL5eSkqKUlJS7I7hWJwpAgAAAOBqlCIAAAAArkYpAgAAAOBqfKcIAACgDTBNU36/P+jbkBT06994vV6usYOQQikCAAAIcaZpasqUKdq5c6fdUVpFYmKicnJyKEYIGQyfAwAAAOBqnCkCAAAIcYZhKCcnJ6jD5/x+v9LS0iRJhYWFQb0WDsPnEGooRQAAAG2AYRjy+XyWbMvr9Vq2LSAUMHwOAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKuF2x0ACAa/3293hFZx6n44ZZ+8Xq8Mw7A7BgAAQCNKERwpLS3N7gitzin7VFRUJJ/PZ3cMAACARrYOnwsEArrzzjv10EMPNS7bvn270tPT1adPHw0bNkwrV660MSEAAAAAp7P1TNGzzz6rLVu26NJLL5UkVVVVacKECZo6darGjRun4uJiTZo0Sb169VJycrKdUdFGPTv4kKI8pt0xzov5r/htecRZTcDQ5A/i7I4BAABwRraVoo0bN2rdunW66aabGpetW7dOsbGxyszMlCQNHDhQqampysvLC+1SFKizOwGkMx6HKI+pKI8NWfAf2nYxBQDg65imGfTv/pr/+pTUiu/luvH7v7aUosrKSj3yyCN67rnnlJub27h89+7dSkhIaPLYHj16qKCgoMXbCAQC5xvza9XX1zfevmD7iqBuC3CSQCAQ9H+faLlTjwnHCHAn3gfOjWmamjZtmj755BO7o7SaxMREPfPMM44oRs19HVteihoaGpSVlaXx48friiuuaHLf8ePHT/sCttfrVXV1dYu3U1JScl45v4lTZgIDrLZjxw5FRUXZHQP/oaampvE2xwhwJ94Hzo1pmjp+/LjdMVrVsWPHtG3bNkeUouayvBQtXbpUkZGRuvPOO0+7z+fz6ejRo02W+f1+RUdHt3g7SUlJ8niCN27qxIkTjbePXvVDyRMRtG2hmQJ1nLVrA5KTk5l9LgSd+p7GMQLcifeBc7ds2bKgfmDu9/s1duxYSVJBQYG8Xm/QtiU5a/hcIBBo1skSy0vRG2+8oYMHD6pv376S/n3G5fe//71mzZqlDz/8sMnjS0tL1bNnzxZvx+PxBLUUNVm3J4JSBDRTsP9t4tycekw4RoA78T5wfmJiYoK27lOPRXR0NIU1CCyfknvt2rX6+OOPtWXLFm3ZskW33HKLbrnlFm3ZskXDhw9XRUWFcnNzVVdXp02bNmn16tUaM2aM1TEBAAAAuISt1yn6T+3bt9fy5cu1du1aXXvttZozZ47mzJmjAQMG2B0NAAAAgEPZep0iSXriiSea/DkpKUkrVvC9EAAAAADWCKkzRQAAAABgNUoRAAAAAFejFAEAAABwNUoRAAAAAFejFAEAAABwNUoRAAAAAFejFAEAAABwtXMuRYcOHWrNHAAAAABgixaVovr6ei1evFjXXHONhg0bpr1792rMmDE6ePBgsPIBAAAAQFC1qBTl5ORo06ZNys7OVkREhDp06KDOnTtr3rx5wcoHAAAAAEEV3pIHr169Wr/97W8VHx8vwzDUrl07LViwQMOHDw9WPgAAAAAIqhadKaqurlZcXJwkyTRNSZLX61VYGPM1AAAAAGibWtRmevfurWeffVaSZBiGJOmVV15RUlJS6ycDAAAAAAu0aPjcI488orvuukuFhYU6fvy4fvCDH+j48eN66aWXgpUPOC81AbsTQOI4AACA0NaiUtS1a1e9/fbbWr9+vfbv36/OnTvr+uuvV0xMTLDyAedl8gcd7I6A/3By6C0AAECoaFEp2r9/v6SvhtH17t1bkvTll1/qxIkT+ta3vqXIyMhWDwgAAAAAwdSiUjR8+HA1NDSc8b6wsDClpKRo4cKFjZMxAHZ7dnClojx2p0BN4N9n7U5+HxEAACBUtKgUPfzww1q/fr1mz56trl27qry8XE8++aQSExN100036fnnn9eCBQv01FNPBSsv0CJRHlGKAAAA8LVaNPvcyy+/rKefflqXX365IiMj1b17dy1cuFC/+93vlJCQoLlz5+r9998PVlYAAAAAaHUtKkWHDx+Wx9P0Y3fDMFRZWSlJ8vl8Zx1eBwAAAAChqEWl6LrrrtMDDzygPXv2qK6uTnv27NHDDz+sQYMGqba2VkuWLNGVV14ZrKwAAAAA0OpaVIoeffRRBQIBjRgxQsnJyfr+97+vhoYGPfbYY9qyZYv+9Kc/6ac//WmwsgIAAABAq2vRRAuxsbF68cUXdeDAAf3zn/+UaZpatWqVhg0bpm3btumNN94IVk4ACDmmacrv99sdo1Wcuh9O2Sev18tshwCAZmlRKTpp7969evHFF/Xee++pZ8+eysrKau1cABDy/H6/Ro4caXeMVpeWlmZ3hFZRVFQkn89ndwwAQBvQ7FLU0NCgtWvX6qWXXtLu3btVX1+vpUuX6rrrrgtmPgAAAAAIqmaVopdfflm/+c1v1NDQoDvuuEMvvPCCvv/97yshISHY+QCgTTjW+w6ZYed08j10mOZXv7fhIWdGQ71itv3W7hgAgDamWf+DL1iwQBkZGXrooYcUGRkZ7EwA0OaYYeGSJ8LuGK5n2h0AANAmNWv2uZ/+9KfavHmzhgwZosWLF+vAgQN8eRUAAACAIzTrTFFmZqYyMzO1ceNGvfrqqxo+fLgCgYA2btyo1NTU0y7oCgCAW1gxC6H5r6GNwf5Akhn7ALhViwbADxw4UAMHDtS+ffuUn5+vJ554Qk8++aRuvfVWPfTQQ8HKCABASDJNU1OmTNHOnTvtjtIqEhMTlZOTQzEC4DotunjrSZdeeqmysrL0/vvva8aMGfroo49aOxcAAAAAWOK8pkqKjIzU2LFjNXbs2NbKAwBAm2EYhnJycoI6fM7v9zdeO6qwsFBerzdo22L4HAC3auPzxwIAYC/DMCy7SKzX6+WCtAAQBOc0fA4AAAAAnIJSBAAAAMDVKEUAAAAAXI1SBAAAAMDVmGgBAADgHFlx8V6rnLofTtkniVkV0TyUIgAAgHPk9/s1cuRIu2O0upPTwDtBUVERszbiGzF8DgAAAICrcaYIAFpDoM7uBJA4DrDVsd53yAxr4z9ameZXv7fx4WZGQ71itv3W7hhoQ9r4v1wAsI958ocHSRdsX2FjEpzJqccHsIIZFi55IuyOAUn860dLMXwOAAAAgKtxpggAztGpsxkdveqHfEIcCgJ1jWftmG0KANBclCIAaA2eCEoRAABtFMPnAAAAALgapQgAAACAq1GKAAAAALgapQgAAACAq1GKAAAAALiaLaVo48aNSk9P19VXX61BgwZp7ty58vv9kqTt27crPT1dffr00bBhw7Ry5Uo7IgIAAABwCctL0aFDh3Tvvffqjjvu0JYtW1RYWKiPPvpIv/71r1VVVaUJEyZo1KhRKi4u1rx587RgwQLt2LHD6pgAAAAAXMLy6xTFxcVpw4YNiomJkWmaOnLkiGpqahQXF6d169YpNjZWmZmZkqSBAwcqNTVVeXl5Sk5OtjoqAAAAABew5eKtMTExkqQhQ4bowIED6tu3r0aPHq1nnnlGCQkJTR7bo0cPFRQUtHgbgUCgVbLatX7AqQKBgGP+/ThlP5zq+PHjjjhGJ4eXS87ZJ0nyer0yDMPuGOfNKcfDyZzw/86p+Z2wP1Zq7nNlSyk6ad26daqqqtLMmTM1depUxcfHy+fzNXmM1+tVdXV1i9ddUlLSWjHPqKamJqjrB5xqx44dioqKsjtGq+B9ILSNHTvW7gitzkn7NH/+fEe8F/A+EPqc8P/Oqa8zJ+xPKLK1FHm9Xnm9XmVlZSk9PV133nmnjh492uQxfr9f0dHRLV53UlKSPB5Pa0U9zYkTJ4K2bsDJkpOTT/vwo63ifQA4d055L+B9IPQ54bV26uvMCftjpUAg0KyTJZaXoo8//lizZ8/Wm2++qcjISElSbW2tIiIi1KNHD3344YdNHl9aWqqePXu2eDsejyeopSiY6wacLNj/Nq3klP1wumcHH1KUx7Q7xnkx/xW/rY82qwkYmvxBnCTnvBc4YR+czgmvtVPzO2F/QpHlpahXr17y+/16+umn9cADD+iLL77QwoULNXbsWI0YMUJPP/20cnNzlZmZqa1bt2r16tV67rnnrI4JAHCIKI+pKH5+CBFtu5wCcC7LS1F0dLSWLVum+fPna9CgQbrggguUmpqqSZMmKTIyUsuXL9e8efO0ZMkSxcXFac6cORowYIDVMQEAAAC4hC3fKerRo4eWL19+xvuSkpK0YsUKixMBAAAAcCvLL94KAAAAAKGEUgQAAADA1ShFAAAAAFyNUgQAAADA1Wy9eCsAAMFWE7A7AU7iWAAIVZQiAICjTf6gg90RcAamyTWLAIQOhs8BAAAAcDXOFAEAHO3ZwZWK8tidAtJXw+dOnrkzDMPmNHALv99vd4Tzduo+OGF/TvJ6vSHzXkApAgA4WpRHlCLAxdLS0uyO0KqctD9FRUXy+Xx2x5DE8DkAAAAALseZIgAAADjes4MPKcrTdif4ODk3SYiMNjtnNQFDkz+IszvGaShFAAAAcLwoj8lQ2pAQmsWU4XMAAAAAXI1SBAAAAMDVKEUAAAAAXI1SBAAAAMDVmGihFRgN9SH6lbFmcsh0JkZDvd0RAAAA0AZRilpBzLbf2h0BAAAAwDli+BwAAAAAV+NM0Tnyer0qKiqyO8Z58/v9SktLkyQVFhbK6/XanOj8nbpPAAAAwDehFJ0jwzDk8/nsjtGqvF6v4/YJAAAA+CYMnwMAAADgapQiAAAAAK5GKQIAAADgapQiAAAAAK5GKQIAAADgapQiAAAAAK5GKQIAAADgalynCAAAoDUE6uxOgJM4FmghShEAAMA5Mk2z8fYF21fYmATA+WD4HAAAAABX40wRAADAOTIMo/H20at+KHkibEyDRoE6ztyhRShFAAAArcETQSkC2iiGzwEAAABwNUoRAAAAAFejFAEAAABwNb5TBEerCRiSzG98XCg7OdvrKd/lbXO+Og4AAAChiVIER5v8QZzdEQAAABDiGD4HAAAAwNU4UwTH8Xq9KioqsjtGq/D7/UpLS5MkFRYWyuv12pzo/DlhHwAAgLNQiuA4hmHI5/PZHaPVeb1eR+4XAACA3Rg+BwAAAMDVKEUAAAAAXI1SBAAAAMDVKEUAAAAAXI1SBAAAAMDVmH0OAFqB0VAv0+4Q58v81x4Yhr05zoPRUG93BABAG0QpAoBWELPtt3ZHAAAA54jhcwAAAABcjTNFAHCOvF6vioqK7I7RKvx+v9LS0iRJhYWF8nq9Nic6P6fuDwAA38SWUrRr1y4tXLhQn3zyiSIiIjRo0CA99NBDiouL0/bt2/X444+rtLRU7du318SJE5Wenm5HTAD4WoZhyOfz2R2j1Xm9XkfuFwAAZ2P58Dm/36977rlHffr00QcffKC33npLR44c0ezZs1VVVaUJEyZo1KhRKi4u1rx587RgwQLt2LHD6pgAAAAAXMLyM0X79+/XFVdcoUmTJsnj8SgyMlLjxo3TrFmztG7dOsXGxiozM1OSNHDgQKWmpiovL0/JyclWRwUAAGg2ZqEMHcxEiZayvBR1795dy5Yta7LsnXfe0ZVXXqndu3crISGhyX09evRQQUFBi7cTCATOK6dbnPo8BQIBnrcQw/GBVZz2Wmvr+d3ACa8zqelrjVkogZax4n2gueu3daIF0zT1zDPPaP369Xr11Vf1m9/85rRx7F6vV9XV1S1ed0lJSWvFdLSamprG2zt27FBUVJSNafCfOD6witNea6fuD0KTE15nEq814HyE0vuAbaXo2LFjevjhh/XJJ5/o1VdfVa9eveTz+XT06NEmj/P7/YqOjm7x+pOSkuTxeForrmOdOHGi8XZycjJfrg4xHB9YxWmvtVP3B6HJCa8z6asPeN966y27Y7QKv9+vsWPHSpIKCgra/CyUUtN9Quix4n0gEAg062SJLaWorKxMP/7xj3XJJZeooKBAcXFxkqSEhAR9+OGHTR5bWlqqnj17tngbHo+HUtQMpz5HPGehh+MDqzjttdbW87uBE15nJ8XExNgdoVWcejyio6MdUVqd8hpzqlB6H7B89rmqqirddddduvrqq/Xiiy82FiJJGj58uCoqKpSbm6u6ujpt2rRJq1ev1pgxY6yOCQAAAMAlLD9TtGrVKu3fv19FRUVau3Ztk/v+8pe/aPny5Zo3b56WLFmiuLg4zZkzRwMGDLA6ZsgwTVN+vz9o6z913cHczkler1dGG5/R5qRgHxvJ2uPjpGMDAMB/qmn783o4QqgeB8tL0fjx4zV+/Piz3p+UlKQVK1ZYmCh0maapKVOmaOfOnZZsz4qrvycmJionJ6fN//Bt9bGRgn98nHJsAAA4yTT/PUn65A862JgEZ3Lq8bGb5cPnAAAAACCU2DolN76eYRjKyckJ+rCpky3dijMEThmiZdWxkaw7Pk45NgAAnHTq/2vPDq5UVGh8p9/VagL/PmsXSj93UIpCnGEYjpj9xYk4NgAAtB1RHlGKcFYMnwMAAADgapQiAAAAAK5GKQIAAADgapQiAAAAAK5GKQIAAADgapQiAAAAAK5GKQIAAADgapQiAAAAAK5GKQIAAADgapQiAAAAAK5GKQIAAADgauF2BwAAIJhqAoYk0+4Y58X8V3zDsDfH+frqWABA6KEUAQAcbfIHcXZHAACEOIbPAQAAAHA1zhQBABzH6/WqqKjI7hitwu/3Ky0tTZJUWFgor9drc6LW4ZT9AOAMlCIAgOMYhiGfz2d3jFbn9XoduV8AYDeGzwEAAABwNUoRAAAAAFejFAEAAABwNUoRAAAAAFdjogUAAAA4Xlu/kDMXcQ4uShEAAAAcjws54+swfA4AAACAq3GmCAAAAI7klAs5cxHn4KMUAQAAwJGceCFnLuIcHAyfAwAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArmZrKTp06JCGDx+uzZs3Ny7bvn270tPT1adPHw0bNkwrV660MSEAAAAAp7OtFG3dulXjxo1TWVlZ47KqqipNmDBBo0aNUnFxsebNm6cFCxZox44ddsUEAAAA4HC2lKLCwkLNnDlT06dPb7J83bp1io2NVWZmpsLDwzVw4EClpqYqLy/PjpgAAAAAXCDcjo0OHjxYqampCg8Pb1KMdu/erYSEhCaP7dGjhwoKClq8jUAgcN45AcAtTn3PDAQCvIeGEI4NrMJrLXRxbM5dc58rW0pRp06dzrj8+PHj8vl8TZZ5vV5VV1e3eBslJSXnlA0A3Kimpqbx9o4dOxQVFWVjGpyKYwOr8FoLXRyb4LOlFJ2Nz+fT0aNHmyzz+/2Kjo5u8bqSkpLk8XhaKxoAONqJEycabycnJ5/2ARXsw7GBVXithS6OzbkLBALNOlkSUqUoISFBH374YZNlpaWl6tmzZ4vX5fF4KEUA0Eynvl/y/hlaODawCq+10MWxCb6Quk7R8OHDVVFRodzcXNXV1WnTpk1avXq1xowZY3c0AAAAAA4VUqWoffv2Wr58udauXatrr71Wc+bM0Zw5czRgwAC7owEAAABwKNuHz3322WdN/pyUlKQVK1bYlAYAAACA24TUmSIAAAAAsBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrUYoAAAAAuBqlCAAAAICrhdsdAACAtsw0Tfn9/qCt/9R1B3M7kuT1emUYRlC3AQChiFIEAMA5Mk1TU6ZM0c6dOy3ZXlpaWlDXn5iYqJycHIoRANdh+BwAAAAAV+NMEQAA58gwDOXk5AR9WJtpmo3bCyaGzwFwK0oRAADnwTAM+Xw+u2MAAM4Dw+cAAAAAuBpnigAAANoAZjoMXU46NpLzjk9zUIoAAABCHDMdhi6nHRvJWcenuRg+BwAAAMDVOFMEAAAQ4pjpMHQ57dhIzjo+zUUpAgAAaAOY6TB0cWzaPobPAQAAAHA1ShEAAAAAV2P4HACEuGBP9SoxFS8AwN0oRQAQwqye6lViKl4AgPuE5PC5yspK3Xffferbt6+uvfZazZs3T/X19XbHAgAAAOBAIXmm6P7771d8fLz+/Oc/q6KiQhMnTlRubq7uueceu6MBgKWsmupVYipeAIB7hVwp2rNnjz766CO9//778vl86tq1q+677z499dRTlCIArsRUrwAABFfIDZ/bvXu3YmNjFR8f37js8ssv1/79+/Xll1/amAwAAACAE4XcmaLjx4+f9onoyT9XV1frwgsvbNZ6AoFAq2cDAAAA0HY0txOEXClq166dTpw40WTZyT9HR0c3ez0lJSWtmgsAAACAM4VcKerZs6eOHDmiiooKdezYUZL0t7/9TZ07d9YFF1zQ7PUkJSXJ4/EEKyYAAACAEBcIBJp1siTkStG3v/1tXXPNNZo/f74ee+wxHT58WM8995zGjh3bovV4PB5KEQAAAIBvFHITLUjSkiVLVF9frxtuuEG33367rrvuOt133312xwIAAADgQCF3pkiSOnbsqCVLltgdAwAAAIALhOSZIgAAAACwCqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKtRigAAAAC4GqUIAAAAgKuF2x2gtZmmKUkKBAI2JwEAAABgp5Od4GRHOBvHlaKGhgZJUklJic1JAAAAAISCkx3hbAzzm2pTG9PQ0KD6+nqFhYXJMAy74wAAAACwiWmaamhoUHh4uMLCzv7NIceVIgAAAABoCSZaAAAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCIAAAAArkYpAgAAAOBqlCI40qFDhzR8+HBt3rzZ7igALLZr1y6NHz9e/fv316BBgzRr1iwdOnTI7lgALLRx40alp6fr6quv1qBBgzR37lz5/X67YyGEUYrgOFu3btW4ceNUVlZmdxQAFvP7/brnnnvUp08fffDBB3rrrbd05MgRzZ492+5oACxy6NAh3Xvvvbrjjju0ZcsWFRYW6qOPPtKvf/1ru6MhhFGK4CiFhYWaOXOmpk+fbncUADbYv3+/rrjiCk2aNEmRkZFq3769xo0bp+LiYrujAbBIXFycNmzYoNGjR8swDB05ckQ1NTWKi4uzOxpCGKUIjjJ48GC9++67+sEPfmB3FAA26N69u5YtWyaPx9O47J133tGVV15pYyoAVouJiZEkDRkyRKmpqerUqZNGjx5tcyqEMkoRHKVTp04KDw+3OwaAEGCaphYvXqz169frkUcesTsOABusW7dO77//vsLCwjR16lS74yCEUYoAAI5z7NgxTZ06VatXr9arr76qXr162R0JgA28Xq/i4+OVlZWlP//5z6qqqrI7EkIUpQgA4ChlZWUaM2aMjh07poKCAgoR4DIff/yxvv/976u2trZxWW1trSIiIuTz+WxMhlBGKQIAOEZVVZXuuusuXX311XrxxRf5YjXgQr169ZLf79fTTz+t2tpa7du3TwsXLtTYsWMVGRlpdzyEKL58AQBwjFWrVmn//v0qKirS2rVrm9z3l7/8xaZUAKwUHR2tZcuWaf78+Ro0aJAuuOACpaamatKkSXZHQwgzTNM07Q4BAAAAAHZh+BwAAAAAV6MUAQAAAHA1ShEAAAAAV6MUAQAAAHA1ShEAAAAAV6MUAQAAAHA1ShEAAAAAV+PirQCAkDFs2DB98cUXCg//6r8n0zQVExOj1NRUZWVlKSzs7J/lDRs2TJMnT9bo0aOtigsAcAhKEQAgpPziF79oUmw+++wz3X333fL5fJo6daqNyQAATsXwOQBASOvVq5f69eunTz/9VNXV1Xrsscc0cOBA9e3bVz/+8Y+1b9++0/7OgQMHdP/992vYsGG66qqrdMMNN6igoKDx/vz8fN14443q27evUlNTtXLlysb7cnJyNGTIEPXv319jxozRH/7wB0v2EwBgH0oRACBk1dXVafPmzdq0aZMGDRqkxx57TCUlJVq1apU2bNigjh07asaMGaf9vTlz5igiIkJvv/22Pv74Y/33f/+35s6dq+PHj2vv3r1asGCBfv3rX2vLli2aNWuW5s6dq4MHD2rTpk167bXXtHLlSm3evFnp6el65JFHVFdXZ8PeAwCswvA5AEBI+cUvfqH58+c3/rlz584aP368xo0bp2uuuUbPP/+8Lr74YknSww8/rD179py2jscff1zR0dGKiIjQ/v37FR0dLb/fr6qqKnk8HpmmqRUrVmjEiBEaOHCgtm3bprCwMO3bt09VVVV6/fXXNXToUKWnp2vcuHEyDMOy/QcAWI9SBAAIKY8++ugZJ0v44osvVFtbq0suuaRx2YUXXqikpKTTHrt37149+eST+vvf/65vf/vbuuyyyyRJDQ0N6tKli1555RUtW7ZMP/nJTxQIBDR69GhlZWWpT58+ysnJabzf6/Xqzjvv1MSJE792kgcAQNtGKQIAtAkdOnRQZGSk/vGPf6h79+6SpMrKSr3wwgu6//77Gx9XV1ene++9VzNmzFBGRoYMw9DOnTv15ptvNv6dQCCgX/3qV2poaNDHH3+sqVOn6jvf+Y6GDh2qDh066MUXX1Rtba02btyoyZMn68orr9T1119vw14DAKzAx14AgDYhLCxMo0aNUk5Ojg4cOKCamho988wz2rZtm7xeb+Pj6urq5Pf75fV6ZRiG9u/fr6eeeqrxvv379+tHP/qRNm7cqLCwMMXHx0uS2rdvr5KSEt1zzz3atWuXIiMj1aFDh8b7AADOxZkiAECb8dBDD2nx4sVKT0+X3+9X//79lZ2d3eQx7dq10/z585Wdna3HH39cHTp00O23367S0lL99a9/1YgRI/Szn/1MP//5z3Xw4EFdcMEFysjI0MiRI2UYhv7+979r4sSJOnz4sDp06KDZs2frqquusmmPAQBWMEzTNO0OAQAAAAB2YfgcAAAAAFejFAEAAABwNUoRAAAAAFejFAEAAABwNUoRAAAAAFejFAEAAABwNUoRAAAAAFejFAEAAABwNUoRAAAAAFejFAEAAABwNUoRAAAAAFejFAEAAABwtf8Pa1TJKOrjLTcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x='Pclass', y='Age', hue=\"Sex\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to above interpretation, also median of male's age is more than female's in each class quality. Lets find these median values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Sex   \n",
       "1       female   36.500\n",
       "        male     40.000\n",
       "2       female   28.000\n",
       "        male     32.000\n",
       "3       female   19.000\n",
       "        male     24.000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Pclass', 'Sex']).Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     24.000\n",
       "1     36.500\n",
       "2     19.000\n",
       "3     36.500\n",
       "4     24.000\n",
       "       ...  \n",
       "151   36.500\n",
       "152   24.000\n",
       "153   24.000\n",
       "154   24.000\n",
       "155   40.000\n",
       "Name: Age, Length: 156, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Pclass', 'Sex']).Age.transform(\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'] = df['Age'].fillna(df.groupby(['Pclass', 'Sex']).Age.transform(\"median\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          125\n",
       "Embarked         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.283</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.600</td>\n",
       "      <td>C2</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>14.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 12 columns</p>\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",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male 22.000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                               Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                             Allen, Mr. William Henry    male 35.000      0   \n",
       "..                                                 ...     ...    ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female 22.000      1   \n",
       "152                                   Meo, Mr. Alfonzo    male 55.500      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male 40.500      0   \n",
       "154                              Olsen, Mr. Ole Martin    male 24.000      0   \n",
       "155                        Williams, Mr. Charles Duane    male 51.000      0   \n",
       "\n",
       "     Parch            Ticket   Fare Cabin Embarked  \n",
       "0        0         A/5 21171  7.250   NaN        S  \n",
       "1        0          PC 17599 71.283   C85        C  \n",
       "2        0  STON/O2. 3101282  7.925   NaN        S  \n",
       "3        0            113803 53.100  C123        S  \n",
       "4        0            373450  8.050   NaN        S  \n",
       "..     ...               ...    ...   ...      ...  \n",
       "151      0            113776 66.600    C2        S  \n",
       "152      0        A.5. 11206  8.050   NaN        S  \n",
       "153      2          A/5. 851 14.500   NaN        S  \n",
       "154      0         Fa 265302  7.312   NaN        S  \n",
       "155      1          PC 17597 61.379   NaN        C  \n",
       "\n",
       "[156 rows x 12 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alt_titanic = pd.read_excel(\"titanic3.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alt_titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alt_titanic.loc[:,['survived','boat', 'body']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alt_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alt_titanic.sample(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cabin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "77 percent of the Cabin data are missing. We can't fill these missing values accurately enough. So let's drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Cabin', axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <th>1</th>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>PC 17599</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000</td>\n",
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       "      <td>8.050</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.600</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>14.500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.312</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.379</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 11 columns</p>\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",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male 22.000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                               Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                             Allen, Mr. William Henry    male 35.000      0   \n",
       "..                                                 ...     ...    ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female 22.000      1   \n",
       "152                                   Meo, Mr. Alfonzo    male 55.500      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male 40.500      0   \n",
       "154                              Olsen, Mr. Ole Martin    male 24.000      0   \n",
       "155                        Williams, Mr. Charles Duane    male 51.000      0   \n",
       "\n",
       "     Parch            Ticket   Fare Embarked  \n",
       "0        0         A/5 21171  7.250        S  \n",
       "1        0          PC 17599 71.283        C  \n",
       "2        0  STON/O2. 3101282  7.925        S  \n",
       "3        0            113803 53.100        S  \n",
       "4        0            373450  8.050        S  \n",
       "..     ...               ...    ...      ...  \n",
       "151      0            113776 66.600        S  \n",
       "152      0        A.5. 11206  8.050        S  \n",
       "153      2          A/5. 851 14.500        S  \n",
       "154      0         Fa 265302  7.312        S  \n",
       "155      1          PC 17597 61.379        C  \n",
       "\n",
       "[156 rows x 11 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is just 1 missing values in Embarked column and we can't fill itaccurately enough. So let's drop just this row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 155 entries, 0 to 155\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  155 non-null    int64  \n",
      " 1   Survived     155 non-null    int64  \n",
      " 2   Pclass       155 non-null    int64  \n",
      " 3   Name         155 non-null    object \n",
      " 4   Sex          155 non-null    object \n",
      " 5   Age          155 non-null    float64\n",
      " 6   SibSp        155 non-null    int64  \n",
      " 7   Parch        155 non-null    int64  \n",
      " 8   Ticket       155 non-null    object \n",
      " 9   Fare         155 non-null    float64\n",
      " 10  Embarked     155 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 14.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survive (target feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0   0.658\n",
       "1   0.342\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Survived.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Survived');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's examine the affect of each feature on survival status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  1          0.709\n",
       "        0          0.291\n",
       "male    0          0.860\n",
       "        1          0.140\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Sex\").Survived.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Sex', hue='Survived');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Pclass', hue='Survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.groupby(\"Pclass\")   fix during break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIVWlij60Y9+lCFDhmTo0KHtf6ZOnZokWbx4cSZOnJihQ4dm1KhRueOOO8oxIgAAUBDV5Vh06dKlOeOMM3L99dd32N7Q0JBzzz03F1xwQc4666w8/fTTOf/88zN48OAcdthh5RgVAADo5spypmjp0qX58Ic//KbtCxcuTF1dXSZNmpTq6uocc8wxGTNmTObOnVuGKQEAgCIo+Zmi1tbW/PKXv0xtbW1mzZqVlpaWnHDCCbnkkkuyYsWKDBo0qMP+AwYMyPz58zu9TktLS6f2r6qq6vQaXVlnjx8AALqbbf2ZuORRtGHDhgwZMiSnnHJKZsyYkZdeeimXXnpppk6dmn79+qW2trbD/jU1Ndm8eXOn11m6dOk271tbW5shQ4Z0eo2ubPny5dmyZUu5xwAAgC6v5FHUt2/fDpfD1dbWZurUqTnzzDMzbty4NDY2dti/sbExvXv37vQ69fX13e7sT2cMHjy43CMAAEBZtbS0bNPJkpJH0bJly3Lffffl4osvTkVFRZKkqakplZWVOeyww/JP//RPHfZfuXJlBg4c2Ol1qqqqCh1FRT52AADojJLfaKGuri5z587NrFmzsnXr1qxduzY33nhjPvGJT+SUU07J+vXrM2fOnDQ3N+eJJ57Ivffem/Hjx5d6TAAAoCBKHkX9+/fPLbfckgceeCDDhw/P+PHjU19fn6uvvjp77bVXZs+enfvvvz8jRozI9OnTM3369IwcObLUYwIAAAVRlvcpGj58eObNm/eWj9XX17/tYwAAADtaWd6nCAAAoKsQRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQhNFAABAoZU1ilpaWjJ58uRcdtll7dsWL16ciRMnZujQoRk1alTuuOOOMk4IAAB0d2WNoptvvjmLFi1q/7ihoSHnnntuxo4dm6effjrXXnttrr/++ixZsqSMUwIAAN1Z2aLo8ccfz8KFC/Oxj32sfdvChQtTV1eXSZMmpbq6Osccc0zGjBmTuXPnlmtMAACgm6sux6IvvvhirrzyysycOTNz5sxp375ixYoMGjSow74DBgzI/PnzO71GS0tLp/avqqrq9BpdWWePHwAAuptt/Zm45FHU2tqaqVOn5uyzz84hhxzS4bFXX301tbW1HbbV1NRk8+bNnV5n6dKl27xvbW1thgwZ0uk1urLly5dny5Yt5R4DAAC6vJJH0S233JKePXtm8uTJb3qstrY2r7zySodtjY2N6d27d6fXqa+v73Znfzpj8ODB5R4BAADKqqWlZZtOlpQ8iu6+++6sW7cuw4YNS/KH6EmSf/3Xf820adPy6KOPdth/5cqVGThwYKfXqaqqKnQUFfnYAQCgM0p+o4X7778/P//5z7No0aIsWrQop512Wk477bQsWrQoo0ePzvr16zNnzpw0NzfniSeeyL333pvx48eXekwAAKAgutSbt+61116ZPXt27r///owYMSLTp0/P9OnTM3LkyHKPBgAAdFNlufvcG91www0dPq6vr8+8efPKNA0AAFA0XepMEQAAQKmJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBonY6i88477y23f/KTn9zuYQAAAEqtelt2+u///u/cddddSZJHHnkkN998c4fHN23alOXLl+/w4QAAAHa2bYqi973vfVmxYkU2bNiQlpaWPPnkkx0e32233fLFL35xpwwIAACwM21TFFVWVubv/u7vkiTTp0/PNddcs1OHAgAAKJVtiqI3uuaaa9LU1JQNGzaktbW1w2Pve9/7dthgAAAApdDpKLr//vtz1VVXZdOmTe3b2traUlFRkV/96lc7dDgAAICdrdNRNGPGjEyaNCmf+MQnUl3d6acDAAB0KZ2umt///vf5/Oc/L4gAAIBuodPvU3TooYdm5cqVO2MWAACAkuv06Z4jjzwyn/70p/Pxj388ffv27fDY5z//+R02GAAAQCl0OoqeffbZDBw4MKtWrcqqVavat1dUVOzQwQAAAEqh01H0ve99b2fMAQAAUBadjqK77rrrbR8bO3bsdowCAABQeu/pltxv1NDQkC1btuSoo44SRQAAwC6n01H0b//2bx0+bmtry3e+851s3LhxR80EAABQMp2+Jff/VVFRkb/+67/O3XffvSPmAQAAKKntjqIkWb16tbvPAQAAu6ROXz43efLkDgHU3Nyc5cuX5/TTT9+hgwEAAJRCp6NoxIgRHT6urKzMpz/96Xz0ox/dYUMBAACUSqej6POf/3z7f7/44ovZc889U13d6ZcBAADoEjr9O0XNzc257rrrMnTo0Bx33HE56qijctVVV6WpqWlnzAcAALBTdTqKZs6cmSeffDLf/OY3c9999+Wb3/xmFi9enG9+85s7YTwAAICdq9PXvd1777257bbbcsABByRJDj744Bx88MGZNGlSpk2btsMHBAAA2Jk6faaooaEh+++/f4dt+++/fxobG3fYUAAAAKXS6SgaPHhw5s2b12HbvHnzMmjQoB02FAAAQKl0+vK5Cy+8MJ/5zGdyzz335IADDsiaNWuycuXK3HrrrTtjPgAAgJ2q01E0bNiwXHnllVm8eHGqq6tz0kkn5cwzz8yRRx65M+YDAADYqTodRTNmzMiCBQty22235cADD8wDDzyQ6667Lg0NDTnnnHN2xowAAAA7Tad/p2j+/Pn57ne/mwMPPDBJcvLJJ+e2227L3Llzd/RsAAAAO12no2jTpk1vefe5zZs377CheO/26VOTttaWco+xQ3W34wEAoGvp9OVzhx56aL797W9nypQp7dtmz56dQw45ZIcOxnvTp6ZnKiqrsv7Oy9K8/tflHme79eh7UPqOu6HcYwAA0I11Ooouu+yyfOYzn8kPf/jD9O/fP88//3y2bt2aWbNm7Yz5eI+a1/86zc//qtxjAABAl/eezhQtXLgwDz74YNatW5f9998/J554Yvr06bMz5gMAANipOh1FSbLnnntm7NixO3gUAACA0uv0jRYAAAC6E1EEAAAUmigCAAAKTRQBAACFVpYoevzxxzNx4sQceeSROfbYY/OVr3wljY2NSZLFixdn4sSJGTp0aEaNGpU77rijHCMCAAAFUfIo2rBhQ/7mb/4mf/EXf5FFixZlwYIFeeqpp/Ltb387DQ0NOffcczN27Ng8/fTTufbaa3P99ddnyZIlpR4TAAAoiPd0S+7tsffee+exxx7L7rvvnra2tmzcuDGvvfZa9t577yxcuDB1dXWZNGlSkuSYY47JmDFjMnfu3Bx22GGlHhUAACiAslw+t/vuuydJTjjhhIwZMyb9+vXLuHHjsmLFigwaNKjDvgMGDMiyZcvKMSYAAFAAJT9T9EYLFy5MQ0NDLrnkklxwwQXZb7/9Ultb22GfmpqabN68udOv3dLS0qn9q6qqOr0GpdPZrycAAGzrz5BljaKamprU1NRk6tSpmThxYiZPnpxXXnmlwz6NjY3p3bt3p1976dKl27xvbW1thgwZ0uk1KJ3ly5dny5Yt5R4DAIBuqORR9POf/zxXXHFF7rnnnvTs2TNJ0tTUlB49emTAgAF59NFHO+y/cuXKDBw4sNPr1NfXO/vTjQwePLjcIwAAsItpaWnZppMlJY+iwYMHp7GxMTfddFMuvvjivPDCC/nqV7+aCRMm5JRTTslNN92UOXPmZNKkSXnmmWdy7733ZubMmZ1ep6qqShR1I76WAADsLCWPot69e2fWrFm57rrrcuyxx6ZPnz4ZM2ZMzj///PTs2TOzZ8/OtddemxkzZmTvvffO9OnTM3LkyFKPCQAAFERZfqdowIABmT179ls+Vl9fn3nz5pV4IgAAoKjKcktuAACArkIUAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0Uwf9qaW0t9wg7VHc7HgCAnaW63ANAV1FVWZnptz+c1esayj3Kdvvgvnvmmr88vtxjAADsEkQRvMHqdQ1Z9rsN5R4DAIAScvkcAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCK0sULVu2LGeffXaGDx+eY489NtOmTcuGDX+4DfLixYszceLEDB06NKNGjcodd9xRjhEBAICCKHkUNTY25pxzzsnQoUPzyCOP5L777svGjRtzxRVXpKGhIeeee27Gjh2bp59+Otdee22uv/76LFmypNRjAgAABVHyKFq7dm0OOeSQnH/++enZs2f22muvnHXWWXn66aezcOHC1NXVZdKkSamurs4xxxyTMWPGZO7cuaUeEwAAKIjqUi940EEHZdasWR22/eQnP8mhhx6aFStWZNCgQR0eGzBgQObPn9/pdVpaWjq1f1VVVafXoHQ6+/V8L7rj90ApPm8AAF3Vtv4sVPIoeqO2trZ885vfzIMPPpjvf//7+e53v5va2toO+9TU1GTz5s2dfu2lS5du8761tbUZMmRIp9egdJYvX54tW7bstNfvrt8DO/vzBgDQHZQtijZt2pTLL788v/zlL/P9738/gwcPTm1tbV555ZUO+zU2NqZ3796dfv36+vpu+S//RTV48OByj7BL8nkDAIqspaVlm06WlCWK1qxZk89+9rN53/vel/nz52fvvfdOkgwaNCiPPvpoh31XrlyZgQMHdnqNqqoqUdSN+Fq+Nz5vAADvruQ3WmhoaMinPvWpHHnkkbn11lvbgyhJRo8enfXr12fOnDlpbm7OE088kXvvvTfjx48v9ZgAAEBBlPxM0Z133pm1a9fmxz/+ce6///4Ojz377LOZPXt2rr322syYMSN77713pk+fnpEjR5Z6TAAAoCBKHkVnn312zj777Ld9vL6+PvPmzSvhRAAAQJGV/PI5AACArkQUAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKIIAAAoNFEEAAAUmiiCbmifPjVpa20p9xg7THc6FgCg66ku9wDAjtenpmcqKquy/s7L0rz+1+UeZ7v06HtQ+o67odxjAADdmCiCbqx5/a/T/Pyvyj0GAECX5vI5AACg0EQRAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKIIAAAoNFEEAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQitrFG3YsCGjR4/Ok08+2b5t8eLFmThxYoYOHZpRo0bljjvuKOOEAABAd1e2KHrmmWdy1llnZc2aNe3bGhoacu6552bs2LF5+umnc+211+b666/PkiVLyjUmAADQzZUlihYsWJBLLrkkF110UYftCxcuTF1dXSZNmpTq6uocc8wxGTNmTObOnVuOMQEAgAKoLseixx13XMaMGZPq6uoOYbRixYoMGjSow74DBgzI/PnzO71GS0tLp/avqqrq9BqUTme/nu+F74GurRTfAwBA97KtPz+UJYr69ev3lttfffXV1NbWdthWU1OTzZs3d3qNpUuXbvO+tbW1GTJkSKfXoHSWL1+eLVu27LTX9z3Q9e3s7wEAoLjKEkVvp7a2Nq+88kqHbY2Njendu3enX6u+vt6//HcjgwcPLvcIlJnvAQCgs1paWrbpZEmXiqJBgwbl0Ucf7bBt5cqVGThwYKdfq6qqShR1I76W+B4AAHaWLvU+RaNHj8769eszZ86cNDc354knnsi9996b8ePHl3s0AACgm+pSUbTXXntl9uzZuf/++zNixIhMnz4906dPz8iRI8s9GgAA0E2V/fK55cuXd/i4vr4+8+bNK9M0AABA0XSpM0UAAAClJooAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQhNFAABAoYkiAACg0EQRAABQaKII4H+1tLaWe4QdqrsdDwDsLNXlHgCgq6iqrMz02x/O6nUN5R5lu31w3z1zzV8eX+4xAGCXIIoA3mD1uoYs+92Gco8BAJSQy+cAAIBCE0UAAEChiSIAAKDQRBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgAAoNBEEQD8r5bW1nKPsEN1t+MB2Fmqyz0AAHQVVZWVmX77w1m9rqHco2y3D+67Z675y+PLPQbALkEUAcAbrF7XkGW/21DuMQAoIZfPAQAAhSaKAACAQhNFAABAoYkiAID/1d3u2Nfdjgd2FjdaAAD4X+5ACMUkigAA3sAdCKF4XD4HAAAUmigCAAAKTRQBAACFJooAAIBCE0UA0A3t06cmba0t5R5jh+pux0PX1Z1uZd6djmVncvc5AOiG+tT0TEVlVdbfeVma1/+63ONstx59D0rfcTeUewwKorvcmt1t2bedKAKAbqx5/a/T/Pyvyj0G7HLcmr1YXD4HAAAUmigCAAAKTRQBAACFJooAAIBCE0UAAN2Q27LDtnP3OQCAbsht2WHbiSIAgG7Mbdnh3XXJy+defPHFTJkyJcOGDcuIESNy7bXXZuvWreUeCwAA6Ia6ZBRdeOGF6dWrVx5++OHMnz8/jz/+eObMmVPusQAAgG6oy0XRc889l6eeeipTp05NbW1tDjjggEyZMiVz584t92gAAEA31OV+p2jFihWpq6vLfvvt177t4IMPztq1a/Pyyy9njz32eMfnt7W1JUmamppSVVW1zetWVVVlYP8907Oq4r0N3kUcsE/vtLS0pKrfoLRW9iz3ONutap8D09LSkpaWnX+3me7yPZB0r+8D3wPvzR/326Nkn7fupDt9D3SnvweS0v1d4Hug6/L/g87z/4K0H/vrjfB2KtrebY8Su/vuu/ONb3wjP/vZz9q3rVmzJqNHj85DDz2U/v37v+Pzm5qasnTp0p08JQAAsKuor69Pz55v/48DXe5MUa9evbJly5YO217/uHfv3u/6/Orq6tTX16eysjIVFbt23QMAAO9dW1tbWltbU139ztnT5aJo4MCB2bhxY9avX5++ffsmSVatWpX+/funT58+7/r8ysrKd6xAAACAN+pyN1o48MADc9RRR+W6667Lpk2b8tvf/jYzZ87MhAkTyj0aAADQDXW53ylKkvXr1+fLX/5ynnzyyVRWVmbs2LG55JJLOnXjBAAAgG3RJaMIAACgVLrc5XMAAAClJIoAAIBCE0UAAEChiSIAAKDQRFE38uKLL2bKlCkZNmxYRowYkWuvvTZbt24t91iUwYYNGzJ69Og8+eST5R6FElu2bFnOPvvsDB8+PMcee2ymTZuWDRs2lHssSuzxxx/PxIkTc+SRR+bYY4/NV77ylTQ2NpZ7LEqspaUlkydPzmWXXVbuUSiDH/3oRxkyZEiGDh3a/mfq1KnlHqvLEkXdyIUXXphevXrl4Ycfzvz58/P4449nzpw55R6LEnvmmWdy1llnZc2aNeUehRJrbGzMOeeck6FDh+aRRx7Jfffdl40bN+aKK64o92iU0IYNG/I3f/M3+Yu/+IssWrQoCxYsyFNPPZVvf/vb5R6NErv55puzaNGico9BmSxdujRnnHFGnn322fY/N954Y7nH6rJEUTfx3HPP5amnnsrUqVNTW1ubAw44IFOmTMncuXPLPRoltGDBglxyySW56KKLyj0KZbB27doccsghOf/889OzZ8/stddeOeuss/L000+XezRKaO+9985jjz2WcePGpaKiIhs3bsxrr72Wvffeu9yjUUKPP/54Fi5cmI997GPlHoUyWbp0aT784Q+Xe4xdhijqJlasWJG6urrst99+7dsOPvjgrF27Ni+//HIZJ6OUjjvuuPz0pz/Nn/3Zn5V7FMrgoIMOyqxZszq80fVPfvKTHHrooWWcinLYfffdkyQnnHBCxowZk379+mXcuHFlnopSefHFF3PllVfmpptuSm1tbbnHoQxaW1vzy1/+Mj/72c9y0kkn5U/+5E9y1VVXpaGhodyjdVmiqJt49dVX3/QX3+sfb968uRwjUQb9+vVLdXV1ucegC2hra8s3vvGNPPjgg7nyyivLPQ5lsnDhwvz7v/97Kisrc8EFF5R7HEqgtbU1U6dOzdlnn51DDjmk3ONQJhs2bMiQIUNyyimn5Ec/+lHmzZuX3/zmN36n6B346amb6NWrV7Zs2dJh2+sf9+7duxwjAWWyadOmXH755fnlL3+Z73//+xk8eHC5R6JMampqUlNTk6lTp2bixIlpaGjInnvuWe6x2IluueWW9OzZM5MnTy73KJRR3759O/wKRW1tbaZOnZozzzwzmzZtaj+bzP/nTFE3MXDgwGzcuDHr169v37Zq1ar0798/ffr0KeNkQCmtWbMm48ePz6ZNmzJ//nxBVEA///nP8/GPfzxNTU3t25qamtKjRw+XUhXA3XffnaeeeirDhg3LsGHDct999+W+++7LsGHDyj0aJbRs2bJ8/etfT1tbW/u2pqamVFZWpmfPnmWcrOsSRd3EgQcemKOOOirXXXddNm3alN/+9reZOXNmJkyYUO7RgBJpaGjIpz71qRx55JG59dZb/WJ9QQ0ePDiNjY256aab0tTUlN/97nf56le/mgkTJvhhqADuv//+/PznP8+iRYuyaNGinHbaaTnttNPcha5g6urqMnfu3MyaNStbt27N2rVrc+ONN+YTn/iEvwfehijqRmbMmJGtW7fm5JNPzplnnpnjjz8+U6ZMKfdYQInceeedWbt2bX784x/nqKOO6vDeFBRH7969M2vWrKxYsSLHHntsJk+enI985CNuzQ4F0r9//9xyyy154IEHMnz48IwfPz719fW5+uqryz1al1XR9sbzagAAAAXjTBEAAFBooggAACg0UQQAABSaKAIAAApNFAEAAIUmigAAgEITRQAAQKGJIgC6nIaGhnzpS1/KCSeckCOOOCLHHXdcLr300jz//PNJklNPPTX33HNPkmTy5Mn51re+9bav1dTUlJtuuikf/ehHM3To0IwcOTJf+MIXsmrVqpIcCwBdnygCoMu56KKL8tJLL2X+/Pn5xS9+kbvuuitNTU05++yzs3Xr1vzLv/xLTj/99G16ra985St59tlnM2fOnDz77LNZuHBh+vfvn0mTJuXll1/eyUcCwK5AFAHQ5TzzzDMZPXp0+vXrlyTp27dvrrjiihx++OF5+eWXM2rUqNx5553t+69ZsyaTJ0/O0UcfnT//8z/PkiVLOrzW8ccfnw984ANJkj322CPTpk3LSSedlBdeeCHJH8423XDDDRk3blyOOOKIjBs3LosWLSrhEQNQTtXlHgAA/q9TTz01X/ziF7No0aIMHz48hx9+eN7//vfnhhtueMv9H3jggdxyyy054ogjMmvWrHz2s5/NT3/60+yxxx459dRTc/PNN2f16tUZOXJkDj/88Hzwgx/M9ddf3+E1fvCDH+Qf/uEfcuSRR+bWW2/Neeedl4ULF2avvfYqxSEDUEbOFAHQ5VxzzTW5+uqr8/vf/z5XX311Ro0aldGjR7f/HtH/NWHChBx99NHp0aNHPve5z2W33XbLQw89lCQ5//zz83d/93fZvHlzvvrVr+bjH/94jj/++MyZM6fDa4wfPz4jR45Mz54987nPfS61tbV58MEHd/ahAtAFOFMEQJdTWVmZM844I2eccUba2tqyatWq3H333Zk2bVr7JXVv9PqlcUlSUVGR/v3753/+53/at40aNSqjRo1K8odL7RYuXJivf/3r6d27dyZOnJgkOfDAA9/0Gq9fXgdA9+ZMEQBdysMPP5yhQ4dm48aNSf4QKAMGDMjFF1+cIUOG5D//8z/f9Jx169a1/3dra2vWrl2b97///Vm1alXq6+vzX//1X+2P/9Ef/VHOOeecnHTSSfnVr37Vvv2NEfX6a+y///474QgB6GpEEQBdytFHH5199tknl19+eZYvX57m5uZs2rQp99xzT37zm9/kxBNPfNNz5s+fn8WLF6epqSnf+ta3Ul1dnRNOOCEHHXRQDj300Fx99dVZsmRJXnvttWzZsiUPPfRQnnzyyYwePbr9Ne644478x3/8R5qamvL3f//3aWtry0knnVTCIwegXFw+B0CXUlNTk9tvvz0333xzzjvvvLz44ovp0aNHjjjiiNx22205+OCD3/Scj33sY/niF7+YNWvW5MMf/nBuvfXW9OrVK0nyne98JzNnzszUqVPzP//zP6msrMyHPvSh3HjjjTnmmGPaX2P48OH58pe/nJUrV2bIkCGZPXt2+vTpU7LjBqB8Ktra2trKPQQAlNPkyZMzfPjwfOELXyj3KACUgcvnAACAQhNFAABAobl8DgAAKDRnigAAgEITRQAAQKGJIgAAoNBEEQAAUGiiCAAAKDRRBAAAFJooAgAACk0UAQAAhSaKAACAQvt/RyHuhSr9KM8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='SibSp', hue='Survived');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Parch', hue='Survived');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='Embarked', hue='Survived');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0   24.000\n",
       "1   24.000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Survived\").Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = df, x = \"Survived\", y = \"Age\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data = df, x = \"Age\", hue = \"Survived\", fill=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/DCgPRaIDYXA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x117d1f390>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"DCgPRaIDYXA\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0   13.727\n",
       "1   15.246\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Survived\").Fare.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = df, x = \"Survived\", y = \"Fare\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data = df, x = \"Fare\", hue = \"Survived\", fill=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Some Feature Engineering</p>\n",
    "\n",
    "<a id=\"4\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From \"Ticket\" to \"is_group\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticket\n",
       "2651          2\n",
       "W./C. 6608    2\n",
       "35281         2\n",
       "19950         2\n",
       "CA 2144       2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Ticket.value_counts(dropna = False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticket\n",
       "14311              1\n",
       "370371             1\n",
       "S.C./A.4. 23567    1\n",
       "330958             1\n",
       "PC 17597           1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Ticket.value_counts(dropna = False).tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticket\n",
       "2651               2\n",
       "W./C. 6608         2\n",
       "35281              2\n",
       "19950              2\n",
       "CA 2144            2\n",
       "                  ..\n",
       "14311              1\n",
       "370371             1\n",
       "S.C./A.4. 23567    1\n",
       "330958             1\n",
       "PC 17597           1\n",
       "Name: count, Length: 144, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket = df.Ticket.value_counts()\n",
    "ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticket\n",
       "2651            2\n",
       "W./C. 6608      2\n",
       "35281           2\n",
       "19950           2\n",
       "CA 2144         2\n",
       "347082          2\n",
       "S.O.C. 14879    2\n",
       "237736          2\n",
       "11668           2\n",
       "349909          2\n",
       "113803          2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticket[ticket != 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2651',\n",
       " 'W./C. 6608',\n",
       " '35281',\n",
       " '19950',\n",
       " 'CA 2144',\n",
       " '347082',\n",
       " 'S.O.C. 14879',\n",
       " '237736',\n",
       " '11668',\n",
       " '349909',\n",
       " '113803']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_list = list(ticket[ticket != 1].index)\n",
    "group_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      1\n",
       "4      0\n",
       "      ..\n",
       "151    0\n",
       "152    0\n",
       "153    0\n",
       "154    0\n",
       "155    0\n",
       "Name: is_group, Length: 155, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_group\"] = df.Ticket.isin(group_list) * 1\n",
    "df.is_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='is_group', hue='Survived');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From \"SibSp\" and \"Parch\" to \"is_alone\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "151    0\n",
       "152    1\n",
       "153    0\n",
       "154    1\n",
       "155    0\n",
       "Name: is_alone, Length: 155, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_alone\"] = ((df.SibSp == 0) & (df.Parch == 0)) * 1\n",
    "df.is_alone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>is_group</th>\n",
       "      <th>is_alone</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>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.283</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.925</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.600</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>14.500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.312</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.379</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 13 columns</p>\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",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male 22.000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                               Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                             Allen, Mr. William Henry    male 35.000      0   \n",
       "..                                                 ...     ...    ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female 22.000      1   \n",
       "152                                   Meo, Mr. Alfonzo    male 55.500      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male 40.500      0   \n",
       "154                              Olsen, Mr. Ole Martin    male 24.000      0   \n",
       "155                        Williams, Mr. Charles Duane    male 51.000      0   \n",
       "\n",
       "     Parch            Ticket   Fare Embarked  is_group  is_alone  \n",
       "0        0         A/5 21171  7.250        S         0         0  \n",
       "1        0          PC 17599 71.283        C         0         0  \n",
       "2        0  STON/O2. 3101282  7.925        S         0         1  \n",
       "3        0            113803 53.100        S         1         0  \n",
       "4        0            373450  8.050        S         0         1  \n",
       "..     ...               ...    ...      ...       ...       ...  \n",
       "151      0            113776 66.600        S         0         0  \n",
       "152      0        A.5. 11206  8.050        S         0         1  \n",
       "153      2          A/5. 851 14.500        S         0         0  \n",
       "154      0         Fa 265302  7.312        S         0         1  \n",
       "155      1          PC 17597 61.379        C         0         0  \n",
       "\n",
       "[155 rows x 13 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='is_alone', hue='Survived');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_group  is_alone\n",
       "0         1           83\n",
       "          0           50\n",
       "1         0           21\n",
       "          1            1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"is_group\").is_alone.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>is_group</th>\n",
       "      <th>is_alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.191</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.028</td>\n",
       "      <td>-0.024</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.191</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.018</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>-0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0.008</td>\n",
       "      <td>-0.102</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-0.602</td>\n",
       "      <td>-0.129</td>\n",
       "      <td>0.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.066</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.349</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>0.060</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>0.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.062</td>\n",
       "      <td>0.082</td>\n",
       "      <td>-0.349</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.462</td>\n",
       "      <td>-0.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.028</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>0.398</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.323</td>\n",
       "      <td>-0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>-0.024</td>\n",
       "      <td>0.018</td>\n",
       "      <td>-0.602</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.260</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.337</td>\n",
       "      <td>-0.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_group</th>\n",
       "      <td>-0.023</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>-0.129</td>\n",
       "      <td>-0.192</td>\n",
       "      <td>0.462</td>\n",
       "      <td>0.323</td>\n",
       "      <td>0.337</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-0.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_alone</th>\n",
       "      <td>0.018</td>\n",
       "      <td>-0.047</td>\n",
       "      <td>0.192</td>\n",
       "      <td>0.165</td>\n",
       "      <td>-0.639</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.373</td>\n",
       "      <td>-0.405</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived  Pclass    Age  SibSp  Parch   Fare  \\\n",
       "PassengerId        1.000    -0.191   0.008  0.066 -0.138 -0.028 -0.024   \n",
       "Survived          -0.191     1.000  -0.102 -0.126 -0.062  0.044  0.018   \n",
       "Pclass             0.008    -0.102   1.000 -0.400  0.082  0.011 -0.602   \n",
       "Age                0.066    -0.126  -0.400  1.000 -0.349 -0.194  0.060   \n",
       "SibSp             -0.138    -0.062   0.082 -0.349  1.000  0.398  0.279   \n",
       "Parch             -0.028     0.044   0.011 -0.194  0.398  1.000  0.260   \n",
       "Fare              -0.024     0.018  -0.602  0.060  0.279  0.260  1.000   \n",
       "is_group          -0.023    -0.098  -0.129 -0.192  0.462  0.323  0.337   \n",
       "is_alone           0.018    -0.047   0.192  0.165 -0.639 -0.500 -0.373   \n",
       "\n",
       "             is_group  is_alone  \n",
       "PassengerId    -0.023     0.018  \n",
       "Survived       -0.098    -0.047  \n",
       "Pclass         -0.129     0.192  \n",
       "Age            -0.192     0.165  \n",
       "SibSp           0.462    -0.639  \n",
       "Parch           0.323    -0.500  \n",
       "Fare            0.337    -0.373  \n",
       "is_group        1.000    -0.405  \n",
       "is_alone       -0.405     1.000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(numeric_only = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's implement some useful methods on \"Name\" and \"Ticket\" features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                Braund, Mr. Owen Harris\n",
       "1      Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                                 Heikkinen, Miss. Laina\n",
       "3           Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                               Allen, Mr. William Henry\n",
       "                             ...                        \n",
       "151                    Pears, Mrs. Thomas (Edith Wearne)\n",
       "152                                     Meo, Mr. Alfonzo\n",
       "153                      van Billiard, Mr. Austin Blyler\n",
       "154                                Olsen, Mr. Ole Martin\n",
       "155                          Williams, Mr. Charles Duane\n",
       "Name: Name, Length: 155, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Owen Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John Bradley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Laina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jacques Heath</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>William Henry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Thomas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Alfonzo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Austin Blyler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Ole Martin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Charles Duane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "0      Owen Harris\n",
       "1     John Bradley\n",
       "2            Laina\n",
       "3    Jacques Heath\n",
       "4    William Henry\n",
       "..             ...\n",
       "151        Thomas \n",
       "152        Alfonzo\n",
       "153  Austin Blyler\n",
       "154     Ole Martin\n",
       "155  Charles Duane\n",
       "\n",
       "[155 rows x 1 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.Name.str.split(\",\").str[1].str.split(\"(\").str[0].str.split('\"').str[0].str.split('.').str[1].str.strip()\n",
    "\n",
    "df.Name.str.extract(\"\\w+\\.\\s(\\w*\\s*\\w*)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>is_group</th>\n",
       "      <th>is_alone</th>\n",
       "      <th>New_Name</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>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Owen Harris Braund</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>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.283</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>John Bradley Cumings</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>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.925</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Laina Heikkinen</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>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Jacques Heath Futrelle</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>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>William Henry Allen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.600</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Thomas  Pears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alfonzo Meo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>14.500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Austin Blyler Billiard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.312</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ole Martin Olsen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.379</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Charles Duane Williams</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 14 columns</p>\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",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male 22.000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                               Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                             Allen, Mr. William Henry    male 35.000      0   \n",
       "..                                                 ...     ...    ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female 22.000      1   \n",
       "152                                   Meo, Mr. Alfonzo    male 55.500      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male 40.500      0   \n",
       "154                              Olsen, Mr. Ole Martin    male 24.000      0   \n",
       "155                        Williams, Mr. Charles Duane    male 51.000      0   \n",
       "\n",
       "     Parch            Ticket   Fare Embarked  is_group  is_alone  \\\n",
       "0        0         A/5 21171  7.250        S         0         0   \n",
       "1        0          PC 17599 71.283        C         0         0   \n",
       "2        0  STON/O2. 3101282  7.925        S         0         1   \n",
       "3        0            113803 53.100        S         1         0   \n",
       "4        0            373450  8.050        S         0         1   \n",
       "..     ...               ...    ...      ...       ...       ...   \n",
       "151      0            113776 66.600        S         0         0   \n",
       "152      0        A.5. 11206  8.050        S         0         1   \n",
       "153      2          A/5. 851 14.500        S         0         0   \n",
       "154      0         Fa 265302  7.312        S         0         1   \n",
       "155      1          PC 17597 61.379        C         0         0   \n",
       "\n",
       "                   New_Name  \n",
       "0        Owen Harris Braund  \n",
       "1      John Bradley Cumings  \n",
       "2           Laina Heikkinen  \n",
       "3    Jacques Heath Futrelle  \n",
       "4       William Henry Allen  \n",
       "..                      ...  \n",
       "151           Thomas  Pears  \n",
       "152             Alfonzo Meo  \n",
       "153  Austin Blyler Billiard  \n",
       "154        Ole Martin Olsen  \n",
       "155  Charles Duane Williams  \n",
       "\n",
       "[155 rows x 14 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"New_Name\"] = df.Name.str.extract(\"\\w+\\.\\s(\\w*\\s*\\w*)\") + \" \" + df.Name.str.extract(\"(\\w+),\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
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       "      <th>is_alone</th>\n",
       "      <th>New_Name</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>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Owen Harris Braund</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>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.283</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>John Bradley Cumings</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>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.925</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Laina Heikkinen</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>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Jacques Heath Futrelle</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>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>William Henry Allen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Pears, Mrs. Thomas (Edith Wearne)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113776</td>\n",
       "      <td>66.600</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Thomas  Pears</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Meo, Mr. Alfonzo</td>\n",
       "      <td>male</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 11206</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Alfonzo Meo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Billiard, Mr. Austin Blyler</td>\n",
       "      <td>male</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>A/5. 851</td>\n",
       "      <td>14.500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Austin Blyler Billiard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Olsen, Mr. Ole Martin</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Fa 265302</td>\n",
       "      <td>7.312</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ole Martin Olsen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Williams, Mr. Charles Duane</td>\n",
       "      <td>male</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17597</td>\n",
       "      <td>61.379</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Charles Duane Williams</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 14 columns</p>\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",
       "151          152         1       1   \n",
       "152          153         0       3   \n",
       "153          154         0       3   \n",
       "154          155         0       3   \n",
       "155          156         0       1   \n",
       "\n",
       "                                                  Name     Sex    Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male 22.000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                               Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                             Allen, Mr. William Henry    male 35.000      0   \n",
       "..                                                 ...     ...    ...    ...   \n",
       "151                  Pears, Mrs. Thomas (Edith Wearne)  female 22.000      1   \n",
       "152                                   Meo, Mr. Alfonzo    male 55.500      0   \n",
       "153                    van Billiard, Mr. Austin Blyler    male 40.500      0   \n",
       "154                              Olsen, Mr. Ole Martin    male 24.000      0   \n",
       "155                        Williams, Mr. Charles Duane    male 51.000      0   \n",
       "\n",
       "     Parch            Ticket   Fare Embarked  is_group  is_alone  \\\n",
       "0        0         A/5 21171  7.250        S         0         0   \n",
       "1        0          PC 17599 71.283        C         0         0   \n",
       "2        0  STON/O2. 3101282  7.925        S         0         1   \n",
       "3        0            113803 53.100        S         1         0   \n",
       "4        0            373450  8.050        S         0         1   \n",
       "..     ...               ...    ...      ...       ...       ...   \n",
       "151      0            113776 66.600        S         0         0   \n",
       "152      0        A.5. 11206  8.050        S         0         1   \n",
       "153      2          A/5. 851 14.500        S         0         0   \n",
       "154      0         Fa 265302  7.312        S         0         1   \n",
       "155      1          PC 17597 61.379        C         0         0   \n",
       "\n",
       "                   New_Name  \n",
       "0        Owen Harris Braund  \n",
       "1      John Bradley Cumings  \n",
       "2           Laina Heikkinen  \n",
       "3    Jacques Heath Futrelle  \n",
       "4       William Henry Allen  \n",
       "..                      ...  \n",
       "151           Thomas  Pears  \n",
       "152             Alfonzo Meo  \n",
       "153  Austin Blyler Billiard  \n",
       "154        Ole Martin Olsen  \n",
       "155  Charles Duane Williams  \n",
       "\n",
       "[155 rows x 14 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             A/5 21171\n",
       "1              PC 17599\n",
       "2      STON/O2. 3101282\n",
       "3                113803\n",
       "4                373450\n",
       "             ...       \n",
       "151              113776\n",
       "152          A.5. 11206\n",
       "153            A/5. 851\n",
       "154           Fa 265302\n",
       "155            PC 17597\n",
       "Name: Ticket, Length: 155, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>113776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>11206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>265302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>17597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0\n",
       "0      21171\n",
       "1      17599\n",
       "2    3101282\n",
       "3     113803\n",
       "4     373450\n",
       "..       ...\n",
       "151   113776\n",
       "152    11206\n",
       "153      851\n",
       "154   265302\n",
       "155    17597\n",
       "\n",
       "[155 rows x 1 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.Ticket.str.replace(\"\\S*\\s\", \"\")\n",
    "\n",
    "df.Ticket.str.extract(\"(\\d*)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Ticket\"] = df.Ticket.str.extract(\"(\\d*)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>is_group</th>\n",
       "      <th>is_alone</th>\n",
       "      <th>New_Name</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>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21171</td>\n",
       "      <td>7.250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Owen Harris Braund</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>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17599</td>\n",
       "      <td>71.283</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>John Bradley Cumings</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>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3101282</td>\n",
       "      <td>7.925</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Laina Heikkinen</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>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.100</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Jacques Heath Futrelle</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>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>William Henry Allen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.458</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>James Moran</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.862</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Timothy J McCarthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.075</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Gosta Leonard Palsson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.133</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Oscar W Johnson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.071</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Nicholas  Nasser</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex    Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male 22.000      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female 38.000      1   \n",
       "2                             Heikkinen, Miss. Laina  female 26.000      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female 35.000      1   \n",
       "4                           Allen, Mr. William Henry    male 35.000      0   \n",
       "5                                   Moran, Mr. James    male 24.000      0   \n",
       "6                            McCarthy, Mr. Timothy J    male 54.000      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male  2.000      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female 27.000      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female 14.000      1   \n",
       "\n",
       "   Parch   Ticket   Fare Embarked  is_group  is_alone                New_Name  \n",
       "0      0    21171  7.250        S         0         0      Owen Harris Braund  \n",
       "1      0    17599 71.283        C         0         0    John Bradley Cumings  \n",
       "2      0  3101282  7.925        S         0         1         Laina Heikkinen  \n",
       "3      0   113803 53.100        S         1         0  Jacques Heath Futrelle  \n",
       "4      0   373450  8.050        S         0         1     William Henry Allen  \n",
       "5      0   330877  8.458        Q         0         1             James Moran  \n",
       "6      0    17463 51.862        S         0         1      Timothy J McCarthy  \n",
       "7      1   349909 21.075        S         1         0   Gosta Leonard Palsson  \n",
       "8      2   347742 11.133        S         0         0         Oscar W Johnson  \n",
       "9      0   237736 30.071        C         1         0        Nicholas  Nasser  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Dropping Unnecessary Features</p>\n",
    "\n",
    "<a id=\"5\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
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       "      <th>is_alone</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.250</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000</td>\n",
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       "      <td>0</td>\n",
       "      <td>71.283</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.925</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.100</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050</td>\n",
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       "      <td>...</td>\n",
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       "      <th>151</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>66.600</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>153</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>40.500</td>\n",
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       "      <td>2</td>\n",
       "      <td>14.500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.312</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>61.379</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex    Age  SibSp  Parch   Fare Embarked  is_group  \\\n",
       "0           0       3    male 22.000      1      0  7.250        S         0   \n",
       "1           1       1  female 38.000      1      0 71.283        C         0   \n",
       "2           1       3  female 26.000      0      0  7.925        S         0   \n",
       "3           1       1  female 35.000      1      0 53.100        S         1   \n",
       "4           0       3    male 35.000      0      0  8.050        S         0   \n",
       "..        ...     ...     ...    ...    ...    ...    ...      ...       ...   \n",
       "151         1       1  female 22.000      1      0 66.600        S         0   \n",
       "152         0       3    male 55.500      0      0  8.050        S         0   \n",
       "153         0       3    male 40.500      0      2 14.500        S         0   \n",
       "154         0       3    male 24.000      0      0  7.312        S         0   \n",
       "155         0       1    male 51.000      0      1 61.379        C         0   \n",
       "\n",
       "     is_alone  \n",
       "0           0  \n",
       "1           0  \n",
       "2           1  \n",
       "3           0  \n",
       "4           1  \n",
       "..        ...  \n",
       "151         0  \n",
       "152         1  \n",
       "153         0  \n",
       "154         1  \n",
       "155         0  \n",
       "\n",
       "[155 rows x 10 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df.drop(['PassengerId', 'Name' , 'Ticket', 'New_Name'], axis=1)\n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Dummy Operation</p>\n",
    "\n",
    "<a id=\"6\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>155 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass    Age  SibSp  Parch   Fare  is_group  is_alone  \\\n",
       "0           0       3 22.000      1      0  7.250         0         0   \n",
       "1           1       1 38.000      1      0 71.283         0         0   \n",
       "2           1       3 26.000      0      0  7.925         0         1   \n",
       "3           1       1 35.000      1      0 53.100         1         0   \n",
       "4           0       3 35.000      0      0  8.050         0         1   \n",
       "..        ...     ...    ...    ...    ...    ...       ...       ...   \n",
       "151         1       1 22.000      1      0 66.600         0         0   \n",
       "152         0       3 55.500      0      0  8.050         0         1   \n",
       "153         0       3 40.500      0      2 14.500         0         0   \n",
       "154         0       3 24.000      0      0  7.312         0         1   \n",
       "155         0       1 51.000      0      1 61.379         0         0   \n",
       "\n",
       "     Sex_female  Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0         False      True       False       False        True  \n",
       "1          True     False        True       False       False  \n",
       "2          True     False       False       False        True  \n",
       "3          True     False       False       False        True  \n",
       "4         False      True       False       False        True  \n",
       "..          ...       ...         ...         ...         ...  \n",
       "151        True     False       False       False        True  \n",
       "152       False      True       False       False        True  \n",
       "153       False      True       False       False        True  \n",
       "154       False      True       False       False        True  \n",
       "155       False      True        True       False       False  \n",
       "\n",
       "[155 rows x 13 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy = pd.get_dummies(data = df_final, drop_first=False)\n",
    "df_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>is_group</th>\n",
       "      <th>is_alone</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.925</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>66.600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.312</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>61.379</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived    Age  SibSp  Parch   Fare  is_group  is_alone  Sex_female  \\\n",
       "0           0 22.000      1      0  7.250         0         0       False   \n",
       "1           1 38.000      1      0 71.283         0         0        True   \n",
       "2           1 26.000      0      0  7.925         0         1        True   \n",
       "3           1 35.000      1      0 53.100         1         0        True   \n",
       "4           0 35.000      0      0  8.050         0         1       False   \n",
       "..        ...    ...    ...    ...    ...       ...       ...         ...   \n",
       "151         1 22.000      1      0 66.600         0         0        True   \n",
       "152         0 55.500      0      0  8.050         0         1       False   \n",
       "153         0 40.500      0      2 14.500         0         0       False   \n",
       "154         0 24.000      0      0  7.312         0         1       False   \n",
       "155         0 51.000      0      1 61.379         0         0       False   \n",
       "\n",
       "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  \\\n",
       "0        True       False       False        True     False     False   \n",
       "1       False        True       False       False      True     False   \n",
       "2       False       False       False        True     False     False   \n",
       "3       False       False       False        True      True     False   \n",
       "4        True       False       False        True     False     False   \n",
       "..        ...         ...         ...         ...       ...       ...   \n",
       "151     False       False       False        True      True     False   \n",
       "152      True       False       False        True     False     False   \n",
       "153      True       False       False        True     False     False   \n",
       "154      True       False       False        True     False     False   \n",
       "155      True        True       False       False      True     False   \n",
       "\n",
       "     Pclass_3  \n",
       "0        True  \n",
       "1       False  \n",
       "2        True  \n",
       "3       False  \n",
       "4        True  \n",
       "..        ...  \n",
       "151     False  \n",
       "152      True  \n",
       "153      True  \n",
       "154      True  \n",
       "155     False  \n",
       "\n",
       "[155 rows x 15 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy = pd.get_dummies(data = df_dummy, columns=[\"Pclass\"], drop_first=False)\n",
    "df_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(df_dummy.corr(), annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#9d4f8c; font-family:newtimeroman; color:#FFF9ED; font-size:175%; text-align:center; border-radius:10px 10px;\">Intro to Machine Learning</p>\n",
    "\n",
    "<a id=\"6\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>\n",
    "\n",
    "We have come to the final segement of Data Analysis with Python, a good opportunity to look forward and see where all this might be going in terms of Machine Learning, a set of algorithms which have transformed statistics into the data science we study today.\n",
    "\n",
    "If you have done some reading on Machine Learning, you may be aware that the [\"neural network\" type algorithms](https://scikit-learn.org/stable/modules/neural_networks_supervised.html) have made some of the biggest and most impressive advances lately, thanks to (a) growing computing power and (b) big data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/znrGfvjWJhQ\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x118219450>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"znrGfvjWJhQ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data = df_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>is_group</th>\n",
       "      <th>is_alone</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.925</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1</td>\n",
       "      <td>22.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>66.600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0</td>\n",
       "      <td>55.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.050</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0</td>\n",
       "      <td>40.500</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0</td>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.312</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "      <td>51.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>61.379</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived    Age  SibSp  Parch   Fare  is_group  is_alone  Sex_female  \\\n",
       "0           0 22.000      1      0  7.250         0         0       False   \n",
       "1           1 38.000      1      0 71.283         0         0        True   \n",
       "2           1 26.000      0      0  7.925         0         1        True   \n",
       "3           1 35.000      1      0 53.100         1         0        True   \n",
       "4           0 35.000      0      0  8.050         0         1       False   \n",
       "..        ...    ...    ...    ...    ...       ...       ...         ...   \n",
       "151         1 22.000      1      0 66.600         0         0        True   \n",
       "152         0 55.500      0      0  8.050         0         1       False   \n",
       "153         0 40.500      0      2 14.500         0         0       False   \n",
       "154         0 24.000      0      0  7.312         0         1       False   \n",
       "155         0 51.000      0      1 61.379         0         0       False   \n",
       "\n",
       "     Sex_male  Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  \\\n",
       "0        True       False       False        True     False     False   \n",
       "1       False        True       False       False      True     False   \n",
       "2       False       False       False        True     False     False   \n",
       "3       False       False       False        True      True     False   \n",
       "4        True       False       False        True     False     False   \n",
       "..        ...         ...         ...         ...       ...       ...   \n",
       "151     False       False       False        True      True     False   \n",
       "152      True       False       False        True     False     False   \n",
       "153      True       False       False        True     False     False   \n",
       "154      True       False       False        True     False     False   \n",
       "155      True        True       False       False      True     False   \n",
       "\n",
       "     Pclass_3  \n",
       "0        True  \n",
       "1       False  \n",
       "2        True  \n",
       "3       False  \n",
       "4        True  \n",
       "..        ...  \n",
       "151     False  \n",
       "152      True  \n",
       "153      True  \n",
       "154      True  \n",
       "155     False  \n",
       "\n",
       "[155 rows x 15 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these machine learning examples, we pick a Series Y column to be predicted, by all the other columns in DataFrame X.  \n",
    "\n",
    "The predicting is done according to a model, which creates itself by using training data, wherein the actual Y values (Survived or not) are provided.\n",
    "\n",
    "Models come in many flavors:  linear and logistical regression, random forest, neural network and more.\n",
    "\n",
    "Once we have a model, we can test it against the training data for its predictive power, and more to the point, we can test it against testing data not yet seen by the model.\n",
    "\n",
    "We would expect a model to be more accurate (have a higher success rate) vis-a-vis the training data it trains on, versus testing data.  Even if not perfect, is our model accurate enough, even on test data, to be usable in the field?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "\n",
    "Often used to predict a true or false outcome, based on features. Think of eligible verus ineligible for a benefit, based on features.  \n",
    "\n",
    "In contrast, linear regression, probably the simplest model, predicts a continuous range of outputs.  Think of predicting a specific salary range or housing price based on features.\n",
    "\n",
    "The curve to imagine, with logistic regression, is called a \"sigmoid\" and it takes on y values between two boundaries, corresponding to our yes / no logic.  As with linear regression, the algorithm is geared to be [multivariate](https://www.hackerearth.com/practice/machine-learning/linear-regression/multivariate-linear-regression-1/tutorial/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python ecosystem is well endowed with machine learning and statistics packages that contain the various model types.  We will turn to `sci-kit learn` for our needs, imported as `sklearn`.  If you installed the Anaconda distro, it's likely already present, otherwise, you'll need to [install it from the cloud](https://scikit-learn.org/stable/install.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = titanic_data.drop(columns = ['Survived'], axis=1)\n",
    "Y = titanic_data['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting into test and training data sets, after we've already \"cut the cake\" between X and Y, gives us a total of four data sets we need to keep track of.  The training and test sets go together.\n",
    "\n",
    "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/39098603200/in/album-72177720296706479/\" title=\"P1040552\"><img src=\"https://live.staticflickr.com/785/39098603200_613a131381.jpg\" width=\"500\" height=\"334\" alt=\"P1040552\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a step along the way, almost always taken.\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Survived column is missing from X_train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>is_group</th>\n",
       "      <th>is_alone</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_1</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>19.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.150</td>\n",
       "      <td>0</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>33.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.896</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>21.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>29.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>32.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>24.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.896</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>3.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41.579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.029</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>26.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14.454</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>124 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  SibSp  Parch   Fare  is_group  is_alone  Sex_female  Sex_male  \\\n",
       "109 19.000      1      0 24.150         0         0        True     False   \n",
       "130 33.000      0      0  7.896         0         1       False      True   \n",
       "72  21.000      0      0 73.500         1         1       False      True   \n",
       "66  29.000      0      0 10.500         0         1        True     False   \n",
       "123 32.500      0      0 13.000         0         1        True     False   \n",
       "..     ...    ...    ...    ...       ...       ...         ...       ...   \n",
       "76  24.000      0      0  7.896         0         1       False      True   \n",
       "43   3.000      1      2 41.579         0         0        True     False   \n",
       "22  15.000      0      0  8.029         0         1        True     False   \n",
       "73  26.000      1      0 14.454         0         0       False      True   \n",
       "15  55.000      0      0 16.000         0         1        True     False   \n",
       "\n",
       "     Embarked_C  Embarked_Q  Embarked_S  Pclass_1  Pclass_2  Pclass_3  \n",
       "109       False        True       False     False     False      True  \n",
       "130        True       False       False     False     False      True  \n",
       "72        False       False        True     False      True     False  \n",
       "66        False       False        True     False      True     False  \n",
       "123       False       False        True     False      True     False  \n",
       "..          ...         ...         ...       ...       ...       ...  \n",
       "76        False       False        True     False     False      True  \n",
       "43         True       False       False     False      True     False  \n",
       "22        False        True       False     False     False      True  \n",
       "73         True       False       False     False     False      True  \n",
       "15        False       False        True     False      True     False  \n",
       "\n",
       "[124 rows x 14 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109    1\n",
       "130    0\n",
       "72     0\n",
       "66     1\n",
       "123    1\n",
       "      ..\n",
       "76     0\n",
       "43     1\n",
       "22     1\n",
       "73     0\n",
       "15     1\n",
       "Name: Survived, Length: 124, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train  #  the Survived column we want to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()  # the model type we plan to use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train)   # a training session molds the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_prediction = model.predict(X_train)  # test it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  0.8467741935483871\n"
     ]
    }
   ],
   "source": [
    "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
    "print('Accuracy score of training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expected drop-off in accuracy, given new data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of test data :  0.7741935483870968\n"
     ]
    }
   ],
   "source": [
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
    "print('Accuracy score of test data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest\n",
    "\n",
    "This well-known algorithm creates several Decision Trees, which are yes / no decision sequences aimed at classifying your samples.  Think of the game of \"20 questions\" wherein one person thinks of an object, and the other person tries to guess it within 20 yes or no questions.  \"Is it bigger than a bread box?\" is a typical question people use to allude to this little game.  \n",
    "\n",
    "A decision tree is a formalized series of such question.  A lot of decision trees make a random forest.  The many trees get to vote on each sample.  Having several classifiers in the picture working together is what we call an ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf_model = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  1.0\n"
     ]
    }
   ],
   "source": [
    "X_train_prediction = rf_model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
    "print('Accuracy score of training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of test data :  0.7741935483870968\n"
     ]
    }
   ],
   "source": [
    "X_test_prediction = rf_model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
    "print('Accuracy score of test data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-Layer Perceptron\n",
    "\n",
    "<!---\n",
    "![](https://scikit-learn.org/stable/_images/multilayerperceptron_network.png)\n",
    "--->\n",
    "\n",
    "[Neural Network Playground](http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.93678&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "mlp_model = rf_model = MLPClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier()"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  0.6693548387096774\n"
     ]
    }
   ],
   "source": [
    "X_train_prediction = mlp_model.predict(X_train)\n",
    "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
    "print('Accuracy score of training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of test data :  0.6129032258064516\n"
     ]
    }
   ],
   "source": [
    "X_test_prediction = mlp_model.predict(X_test)\n",
    "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
    "print('Accuracy score of test data : ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/52624510808/in/dateposted-public/\" title=\"facebook_meme_titanic\"><img src=\"https://live.staticflickr.com/65535/52624510808_0295c53dc1.jpg\" width=\"398\" height=\"500\" alt=\"facebook_meme_titanic\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"background-color:#FDFEFE; font-family:newtimeroman; color:#9d4f8c; font-size:150%; text-align:center; border-radius:10px 10px;\">The End of The Session - 12</p>\n",
    "\n",
    "<a id=\"7\"></a>\n",
    "<a href=\"#toc\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" \n",
    "style=\"color:blue; background-color:#dfa8e4\" data-toggle=\"popover\">Content</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: center;\"><img src=\"https://docs.google.com/uc?id=1lY0Uj5R04yMY3-ZppPWxqCr5pvBLYPnV\" class=\"img-fluid\" \n",
    "alt=\"CLRSWY\"></p>\n",
    "\n",
    "## <p style=\"background-color:#FDFEFE; font-family:newtimeroman; color:#9d4f8c; font-size:100%; text-align:center; border-radius:10px 10px;\">WAY TO REINVENT YOURSELF</p>\n",
    "\n",
    "____"
   ]
  }
 ],
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