EDA-TITANIC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bdfffc35-4784-4a80-b2b7-fa68f1f341c4",
    "_uuid": "5ec865618a600b6ec734f2f42360511ad9b97c9e"
   },
   "source": [
    "# Importing important libraries\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "1ccc646e-3906-45b0-8fb6-813d9817878a",
    "_uuid": "7ef37314af94e5550608dab39e2ef8eaa02fa4e8"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "1fadbae8-73fc-40c4-80fa-d62dc1360368",
    "_uuid": "86558aeb9c37dd924a33288186bc3bf468f15ed8"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gathering the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "cc67b041-a52d-461d-9039-d28e182d94fa",
    "_uuid": "dd06df984ddcd00c3a2b4e97a844794407088489",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_data=pd.read_csv(r\"D:\\Prodigy InfoTech\\archive\\train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "3fdca55c-7dcb-4eb0-b7da-b140f943a646",
    "_uuid": "d195d7ce719c98f9fd27398fda67f09c48d4e035"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "9f87a7d4-11a6-47d7-a604-8fc6e4963a14",
    "_uuid": "92f7bbb6010c4f1d6b32b00750cd9ea7555cea38",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
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       "      <td>S</td>\n",
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       "    <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>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <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>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
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       "    <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>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <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>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <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>14.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>C</td>\n",
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       "  </tbody>\n",
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       "</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  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop([\"PassengerId\",'Ticket'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From common sense, columns such as PassengerId, Name and Ticket number shouldn't be related to the survival probability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data cleaning and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "e43a6271-1b69-43d7-a45b-f8b84c940dad",
    "_uuid": "a7b00e3add28398149621d710b28cfd43a7e8315"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### there are missing values in Age and Cabin columns which needs to be handeled properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In the training set:**\n",
    "\n",
    "**1- 38.3% people survived**\n",
    "\n",
    "**2- More number of people were actually in 3rd class**\n",
    "\n",
    "**3- 50% of passengers were in between the age of 20 to 38**\n",
    "\n",
    "**Since the survival rate is 0.38, even if I decide to give a submission of all passengers being perished, I would still be having a accuracy of 62%. So accuracy cannot be considered as the only measure in saying how good the model is.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It indicates that there are total of 891 passesnger details among which 177 people's Age is missing and 687 people's Cabin details are missing. And 2 people's Embarkation details are missing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "c57466e5-c0fc-4e3a-86ee-c219c25cfb4e",
    "_uuid": "474928cc7dc656e5da644fa453edcd1d21841915"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t+vrrrxUYGKipU6dq8eLF8vX1bXM7GzZs0IoVK3T48GF5eHho7NixSk1N1ejRo897rgAAAL1dl2MwNTVVR44c0T333KNf/vKX+qd/+ie5ubld8IS+++47ZWVl6brrrlNMTIwGDhyoEydOaOfOnbr//vt1//33KyMjo3l8bW2tYmJiVFRUpFtuuUUzZsxQcXGxMjMztWfPHu3cuVNeXl5220hPT9fTTz+tSy+9VHPmzFFtba3eeustTZ48WZs2bdKNN954wfsBAADQG1isVmv7h/U6EBoaqjFjxuh//ud/unVCjY2Nampqkru7fafW1NTo5z//uQ4fPqy8vDyNGDFCkrR06VKlpaUpKSlJTz75ZPN42/Lk5GSlpqY2Ly8tLdW4ceM0ZMgQvffee/Lz85MklZSU6NZbb1VQUJD279/favvOclVmgbOnABjp08Sxzp6CQ9U8dq+zpwAYyeeFNc6eQitdfgJJU1OTrr766u6ciyTJzc2tzRDz8fFRdHS0JOnIkSPNc1i5cqW8vb2VnJxsN37BggXy9/fXqlWr7E5jZ2VlqaGhQQsXLmwOQUkaMWKEZs2apaNHjyonJ6fb9wsAAMAVdTkGr7/+en322WfdOZcO1dXVKScnRxaLRVdccYWks0f5jh8/rnHjxrU6Fezp6anx48eroqKiOR6lf3xxti0sW7It27t3r6N2AwAAwKV0+Vzov//7v2vKlCl6++23FRsb251zknT2RpIVK1bozJkz+uabb7R7924dO3ZMKSkpGj58uKSzMShJw4YNa3MdLce1fO3t7a2goKAOx5+Purq6zu0UgF6D328AjtATf1s8PT07Nb7LMbhz505NmDBBc+bMUVRUlK6++uo279y1WCytTuGej6qqKi1fvrz5Zw8PDz399NN65JFHmpdVV1dLkt3p3pZ8fHzsxtleBwQEnPf4jlRUVLT5dToAer/y8nJnT8GhfursCQCGcvTfFjc3t3YPkrWnyzH4zDPPNL/Ozc21e25xS12NwbCwMFmtVjU2NurYsWN666239PTTTys/P19vvvmmS9zgERIS0gNbOXnuIQC63eDBg509BYeqd/YEAEO54t+WC3ocXU9wc3NTWFiYHnvsMbm5uel3v/ud/vu//1sPPvhg85HIqqqqNj9bU1MjSXZHLH19fds98tfW+I509jAsgN6jr/9+E4OAc7ji35Ze9Ti6W265RdLZI5EPPvhg8zV+LW8Qacl27Z9tnO11QUGBKisrW1032NZ4AACAvqzLdxM7w1dffSVJzaeIhw8fruDgYOXn56u2ttZubF1dnfbt26fg4GC7c+dRUVGSpOzs7Fbrty2zjQEAAOjrunxksDNfv9KZuCosLFRYWFirm0JOnjypp556SpJ02223STp7PWJ8fLzS0tKUlpZm96XTzz//vKxWq371q1/ZPb5u9uzZevnll5Wenq4pU6bYfen02rVrNXToUE2cOPG85wsAANCbdTkG77jjjlbPCG7Pd999d97rXb16tVauXKkJEyYoNDRUF198scrLy/Xuu+/q1KlTmjp1qmbOnNk8PikpSTt27FBGRoYKCws1atQoFRcXa/fu3Ro5cqSSkpLs1h8eHq7FixdryZIlioqKUmxsrE6fPq1Nmzapvr5eGRkZLnFzCgAAQE/ocvUkJye3GYPV1dX65JNPtG/fPk2aNEmjR4/u1HpjY2NVXV2tjz76SHl5eTp9+rQGDhyoyMhIzZo1S9OnT7fbrpeXl7Zu3arly5dry5Ytys3NVVBQkBITE5WSktLqy6gladGiRQoNDdWKFSv0xhtvyMPDQ2PHjlVqaqrGjBnT+X8MAACAXqrLzyY+l7fffluJiYnatm2bRo0a5YhNGIFnEwPOwbOJAThCn3o28bnExsZqwoQJzdf5AQAAwPU49G7in/3sZ9q/f78jNwEAAIAL4NAYLCwsPO+bTAAAANDzunwDSXvP1mtsbFRFRYVWr16tnJwcTZkypcuTAwAAgGN1OQavvvrqDo/6NTU1KSwsTEuXLu3qJgAAAOBgXY7BWbNmtRmD/fr1k7+/v0aPHq2YmBiXfAYfAAAAzupyDK5YsaI75wEAAAAn6FXPJgYAAED3uuDnrtXW1mr79u0qKipSdXW1fHx8NHLkSMXExLT59A8AAAC4jguKwa1bt2r+/PmyWq1qavrHg0wsFov8/PyUkZGhqVOnXvAkAQAA4BhdjsGCggLNmTNHbm5u+pd/+RdNmDBBgYGBOnHihHJzc7V69Wo9+OCD2rZtm8aO7duPdQIAAOituhyD6enpGjBggN59911deeWVdu/FxcXpwQcf1KRJk5Senq5169Zd8EQBAADQ/bp8A0lBQYHi4uJahaDNlVdeqWnTpqmgoKDLkwMAAIBjdTkGv//+ewUEBHQ4JiAgQN9//31XNwEAAAAH63IMhoaG6v333+9wzF/+8heFhoZ2dRMAAABwsC7HYFxcnD7++GP9+te/1vHjx+3e++qrr5SQkKCPP/5YcXFxFzxJAAAAOEaXbyB59NFHlZ2drXXr1mnz5s0aNmyYAgICdOLECR05ckQ//PCDrr32Wj366KPdOF0AAAB0py4fGbzooou0bds2PfHEEwoODtbhw4e1Z88eHT58WCEhIXriiSe0bds2XXTRRd05XwAAAHSjC/rS6f79+ys5OVnJycmqqalRTU2NfHx85OPj013zAwAAgAN1+cjghx9+qNTUVFVWVkqSfHx8FBIS0hyClZWVSk1N1f79+7tnpgAAAOh2XY7BV199VTt37lRQUFCb7wcFBWnXrl3KzMzs8uQAAADgWF2OwYMHDyoyMrLDMePHj+fIIAAAgAvrcgyeOHFCISEhHY4JCgrSN99809VNAAAAwMG6HIN+fn4qLy/vcExZWZm8vLy6ugkAAAA4WJdj8Prrr9e2bdt07NixNt8vLy/X9u3bNXbs2C5PDgAAAI7V5Rh8+OGHdfr0ad1+++1as2aNvvrqK0lnnz6yevVq3X777fr+++/1yCOPdNtkAQAA0L26/D2D48eP1/Lly/XEE0/o4YcfliRZLBY1NTVJkvr166dnnnlGUVFR3TNTAAAAdLsL+tLpuXPnKioqSm+88YYOHDigqqoq+fn56dprr9WcOXN05ZVXdtc8AQAA4AAXFIOSdOWVV+q5557rjrkAAACgh3X5mkEAAAD0fsQgAACAwYhBAAAAgxGDAAAABiMGAQAADEYMAgAAGIwYBAAAMBgxCAAAYDBiEAAAwGDEIAAAgMGIQQAAAIMRgwAAAAYjBgEAAAxGDAIAABiMGAQAADAYMQgAAGAwYhAAAMBgxCAAAIDBiEEAAACDEYMAAAAGIwYBAAAMRgwCAAAYjBgEAAAwmMvFYEVFhTIzMxUXF6eIiAgFBATo8ssvV3x8vD766KM2P1NdXa3U1FRFREQoMDBQERERSk1NVXV1dbvb2bBhg6KjoxUSEqKwsDDNnDlTBw8edNRuAQAAuCSXi8HXXntNqamp+uKLL3TzzTfrkUceUWRkpLZv365JkyZp8+bNduNra2sVExOjzMxMXXbZZUpMTNQVV1yhzMxMxcTEqLa2ttU20tPTNXfuXH399deaM2eO4uLilJ+fr8mTJ2vPnj09tasAAABO5+7sCfzYmDFjtH37do0fP95u+b59+xQbG6sFCxZoypQpGjBggCQpIyNDRUVFSkpK0pNPPtk8funSpUpLS1NGRoZSU1Obl5eWlmrZsmUKDw/Xe++9Jz8/P0nSQw89pFtvvVXz58/X/v375e7ucv80AAAA3c7ljgxOnTq1VQhK0vjx43XjjTfq5MmTOnTokCSpqalJK1eulLe3t5KTk+3GL1iwQP7+/lq1apWampqal2dlZamhoUELFy5sDkFJGjFihGbNmqWjR48qJyfHQXsHAADgWlwuBjvi4eEhSXJzc5N09ijf8ePHNW7cOHl5edmN9fT01Pjx41VRUaEjR440L8/NzZUkRUdHt1q/bdnevXsdMn8AAABX02tisLy8XB988IGCgoJ01VVXSTobg5I0bNiwNj8zfPhwu3G2197e3goKCjqv8QAAAH1Zr7gwrr6+Xg899JD+7//+T08++WTzkUHb3cItT/e25OPjYzfO9jogIOC8x3ekrq7u/HYAQK/D7zcAR+iJvy2enp6dGu/yMXjmzBk9/PDD2rdvn+6//37NmjXL2VNqVlFRocbGRmdPA4ADlJeXO3sKDvVTZ08AMJSj/7a4ubm1e8a0PS4dg01NTZo/f77Wr1+vu+++Wy+88ILd+76+vpKkqqqqNj9fU1NjN872ur0jf22N70hISMh5jbswJ3tgGwB+bPDgwc6egkPVO3sCgKFc8W+Ly8bgmTNnNG/ePGVlZWnGjBlasWKF+vWzv8TRdo1fyxtEWrJd+2cbZ3tdUFCgysrKVtcNtjW+I509DAug9+jrv9/EIOAcrvi3xSVvIGkZgnfddZf++Mc/Nl8n2NLw4cMVHBys/Pz8Vl8uXVdXp3379ik4ONjucGlUVJQkKTs7u9X6bMtsYwAAAPo6l4vBM2fO6JFHHlFWVpamTZum1157rc0QlCSLxaL4+HidOnVKaWlpdu89//zzslqtio+Pl8ViaV4+e/Zsubu7Kz093e70cklJidauXauhQ4dq4sSJjtk5AAAAF+Nyp4mXL1+u1atXy9vbW+Hh4Xr22WdbjYmJidHVV18tSUpKStKOHTuUkZGhwsJCjRo1SsXFxdq9e7dGjhyppKQku8+Gh4dr8eLFWrJkiaKiohQbG6vTp09r06ZNqq+vV0ZGBk8fAQAAxnC56ikrK5MknTp1Ss8991ybY0JDQ5tj0MvLS1u3btXy5cu1ZcsW5ebmKigoSImJiUpJSWn1ZdSStGjRIoWGhmrFihV644035OHhobFjxyo1NVVjxoxx3M4BAAC4GIvVam069zA4y1WZBc6eAmCkTxPHOnsKDlXz2L3OngJgJJ8X1jh7Cq243DWDAAAA6DnEIAAAgMGIQQAAAIMRgwAAAAYjBgEAAAxGDAIAABiMGAQAADAYMQgAAGAwYhAAAMBgxCAAAIDBiEEAAACDEYMAAAAGIwYBAAAMRgwCAAAYjBgEAAAwGDEIAABgMGIQAADAYMQgAACAwYhBAAAAgxGDAAAABiMGAQAADEYMAgAAGIwYBAAAMBgxCAAAYDBiEAAAwGDEIAAAgMGIQQAAAIMRgwAAAAYjBgEAAAxGDAIAABiMGAQAADAYMQgAAGAwYhAAAMBgxCAAAIDBiEEAAACDEYMAAAAGIwYBAAAMRgwCAAAYjBgEAAAwGDEIAABgMGIQAADAYMQgAACAwYhBAAAAgxGDAAAABiMGAQAADEYMAgAAGIwYBAAAMBgxCAAAYDBiEAAAwGDEIAAAgMGIQQAAAIMRgwAAAAYjBgEAAAzmkjG4bt06Pfroo7r55psVGBgof39/ZWVltTu+urpaqampioiIUGBgoCIiIpSamqrq6up2P7NhwwZFR0crJCREYWFhmjlzpg4ePOiI3QEAAHBZLhmDS5Ys0Ztvvqny8nIFBQV1OLa2tlYxMTHKzMzUZZddpsTERF1xxRXKzMxUTEyMamtrW30mPT1dc+fO1ddff605c+YoLi5O+fn5mjx5svbs2eOo3QIAAHA5LhmDL7/8sgoLC1VaWqoHHnigw7EZGRkqKipSUlKSNm/erN///vfauHGjkpOTVVRUpIyMDLvxpaWlWrZsmcLDw7V371794Q9/0Isvvqhdu3bJ3d1d8+fPV0NDgyN3DwAAwGW4ZAzefPPNCg0NPee4pqYmrVy5Ut7e3kpOTrZ7b8GCBfL399eqVavU1NTUvDwrK0sNDQ1auHCh/Pz8mpePGDFCs2bN0tGjR5WTk9N9OwMAAODCXDIGz1dpaamOHz+ucePGycvLy+49T09PjR8/XhUVFTpy5Ejz8tzcXElSdHR0q/XZlu3du9eBswYAAHAd7s6ewIUoLS2VJA0bNqzN94cPH948ruVrb2/vNq9FbDnmfNTV1XV6zgB6B36/AThCT/xt8fT07NT4Xh2DtruFW57ubcnHx8dunO11QEDAeY/vSEVFhRobG897vgB6j/LycmdPwaF+6uwJAIZy9N8WNze3dg+StadXx6CzhYSE9MBWTvbANgD82ODBg509BYeqd/YEAEO54t+WXh2Dvr6+kqSqqqo236+pqbEbZ3vd3pG/tsZ3pLOHYQH0Hn3995sYBJzDFf+29OobSGzX+LW8QaQl27V/tnG216dOnVJlZeV5jQcAAOjLen0MBgcHKz8/v9WXS9fV1Wnfvn0KDg62O3ceFRUlScrOzm61Ptsy2xgAAIC+rlfHoMViUXx8vE6dOqW0tDS7955//nlZrVbFx8fLYrE0L589e7bc3d2Vnp5ud3q5pKREa9eu1dChQzVx4sQe2wcAAABncslrBv/0pz8pLy9PknTo0CFJ0sqVK5u/IzAmJkZ33HGHJCkpKUk7duxQRkaGCgsLNWrUKBUXF2v37t0aOXKkkpKS7NYdHh6uxYsXa8mSJYqKilJsbKxOnz6tTZs2qb6+XhkZGXJ3d8l/FgAAgG7nktWTl5enNWvW2C378MMP9eGHH0qSQkNDm2PQy8tLW7du1fLly7Vlyxbl5uYqKChIiYmJSklJafVl1JK0aNEihYaGasWKFXrjjTfk4eGhsWPHKjU1VWPGjHH8DgIAALgIi9VqbTr3MDjLVZkFzp4CYKRPE8c6ewoOVfPYvc6eAmAknxfWnHtQD+vV1wwCAADgwhCDAAAABiMGAQAADEYMAgAAGIwYBAAAMBgxCAAAYDBiEAAAwGDEIAAAgMGIQQAAAIMRgwAAAAYjBgEAAAxGDAIAABiMGAQAADAYMQgAAGAwYhAAAMBgxCAAAIDBiEEAAACDEYMAAAAGIwYBAAAMRgwCAAAYjBgEAAAwGDEIAABgMGIQAADAYMQgAACAwYhBAAAAgxGDAAAABiMGAQAADEYMAgAAGIwYBAAAMBgxCAAAYDBiEAAAwGDEIAAAgMGIQQAAAIMRgwAAAAYjBgEAAAxGDAIAABiMGAQAADAYMQgAAGAwYhAAAMBgxCAAAIDBiEEAAACDEYMAAAAGIwYBAAAMRgwCAAAYjBgEAAAwGDEIAABgMGIQAADAYMQgAACAwYhBAAAAgxGDAAAABiMGAQAADEYMAgAAGMzoGDxw4IBmzpypsLAwhYSEKDo6Whs2bHD2tAAAAHqMu7Mn4Cx79uzR9OnT1b9/f911113y9fXVO++8o7lz56qsrEwLFy509hQBAAAczsgYbGho0Pz582WxWLRt2zZdc801kqSUlBRNmjRJy5Yt07Rp0zR8+HAnzxQAAMCxjDxNnJOTo6NHj2rGjBnNIShJPj4+evzxx9XQ0KCsrCwnzhAAAKBnGHlkMDc3V5IUHR3d6j3bsr179/bonNoz0NPI/0UAHMzi5ePsKQBwEUaWRmlpqSS1eRrY399fgwYNah7jbLkPjHH2FAD0Qd5LXnP2FAC4CCNPE1dXV0uSfH1923zfx8eneQwAAEBfZmQMAgAA4CwjY9B2RLC9o381NTXtHjUEAADoS4yMQdu1gm1dF2i1WvXtt9/ytTIAAMAIRsZgVFSUJCk7O7vVe7ZltjEAAAB9mcVqtTY5exI9raGhQdddd52OHz+u3bt36+qrr5Z09vTwpEmT9Pnnn+vDDz9UeHi4k2cKAADgWEbGoHT2i6enT5+uAQMGaPr06fLx8dE777yjL7/8Ur/5zW+0aNEiZ08RAADA4Yw8TSxJEydO1M6dOxUZGanNmzfr9ddf1yWXXKLXXnuNEMQFOXDggGbOnKmwsDCFhIQoOjpaGzZscPa0APRS69at06OPPqqbb75ZgYGB8vf35ylZ6FZGfum0zbXXXquNGzc6exroQ/bs2aPp06erf//+uuuuu+Tr66t33nlHc+fOVVlZmRYuXOjsKQLoZZYsWaLy8nINGjRIQUFBKi8vd/aU0McYe2QQ6G4NDQ2aP3++LBaLtm3bppdeeklLlixRbm6uRowYoWXLlrnMk20A9B4vv/yyCgsLVVpaqgceeMDZ00EfRAwC3SQnJ0dHjx7VjBkzdM011zQv9/Hx0eOPP66GhgZO7QDotJtvvlmhoaHOngb6MGIQ6Ca5ubmSpOjo6Fbv2Zbt3bu3R+cEAMC5EINAN7GdAm7rC8v9/f01aNAgThMDAFwOMQh0E9vjDdt7lKGPj0+7j0AEAMBZiEEAAACDEYNAN7EdEWzv6F9NTU27Rw0BAHAWYhDoJrZrBdu6LtBqterbb79t83pCAACciRgEuklUVJQkKTs7u9V7tmW2MQAAuApiEOgmN910k4YMGaKNGzeqsLCweXlNTY2effZZubu767777nPiDAEAaM1itVqbnD0JoK/IycnR9OnTNWDAAE2fPl0+Pj5655139OWXX+o3v/kNz70G0Gl/+tOflJeXJ0k6dOiQPvnkE0VGRmro0KGSpJiYGN1xxx3OnCJ6OWIQ6GZ//etftWzZMhUUFKi+vl5XXHGFEhISdPfddzt7agB6oYSEBK1Zs6bd91NSUvTEE0/04IzQ1xCDAAAABuOaQQAAAIMRgwAAAAYjBgEAAAxGDAIAABiMGAQAADAYMQgAAGAwYhAAAMBgxCAAAIDBiEEAcCEjR47UyJEjnT2NZq42HwDdz93ZEwAARzt9+rRWrFiht99+W6WlpWpoaNCgQYMUFhamyMhI/fM//3Pzc14BwDTEIIA+raamRrfffrs+/fRTDRs2THfffbf8/Pz0v//7vyopKdELL7ygoUOHukwMbtmyxdlTAGAYYhBAn7ZixQp9+umnio+P10svvSSLxWL3/hdffKEffvjBSbNrzVWiFIA5uGYQQJ+2f/9+SdLcuXNbhaAkDRkyRJdffnnzz/7+/oqJiWlzXW1dP5eQkCB/f3998cUXevXVVxUZGanAwEAlJCRo+fLl8vf319q1a9tc3/r16+Xv76+0tLR2t9GVdUhnI3fevHmKiIhQYGCgfvaznykhIUFlZWVtrmfbtm265ZZb9NOf/lSXXXaZ5s+fL6vV2uZYAH0LMQigTxs4cKAk6ejRow7dTnJysp577jldc801SkhI0FVXXaV77rlH0tlga8v69etlsVh09913t7verqzjo48+0sSJE7VmzRqNGjVKv/71r3XDDTdow4YNuvXWW/XFF1/YrWPNmjWaPXu2SktLdc899+jee+9Vfn6+YmNjVV9f35l/BgC9EKeJAfRpsbGxWr9+vebNm6eDBw/qlltu0TXXXCN/f/9u3c6nn36qnJwcDR482G55ZGSk/vKXv6iyslJBQUHNy0+cOKEPPvhAkZGRGjJkSLvrHTJkSKfWUV9frwceeEBNTU16//337Y4y5uXl6Y477lBKSorWrVsnSaqurlZKSoq8vLyUnZ2t8PBwSdJvf/tbxcbG6quvvmq1TwD6Fo4MAujTYmJi9NRTT+nMmTN68cUXFRsbqyFDhmj06NF6/PHHVVpa2i3bmTdvXpvRdPfdd6uxsVEbN260W75x40Y1NDR0eFSwK+vYuXOnysrKNH/+/FantG+44QZNmTJFu3fvVnV1taSzp4erq6s1e/bs5hCUJA8PD/32t789944D6PWIQQB93vz581VSUqI333xTCQkJuuGGG3Ts2DH9x3/8h6KiorR9+/YL3sa1117b5vK77rpL/fv3b3Wad926derfv7/i4uLOue7OrOOjjz6SJH3++edatmxZq/++/vprnTlzpjmCi4uLJUnjx49vtd2xY8fK3Z0TSEBfx285ACP4+Pho2rRpmjZtmiSpqqpKTz/9tP7zP/9T8+bN02233ab+/ft3ef0BAQFtLvf399ekSZO0detW/e1vf9Pll1+uzz//XB9//LHuvPPO8zpd3Zl1nDx5UlL71xja1NbWSlLzEcKf/OQnrca4ubnpkksuOef8APRuHBkEYCQ/Pz89++yzGjx4sL799lsdOnRIkmSxWNTY2NjmZ2zh1Ja27lS2+fFNILbr9WzLz8f5rsPHx0eStHbtWlmt1nb/mzBhgiTJ19dXkvTNN9+02mZjY6O+++67854jgN6JGARgLIvFoosvvthumb+/vyoqKlqN/fLLL1VVVdWl7UyePFn+/v5av369zpw5ow0bNmjgwIGaNGlSt6/juuuuk/SPr9Q5l4iICEnSvn37Wr1XUFCghoaG854jgN6JGATQp/3Xf/2XDhw40OZ7W7Zs0d/+9jf5+flpxIgRkqTRo0errKxMe/bsaR73ww8/6N/+7d+6PAfbdX1lZWV68cUX9eWXXyouLq5Tp6XPdx1TpkzRpZdeqldffVV79+5ttZ76+nrl5eXZjff19VVWVpb+/ve/241bsmRJF/YWQG9jsVqtTc6eBAA4yn333aft27dr2LBhGjdunIKDg3Xq1CkVFRUpLy9P/fr102uvvaYZM2ZIkv785z9rxowZuvjiizV9+nRddNFF+uCDD+Tn56fy8nJ5eHioqKioef0JCQlas2aNPvnkE4WFhbU7jw8//FC33367PDw8VF9fr127dmncuHGtxtnuAG65jc6u48CBA5oxY4a+++473XTTTc2he+zYMeXl5emSSy6xO3K4evVqJSYmytfXV3fddZd8fX21a9cueXp6qrKystU+A+hbiEEAfdrnn3+uHTt26P3339eRI0dUWVkpSQoODlZkZKQeeughjRo1yu4zmzdv1rPPPqu///3vGjhwoGJjY/W73/1ON9xwgyR1KQYladSoUfriiy80ZMgQffzxx22O6SgGz3cdklRRUaGXXnpJu3fv1rFjxzRgwAAFBwdr3Lhxmj59um666Sa78Vu3btVzzz2nkpIS+fr66he/+IWeeuop3XjjjR3OB0DvRwwCAAAYjGsGAQAADEYMAgAAGIwYBAAAMBgxCAAAYDBiEAAAwGDEIAAAgMGIQQAAAIMRgwAAAAYjBgEAAAxGDAIAABiMGAQAADAYMQgAAGAwYhAAAMBg/w9gsxHnMJGlpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(x='Survived', data=train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f14422da-af0a-431a-89ce-6ea5993aca91",
    "_uuid": "62f5615b17798fb758e14fab7be9d99369e791ea"
   },
   "source": [
    "From the above graph it is clear that not many persons survived.\n",
    "Out of 891 persons in training dataset only 350, 38.4% of total training dataset survived. We will get more insight of data by exploring more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8ba3935e-5f02-4d93-95dc-cfd80fab8d30",
    "_uuid": "f678cba564076a54d7d05a1e4fbaf30436066fb0"
   },
   "source": [
    "Here we'll explore features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "76992ff6-10cb-409a-9465-e56b335faa74",
    "_uuid": "ef6e3bed7a9c2f718dd269619c30c1105233ea54"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the data by 'Survived' and 'Sex' columns and count the number of rows in each group\n",
    "grouped_data = train_data.groupby(['Survived','Sex'])['Survived'].count()\n",
    "\n",
    "# Reset the index to create a DataFrame\n",
    "pivoted_df = grouped_data.reset_index(name='count')\n",
    "# Plot the data using seaborn bar plot\n",
    "sb.barplot(x='Survived', y='count', hue='Sex', data=pivoted_df)\n",
    "# Set plot labels and title\n",
    "plt.xlabel('Survived')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count of passengers by Survived and Sex')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived  Sex   \n",
       "0         female     81\n",
       "          male      468\n",
       "1         female    233\n",
       "          male      109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Survived','Sex'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ac056788-6cc2-4411-a19f-73b83bd6f21b",
    "_uuid": "eed11c2d5077018577d778cb3d03e32214c7c6ce"
   },
   "source": [
    "It is clear that 233 female survived out of 344. And out of 549 male 109 survived. The survival ratio of female is much greater than that of male. It can be seen clearly in following graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "7b0bc1e0-a8d9-4848-807e-815392d12b76",
    "_uuid": "bf5d8e2522bb00dc2fc1c22ff0d821290e8b418b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8eLVu3dpW37NnT33yySd66623tHTpUvn4+CgkJESvvvqqOnfuXOI+8u8FmJSUJH9/f3Xs2NFhY7Rt21axsbF65513tGnTJu3evVtVq1aVn5+fgoODNWTIkAL1I0eOlJeXl+bMmaOYmBh5eXnpscce0/Tp09WtW7cS7a+9mNLS0qxO7QA31XzhHme3gFI4EtbB2S0AKCPnzp0r9mpXwN7s+fPGOYMAAAAGRhgEAAAwMMIgAACAgREGAQAADKxchMGoqChZLBZZLJZib/CYnp6uiIgIBQYGqnbt2goMDFRERMRN72S+fPly9ejRQ3Xq1FGDBg305JNPav/+/Y7aDQAAAJfj8mHwxx9/1BtvvCFPT89ia7KyshQcHKyFCxfqvvvuU1hYmJo2baqFCxcqODjY9mzAG82dO1fjxo3Tr7/+qjFjxmjQoEGKj49Xnz59CtwEEwAAoCJz6TCYm5ur0NBQBQYGKjg4uNi6qKgoHTp0SJMmTdKXX36pv//971qxYoWmTp2qQ4cOKSoqqkB9QkKCIiMj1bhxY+3cuVOvv/665s+fr40bN8rd3V0TJ050+qNhAAAAyoJLh8H58+fr8OHDeu+99+Tm5lZkjdVq1aJFi1S9enVNnTq1wLoXXnhBFotFn3/+uazW/95OcfHixcrJydHkyZPl7e1tW96sWTMNHz5ciYmJ2r59u2N2CgAAwIW4bBg8evSoZs2apSlTptge8VKUhIQEnTlzRh07dix0KNnDw0NdunRRSkqKTpw4YVseGxsrSerRo0eh8fKXFfWsQQAAgIrGJR9Hl5OTo7CwMDVp0kTPP//8TWsTEhIkSQEBAUWub9Soka3uxtfVq1eXr6/vTetvJTs7+5Y1MDZ+RgDjyMvLU15enrPbgEHk5eUV+zvGw8PjjsZyyTA4d+5cHT58WN98840qV65809r8q4VvPNx7I7PZXKAu/3Vxj3Apqr44KSkpys3NvWUdjOvUqVPObgFAGalSpYquXr3q7DZgENnZ2UVmFTc3t2InyIrjcmHw0KFDmjNnjiZMmFDgodauqE6dOmWwlYtlsA04Sr169ZzdAoAycunSJVWpUsXZbcAgPDw8ijzCWRIuFwZDQ0PVsGFDTZs27bbqvby8JF3/R1iUjIyMAnX5r4ub+Suqvjh3Og0L4+FnBDCOjIwMVarksqfio4KpVKmS3X7HuFwYPHz4sCQVm3Z79eolSfr888/Vv39/2zl+N14gcqP8c//y6/Jf79mzR6mpqYW2U1Q9AABAReVyYXD06NFFLt+1a5cSEhL02GOP6e6771b9+vUlXQ9tfn5+io+PV1ZWVoErirOzs7Vr1y75+fkVOH4eFBSkPXv2aMuWLRoxYkSB7WzZssVWAwCAvTRfuMfZLRTrSFgHu4zz/fffKzIyUnv27NG1a9fUtGlThYaG6sknn7TL+HAMlwuD7777bpHLQ0NDlZCQoBdeeEEPPvigbbnJZNLo0aM1e/ZszZ49W6+99ppt3dtvv620tDT9+c9/lslksi0fNWqU3n33Xc2dO1f9+vWzXXxy7NgxLVmyRA0bNtRDDz3koD0EAKDi2bFjh4YMGaIqVapo8ODB8vLy0ldffaVx48YpOTlZkydPdnaLKIbLhcGSmDRpktavX6+oqCgdPHhQrVu31uHDh7Vp0ya1aNFCkyZNKlDfuHFjTZs2TTNnzlRQUJBCQkJ0+fJlrVy5UteuXVNUVJTc3SvEXw0AAA6Xk5OjiRMnymQyae3atWrVqpUkKTw8XL1791ZkZKQGDhzIKVguqkKc6erp6ak1a9YoLCxMP/30k9577z0dO3ZMYWFhWrNmTZHPNZ4yZYo++OAD1apVSx9//LFWrlypDh06aOPGjcwKAgBwB7Zv367ExEQ98cQTtiAoXb9d24svvqicnBwtXrzYiR3iZsrN9Fd0dLSio6OLXe/t7a033nhDb7zxxm2POXToUA0dOtQe7QEAYFg82at8qxAzgwAAwHludicOi8WimjVr3taTveAchEEAAFAq+ffuLe4evWaz+bae7AXnIAwCAAAYGGEQAACUSv6M4M2e7nU7T/aCcxAGAQBAqeSfK1jUeYFpaWm6cOECt5VxYYRBAABQKvlP7cp/iteNeLKX6yMMAgCAUnn44Yfl7++vFStW6ODBg7blGRkZeuutt+Tu7q6RI0c6sUPcTLm5zyAAAHBN7u7ueueddzRkyBD169dPQ4YMkdls1ldffaWTJ0/qr3/9qxo3buzsNlEMwiAAACi1hx56SBs2bFBkZKS+/PJLXbt2TU2bNtXLL7/MAx5cHGEQAIAycCSsg7NbcLh27dppxYoVzm4Dd4hzBgEAAAyMMAgAAGBghEEAAAADIwwCAAAYGGEQAADAwAiDAAAABkYYBAAAMDDCIAAAgIERBgEAAAyMMAgAAGBghEEAAAADIwwCAAAYGGEQAADAwAiDAAAABubu7AYAADCCjOdHOLuFYpnnxZR6jKVLlyouLk4HDhzQ0aNHdfXqVS1YsECjRo2yQ4dwJMIgAAAotZkzZ+rUqVOqWbOmfH19derUKWe3hNvEYWIAAFBq7777rg4ePKiEhASNHTvW2e3gDjAzCAAASu2RRx5xdgsoIWYGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgXHTaQAAUGqfffaZ4uLiJElHjx6VJC1atEixsbGSpODgYPXv399p/aF4hEEAAMqAeV6Ms1twqLi4OMXEFNzH3bt3a/fu3ZKk+vXrEwZdFGEQAACUWnR0tKKjo53dBkqAcwYBAAAMjDAIAABgYIRBAAAAAyMMAgAAGBhhEAAAwMAIgwAAAAZGGAQAADAwwiAAAHZitVqd3QIMwN4/Z4RBAADswMPDQ9nZ2c5uAwaQnZ0tDw8Pu41HGAQAwA48PT2VmZmpK1euMEMIh7Barbpy5YoyMzPl6elpt3F5HB0AAHZQqVIl1axZU1lZWTp//ryz20EF5eHhoZo1a6pSJfvN5xEGAQCwk0qVKslsNstsNju7FeC2cZgYAADAwAiDAAAABkYYBAAAMDDCIAAAgIERBgEAAAyMMAgAAGBghEEAAAADIwwCAAAYGGEQAADAwAiDAAAABkYYBAAAMDDCIAAAgIERBgEAAAzM5cJgWlqapk6dql69eqlJkyaqXbu2mjVrpgEDBmjVqlWyWq2FPpOenq6IiAgFBgaqdu3aCgwMVEREhNLT04vdzvLly9WjRw/VqVNHDRo00JNPPqn9+/c7ctcAAABcjiktLa1wunKiEydOqFu3bmrfvr0CAgLk4+Ojc+fOacOGDTp37pz++Mc/KioqylaflZWlvn376tChQ+revbtatWqlw4cP65tvvlGLFi20YcMGeXp6FtjG3LlzNWPGDNWtW1chISHKysrSv/71L2VnZ2vlypXq1q1bWe92sZov3OPsFlAKR8I6OLsFAABuyuXCYG5urqxWq9zd3Qssz8jIUK9evfTDDz8oLi5OzZo1kyS98cYbmj17tiZNmqTXXnvNVp+/fOrUqYqIiLAtT0hIUMeOHeXv76/NmzfL29tbknTs2DE9+uij8vX11d69ewtt31kIg+UbYRAA4Opc7jCxm5tbkUHMbDarR48ekq7PHkqS1WrVokWLVL16dU2dOrVA/QsvvCCLxaLPP/+8wKHlxYsXKycnR5MnT7YFQUlq1qyZhg8frsTERG3fvt0RuwYAAOByXC4MFic7O1vbt2+XyWRS06ZNJV2f5Ttz5ow6duxY6FCwh4eHunTpopSUFFt4lKTY2FhJsgXLG+Uv27lzp6N2AwAAwKW4xrHQIqSlpSk6Olp5eXk6f/68Nm3apNOnTys8PFyNGjWSdD0MSlJAQECRY9xYd+Pr6tWry9fX96b1tyM7O/vOdgqGw88IAKCseXh43FG9y4bBS5cuadasWbb3lStX1owZM/Tcc8/ZluVfLXzj4d4bmc3mAnX5r2vVqnXb9TeTkpKi3Nzc26qFMZ06dcrZLQAADMTNza3YSbLiuGwYbNCggdLS0pSbm6vTp0/rX//6l2bMmKH4+Hh9+umnLnGBR506dcpgKxfLYBtwlHr16jm7BQAAbsr5ieoW3Nzc1KBBAz3//PNyc3PTq6++qn/+8596+umn5eXlJen6LGJRMjIyJMlWl/+6uJm/oupv5k6nYWE8/IwAAFxdubmARJK6d+8u6b8XgeSf43fjBSI3yj/3L78u/3VmZqZSU1Nvqx4AAKAiK1dh8OzZs5JkO0TcqFEj+fn5KT4+XllZWQVqs7OztWvXLvn5+RU4dh4UFCRJ2rJlS6Hx85fl1wAAAFR0LhcGDx48WORh34sXL2r69OmSpJ49e0qSTCaTRo8erczMTM2ePbtA/dtvv620tDSNHj1aJpPJtnzUqFFyd3fX3LlzC2zn2LFjWrJkiRo2bKiHHnrIEbsGAADgclzuCSTTpk3TokWL1LVrV9WvX1933XWXTp06pa+//lqZmZl6/PHH9emnn6pSpes59n8fR9e6dWsdPnxYmzZtKvZxdHPmzNHMmTNtj6O7fPmyVq5cqStXrmjlypUuFQZ5Akn5xhNIAACuzuXCYFxcnBYtWqR9+/bp7Nmzunz5snx8fNSqVSsNHz5cQ4YMKTDTJ/33NjSrV69WamqqfH199fjjjys8PLzY284sW7ZM0dHR+uGHH1S5cmV16NBBERERatu2bVns5m0jDJZvhEEAgKtzuTCIggiD5RthEADg6lzunEEAAACUHcIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgZU4DMbExOjw4cM3rTl69KhiYmJKugkAAAA4WInDYFhYmNauXXvTmq+//lrPPvtsSTcBAAAAB3PoYeLc3FxVqsSRaAAAAFfl0KR28OBB+fj4OHITAAAAKAX3OykeMGBAgfdffPGFYmNjC9Xl5uYqJSVFycnJGjRoUOk6BAAAgMPcURi8MfiZTCYlJycrOTm5UF2lSpXk4+OjgQMHKjIysvRdAgAAwCHuKAxevHjR9trHx0fTpk1TeHi43ZsCAABA2bijMHijr776SvXr17dnLwAAAChjJQ6DXbt2tWcfAAAAcIISh0FJunr1qtauXavvv/9ely5dUm5ubqEak8mk9957rzSbAQAAgIOUOAzmXymcmJgoq9VabB1hEAAAwHWVOAxGREToxIkTGjZsmP7whz/o3nvvlZubmz17AwAAgIOVOAxu375dDz/8sN5//3179gMAAIAyVOInkFitVrVs2dKevQAAAKCMlTgMPvjgg/rxxx/t2QsAAADKWInD4N/+9jfFxsZq1apV9uwHAAAAZajE5wxu2LBBXbt21ZgxYxQUFKSWLVvKy8urUJ3JZNLUqVNL1SQAAAAcw5SWllb8fWFuwsfH5/Y2YDLpt99+K8kmIKn5wj3ObgGlcCSsg7NbAADgpkr1ODoAAACUbzyODgAAwMBKfAEJAAAAyr8Szwzu3LnztmuDgoJKuhkAAAA4UInDYP/+/WUymW6rlgtIAAAAXFOJw+DUqVOLDIPp6en6z3/+o127dql3795q06ZNqRoEAACA45Q4DL700ks3Xb9q1SqFhYXdsg4AAADO47ALSEJCQtS1a1dNnz7dUZsAAABAKTn0auL7779fe/fudeQmAAAAUAoODYMHDx687YtMAAAAUPZKfM7gqVOnilyem5urlJQUffHFF9q+fbv69etX4uYAAADgWCUOgy1btrzprJ/ValWDBg30xhtvlHQTAAAAcLASh8Hhw4cXGQYrVaoki8WiNm3aKDg4WB4eHqVqEAAAAI5T4jAYHR1tzz4AAADgBDybGAAAwMBKPDOYLysrS+vWrdOhQ4eUnp4us9msFi1aKDg4WJ6envboEQAAAA5SqjC4Zs0aTZw4UWlpabJarbblJpNJ3t7eioqK0uOPP17qJgEAwHUZz49wdgsoBfO8GGe3UEiJw+CePXs0ZswYubm56U9/+pO6du2q2rVr69y5c4qNjdUXX3yhp59+WmvXrlWHDh3s2TMAAADspMRhcO7cuapataq+/vprPfDAAwXWDRo0SE8//bR69+6tuXPnaunSpaVuFAAAAPZX4gtI9uzZo0GDBhUKgvkeeOABDRw4UHv27ClxcwAAAHCsEofBK1euqFatWjetqVWrlq5cuVLSTQAAAMDBShwG69evr61bt9605ttvv1X9+vVLugkAAAA4WInD4KBBg3TgwAE988wzOnPmTIF1Z8+eVWhoqA4cOKBBgwaVukkAAAA4RokvIPnLX/6iLVu2aOnSpfryyy8VEBCgWrVq6dy5czpx4oSuXr2qdu3a6S9/+Ysd2wUAAIA9lXhmsFq1alq7dq1eeukl+fn56YcfftCOHTv0ww8/qE6dOnrppZe0du1aVatWzZ79AgAAwI5KddPpKlWqaOrUqZo6daoyMjKUkZEhs9kss9lsr/4AAADgQCWeGdy9e7ciIiKUmpoqSTKbzapTp44tCKampioiIkJ79+61T6cAAACwuxKHwQULFmjDhg3y9fUtcr2vr682btyohQsXlrg5AAAAOFaJw+D+/fvVqVOnm9Z06dKFmUEAAAAXVuIweO7cOdWpU+emNb6+vjp//nxJNwEAAAAHK3EY9Pb21qlTp25ak5ycLE9Pz5JuAgAAAA5W4jD44IMPau3atTp9+nSR60+dOqV169apQ4cOJW4OAAAAjlXiMPjss8/q8uXL6tu3r2JiYnT27FlJ158+8sUXX6hv3766cuWKnnvuObs1CwAAAPsq8X0Gu3TpolmzZumll17Ss88+K0kymUyyWq2SpEqVKunNN99UUFCQfToFAACA3ZXqptPjxo1TUFCQPv74Y33//fe6dOmSvL291a5dO40ZM0YPPPCAvfoEAACAA5QqDErSAw88oDlz5tijFwAAAJSxEp8z6CgpKSlauHChBg0apMDAQNWqVUtNmjTR6NGjtW/fviI/k56eroiICAUGBqp27doKDAxURESE0tPTi93O8uXL1aNHD9WpU0cNGjTQk08+qf379ztqtwAAAFySy4XBDz74QBEREUpKStIjjzyi5557Tp06ddK6devUu3dvffnllwXqs7KyFBwcrIULF+q+++5TWFiYmjZtqoULFyo4OFhZWVmFtjF37lyNGzdOv/76q8aMGaNBgwYpPj5effr00Y4dO8pqVwEAAJyu1IeJ7a1t27Zat26dunTpUmD5rl27FBISohdeeEH9+vVT1apVJUlRUVE6dOiQJk2apNdee81W/8Ybb2j27NmKiopSRESEbXlCQoIiIyPVuHFjbd68Wd7e3pKk8ePH69FHH9XEiRO1d+9eubu73F8NAACA3bnczODjjz9eKAhK169e7tatmy5evKijR49KkqxWqxYtWqTq1atr6tSpBepfeOEFWSwWff7557YrnCVp8eLFysnJ0eTJk21BUJKaNWum4cOHKzExUdu3b3fQ3gEAALgWlwuDN1O5cmVJkpubm6Trs3xnzpxRx44dCz3pxMPDQ126dFFKSopOnDhhWx4bGytJ6tGjR6Hx85ft3LnTIf0DAAC4mnJzLPTUqVPatm2bfH191bx5c0nXw6AkBQQEFPmZRo0a2epufF29enX5+vretP52ZGdn39lOwHD4GQEA3Kgsfi94eHjcUX25CIPXrl3T+PHj9fvvv+u1116zzQzmXy184+HeG5nN5gJ1+a9r1ap12/U3k5KSotzc3NvbCRjSrZ7fDQB36h5nN4BScfTvBTc3t2InyYrj8mEwLy9Pzz77rHbt2qU//vGPGj58uLNbsqlTp04ZbOViGWwDjlKvXj1ntwCggrnm7AZQKq74e8Glw6DVatXEiRO1bNkyDR06VPPmzSuw3svLS5J06dKlIj+fkZFRoC7/dXEzf0XV38ydTsPCePgZAWBvhMHyzRV/L7jsBSR5eXl67rnn9Pnnn+uJJ55QdHS0KlUq2G7+OX43XiByo/xz//Lr8l9nZmYqNTX1tuoBAAAqMpcMg3l5eZowYYIWL16swYMH6//+7/9s5wneqFGjRvLz81N8fHyhm0tnZ2dr165d8vPzK3DsPCgoSJK0ZcuWQuPlL8uvAQAAqOhcLgzmzwguXrxYAwcO1AcffFBkEJQkk8mk0aNHKzMzU7Nnzy6w7u2331ZaWppGjx4tk8lkWz5q1Ci5u7tr7ty5BQ4vHzt2TEuWLFHDhg310EMPOWbnAAAAXIzLnTM4a9YsffHFF6pevboaN26st956q1BNcHCwWrZsKUmaNGmS1q9fr6ioKB08eFCtW7fW4cOHtWnTJrVo0UKTJk0q8NnGjRtr2rRpmjlzpoKCghQSEqLLly9r5cqVunbtmqKionj6CAAAMAyXSz3JycmSpMzMTM2ZM6fImvr169vCoKenp9asWaNZs2Zp9erVio2Nla+vr8LCwhQeHl7oZtSSNGXKFNWvX1/R0dH6+OOPVblyZXXo0EERERFq27at43YOAADAxZjS0tKsty6DszRfuMfZLaAUjoR1cHYLACqYjOdHOLsFlIJ5XoyzWyjE5c4ZBAAAQNkhDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADMzd2Q0AFVnG8yOc3QJKyDwvxtktAECZcMmZwaVLl+ovf/mLHnnkEdWuXVsWi0WLFy8utj49PV0REREKDAxU7dq1FRgYqIiICKWnpxf7meXLl6tHjx6qU6eOGjRooCeffFL79+93xO4AAAC4LJcMgzNnztSnn36qU6dOydfX96a1WVlZCg4O1sKFC3XfffcpLCxMTZs21cKFCxUcHKysrKxCn5k7d67GjRunX3/9VWPGjNGgQYMUHx+vPn36aMeOHY7aLQAAAJfjkmHw3Xff1cGDB5WQkKCxY8fetDYqKkqHDh3SpEmT9OWXX+rvf/+7VqxYoalTp+rQoUOKiooqUJ+QkKDIyEg1btxYO3fu1Ouvv6758+dr48aNcnd318SJE5WTk+PI3QMAAHAZLhkGH3nkEdWvX/+WdVarVYsWLVL16tU1derUAuteeOEFWSwWff7557JarbblixcvVk5OjiZPnixvb2/b8mbNmmn48OFKTEzU9u3b7bczAAAALswlw+DtSkhI0JkzZ9SxY0d5enoWWOfh4aEuXbooJSVFJ06csC2PjY2VJPXo0aPQePnLdu7c6cCuAQAAXEe5vpo4ISFBkhQQEFDk+kaNGtnqbnxdvXr1Is9FvLHmdmRnZ99xzwDKB/59A3CEsvhu8fDwuKP6ch0G868WvvFw743MZnOBuvzXtWrVuu36m0lJSVFubu5t9wug/Dh16pSzWwCKdI+zG0CpOPq7xc3NrdhJsuKU6zDobHXq1CmDrVwsg20A+F/16tVzdgtAka45uwGUiit+t5TrMOjl5SVJunTpUpHrMzIyCtTlvy5u5q+o+pu502lYAOUH/77hqgiD5ZsrfreU6wtI8s/xu/ECkRvln/uXX5f/OjMzU6mpqbdVDwAAUJGV+zDo5+en+Pj4QjeXzs7O1q5du+Tn51fg2HlQUJAkacuWLYXGy1+WXwMAAFDRleswaDKZNHr0aGVmZmr27NkF1r399ttKS0vT6NGjZTKZbMtHjRold3d3zZ07t8Dh5WPHjmnJkiVq2LChHnrooTLbBwAAAGdyyXMGP/vsM8XFxUmSjh49KklatGiR7R6BwcHB6t+/vyRp0qRJWr9+vaKionTw4EG1bt1ahw8f1qZNm9SiRQtNmjSpwNiNGzfWtGnTNHPmTAUFBSkkJESXL1/WypUrde3aNUVFRcnd3SX/WgAAAOzOJVNPXFycYmJiCizbvXu3du/eLUmqX7++LQx6enpqzZo1mjVrllavXq3Y2Fj5+voqLCxM4eHhhW5GLUlTpkxR/fr1FR0drY8//liVK1dWhw4dFBERobZt2zp+BwEAAFyEKS0tzXrrMjhL84V7nN0CSmH3T/Oc3QJKyDwv5tZFgBNkPD/C2S2gFFzxu6VcnzMIAACA0iEMAgAAGBhhEAAAwMAIgwAAAAZGGAQAADAwwiAAAICBueR9BgEAjsVtq8qv3c5uABUOM4MAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGRhgEAAAwMMIgAACAgREGAQAADIwwCAAAYGCEQQAAAAMjDAIAABgYYRAAAMDACIMAAAAGZugw+P333+vJJ59UgwYNVKdOHfXo0UPLly93dlsAAABlxt3ZDTjLjh07NGTIEFWpUkWDBw+Wl5eXvvrqK40bN07JycmaPHmys1sEAABwOEOGwZycHE2cOFEmk0lr165Vq1atJEnh4eHq3bu3IiMjNXDgQDVq1MjJnQIAADiWIcPg9u3blZiYqFGjRtmCoCSZzWa9+OKLGjt2rBYvXqxXX33ViV1e5+NhyP9FFYbJ0+zsFoAi8d1SfvG9Ansz5LdBbGysJKlHjx6F1uUv27lzZ5n2VJzYsW2d3QJK5QNnNwAUie+W8ozvFdiXIS8gSUhIkKQiDwNbLBbVrFnTVgMAAFCRGTIMpqenS5K8vLyKXG82m201AAAAFZkhwyAAAACuM2QYzJ8RLG72LyMjo9hZQwAAgIrEkGEw/1zBos4LTEtL04ULF7itDAAAMARDhsGgoCBJ0pYtWwqty1+WXwMAAFCRmdLS0qzObqKs5eTkqH379jpz5ow2bdqkli1bSrp+eLh379766aeftHv3bjVu3NjJnQIAADiWIcOgdP3G00OGDFHVqlU1ZMgQmc1mffXVVzp58qT++te/asqUKc5uEQAAwOEMGwYl6bvvvlNkZKT27Nmja9euqWnTpgoNDdXQoUOd3RoAAECZMHQYBACgvLh48aKysrJUt25dZ7eCCsaQF5AAjpaZmakDBw5o165dzm4FQDl26dIlhYeH67777lOjRo3UqlUr27p9+/bpySef1IEDB5zXICoEwiBgRydPntSIESPk7++vHj16aMCAAbZ1u3fvVseOHbVjxw4ndgigvLh48aJ69uypDz74QPfee6/uv/9+Wa3/PZjXvHlzxcfHa9myZU7sEhUBYRCwk1OnTqlXr17atGmT+vXrpw4dOhT44m7fvr0uXLiglStXOrFLAOVFZGSkfv75Z/3jH//Qtm3bFBISUmB9tWrV1KVLF23fvt1JHaKiIAwCdhIZGam0tDStXbtWn332mR555JEC693d3dW5c2fFx8c7p0EA5cr69evVp08fDR48uNiaevXqKSUlpQy7QkVEGATsZMuWLerfv786duxYbE3dunX54gZwW1JTU9W0adOb1nh4eOjy5ctl1BEqKsIgYCcXL15U/fr1b1l39erVMugGQHlXo0YN/fLLLzetOX78uHx9fcuoI1RUhEHATmrVqqUTJ07ctObYsWPcFgLAbenSpYvWr19f7NGEH374QZs3by50SgpwpwiDgJ10795dGzZs0NGjR4tcv2vXLn377bfq1atXGXcGoDyaPHmycnJy1KdPHy1fvlwXLlyQJP3444/67LPP9Pjjj6tq1aqaOHGikztFecdNpwE7OXnypLp16yZJmjRpkn788UetWLFCS5cuVXx8vBYuXKi77rpLsbGxuueee5zcLYDyYN26dXrmmWeUmZkpSbJarTKZTLJarTKbzfroo4/Uu3dvJ3eJ8o4wCNjRvn37NHbsWJ06dcr2hZ3/37p16+qzzz5TmzZtnN0mgHLk4sWL+uKLL/Tdd9/p4sWLMpvNat++vUaNGqWaNWs6uz1UAIRBwM5ycnK0fv36Ql/c/fr1U5UqVZzdHgAABRAGAQAADMzd2Q0AAABp586dJf5sUFCQHTuB0TAzCJRQTExMiT87YsQIO3YCoCLw8fGRyWQq0Wd/++03O3cDIyEMAiVUki/u/AtK+OIG8L8iIyNLHAanTZtm525gJIRBoIS++OKLEn925MiRduwEAICSIwwCAAAYGE8gAQAAMDCuJgYAwEVlZGToww8/1LZt23T27Fn9/vvvhWpMJpMOHDhQ9s2hwiAMAnZ0+vRpzZkzx/bFffXq1UI1JpPJ9oxRACjO+fPn1bt3byUmJspsNisjI0NeXl66du2arly5Ikny8/OTuzu/ylE6HCYG7CQpKUkPP/ywFi1aJE9PT/3++++qW7euGjduLDc3N1mtVjVv3lydO3d2dqsAyoE333xTiYmJev/993Xy5ElJUlhYmFJSUrR582a1a9dO9evX1+7du53cKco7wiBgJ2+++abS09O1atUq281jR40apT179ujgwYPq27evLl++rH/+859O7hRAefD111/r4Ycf1rBhwwrdcqZt27ZasWKFkpOT9eabbzqpQ1QUhEHATr799lv16tVLXbt2tS2zWq9frO/n56dPP/1UkjRjxgxntAegnElNTVXLli1t793c3JSdnW17b7FY1LNnT/373/92QneoSAiDgJ1cuHBBTZo0sb13d3e3ndcjSVWrVlX37t21ceNGZ7QHoJzJPz8wn8Vi0S+//FKgxmw269y5c2XdGioYwiBgJzVr1lRWVlaB98nJyQVq3NzcdOnSpbJuDUA55O/vX+A7pGXLltq2bZsuXrwoSbpy5Yo2bNigunXrOqtFVBCEQcBOAgIClJSUZHvftm1bbd682bbs/PnzWr16tfz9/Z3SH4DypXv37tq+fbsuX74sSfrTn/6kc+fOqWvXrvrjH/+ozp07KzExkScaodQIg4Cd9OrVSzt27FBaWpokKTQ0VJmZmQoKClL37t3Vrl07paamavz48c5tFEC5MHbsWEVFRdlON3n88cc1ffp0ZWZmavXq1fr111/17LPPauLEiU7uFOUdj6MD7CQ9PV3Hjx/X/fffL7PZLEn697//rTfffFNJSUmqV6+e/vznP2vcuHFO7hRAeZabm6sLFy6oVq1aha4yBkqCMAgAAGBg3LYcsLO8vDz98ssvOnPmTIErAW8UFBRUxl0BKM/y8vL066+/FvudUq9evTLuCBUJYRCwE6vVqnnz5mnhwoX67bffblp7q/UAIEkrVqxQVFSUfvjhB+Xm5hZZwyMuUVqEQcBOXnvtNUVFRalWrVoaNWqUfH19eWYogBJ799139be//U2VK1dWly5d+E6Bw3DOIGAnTZo0kcVi0ZYtW1S9enVntwOgnAsMDJQkbdy4Uffee6+Tu0FFxq1lADvJyspS7969CYIA7OLChQsaMGAAQRAORxgE7KRFixY6e/ass9sAUEHcd999tvuWAo5EGATsZMqUKVq3bp0OHDjg7FYAVADPPfec1q1bV+ixloC9cc4gYEerVq3SlClT9NhjjykwMNB28+n/NWLEiDLuDEB59O6772rhwoV6+umnb/qdwu2qUBpclgTYye+//661a9fqwoULWrRokSQVejqA1WqVyWQiDAK4LZcuXVJ6erreeOONm9ZxuyqUBmEQsJOXX35Zy5cvV/PmzRUSEsJtIACUyuuvv663335bd999twYPHsx3ChyGw8SAnTRu3Fj16tXTpk2b+MIGUGoPPPCAqlevzu2q4HBcQALYye+//65u3boRBAHYRVpaGrerQpkgDAJ20qpVK504ccLZbQCoIB544AGlpqY6uw0YAGEQsJNXX31Vmzdv1oYNG5zdCoAKYPLkyVq7di23q4LDcTwLsJOtW7eqa9euGjlypLp166YWLVoUeRsIk8mkqVOnOqFDAOVJWlqaHnnkEfXp00dDhw4t9jtF4nZVKB0uIAHsxMfH57bqTCYTt4EAcEs+Pj4ymUyyWv/7a7q421XxnYLSYGYQsJOvvvrK2S0AqEAWLFjg7BZgEMwMAgAAGBgXkAAAABgYYRAAABf21Vdf6U9/+pO6dOmiNm3a2JYfP35cUVFRSklJcWJ3qAg4ZxAAABeUl5enp59+WqtWrZIkVatWTVeuXLGtt1gsmjFjhnJzc/XCCy84q01UAMwMAgDgghYsWKB///vfGjNmjJKSkvTcc88VWF+7dm117txZX3/9tZM6REVBGAQAwAXFxMSoTZs2mjt3rry8vArdVkaSAgICdPLkSSd0h4qEMAgAgAs6ceKEunTpctOaGjVqcI9BlBphEAAAF+Th4aGMjIyb1pw6dUre3t5l1BEqKsIgAAAuqGXLltq8ebN+//33ItdfvHhR33zzjdq3b1/GnaGiIQwCAOCCxo8fr19++UVPPfVUodvHJCYmatSoUUpPT9f48eOd1CEqCp5AAgCAi5o+fbrmzZsnk8kkT09PZWVl2c4TtFqtevHFFxUREeHsNlHOEQYBAHABO3fuVP369VWvXr0Cy7du3aoPPvhA3333nS5evCiz2az27dtr/PjxevTRR53ULSoSwiAAAC6gRo0aCg8PV3h4uCRpwIABGjlypEaMGOHkzlDRcc4gAAAuwN3dXTk5Obb3sbGxSk5OdmJHMArCIAAALuDee+9VfHy88vLybMuKutE0YG8cJgYAwAXMnDlTc+fOldlslo+Pj5KTk+Xt7X3L+wiaTCYdOHCgbJpEheTu7AYAAIAUHh6uatWq6ZtvvtGZM2dkMplktVpltd58zuZW64FbYWYQAAAX5OPjo2nTptkuKAEchXMGAQBwQeHh4eratauz24ABMDMIAABgYMwMAgAAGBhhEAAAwMAIgwAAAAZGGAQAADAw7jMIAHZw+fJlRUdHa9WqVUpISFBOTo5q1qypBg0aqFOnTnrqqafUsGFDZ7cJAIUQBgGglDIyMtS3b18dOXJEAQEBGjp0qLy9vfXLL7/o2LFjmjdvnho2bEgYBOCSCIMAUErR0dE6cuSIRo8erXfeeafQ82STkpJ09epVJ3UHADfHOYMAUEp79+6VJI0bN65QEJQkf39/NWnSpMCyc+fO6aWXXlKbNm1Uu3ZtBQQEaPTo0Tp69Kitxmq1atCgQbJYLFq1alWBz+fl5WnAgAGyWCxas2aNA/YKgFEQBgGglHx8fCRJiYmJt1WfmJioRx55RO+//74CAgL05z//Wb169dLmzZvVq1cv7du3T5JkMpn0/vvv6+6779akSZN0+vRp2xhRUVHasWOHxowZo/79+9t/pwAYBk8gAYBSWrt2rUaNGiUvLy+NHTtW3bt3V6tWrWSxWIqs79Onj7777jstX75c3bt3ty3/+eef1b17d9WrV0+7du2yLd+wYYOGDx+uzp07a+3atfrPf/6j3r17KyAgQNu2bVO1atUcvYsAKjBmBgGglIKDgzV9+nTl5eVp/vz5CgkJkb+/v9q0aaMXX3xRCQkJttr//Oc/io+P14gRIwoEQUlq3LixnnrqKR09erTA4eK+fftq3LhxiouL0/Tp0/X//t//k8lk0ocffkgQBFBqzAwCgJ1kZGRo8+bNio+P14EDB7Rv3z5du3ZNHh4e+vjjj9WvXz/94x//0OTJk9WnTx+1bt260Bj79+/X119/rX/+858KCQmxLc/OzlaPHj1sIfH111/Xs88+W1a7BqAC42piALATs9msgQMHauDAgZKkS5cuacaMGfroo480YcIE9ezZUxcvXpQkbdy4URs3bix2rKysrALvPTw81KtXLx09elR33XWX/vCHPzhsPwAYC4eJAcBBvL299dZbb6levXq6cOGCjh49KrPZLEmaPXu20tLSiv0zcuTIAmPt2bNHCxYsUI0aNXT58mW9+OKLztglABUQYRAAHMhkMumuu+6yvW/fvr2k/96O5nakp6dr3Lhxqly5statW6d+/fpp2bJlWrp0qd37BWA8hEEAKKVPPvlE33//fZHrVq9erePHj8vb21vNmjVTu3bt1L59e61YsUL/+te/CtXn5eUpNja2wLLJkyfr5MmTev3119W0aVO999578vPz05QpU5SUlOSIXQJgIFxAAgClNHLkSK1bt04BAQHq2LGj/Pz8lJmZqUOHDikuLk6VKlXSBx98oCeeeELS9SeSDBgwQKdOndKDDz6o1q1bq2rVqjp9+rT27t2r8+fPKzU1VZK0ZMkSPfPMM3rssccUExNj2+a2bds0aNAgtW/fXuvXr5e7O6eAAygZwiAAlNJPP/2k9evXa+vWrTpx4oQtyPn5+alTp04aP358oSuH09LS9N5772ndunVKTEyUm5ubfH191bZtWz3++OMaMGCAkpKS1K1bN3l6emrnzp2qWbNmgTFeeeUVvfvuu5oyZYr++te/ltXuAqhgCIMAAAAGxjmDAAAABkYYBAAAMDDCIAAAgIERBgEAAAyMMAgAAGBghEEAAAADIwwCAAAYGGEQAADAwAiDAAAABkYYBAAAMDDCIAAAgIERBgEAAAyMMAgAAGBg/x8NEsGgwbD2CAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data[['Sex','Survived']].groupby(['Sex']).mean().plot.bar()\n",
    "sb.countplot(x='Sex', hue='Survived', data=train_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "14c2aff4-a9af-49f5-ae09-2f7ead2e108b",
    "_uuid": "90cc96f012bb3c866ed40bc88e20ff8a1eb4028d"
   },
   "source": [
    "Let's explore more features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "013a7ce7-accc-4272-9df7-c78289478cc6",
    "_uuid": "c949094d68749de7822e975aed9e20327a98f5dd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.countplot(x='Pclass', hue='Survived', data=train_data)\n",
    "plt.title('Pclass: Survived vs Dead')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "22af9369-c7e2-49d2-b838-c51dbf33fdf6",
    "_uuid": "f8630bf3c9131f866b44c523ffdad4ecf4fd9bfa"
   },
   "source": [
    "Wow.... That looks amazing. It is usually said that Money can't buy Everything, But it is clearly seen that pasangers of Class 1 are given high priority while Rescue. There are greater number of passangers in Class 3 than Class 1 and Class 2 but very few, almost 25% in Class 3 survived. In Class 2, survivail and non-survival rate is 49% and 51% approx.\n",
    "While in Class 1 almost 68% people survived. So money and status matters here.\n",
    "\n",
    "Let's dive in again into data to check more interesting observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "9c3d4ad1-18e5-416d-a26d-74fbdca0b94a",
    "_uuid": "26243d12a3c5fad03467180a23a39d5e63131fd9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_14b7f_row0_col0, #T_14b7f_row0_col1, #T_14b7f_row0_col3, #T_14b7f_row3_col2 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row0_col2, #T_14b7f_row1_col2 {\n",
       "  background-color: #f1f866;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row1_col0 {\n",
       "  background-color: #96cb66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row1_col1 {\n",
       "  background-color: #a3d166;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row1_col3 {\n",
       "  background-color: #cfe766;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row2_col0 {\n",
       "  background-color: #a7d366;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row2_col1, #T_14b7f_row2_col3 {\n",
       "  background-color: #85c266;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row2_col2 {\n",
       "  background-color: #6eb666;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_14b7f_row3_col0 {\n",
       "  background-color: #cde666;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row3_col1 {\n",
       "  background-color: #f0f866;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row3_col3 {\n",
       "  background-color: #f7fb66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_14b7f_row4_col0, #T_14b7f_row4_col1, #T_14b7f_row4_col2, #T_14b7f_row4_col3 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_14b7f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >Pclass</th>\n",
       "      <th id=\"T_14b7f_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_14b7f_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_14b7f_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_14b7f_level0_col3\" class=\"col_heading level0 col3\" >All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Sex</th>\n",
       "      <th class=\"index_name level1\" >Survived</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_14b7f_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"2\">female</th>\n",
       "      <th id=\"T_14b7f_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
       "      <td id=\"T_14b7f_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_14b7f_row0_col1\" class=\"data row0 col1\" >6</td>\n",
       "      <td id=\"T_14b7f_row0_col2\" class=\"data row0 col2\" >72</td>\n",
       "      <td id=\"T_14b7f_row0_col3\" class=\"data row0 col3\" >81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_14b7f_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
       "      <td id=\"T_14b7f_row1_col0\" class=\"data row1 col0\" >91</td>\n",
       "      <td id=\"T_14b7f_row1_col1\" class=\"data row1 col1\" >70</td>\n",
       "      <td id=\"T_14b7f_row1_col2\" class=\"data row1 col2\" >72</td>\n",
       "      <td id=\"T_14b7f_row1_col3\" class=\"data row1 col3\" >233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_14b7f_level0_row2\" class=\"row_heading level0 row2\" rowspan=\"2\">male</th>\n",
       "      <th id=\"T_14b7f_level1_row2\" class=\"row_heading level1 row2\" >0</th>\n",
       "      <td id=\"T_14b7f_row2_col0\" class=\"data row2 col0\" >77</td>\n",
       "      <td id=\"T_14b7f_row2_col1\" class=\"data row2 col1\" >91</td>\n",
       "      <td id=\"T_14b7f_row2_col2\" class=\"data row2 col2\" >300</td>\n",
       "      <td id=\"T_14b7f_row2_col3\" class=\"data row2 col3\" >468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_14b7f_level1_row3\" class=\"row_heading level1 row3\" >1</th>\n",
       "      <td id=\"T_14b7f_row3_col0\" class=\"data row3 col0\" >45</td>\n",
       "      <td id=\"T_14b7f_row3_col1\" class=\"data row3 col1\" >17</td>\n",
       "      <td id=\"T_14b7f_row3_col2\" class=\"data row3 col2\" >47</td>\n",
       "      <td id=\"T_14b7f_row3_col3\" class=\"data row3 col3\" >109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_14b7f_level0_row4\" class=\"row_heading level0 row4\" >All</th>\n",
       "      <th id=\"T_14b7f_level1_row4\" class=\"row_heading level1 row4\" ></th>\n",
       "      <td id=\"T_14b7f_row4_col0\" class=\"data row4 col0\" >216</td>\n",
       "      <td id=\"T_14b7f_row4_col1\" class=\"data row4 col1\" >184</td>\n",
       "      <td id=\"T_14b7f_row4_col2\" class=\"data row4 col2\" >491</td>\n",
       "      <td id=\"T_14b7f_row4_col3\" class=\"data row4 col3\" >891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x26f71e10490>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([train_data.Sex,train_data.Survived],train_data.Pclass,margins=True).style.background_gradient(cmap='summer_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "458dff98-f6bb-4f40-8d1d-0aa1b19893aa",
    "_uuid": "0f629690be27786abfadf19b3a5f2f5121c09772"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 637.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.catplot(x=None, y='Pclass', hue='Sex', data=train_data, kind='count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "dfe8ad42-528f-400f-bff1-493dba5f3dfd",
    "_uuid": "bfbbe611e3f4f089ed5ffcf17d994485127af4f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oldest person Survived was of: 80.0\n",
      "Youngest person Survived was of: 0.42\n",
      "Average person Survived was of: 29.69911764705882\n"
     ]
    }
   ],
   "source": [
    "print('Oldest person Survived was of:',train_data['Age'].max())\n",
    "print('Youngest person Survived was of:',train_data['Age'].min())\n",
    "print('Average person Survived was of:',train_data['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "623f7637-6bf0-4c11-bc39-f32e7c2cc1d7",
    "_uuid": "392a729823d49f221d95aa57dcc9d03223bf80ba"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "sb.violinplot(x='Pclass', y='Age', hue='Survived', data=train_data, split=True, ax=ax[0])\n",
    "ax[0].set_title('PClass and Age vs Survived')\n",
    "ax[0].set_yticks(range(0,110,10))\n",
    "sb.violinplot(x='Sex', y='Age', hue='Survived', data=train_data, split=True, ax=ax[1])\n",
    "ax[1].set_title('Sex and Age vs Survived')\n",
    "ax[1].set_yticks(range(0,110,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b948bf43-0a2b-4159-a0ec-ead2728110ca",
    "_uuid": "e7f8efa74236d35aa4aeb3b5acd6f51a91e770ce"
   },
   "source": [
    "From above violen plots, following observations are clear,\n",
    "1) The no of children is increasing from Class 1 to 3, the number of children in Class 3 is greater than other two. \n",
    "2) Survival rate of children, for age 10 and below is good irrespective of Class\n",
    "3) Survival rate between age 20-30 is well and is quite better for women.\n",
    "\n",
    "Now, in Age feature we have 177 null values filled with NaN. We have to deal with it. But we can't enter mean of age in every NaN column, because our average/mean is 29 and we cannot put 29 for a child or some olde man. So we have to discover something better. \n",
    "Let's do something more interesting with dataset by exploring more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7845ce06-5e0f-4850-af21-6e47e3a60c3d",
    "_uuid": "0021a84e580c8010d11082766b48d1dc755c5f2a"
   },
   "source": [
    "What is, if I look at 'Name' feature, It looks interesting. Let's check it...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "1aa29a1a-34dd-4270-8f8c-8022dd806ba0",
    "_uuid": "00ba326def819939d616f175f150b09026e5f623"
   },
   "outputs": [],
   "source": [
    "train_data['Initial']=0\n",
    "for i in train_data:\n",
    "    train_data['Initial']=train_data.Name.str.extract('([A-Za-z]+)\\.') #extracting Name initials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "9289e051-d21d-4fc9-b774-224a436ba5c9",
    "_uuid": "4a1568cce94ad94c6049266831cb6ad2acbdfb06"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b10a4_row0_col0, #T_b10a4_row0_col1, #T_b10a4_row0_col3, #T_b10a4_row0_col4, #T_b10a4_row0_col5, #T_b10a4_row0_col7, #T_b10a4_row0_col8, #T_b10a4_row0_col12, #T_b10a4_row0_col15, #T_b10a4_row0_col16, #T_b10a4_row1_col2, #T_b10a4_row1_col6, #T_b10a4_row1_col9, #T_b10a4_row1_col10, #T_b10a4_row1_col11, #T_b10a4_row1_col13, #T_b10a4_row1_col14 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b10a4_row0_col2, #T_b10a4_row0_col6, #T_b10a4_row0_col9, #T_b10a4_row0_col10, #T_b10a4_row0_col11, #T_b10a4_row0_col13, #T_b10a4_row0_col14, #T_b10a4_row1_col0, #T_b10a4_row1_col1, #T_b10a4_row1_col3, #T_b10a4_row1_col4, #T_b10a4_row1_col5, #T_b10a4_row1_col7, #T_b10a4_row1_col8, #T_b10a4_row1_col12, #T_b10a4_row1_col15, #T_b10a4_row1_col16 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b10a4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Initial</th>\n",
       "      <th id=\"T_b10a4_level0_col0\" class=\"col_heading level0 col0\" >Capt</th>\n",
       "      <th id=\"T_b10a4_level0_col1\" class=\"col_heading level0 col1\" >Col</th>\n",
       "      <th id=\"T_b10a4_level0_col2\" class=\"col_heading level0 col2\" >Countess</th>\n",
       "      <th id=\"T_b10a4_level0_col3\" class=\"col_heading level0 col3\" >Don</th>\n",
       "      <th id=\"T_b10a4_level0_col4\" class=\"col_heading level0 col4\" >Dr</th>\n",
       "      <th id=\"T_b10a4_level0_col5\" class=\"col_heading level0 col5\" >Jonkheer</th>\n",
       "      <th id=\"T_b10a4_level0_col6\" class=\"col_heading level0 col6\" >Lady</th>\n",
       "      <th id=\"T_b10a4_level0_col7\" class=\"col_heading level0 col7\" >Major</th>\n",
       "      <th id=\"T_b10a4_level0_col8\" class=\"col_heading level0 col8\" >Master</th>\n",
       "      <th id=\"T_b10a4_level0_col9\" class=\"col_heading level0 col9\" >Miss</th>\n",
       "      <th id=\"T_b10a4_level0_col10\" class=\"col_heading level0 col10\" >Mlle</th>\n",
       "      <th id=\"T_b10a4_level0_col11\" class=\"col_heading level0 col11\" >Mme</th>\n",
       "      <th id=\"T_b10a4_level0_col12\" class=\"col_heading level0 col12\" >Mr</th>\n",
       "      <th id=\"T_b10a4_level0_col13\" class=\"col_heading level0 col13\" >Mrs</th>\n",
       "      <th id=\"T_b10a4_level0_col14\" class=\"col_heading level0 col14\" >Ms</th>\n",
       "      <th id=\"T_b10a4_level0_col15\" class=\"col_heading level0 col15\" >Rev</th>\n",
       "      <th id=\"T_b10a4_level0_col16\" class=\"col_heading level0 col16\" >Sir</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Sex</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b10a4_level0_row0\" class=\"row_heading level0 row0\" >female</th>\n",
       "      <td id=\"T_b10a4_row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col2\" class=\"data row0 col2\" >1</td>\n",
       "      <td id=\"T_b10a4_row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col4\" class=\"data row0 col4\" >1</td>\n",
       "      <td id=\"T_b10a4_row0_col5\" class=\"data row0 col5\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "      <td id=\"T_b10a4_row0_col7\" class=\"data row0 col7\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col8\" class=\"data row0 col8\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col9\" class=\"data row0 col9\" >182</td>\n",
       "      <td id=\"T_b10a4_row0_col10\" class=\"data row0 col10\" >2</td>\n",
       "      <td id=\"T_b10a4_row0_col11\" class=\"data row0 col11\" >1</td>\n",
       "      <td id=\"T_b10a4_row0_col12\" class=\"data row0 col12\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col13\" class=\"data row0 col13\" >125</td>\n",
       "      <td id=\"T_b10a4_row0_col14\" class=\"data row0 col14\" >1</td>\n",
       "      <td id=\"T_b10a4_row0_col15\" class=\"data row0 col15\" >0</td>\n",
       "      <td id=\"T_b10a4_row0_col16\" class=\"data row0 col16\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b10a4_level0_row1\" class=\"row_heading level0 row1\" >male</th>\n",
       "      <td id=\"T_b10a4_row1_col0\" class=\"data row1 col0\" >1</td>\n",
       "      <td id=\"T_b10a4_row1_col1\" class=\"data row1 col1\" >2</td>\n",
       "      <td id=\"T_b10a4_row1_col2\" class=\"data row1 col2\" >0</td>\n",
       "      <td id=\"T_b10a4_row1_col3\" class=\"data row1 col3\" >1</td>\n",
       "      <td id=\"T_b10a4_row1_col4\" class=\"data row1 col4\" >6</td>\n",
       "      <td id=\"T_b10a4_row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "      <td id=\"T_b10a4_row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "      <td id=\"T_b10a4_row1_col7\" class=\"data row1 col7\" >2</td>\n",
       "      <td id=\"T_b10a4_row1_col8\" class=\"data row1 col8\" >40</td>\n",
       "      <td id=\"T_b10a4_row1_col9\" class=\"data row1 col9\" >0</td>\n",
       "      <td id=\"T_b10a4_row1_col10\" class=\"data row1 col10\" >0</td>\n",
       "      <td id=\"T_b10a4_row1_col11\" class=\"data row1 col11\" >0</td>\n",
       "      <td id=\"T_b10a4_row1_col12\" class=\"data row1 col12\" >517</td>\n",
       "      <td id=\"T_b10a4_row1_col13\" class=\"data row1 col13\" >0</td>\n",
       "      <td id=\"T_b10a4_row1_col14\" class=\"data row1 col14\" >0</td>\n",
       "      <td id=\"T_b10a4_row1_col15\" class=\"data row1 col15\" >6</td>\n",
       "      <td id=\"T_b10a4_row1_col16\" class=\"data row1 col16\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x26f71ec06a0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_data.Initial,train_data.Sex).T.style.background_gradient(cmap='summer_r')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8002063e-0472-4277-aa71-898a82691958",
    "_uuid": "e57a594cb3ec11145e1538b030834963b1a38fff"
   },
   "source": [
    "There are many names which are not relevant like Mr, Mrs etc. So I will replace them with some relevant names,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_cell_guid": "2c766ba5-c271-41bb-9375-9a7ba28c761e",
    "_uuid": "be92506f66b163d85cab44e4eca42d832aa01a85"
   },
   "outputs": [],
   "source": [
    "train_data['Initial'].replace(['Mlle','Mme','Ms','Dr','Major','Lady','Countess',\n",
    "                               'Jonkheer','Col','Rev','Capt','Sir','Don'],['Miss',\n",
    "                                'Miss','Miss','Mr','Mr','Mrs','Mrs','Other','Other','Other','Mr','Mr','Mr'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "40c865d9-4c7d-4fc4-a7fc-177e56fea22e",
    "_uuid": "2497881aa84c4f77dbfcf1252b2264ba697b5d12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Initial\n",
       "Master     4.574167\n",
       "Miss      21.860000\n",
       "Mr        32.739609\n",
       "Mrs       35.981818\n",
       "Other     45.888889\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby('Initial')['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_cell_guid": "bb997eb2-a34e-4401-ab16-53c1e0789e02",
    "_uuid": "66b23092f07041fad20e6bd68aba4874982d42e6"
   },
   "outputs": [],
   "source": [
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Mr'),'Age']=33\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Mrs'),'Age']=36\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Master'),'Age']=5\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Miss'),'Age']=22\n",
    "train_data.loc[(train_data.Age.isnull()) & (train_data.Initial=='Other'),'Age']=46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_cell_guid": "f962c1fe-35d1-475b-9635-d57e7c420315",
    "_uuid": "438fcbe0457287ec094b253c81f2a9fecf6e3c56"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.Age.isnull().any()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_cell_guid": "b6d56cd3-a67f-4390-a810-0607b4933f8b",
    "_uuid": "7f0f35d293722d47ca53c380fab2249cc5d2a964"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,20))\n",
    "train_data[train_data['Survived']==0].Age.plot.hist(ax=ax[0],bins=20,edgecolor='black',color='red')\n",
    "ax[0].set_title('Survived = 0')\n",
    "x1=list(range(0,85,5))\n",
    "ax[0].set_xticks(x1)\n",
    "train_data[train_data['Survived']==1].Age.plot.hist(ax=ax[1],bins=20,edgecolor='black',color='green')\n",
    "x2=list(range(0,85,5))\n",
    "ax[1].set_xticks(x2)\n",
    "ax[1].set_title('Survived = 1')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6aab367a-e7b7-4770-a3b1-6d59e2c77eec",
    "_uuid": "8ad2ad7f5b3b650fd25a5f5005739bbc4ec08cad"
   },
   "source": [
    "From the above plots, I found the following observations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d48c2228-7dbf-4a69-96e0-fb2688e2592e",
    "_uuid": "fd02e90f9405f908ec564ca976a0467de8fe7f25"
   },
   "source": [
    "(1) First priority during Rescue is given to children and women, as the persons<5 are save by large numbers\n",
    "(2) The oldest saved passanger is of 80\n",
    "(3) The most deaths were between 30-40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "325218e5-1098-4873-86e8-825b2b657497",
    "_uuid": "1eac6b235e6ce9903b3e8bd3c1074f6f5e18fd46"
   },
   "source": [
    "Let's explore some more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f789c8de-c54a-4de6-8d48-23326f0047a2",
    "_uuid": "664569712e94f4c1fa4329f718763bb04a39a156"
   },
   "source": [
    "# Feature: Sib Sp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "411eafc5-f8fc-43e8-a521-425ed3e4eca7",
    "_uuid": "b0855581eb534395babfc51f13069e6a55340794"
   },
   "source": [
    "Sib Sp feature indicates that whether a person is alone or with his family. Siblings=brother,sister, etc\n",
    "and Spouse= husband,wife"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_cell_guid": "9e188e6a-b0c6-4a83-a843-b3551300e933",
    "_uuid": "663a48b6c547bd4eb7ae4a42935f4873c102c946"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_caee3_row0_col0, #T_caee3_row0_col1 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_caee3_row1_col0 {\n",
       "  background-color: #c4e266;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_caee3_row1_col1 {\n",
       "  background-color: #77bb66;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_caee3_row2_col0, #T_caee3_row4_col0 {\n",
       "  background-color: #f9fc66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_caee3_row2_col1 {\n",
       "  background-color: #f0f866;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_caee3_row3_col0, #T_caee3_row3_col1 {\n",
       "  background-color: #fbfd66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_caee3_row4_col1 {\n",
       "  background-color: #fcfe66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_caee3_row5_col0, #T_caee3_row5_col1, #T_caee3_row6_col1 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_caee3_row6_col0 {\n",
       "  background-color: #fefe66;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_caee3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Survived</th>\n",
       "      <th id=\"T_caee3_level0_col0\" class=\"col_heading level0 col0\" >0</th>\n",
       "      <th id=\"T_caee3_level0_col1\" class=\"col_heading level0 col1\" >1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >SibSp</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_caee3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_caee3_row0_col0\" class=\"data row0 col0\" >398</td>\n",
       "      <td id=\"T_caee3_row0_col1\" class=\"data row0 col1\" >210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_caee3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_caee3_row1_col0\" class=\"data row1 col0\" >97</td>\n",
       "      <td id=\"T_caee3_row1_col1\" class=\"data row1 col1\" >112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_caee3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_caee3_row2_col0\" class=\"data row2 col0\" >15</td>\n",
       "      <td id=\"T_caee3_row2_col1\" class=\"data row2 col1\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_caee3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_caee3_row3_col0\" class=\"data row3 col0\" >12</td>\n",
       "      <td id=\"T_caee3_row3_col1\" class=\"data row3 col1\" >4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_caee3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_caee3_row4_col0\" class=\"data row4 col0\" >15</td>\n",
       "      <td id=\"T_caee3_row4_col1\" class=\"data row4 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_caee3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_caee3_row5_col0\" class=\"data row5 col0\" >5</td>\n",
       "      <td id=\"T_caee3_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_caee3_level0_row6\" class=\"row_heading level0 row6\" >8</th>\n",
       "      <td id=\"T_caee3_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_caee3_row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x26f71ffd960>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab([train_data.SibSp],train_data.Survived).style.background_gradient('summer_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_cell_guid": "5e29de27-9fcb-47c3-bcd7-fc941351694a",
    "_uuid": "b45f146653abef8f113657e81a6d49e0c54605ca"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax=plt.subplots(1,2,figsize=(20,8))\n",
    "sb.barplot(x='SibSp', y='Survived', data=train_data, ax=ax[0])\n",
    "ax[0].set_title('SipSp vs Survived in BarPlot')\n",
    "sb.countplot(x='SibSp', hue='Survived', data=train_data, ax=ax[1])\n",
    "ax[1].set_title('SibSp vs Survived in CountPlot')\n",
    "plt.close(2)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_cell_guid": "5bd1649c-f674-4cb8-b566-5daa966f785b",
    "_uuid": "1a734d2421ce6bb7be165841a750fd499ca98aa7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_1d2cd_row0_col0, #T_1d2cd_row0_col1, #T_1d2cd_row0_col2 {\n",
       "  background-color: #008066;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1d2cd_row1_col0 {\n",
       "  background-color: #7bbd66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row1_col1 {\n",
       "  background-color: #8ac466;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row1_col2 {\n",
       "  background-color: #c6e266;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row2_col0, #T_1d2cd_row4_col2 {\n",
       "  background-color: #f6fa66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row2_col1 {\n",
       "  background-color: #eef666;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row2_col2 {\n",
       "  background-color: #f8fc66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row3_col0, #T_1d2cd_row3_col2 {\n",
       "  background-color: #fafc66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row3_col1 {\n",
       "  background-color: #fdfe66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row4_col0, #T_1d2cd_row4_col1, #T_1d2cd_row5_col0, #T_1d2cd_row5_col1, #T_1d2cd_row5_col2, #T_1d2cd_row6_col0, #T_1d2cd_row6_col1 {\n",
       "  background-color: #ffff66;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_1d2cd_row6_col2 {\n",
       "  background-color: #fefe66;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1d2cd\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Pclass</th>\n",
       "      <th id=\"T_1d2cd_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_1d2cd_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_1d2cd_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >SibSp</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1d2cd_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_1d2cd_row0_col0\" class=\"data row0 col0\" >137</td>\n",
       "      <td id=\"T_1d2cd_row0_col1\" class=\"data row0 col1\" >120</td>\n",
       "      <td id=\"T_1d2cd_row0_col2\" class=\"data row0 col2\" >351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d2cd_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_1d2cd_row1_col0\" class=\"data row1 col0\" >71</td>\n",
       "      <td id=\"T_1d2cd_row1_col1\" class=\"data row1 col1\" >55</td>\n",
       "      <td id=\"T_1d2cd_row1_col2\" class=\"data row1 col2\" >83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d2cd_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_1d2cd_row2_col0\" class=\"data row2 col0\" >5</td>\n",
       "      <td id=\"T_1d2cd_row2_col1\" class=\"data row2 col1\" >8</td>\n",
       "      <td id=\"T_1d2cd_row2_col2\" class=\"data row2 col2\" >15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d2cd_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_1d2cd_row3_col0\" class=\"data row3 col0\" >3</td>\n",
       "      <td id=\"T_1d2cd_row3_col1\" class=\"data row3 col1\" >1</td>\n",
       "      <td id=\"T_1d2cd_row3_col2\" class=\"data row3 col2\" >12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d2cd_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_1d2cd_row4_col0\" class=\"data row4 col0\" >0</td>\n",
       "      <td id=\"T_1d2cd_row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "      <td id=\"T_1d2cd_row4_col2\" class=\"data row4 col2\" >18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d2cd_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_1d2cd_row5_col0\" class=\"data row5 col0\" >0</td>\n",
       "      <td id=\"T_1d2cd_row5_col1\" class=\"data row5 col1\" >0</td>\n",
       "      <td id=\"T_1d2cd_row5_col2\" class=\"data row5 col2\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d2cd_level0_row6\" class=\"row_heading level0 row6\" >8</th>\n",
       "      <td id=\"T_1d2cd_row6_col0\" class=\"data row6 col0\" >0</td>\n",
       "      <td id=\"T_1d2cd_row6_col1\" class=\"data row6 col1\" >0</td>\n",
       "      <td id=\"T_1d2cd_row6_col2\" class=\"data row6 col2\" >7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x26f78679660>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_data.SibSp,train_data.Pclass).style.background_gradient('summer_r')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "370a48eb-0a15-4bee-ad4a-c538fde3d75c",
    "_uuid": "35e4a93f1a9943165bb7b348daf2b008820be2a4"
   },
   "source": [
    "There are many interesting facts with this feature. Barplot and FactorPlot shows that if a passanger is alone in ship with no siblings, survival rate is 34.5%. The graph decreases as no of siblings increase. This is interesting because, If I have a family onboard, I will save them instead of saving myself. But there's something wrong, the survival rate for families with 5-8 members is 0%. Is this because of PClass?\n",
    "Yes this is PClass,  The crosstab shows that Person with SibSp>3 were all in Pclass3. It is imminent that all the large families in Pclass3(>3) died."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0fdd2521-4b53-4a04-bad7-0bf62f4932f2",
    "_uuid": "2095912b48d60e66fa9b2f71f7d59ecf9367304f"
   },
   "source": [
    "That are some interesting facts we have observed with Titanic dataset."
   ]
  }
 ],
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