notebooks/titanic-notebook-0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "#source: https://www.kaggle.com/bhaveshsk/getting-started-with-titanic-dataset/data\n",
    "#data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "#data visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#machine learning packages\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading train and test data into the dataframe named **'train_df'** and **'test_df'**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
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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>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
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       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330877</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>54.0</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17463</td>\n",
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       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349909</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>237736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PP 9549</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58.0</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113783</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>347082</td>\n",
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       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350406</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>248706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>382652</td>\n",
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       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>244373</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345763</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2649</td>\n",
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       "      <th>20</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>239865</td>\n",
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       "      <th>21</th>\n",
       "      <td>34.0</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
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       "      <td>248698</td>\n",
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       "      <th>22</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>330923</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113788</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349909</td>\n",
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       "      <th>25</th>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>347077</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19.0</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>330959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>349216</td>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Canavan, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>1280</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>364858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>Palsson, Master. Paul Folke</td>\n",
       "      <td>1</td>\n",
       "      <td>1281</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>349909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>23.0</td>\n",
       "      <td>B24</td>\n",
       "      <td>S</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>Payne, Mr. Vivian Ponsonby</td>\n",
       "      <td>0</td>\n",
       "      <td>1282</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>51.0</td>\n",
       "      <td>D28</td>\n",
       "      <td>S</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>Lines, Mrs. Ernest H (Elizabeth Lindsey James)</td>\n",
       "      <td>1</td>\n",
       "      <td>1283</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>20.2500</td>\n",
       "      <td>Abbott, Master. Eugene Joseph</td>\n",
       "      <td>2</td>\n",
       "      <td>1284</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C.A. 2673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>Gilbert, Mr. William</td>\n",
       "      <td>0</td>\n",
       "      <td>1285</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C.A. 30769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>22.0250</td>\n",
       "      <td>Kink-Heilmann, Mr. Anton</td>\n",
       "      <td>1</td>\n",
       "      <td>1286</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>315153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>18.0</td>\n",
       "      <td>C31</td>\n",
       "      <td>S</td>\n",
       "      <td>60.0000</td>\n",
       "      <td>Smith, Mrs. Lucien Philip (Mary Eloise Hughes)</td>\n",
       "      <td>0</td>\n",
       "      <td>1287</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Colbert, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>1288</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>371109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>48.0</td>\n",
       "      <td>B41</td>\n",
       "      <td>C</td>\n",
       "      <td>79.2000</td>\n",
       "      <td>Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1289</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>Larsson-Rondberg, Mr. Edvard A</td>\n",
       "      <td>0</td>\n",
       "      <td>1290</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>Conlon, Mr. Thomas Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>1291</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>30.0</td>\n",
       "      <td>C7</td>\n",
       "      <td>S</td>\n",
       "      <td>164.8667</td>\n",
       "      <td>Bonnell, Miss. Caroline</td>\n",
       "      <td>0</td>\n",
       "      <td>1292</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>Gale, Mr. Harry</td>\n",
       "      <td>0</td>\n",
       "      <td>1293</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>Gibson, Miss. Dorothy Winifred</td>\n",
       "      <td>1</td>\n",
       "      <td>1294</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>Carrau, Mr. Jose Pedro</td>\n",
       "      <td>0</td>\n",
       "      <td>1295</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>43.0</td>\n",
       "      <td>D40</td>\n",
       "      <td>C</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>Frauenthal, Mr. Isaac Gerald</td>\n",
       "      <td>0</td>\n",
       "      <td>1296</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>20.0</td>\n",
       "      <td>D38</td>\n",
       "      <td>C</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>Nourney, Mr. Alfred (Baron von Drachstedt\")\"</td>\n",
       "      <td>0</td>\n",
       "      <td>1297</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SC/PARIS 2166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>Ware, Mr. William Jeffery</td>\n",
       "      <td>0</td>\n",
       "      <td>1298</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>50.0</td>\n",
       "      <td>C80</td>\n",
       "      <td>C</td>\n",
       "      <td>211.5000</td>\n",
       "      <td>Widener, Mr. George Dunton</td>\n",
       "      <td>1</td>\n",
       "      <td>1299</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
       "      <td>0</td>\n",
       "      <td>1300</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>334915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>Peacock, Miss. Treasteall</td>\n",
       "      <td>1</td>\n",
       "      <td>1301</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Naughton, Miss. Hannah</td>\n",
       "      <td>0</td>\n",
       "      <td>1302</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>37.0</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>0</td>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>Henriksson, Miss. Jenny Lovisa</td>\n",
       "      <td>0</td>\n",
       "      <td>1304</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A.5. 3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>39.0</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>38.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1309 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age        Cabin Embarked      Fare  \\\n",
       "0    22.0          NaN        S    7.2500   \n",
       "1    38.0          C85        C   71.2833   \n",
       "2    26.0          NaN        S    7.9250   \n",
       "3    35.0         C123        S   53.1000   \n",
       "4    35.0          NaN        S    8.0500   \n",
       "5     NaN          NaN        Q    8.4583   \n",
       "6    54.0          E46        S   51.8625   \n",
       "7     2.0          NaN        S   21.0750   \n",
       "8    27.0          NaN        S   11.1333   \n",
       "9    14.0          NaN        C   30.0708   \n",
       "10    4.0           G6        S   16.7000   \n",
       "11   58.0         C103        S   26.5500   \n",
       "12   20.0          NaN        S    8.0500   \n",
       "13   39.0          NaN        S   31.2750   \n",
       "14   14.0          NaN        S    7.8542   \n",
       "15   55.0          NaN        S   16.0000   \n",
       "16    2.0          NaN        Q   29.1250   \n",
       "17    NaN          NaN        S   13.0000   \n",
       "18   31.0          NaN        S   18.0000   \n",
       "19    NaN          NaN        C    7.2250   \n",
       "20   35.0          NaN        S   26.0000   \n",
       "21   34.0          D56        S   13.0000   \n",
       "22   15.0          NaN        Q    8.0292   \n",
       "23   28.0           A6        S   35.5000   \n",
       "24    8.0          NaN        S   21.0750   \n",
       "25   38.0          NaN        S   31.3875   \n",
       "26    NaN          NaN        C    7.2250   \n",
       "27   19.0  C23 C25 C27        S  263.0000   \n",
       "28    NaN          NaN        Q    7.8792   \n",
       "29    NaN          NaN        S    7.8958   \n",
       "..    ...          ...      ...       ...   \n",
       "388  21.0          NaN        Q    7.7500   \n",
       "389   6.0          NaN        S   21.0750   \n",
       "390  23.0          B24        S   93.5000   \n",
       "391  51.0          D28        S   39.4000   \n",
       "392  13.0          NaN        S   20.2500   \n",
       "393  47.0          NaN        S   10.5000   \n",
       "394  29.0          NaN        S   22.0250   \n",
       "395  18.0          C31        S   60.0000   \n",
       "396  24.0          NaN        Q    7.2500   \n",
       "397  48.0          B41        C   79.2000   \n",
       "398  22.0          NaN        S    7.7750   \n",
       "399  31.0          NaN        Q    7.7333   \n",
       "400  30.0           C7        S  164.8667   \n",
       "401  38.0          NaN        S   21.0000   \n",
       "402  22.0          NaN        C   59.4000   \n",
       "403  17.0          NaN        S   47.1000   \n",
       "404  43.0          D40        C   27.7208   \n",
       "405  20.0          D38        C   13.8625   \n",
       "406  23.0          NaN        S   10.5000   \n",
       "407  50.0          C80        C  211.5000   \n",
       "408   NaN          NaN        Q    7.7208   \n",
       "409   3.0          NaN        S   13.7750   \n",
       "410   NaN          NaN        Q    7.7500   \n",
       "411  37.0          C78        Q   90.0000   \n",
       "412  28.0          NaN        S    7.7750   \n",
       "413   NaN          NaN        S    8.0500   \n",
       "414  39.0         C105        C  108.9000   \n",
       "415  38.5          NaN        S    7.2500   \n",
       "416   NaN          NaN        S    8.0500   \n",
       "417   NaN          NaN        C   22.3583   \n",
       "\n",
       "                                                  Name  Parch  PassengerId  \\\n",
       "0                              Braund, Mr. Owen Harris      0            1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                               Heikkinen, Miss. Laina      0            3   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                             Allen, Mr. William Henry      0            5   \n",
       "5                                     Moran, Mr. James      0            6   \n",
       "6                              McCarthy, Mr. Timothy J      0            7   \n",
       "7                       Palsson, Master. Gosta Leonard      1            8   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      2            9   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)      0           10   \n",
       "10                     Sandstrom, Miss. Marguerite Rut      1           11   \n",
       "11                            Bonnell, Miss. Elizabeth      0           12   \n",
       "12                      Saundercock, Mr. William Henry      0           13   \n",
       "13                         Andersson, Mr. Anders Johan      5           14   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina      0           15   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)       0           16   \n",
       "16                                Rice, Master. Eugene      1           17   \n",
       "17                        Williams, Mr. Charles Eugene      0           18   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...      0           19   \n",
       "19                             Masselmani, Mrs. Fatima      0           20   \n",
       "20                                Fynney, Mr. Joseph J      0           21   \n",
       "21                               Beesley, Mr. Lawrence      0           22   \n",
       "22                         McGowan, Miss. Anna \"Annie\"      0           23   \n",
       "23                        Sloper, Mr. William Thompson      0           24   \n",
       "24                       Palsson, Miss. Torborg Danira      1           25   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...      5           26   \n",
       "26                             Emir, Mr. Farred Chehab      0           27   \n",
       "27                      Fortune, Mr. Charles Alexander      2           28   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"      0           29   \n",
       "29                                 Todoroff, Mr. Lalio      0           30   \n",
       "..                                                 ...    ...          ...   \n",
       "388                               Canavan, Mr. Patrick      0         1280   \n",
       "389                        Palsson, Master. Paul Folke      1         1281   \n",
       "390                         Payne, Mr. Vivian Ponsonby      0         1282   \n",
       "391     Lines, Mrs. Ernest H (Elizabeth Lindsey James)      1         1283   \n",
       "392                      Abbott, Master. Eugene Joseph      2         1284   \n",
       "393                               Gilbert, Mr. William      0         1285   \n",
       "394                           Kink-Heilmann, Mr. Anton      1         1286   \n",
       "395     Smith, Mrs. Lucien Philip (Mary Eloise Hughes)      0         1287   \n",
       "396                               Colbert, Mr. Patrick      0         1288   \n",
       "397  Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...      1         1289   \n",
       "398                     Larsson-Rondberg, Mr. Edvard A      0         1290   \n",
       "399                           Conlon, Mr. Thomas Henry      0         1291   \n",
       "400                            Bonnell, Miss. Caroline      0         1292   \n",
       "401                                    Gale, Mr. Harry      0         1293   \n",
       "402                     Gibson, Miss. Dorothy Winifred      1         1294   \n",
       "403                             Carrau, Mr. Jose Pedro      0         1295   \n",
       "404                       Frauenthal, Mr. Isaac Gerald      0         1296   \n",
       "405       Nourney, Mr. Alfred (Baron von Drachstedt\")\"      0         1297   \n",
       "406                          Ware, Mr. William Jeffery      0         1298   \n",
       "407                         Widener, Mr. George Dunton      1         1299   \n",
       "408                    Riordan, Miss. Johanna Hannah\"\"      0         1300   \n",
       "409                          Peacock, Miss. Treasteall      1         1301   \n",
       "410                             Naughton, Miss. Hannah      0         1302   \n",
       "411    Minahan, Mrs. William Edward (Lillian E Thorpe)      0         1303   \n",
       "412                     Henriksson, Miss. Jenny Lovisa      0         1304   \n",
       "413                                 Spector, Mr. Woolf      0         1305   \n",
       "414                       Oliva y Ocana, Dona. Fermina      0         1306   \n",
       "415                       Saether, Mr. Simon Sivertsen      0         1307   \n",
       "416                                Ware, Mr. Frederick      0         1308   \n",
       "417                           Peter, Master. Michael J      1         1309   \n",
       "\n",
       "     Pclass     Sex  SibSp  Survived              Ticket  \n",
       "0         3    male      1       0.0           A/5 21171  \n",
       "1         1  female      1       1.0            PC 17599  \n",
       "2         3  female      0       1.0    STON/O2. 3101282  \n",
       "3         1  female      1       1.0              113803  \n",
       "4         3    male      0       0.0              373450  \n",
       "5         3    male      0       0.0              330877  \n",
       "6         1    male      0       0.0               17463  \n",
       "7         3    male      3       0.0              349909  \n",
       "8         3  female      0       1.0              347742  \n",
       "9         2  female      1       1.0              237736  \n",
       "10        3  female      1       1.0             PP 9549  \n",
       "11        1  female      0       1.0              113783  \n",
       "12        3    male      0       0.0           A/5. 2151  \n",
       "13        3    male      1       0.0              347082  \n",
       "14        3  female      0       0.0              350406  \n",
       "15        2  female      0       1.0              248706  \n",
       "16        3    male      4       0.0              382652  \n",
       "17        2    male      0       1.0              244373  \n",
       "18        3  female      1       0.0              345763  \n",
       "19        3  female      0       1.0                2649  \n",
       "20        2    male      0       0.0              239865  \n",
       "21        2    male      0       1.0              248698  \n",
       "22        3  female      0       1.0              330923  \n",
       "23        1    male      0       1.0              113788  \n",
       "24        3  female      3       0.0              349909  \n",
       "25        3  female      1       1.0              347077  \n",
       "26        3    male      0       0.0                2631  \n",
       "27        1    male      3       0.0               19950  \n",
       "28        3  female      0       1.0              330959  \n",
       "29        3    male      0       0.0              349216  \n",
       "..      ...     ...    ...       ...                 ...  \n",
       "388       3    male      0       NaN              364858  \n",
       "389       3    male      3       NaN              349909  \n",
       "390       1    male      0       NaN               12749  \n",
       "391       1  female      0       NaN            PC 17592  \n",
       "392       3    male      0       NaN           C.A. 2673  \n",
       "393       2    male      0       NaN          C.A. 30769  \n",
       "394       3    male      3       NaN              315153  \n",
       "395       1  female      1       NaN               13695  \n",
       "396       3    male      0       NaN              371109  \n",
       "397       1  female      1       NaN               13567  \n",
       "398       3    male      0       NaN              347065  \n",
       "399       3    male      0       NaN               21332  \n",
       "400       1  female      0       NaN               36928  \n",
       "401       2    male      1       NaN               28664  \n",
       "402       1  female      0       NaN              112378  \n",
       "403       1    male      0       NaN              113059  \n",
       "404       1    male      1       NaN               17765  \n",
       "405       2    male      0       NaN       SC/PARIS 2166  \n",
       "406       2    male      1       NaN               28666  \n",
       "407       1    male      1       NaN              113503  \n",
       "408       3  female      0       NaN              334915  \n",
       "409       3  female      1       NaN  SOTON/O.Q. 3101315  \n",
       "410       3  female      0       NaN              365237  \n",
       "411       1  female      1       NaN               19928  \n",
       "412       3  female      0       NaN              347086  \n",
       "413       3    male      0       NaN           A.5. 3236  \n",
       "414       1  female      0       NaN            PC 17758  \n",
       "415       3    male      0       NaN  SOTON/O.Q. 3101262  \n",
       "416       3    male      0       NaN              359309  \n",
       "417       3    male      1       NaN                2668  \n",
       "\n",
       "[1309 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv(\"../input/train.csv\")\n",
    "test_df = pd.read_csv(\"../input/test.csv\")\n",
    "df = pd.concat([train_df,test_df])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Printing the names of the columns in a Dataframe gives you an idea of the attributes you are daling with.\n",
    "* This also helps to decide what are the importance of each and every column to complete the task at hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age' 'Cabin' 'Embarked' 'Fare' 'Name' 'Parch' 'PassengerId' 'Pclass'\n",
      " 'Sex' 'SibSp' 'Survived' 'Ticket']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**.head()** function is used to print starting rows of the Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Cabin Embarked     Fare  \\\n",
       "0  22.0   NaN        S   7.2500   \n",
       "1  38.0   C85        C  71.2833   \n",
       "2  26.0   NaN        S   7.9250   \n",
       "3  35.0  C123        S  53.1000   \n",
       "4  35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                Name  Parch  PassengerId  \\\n",
       "0                            Braund, Mr. Owen Harris      0            1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                             Heikkinen, Miss. Laina      0            3   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                           Allen, Mr. William Henry      0            5   \n",
       "\n",
       "   Pclass     Sex  SibSp  Survived            Ticket  \n",
       "0       3    male      1       0.0         A/5 21171  \n",
       "1       1  female      1       1.0          PC 17599  \n",
       "2       3  female      0       1.0  STON/O2. 3101282  \n",
       "3       1  female      1       1.0            113803  \n",
       "4       3    male      0       0.0            373450  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**.tail()** function is used to print ending rows of the Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>0</td>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A.5. 3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>39.0</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>0</td>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>38.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>0</td>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>0</td>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>1</td>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked      Fare                          Name  Parch  \\\n",
       "413   NaN   NaN        S    8.0500            Spector, Mr. Woolf      0   \n",
       "414  39.0  C105        C  108.9000  Oliva y Ocana, Dona. Fermina      0   \n",
       "415  38.5   NaN        S    7.2500  Saether, Mr. Simon Sivertsen      0   \n",
       "416   NaN   NaN        S    8.0500           Ware, Mr. Frederick      0   \n",
       "417   NaN   NaN        C   22.3583      Peter, Master. Michael J      1   \n",
       "\n",
       "     PassengerId  Pclass     Sex  SibSp  Survived              Ticket  \n",
       "413         1305       3    male      0       NaN           A.5. 3236  \n",
       "414         1306       1  female      0       NaN            PC 17758  \n",
       "415         1307       3    male      0       NaN  SOTON/O.Q. 3101262  \n",
       "416         1308       3    male      0       NaN              359309  \n",
       "417         1309       3    male      1       NaN                2668  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**.describe()** is used to view some basic statistical details like percentile, mean, std etc. of a data frame or a series of numeric values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.881138</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>378.020061</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>982.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Fare        Parch  PassengerId       Pclass  \\\n",
       "count  1046.000000  1308.000000  1309.000000  1309.000000  1309.000000   \n",
       "mean     29.881138    33.295479     0.385027   655.000000     2.294882   \n",
       "std      14.413493    51.758668     0.865560   378.020061     0.837836   \n",
       "min       0.170000     0.000000     0.000000     1.000000     1.000000   \n",
       "25%      21.000000     7.895800     0.000000   328.000000     2.000000   \n",
       "50%      28.000000    14.454200     0.000000   655.000000     3.000000   \n",
       "75%      39.000000    31.275000     0.000000   982.000000     3.000000   \n",
       "max      80.000000   512.329200     9.000000  1309.000000     3.000000   \n",
       "\n",
       "             SibSp    Survived  \n",
       "count  1309.000000  891.000000  \n",
       "mean      0.498854    0.383838  \n",
       "std       1.041658    0.486592  \n",
       "min       0.000000    0.000000  \n",
       "25%       0.000000    0.000000  \n",
       "50%       0.000000    0.000000  \n",
       "75%       1.000000    1.000000  \n",
       "max       8.000000    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\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>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>295</td>\n",
       "      <td>1307</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>186</td>\n",
       "      <td>3</td>\n",
       "      <td>1307</td>\n",
       "      <td>2</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>6</td>\n",
       "      <td>914</td>\n",
       "      <td>2</td>\n",
       "      <td>843</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Cabin Embarked                  Name   Sex    Ticket\n",
       "count           295     1307                  1309  1309      1309\n",
       "unique          186        3                  1307     2       929\n",
       "top     C23 C25 C27        S  Connolly, Miss. Kate  male  CA. 2343\n",
       "freq              6      914                     2   843        11"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **.groupby()** is used combine distince values in a column and then various statistical methods are use to calculate multiple results like mean, count, sum, minimum and maximum.\n",
    "* In this case we use **.mean()** to calculate the Mean of Survived column against the Passenger Class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Pclass','Survived']].groupby(['Pclass'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In this case we use **.mean()** to calculate the Mean of Survived column against Sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Sex','Survived']].groupby(['Sex'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In this case we use **.mean()** to calculate the Mean of Survived column against SibSp which dentes the number of Number of Siblings or Spouses aborad the Titanic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['SibSp','Survived']].groupby(['SibSp'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In this case we use **.mean()** to calculate the Mean of Survived column against Parch which denotes the number of Parents or Children aboard the Titanic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000\n",
       "7      9       NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Parch','Survived']].groupby(['Parch'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Plotting of Histogram of Survived against Age is done using **Seaborn** package to get a visual idea whether the age of an individual influenced his chances of survival and the plot shows it did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f01d47597f0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=sns.FacetGrid(df,col='Survived')\n",
    "g.map(plt.hist,'Age',bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Other than age we can see in this plot that Passenger Class also influenced the chances of survival of an individual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/seaborn/axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f01d265a160>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 699.2x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid=sns.FacetGrid(df,col='Survived',row='Pclass',size=3,aspect=1.6)\n",
    "grid.map(plt.hist,'Age',alpha=0.5,bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/usr/local/lib/python3.6/site-packages/seaborn/axisgrid.py:720: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f01d211fd30>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/seaborn/axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f01d21f86d8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Embarked', col='Survived', height=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('After', (1309, 10))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "\n",
    "\"After\", df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dona</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     4\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dona           1     0\n",
       "Dr             1     7\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    61\n",
       "Miss         260     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   757\n",
       "Mrs          197     0\n",
       "Ms             2     0\n",
       "Rev            0     8\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'] = df.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "pd.crosstab(df['Title'], df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'] = df['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    "'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "df['Title'] = df['Title'].replace('Mlle', 'Miss')\n",
    "df['Title'] = df['Title'].replace('Ms', 'Miss')\n",
    "df['Title'] = df['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Embarked     Fare                                               Name  \\\n",
       "0  22.0        S   7.2500                            Braund, Mr. Owen Harris   \n",
       "1  38.0        C  71.2833  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2  26.0        S   7.9250                             Heikkinen, Miss. Laina   \n",
       "3  35.0        S  53.1000       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4  35.0        S   8.0500                           Allen, Mr. William Henry   \n",
       "\n",
       "   Parch  PassengerId  Pclass     Sex  SibSp  Survived  Title  \n",
       "0      0            1       3    male      1       0.0      1  \n",
       "1      0            2       1  female      1       1.0      3  \n",
       "2      0            3       3  female      0       1.0      2  \n",
       "3      0            4       1  female      1       1.0      3  \n",
       "4      0            5       3    male      0       0.0      1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "\n",
    "df['Title'] = df['Title'].map(title_mapping)\n",
    "df['Title'] = df['Title'].fillna(0)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "Age            1046 non-null float64\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Title          1309 non-null int64\n",
      "dtypes: float64(3), int64(5), object(3)\n",
      "memory usage: 122.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 9)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Name', 'PassengerId'], axis=1)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age Embarked     Fare  Parch  Pclass  Sex  SibSp  Survived  Title\n",
       "0  22.0        S   7.2500      0       3    0      1       0.0      1\n",
       "1  38.0        C  71.2833      0       1    1      1       1.0      3\n",
       "2  26.0        S   7.9250      0       3    1      0       1.0      2\n",
       "3  35.0        S  53.1000      0       1    1      1       1.0      3\n",
       "4  35.0        S   8.0500      0       3    0      0       0.0      1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'] = df['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f01d1b400f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O42dms15VXZfkJ4A19HaSt3VHAC/v/t3WLbofvR3ITfSGGW8B/hn4jb1s99/Ntg3SgNmf7c+LmgFgtL5TVc/dc0afI9GXAKdU1R1JzgBeClBVZyd5MfAq4NYkL6iqy5Js7uZdm+R1VXX9lNec1RFDkucAFwGvrKqv9FPwnrqd4GXAZUmuAU6gd5Tw3qr6i2lWOZjeDmQf4KnAt6epaTZHDA8DhwLbuqO1A4BZt0Oawv48nv6sATEAjN/1wCeSnFdVX0mybJqjhv2B7Un2AV5L7w8aSZ5ZVZuBzUleCRya5ADgwar60yTPAJ7TvcYTZnPE0G3jKuD0qrpvto1L8jLg76vqfyXZH3gmvYt4vgm8O8mlVfWtJIcA36uqR4C/AN4BHE7vgr3XT93uLI8Y1gNr6R1NnQpcX1U127ZIfbA/D78/a0AMAGNWVXcn+QPgb5N8n94Q2hlTFnsHsBnY2f18PCn/UXdRUIBNwB3A24HTk3wP2AH84TxL/D16Cf7PuqOb3VW1ehbrvwD4r0l207vo9KKqugUgyU8Cn+m2+y3gvyRZQ2/HcVmSJcCnk7xs6lHPLF0MfCTJA8BXgdPmsS1pr+zPw+/PSV4IfAI4CPj5JO+qqmPmur2WxQMhDUKSS4BLqurGMZciaZ7sz23wY4CSJDXIAKBB+Rvgi+MuQtJA2J8b4CkASZIa5AiAJEkNMgBIktSgkX4McM2aNbVhw4ZRvqSkHzaQ70GwP0sLxpz69EhHAHbt8tbr0qSwP0uLm6cAJElqkAFAkqQGGQAkSWqQAUCSpAYZACRJapABQJKkBhkAJElqkAFAkqQGGQAkSWqQAUCSpAYZACRJapABQJKkBhkAJElqkAFAkqQGGQAkSWpQXwEgyYFJrkzy+SRbkxyfZFmSjUnu734eNOxiJUnSYPQ7AnABsKGqng0cB2wFzgE2VdWRwKZuWpIkLQIzBoAkBwAnABcDVNV3q+pR4GRgXbfYOuCUYRUpSZIGq58RgMOBncCHk9yW5KIkTwdWVNX2bpkdwIphFSlJkgarnwCwFHg+8OdV9Tzg20wZ7q+qAmq6lZOclWRLki07d+6cb72Sxsj+LE2OfgLANmBbVW3upq+kFwi+nGQlQPfzkelWrqoLq2p1Va1evnz5IGqWNCb2Z2lyzBgAqmoH8FCSo7pZJwL3AOuBtd28tcDVQ6lQkiQN3NI+l3sDcGmSfYEHgV+lFx6uSHIm8CXg1cMpUZIkDVpfAaCqbgdWT/PUiYMtR5IkjYJ3ApQkqUEGAEmSGmQAkCSpQQYASZIaZACQJKlBBgBJkhpkAJAkqUEGAEmSGmQAkCSpQQYASZIaZACQJKlBBgBJkhpkAJAkqUEGAEmSGmQAkCSpQQYASZIaZACQJKlBBgBJkhpkAJAkqUEGAEmSGmQAkCSpQQYASZIa1HcASLIkyW1JrummD0+yOckDSS5Psu/wypQkSYM0mxGANwJb95h+P3B+VR0BfA04c5CFSZKk4ekrACRZBbwKuKibDvAy4MpukXXAKcMoUJIkDV6/IwB/AvwW8Fg3fTDwaFXt7qa3AYcMuDZJkjQkMwaAJD8HPFJVt87lBZKclWRLki07d+6cyyYkLRD2Z2ly9DMC8BLgF5J8EfgovaH/C4ADkyztllkFPDzdylV1YVWtrqrVy5cvH0DJksbF/ixNjhkDQFWdW1Wrquow4DTg+qp6LXADcGq32Frg6qFVKUmSBmo+9wF4O/CWJA/Quybg4sGUJEmShm3pzIv8i6q6Ebixe/wg8KLBlyRJkobNOwFKktQgA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAJEkNMgBIktQgA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAJEkNMgBIktQgA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAJEkNMgBIktQgA4AkSQ0yAEiS1KCl4y5AP+z8jffNuMybT3rWCCqRJE0qRwAkSWrQjAEgyaFJbkhyT5K7k7yxm78sycYk93c/Dxp+uZIkaRD6GQHYDby1qo4Gfgr49SRHA+cAm6rqSGBTNy1JkhaBGQNAVW2vqs92j78JbAUOAU4G1nWLrQNOGVaRkiRpsGZ1DUCSw4DnAZuBFVW1vXtqB7BioJVJkqSh6TsAJNkP+Djwpqr6xp7PVVUBtZf1zkqyJcmWnTt3zqtYSeNlf5YmR18fA0yyD70//pdW1VXd7C8nWVlV25OsBB6Zbt2quhC4EGD16tXThgTNnh8V1DjYn6XJ0c+nAAJcDGytqvP2eGo9sLZ7vBa4evDlSZKkYehnBOAlwOnA55Lc3s37beB9wBVJzgS+BLx6OCVKkqRBmzEAVNWngOzl6RMHW44kSRoFbwU8wbxOQJK0NwaAEevnj7KkNsy0PzCga5j8LgBJkhrkCIAkzcOTHcV7BK+FzBEASZIaZACQJKlBngIYoEm9wM9PE0jS5HEEQJKkBhkAJElqkAFAkqQGGQAkSWqQAUCSpAYt2E8BLLQrzyf1Cn9Ji5O3EdZ8OQIgSVKDFuwIgEbDkQ3pydlHNKkMAJI0JPMND4YPDZOnACRJapAjAJLUIL/FUAYAjcwghjMHtWNaaJ8y0Xg51K4WeQpAkqQGOQKggfAISv2Yz++JIzKj4z0G2uAIgCRJDZrXCECSNcAFwBLgoqp630CqkvbCkYZ2zeeiNX9vRsfRg8VjzgEgyRLgg8BJwDbgliTrq+qeQRU3CF7sJU0+/8CP1jD/v/10wujM5xTAi4AHqurBqvou8FHg5MGUJUmShmk+pwAOAR7aY3ob8OL5lTMeHj1oOoP6vfCoReOwUPdr86nL0wuDNfRPASQ5Czirm/xWknv7WO3HgF0zLfSW+RQ2On21ZRGZpPaMpC0j/D3ttz0bqmrNXF5gmP15EZmk9kxSW3jLZLVnNm2ZU59OVc12nd6KyfHAO6vqFd30uQBV9d45bfAHt72lqlbPdzsLwSS1BSarPZPUFli47Vmodc3VJLVnktoCk9WeUbRlPtcA3AIcmeTwJPsCpwHrB1OWJEkapjmfAqiq3UleD1xH72OAH6qquwdWmSRJGpp5XQNQVdcC1w6olj1dOIRtjssktQUmqz2T1BZYuO1ZqHXN1SS1Z5LaApPVnqG3Zc7XAEiSpMXLWwFLktQgA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAJEkNMgBIktQgA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAJEkNMgCMUJLvJ7k9yV1JPpbkR59k2Xcmedso69tLHScnubOre0uSn9nLcjcmOWya+Ud1z92eZGuSkX9bV5JlSTYmub/7edCoa9DksT+PrT//UpK7kzyWZPWoX3+SGABG6ztV9dyqOhb4LnD2uAvqwybguKp6LvB/ARfNcv0/Bc7v2v2TwAcGXWAfzgE2VdWR9Npzzhhq0OSxP4+nP98F/CfgpjG89kQxAIzPzcARAEl+pUvldyT5yNQFk/xaklu65z/++JFGl4Tv6ubf1M07Jsk/dAn9ziRHzqfIqvpW/ct3Rj8dmO33R68Etu2xvc91dS5J8kddu+5M8rpu/n9Msik9K5Pcl+TH59MG4GRgXfd4HXDKPLcnTWV/HlF/rqqtVXXvfLahnqXjLqBFSZYCrwQ2JDkG+F3gp6tqV5Jl06xyVVX9Zbfue4Az6SXv3wNeUVUPJzmwW/Zs4IKqujTJvsCSaV7/cuCoaV7nvKr6q2mW/4/Ae4F/Dbxqls09H7g+yaeB/wF8uKoe7drw9ap6YZKnAH+X5H9U1SeS/CLw68Aa4P+tqh1T6tmf3g53Ov+5qu6ZMm9FVW3vHu8AVsyyDdJe2Z9H3p81IAaA0Xpaktu7xzcDFwOvAz5WVbsAquqr06x3bLejOBDYD7ium/93wCVJrgCu6uZ9BvidJKvo7Wjun7qxqvrl2RRdVZ8APpHkBODdwH+YxbofTnIdvc5/MvC6JMcBLweek+TUbtEDgCOBLwBvoDfM9/dV9d+m2eY3gefOpg17rFtJZnvUI03H/jzm/qz5MQCM1ne6c29PSNLPepcAp1TVHUnOAF4KUFVnJ3kxvRR/a5IXVNVlSTZ3865N8rqqun7Ka87qiOFxVXVTkp9I8mOP7+D6UVX/BHwI+FCSu4BjgQBvqKrrplllFfAYsCLJj1TVY1Pqn+0Rw5eTrKyq7UlWAo/0W7v0JOzP4+nPGhADwPhdTy+Nn1dVX0mybJqjhv2B7Un2AV4LPAyQ5JlVtRnYnOSVwKFJDgAerKo/TfIM4DndazxhNkcMSY4A/md35Px84CnAV2ax/hp6F+B9rzv3d3BX/3XA/5Pk+u65Z3Xz/ze9nctrgLXAW4A/nlL/bI8Y1nfbel/38+pZrCvNhv15+P1ZA2IAGLOqujvJHwB/m+T7wG3AGVMWewewGdjZ/dy/m/9H3UVBoXd17x3A24HTk3yP3vnuP5xnib8I/Eq3ve8Av7zHRUT9eDlwQZJ/7qZ/s6p2JLkIOAz4bHqHTTvpXZz3VuDmqvpUkjuAW5L896raOo82vA+4IsmZwJeAV89jW9Je2Z+H35+7axg+ACwH/nuS26vqFXPdXssyu/deml6SG4EzquqLYy5F0jzZn9vgxwAlSWqQAUCDcgnw6LiLkDQQl2B/nnieApAkqUGOAEiS1KCRfgpgzZo1tWHDhlG+pKQf1teH1Wdif5YWjDn16ZGOAOza1fe9JiQtcPZnaXHzFIAkSQ0yAEiS1CADgCRJDTIASJLUIL8LYAjO33jfjMu8+aRnjaASSZKm5wiAJEkNMgBIktQgA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAJEkNMgBIktQgA4AkSQ0yAEiS1KC+AkCSA5NcmeTzSbYmOT7JsiQbk9zf/Txo2MVKkqTB6HcE4AJgQ1U9GzgO2AqcA2yqqiOBTd20JElaBGYMAEkOAE4ALgaoqu9W1aPAycC6brF1wCnDKlKSJA1WPyMAhwM7gQ8nuS3JRUmeDqyoqu3dMjuAFcMqUpIkDVY/AWAp8Hzgz6vqecC3mTLcX1UF1HQrJzkryZYkW3bu3DnfeiWNkf1Zmhz9BIBtwLaq2txNX0kvEHw5yUqA7ucj061cVRdW1eqqWr18+fJB1CxpTOzP0uSYMQBU1Q7goSRHdbNOBO4B1gNru3lrgauHUqEkSRq4pX0u9wbg0iT7Ag8Cv0ovPFyR5EzgS8Crh1OiJEkatL4CQFXdDqye5qkTB1uOJEkahX5HADRg52+8r6/l3nzSs4ZciSSpRd4KWJKkBhkAJElqkAFAkqQGGQAkSWqQAUCSpAYZACRJapABQJKkBhkAJElqkAFAkqQGGQAkSWqQAUCSpAYZACRJapABQJKkBhkAJElqkAFAkqQGGQAkSWqQAUCSpAYZACRJapABQJKkBhkAJElqkAFAkqQGGQAkSWpQ3wEgyZIktyW5pps+PMnmJA8kuTzJvsMrU5IkDdJsRgDeCGzdY/r9wPlVdQTwNeDMQRYmSZKGp68AkGQV8Crgom46wMuAK7tF1gGnDKNASZI0eP2OAPwJ8FvAY930wcCjVbW7m94GHDLg2iRJ0pDMGACS/BzwSFXdOpcXSHJWki1JtuzcuXMum5C0QNifpcmxtI9lXgL8QpKfBZ4K/CvgAuDAJEu7UYBVwMPTrVxVFwIXAqxevboGUrV+wPkb7+truTef9KwhV6JJZ3+WJseMIwBVdW5Vraqqw4DTgOur6rXADcCp3WJrgauHVqUkSRqo+dwH4O3AW5I8QO+agIsHU5IkSRq2fk4BPKGqbgRu7B4/CLxo8CVJkqRhm1UA0Oj1e35fkqTZ8FbAkiQ1yAAgSVKDDACSJDXIACBJUoMMAJIkNcgAIElSgwwAkiQ1yAAgSVKDvBHQLHljHknSJHAEQJKkBhkAJElqkAFAkqQGGQAkSWqQAUCSpAYZACRJapABQJKkBnkfAEnNm+n+Hm8+6VkjqkQaHUcAJElqkAFAkqQGGQAkSWqQAUCSpAbNGACSHJrkhiT3JLk7yRu7+cuSbExyf/fzoOGXK0mSBqGfEYDdwFur6mjgp4BfT3I0cA6wqaqOBDZ105IkaRGYMQBU1faq+mz3+JvAVuAQ4GRgXbfYOuCUYRUpSZIGa1b3AUh++OdxAAAHz0lEQVRyGPA8YDOwoqq2d0/tAFYMtDIN3EyfdQY/7yxJreg7ACTZD/g48Kaq+kaSJ56rqkpSe1nvLOAsgGc84xnzq1bSWLXan71RkCZRX58CSLIPvT/+l1bVVd3sLydZ2T2/EnhkunWr6sKqWl1Vq5cvXz6ImiWNif1ZmhwzjgCkd6h/MbC1qs7b46n1wFrgfd3Pq4dSoUaqn9ME4BGPJC12/ZwCeAlwOvC5JLd3836b3h/+K5KcCXwJePVwSpQkSYM2YwCoqk8B2cvTJw62HEmSNAreCVCSpAYZACRJatCs7gMwSn5mXZKk4VmwAUCSWuEBj8bBUwCSJDXIEQDNiUcsmg3vpCctPAYASZqnfm+gJS0kngKQJKlBBgBJkhpkAJAkqUEGAEmSGtTERYBesS5J0g9yBECSpAY1MQLQDz/GI0lqiQFAkhrgzZg0lacAJElq0KIeAXDYXpoMwz46bWFfMew2OoIweRZ1ANDCNsgdkjuXtvnHRxo8TwFIktQgA4AkSQ0yAEiS1CADgCRJDfIiQEmLnlf5S7M3rxGAJGuS3JvkgSTnDKooSZI0XHMeAUiyBPggcBKwDbglyfqqumdQxUnD0O+R1CA/WuYXUklaaOZzCuBFwANV9SBAko8CJwMGAA3cOIY//aOtlsy3j437Xg3219mbzymAQ4CH9pje1s2TJEkL3NAvAkxyFnBWN/mtJPf2sdqPAbuGV9VITVJbYLLaM++2vGVAhQxoW/22Z0NVrZnLC9ifgclqz8jaMsi+8iSetD0jqmFQZvPezKlPp6pmu05vxeR44J1V9Ypu+lyAqnrvnDb4g9veUlWr57udhWCS2gKT1Z5Jagss3PYs1LrmapLaM0ltgclqzyjaMp9TALcARyY5PMm+wGnA+sGUJUmShmnOpwCqaneS1wPXAUuAD1XV3QOrTJIkDc28rgGoqmuBawdUy54uHMI2x2WS2gKT1Z5Jagss3PYs1LrmapLaM0ltgclqz9DbMudrACRJ0uLldwFIktSgBRUAFvuthZMcmuSGJPckuTvJG7v5y5JsTHJ/9/OgcdfaryRLktyW5Jpu+vAkm7v36PLuAtBFIcmBSa5M8vkkW5Mcv1jfmyRv7n7H7kry35I8dSG+N4u5T9ufF7ZJ6s8wnj69YALAHrcWfiVwNPCaJEePt6pZ2w28taqOBn4K+PWuDecAm6rqSGBTN71YvBHYusf0+4Hzq+oI4GvAmWOpam4uoPd52WcDx9Fr16J7b5IcAvwGsLqqjqV3Ee5pLLD3ZgL6tP15YZuI/gxj7NNVtSD+AccD1+0xfS5w7rjrmmebrqb3XQn3Aiu7eSuBe8ddW5/1r6LXiV4GXAOE3o0plk73ni3kf8ABwBfornvZY/6ie2/4l7twLqN3Ie81wCsW2nszaX3a/rxw/k1Sf+5qHUufXjAjAEzYrYWTHAY8D9gMrKiq7d1TO4AVYyprtv4E+C3gsW76YODRqtrdTS+m9+hwYCfw4W4I9KIkT2cRvjdV9TDwx8A/AtuBrwO3svDem4np0/bnBWdi+jOMr08vpAAwMZLsB3wceFNVfWPP56oX5Rb8Ry+S/BzwSFXdOu5aBmQp8Hzgz6vqecC3mTI8uIjem4PoffHW4cD/ATwdmNOtfTUz+/OCNDH9GcbXpxdSAHgYOHSP6VXdvEUlyT70dhaXVtVV3ewvJ1nZPb8SeGRc9c3CS4BfSPJF4KP0hg0vAA5M8vj9IxbTe7QN2FZVm7vpK+ntQBbje/MfgC9U1c6q+h5wFb33a6G9N4u+T9ufF6xJ6s8wpj69kALAor+1cJIAFwNbq+q8PZ5aD6ztHq+ldy5xQauqc6tqVVUdRu+9uL6qXgvcAJzaLbYo2gJQVTuAh5Ic1c06kd5XVy+694beMOFPJfnR7nfu8bYstPdmUfdp+/PCNWH9GcbVp8d98cOUCyF+FrgP+J/A74y7njnU/zP0hpzuBG7v/v0svXNtm4D7gU8Cy8Zd6yzb9VLgmu7xTwD/ADwAfAx4yrjrm0U7ngts6d6fvwEOWqzvDfAu4PPAXcBHgKcsxPdmMfdp+/PC/jdJ/blrz8j7tHcClCSpQQvpFIAkSRoRA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAfkCSU5JUkmePuxZJ82ef1t4YADTVa4BPdT8lLX72aU3LAKAndPc8/xl6Xzl5WjfvR5L8Wfed2xuTXJvk1O65FyT52yS3Jrnu8VtwSloY7NN6MgYA7elket+vfR/wlSQvAP4TcBi973M/nd5XUj5+j/QPAKdW1QuADwF/MI6iJe2VfVp7tXTmRdSQ19D7ghDofWHIa+j9jnysqh4DdiS5oXv+KOBYYGPv1tUsofc1lpIWDvu09soAIACSLKP3DWH/NknR6/wFfGJvqwB3V9XxIypR0izYpzUTTwHocacCH6mqf1NVh1XVocAXgK8Cv9idN1xB74tEAO4Flid5YvgwyTHjKFzStOzTelIGAD3uNfzwkcHHgR+n993b9wB/DXwW+HpVfZfeDub9Se6g901pPz26ciXNwD6tJ+W3AWpGSfarqm8lOZjeV1O+pHrfxy1pEbJPC7wGQP25JsmBwL7Au91RSIuefVqOAEiS1CKvAZAkqUEGAEmSGmQAkCSpQQYASZIaZACQJKlBBgBJkhr0/wMggG7S8x50AQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Embarked     Fare  Parch  Pclass  Sex  SibSp  Survived  Title\n",
       "0   22        S   7.2500      0       3    0      1       0.0      1\n",
       "1   38        C  71.2833      0       1    1      1       1.0      3\n",
       "2   26        S   7.9250      0       3    1      0       1.0      2\n",
       "3   35        S  53.1000      0       1    1      1       1.0      3\n",
       "4   35        S   8.0500      0       3    0      0       0.0      1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        guess_df = df[(df['Sex'] == i) & \\\n",
    "                              (df['Pclass'] == j+1)]['Age'].dropna()\n",
    "\n",
    "        # age_mean = guess_df.mean()\n",
    "        # age_std = guess_df.std()\n",
    "        # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "\n",
    "        age_guess = guess_df.median()\n",
    "\n",
    "        # Convert random age float to nearest .5 age\n",
    "        guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "\n",
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        df.loc[ (df.Age.isnull()) & (df.Sex == i) & (df.Pclass == j+1),\\\n",
    "                'Age'] = guess_ages[i,j]\n",
    "\n",
    "df['Age'] = df['Age'].astype(int)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AgeBand'] = pd.cut(df['Age'], 5)\n",
    "df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Age</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Embarked     Fare  Parch  Pclass  Sex  SibSp  Survived  Title  \\\n",
       "0    1        S   7.2500      0       3    0      1       0.0      1   \n",
       "1    2        C  71.2833      0       1    1      1       1.0      3   \n",
       "2    1        S   7.9250      0       3    1      0       1.0      2   \n",
       "3    2        S  53.1000      0       1    1      1       1.0      3   \n",
       "4    2        S   8.0500      0       3    0      0       0.0      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[ df['Age'] <= 16, 'Age'] = 0\n",
    "df.loc[(df['Age'] > 16) & (df['Age'] <= 32), 'Age'] = 1\n",
    "df.loc[(df['Age'] > 32) & (df['Age'] <= 48), 'Age'] = 2\n",
    "df.loc[(df['Age'] > 48) & (df['Age'] <= 64), 'Age'] = 3\n",
    "df.loc[ df['Age'] > 64, 'Age']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>71.2833</td>\n",
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       "      <td>7.9250</td>\n",
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       "    <tr>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Embarked     Fare  Parch  Pclass  Sex  SibSp  Survived  Title\n",
       "0    1        S   7.2500      0       3    0      1       0.0      1\n",
       "1    2        C  71.2833      0       1    1      1       1.0      3\n",
       "2    1        S   7.9250      0       3    1      0       1.0      2\n",
       "3    2        S  53.1000      0       1    1      1       1.0      3\n",
       "4    2        S   8.0500      0       3    0      0       0.0      1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['AgeBand'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
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       "      <th>0</th>\n",
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       "      <td>0.200000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0.136364</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
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      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['FamilySize'] = df['SibSp'] + df['Parch'] + 1\n",
    "\n",
    "df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df['IsAlone'] = 0\n",
    "df.loc[df['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Embarked     Fare  Pclass  Sex  Survived  Title  IsAlone\n",
       "0    1        S   7.2500       3    0       0.0      1        0\n",
       "1    2        C  71.2833       1    1       1.0      3        0\n",
       "2    1        S   7.9250       3    1       1.0      2        1\n",
       "3    2        S  53.1000       1    1       1.0      3        0\n",
       "4    2        S   8.0500       3    0       0.0      1        1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age*Class'] = df.Age * df.Pclass\n",
    "\n",
    "df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['Embarked'] = df['Embarked'].fillna(freq_port)\n",
    "    \n",
    "df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Age  Embarked     Fare  Pclass  Sex  Survived  Title  IsAlone  Age*Class\n",
       "0    1         0   7.2500       3    0       0.0      1        0          3\n",
       "1    2         1  71.2833       1    1       1.0      3        0          2\n",
       "2    1         0   7.9250       3    1       1.0      2        1          3\n",
       "3    2         0  53.1000       1    1       1.0      3        0          2\n",
       "4    2         0   8.0500       3    0       0.0      1        1          6"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'] = df['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   Age  Embarked     Fare  Pclass  Sex  Survived  Title  IsAlone  Age*Class\n",
       "0    1         0   7.2500       3    0       0.0      1        0          3\n",
       "1    2         1  71.2833       1    1       1.0      3        0          2\n",
       "2    1         0   7.9250       3    1       1.0      2        1          3\n",
       "3    2         0  53.1000       1    1       1.0      3        0          2\n",
       "4    2         0   8.0500       3    0       0.0      1        1          6"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'] = df['Fare'].fillna(df['Fare'].dropna().median())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "            FareBand  Survived\n",
       "0    (-0.001, 7.896]  0.197309\n",
       "1    (7.896, 14.454]  0.303571\n",
       "2   (14.454, 31.275]  0.441048\n",
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   "source": [
    "df['FareBand'] = pd.qcut(df['Fare'], 4)\n",
    "df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
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   "execution_count": 39,
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       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Embarked  Fare  Pclass  Sex  Survived  Title  IsAlone  Age*Class\n",
       "0    1         0     0       3    0       0.0      1        0          3\n",
       "1    2         1     3       1    1       1.0      3        0          2\n",
       "2    1         0     1       3    1       1.0      2        1          3\n",
       "3    2         0     3       1    1       1.0      3        0          2\n",
       "4    2         0     1       3    0       0.0      1        1          6\n",
       "5    1         2     1       3    0       0.0      1        1          3\n",
       "6    3         0     3       1    0       0.0      1        1          3\n",
       "7    0         0     2       3    0       0.0      4        0          0\n",
       "8    1         0     1       3    1       1.0      3        0          3\n",
       "9    0         1     2       2    1       1.0      3        0          0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[ df['Fare'] <= 7.91, 'Fare'] = 0\n",
    "df.loc[(df['Fare'] > 7.91) & (df['Fare'] <= 14.454), 'Fare'] = 1\n",
    "df.loc[(df['Fare'] > 14.454) & (df['Fare'] <= 31), 'Fare']   = 2\n",
    "df.loc[ df['Fare'] > 31, 'Fare'] = 3\n",
    "df['Fare'] = df['Fare'].astype(int)\n",
    "\n",
    "df = df.drop(['FareBand'], axis=1)\n",
    "    \n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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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>PassengerId</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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan Cervin</td>\n",
       "      <td>male</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7538</td>\n",
       "      <td>9.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330972</td>\n",
       "      <td>7.6292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Mr. Albert Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2657</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>Davies, Mr. John Samuel</td>\n",
       "      <td>male</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>A/4 48871</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "5          897       3                    Svensson, Mr. Johan Cervin    male   \n",
       "6          898       3                          Connolly, Miss. Kate  female   \n",
       "7          899       2                  Caldwell, Mr. Albert Francis    male   \n",
       "8          900       3     Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female   \n",
       "9          901       3                       Davies, Mr. John Samuel    male   \n",
       "\n",
       "    Age  SibSp  Parch     Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0     330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0     363272   7.0000   NaN        S  \n",
       "2  62.0      0      0     240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0     315154   8.6625   NaN        S  \n",
       "4  22.0      1      1    3101298  12.2875   NaN        S  \n",
       "5  14.0      0      0       7538   9.2250   NaN        S  \n",
       "6  30.0      0      0     330972   7.6292   NaN        Q  \n",
       "7  26.0      1      1     248738  29.0000   NaN        S  \n",
       "8  18.0      0      0       2657   7.2292   NaN        C  \n",
       "9  21.0      2      0  A/4 48871  24.1500   NaN        S  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 9)\n",
      "(418, 8)\n"
     ]
    }
   ],
   "source": [
    "train_df = df[-df['Survived'].isna()]\n",
    "test_df = df[df['Survived'].isna()]\n",
    "test_df = test_df.drop('Survived', axis=1)\n",
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "83.84"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.74"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.28"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.34"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.44"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>77.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>72.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "6               Decision Tree  86.76\n",
       "2               Random Forest  86.64\n",
       "1                         KNN  84.74\n",
       "0     Support Vector Machines  83.84\n",
       "4                  Perceptron  78.34\n",
       "5  Stochastic Gradient Decent  77.44\n",
       "3                 Naive Bayes  72.28"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN',\n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent',\n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn,\n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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