Titanic-preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and prepare the Titanic dataset\n",
    "\n",
    "Many ideas in this notebook are inspired from https://www.kaggle.com/mnassrib/titanic-logistic-regression-with-python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Document dependencies\n",
    "Skip this section. To document the dependencies, execute the following cells after executing all relevant import statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --user watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CPython 3.7.4\n",
      "IPython 7.8.0\n",
      "\n",
      "tensorflow_datasets 3.1.0\n",
      "numpy               1.18.5\n",
      "pandas              0.25.1\n",
      "tensorflow          2.1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "print()\n",
    "%watermark -v\n",
    "print()\n",
    "%watermark -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Titanic dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tensorflow.python.data.ops.dataset_ops.PrefetchDataset'>\n"
     ]
    }
   ],
   "source": [
    "ds = tfds.load('titanic', split='train')\n",
    "print(type(ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': [],\n",
       " 'boat': [],\n",
       " 'body': [],\n",
       " 'cabin': [],\n",
       " 'embarked': [],\n",
       " 'fare': [],\n",
       " 'home.dest': [],\n",
       " 'name': [],\n",
       " 'parch': [],\n",
       " 'pclass': [],\n",
       " 'sex': [],\n",
       " 'sibsp': [],\n",
       " 'ticket': [],\n",
       " 'survived': []}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for elem in tfds.as_numpy(ds.take(1)):\n",
    "    dict_of_lists = {key: [] for key in elem['features']}\n",
    "    dict_of_lists['survived'] = []\n",
    "dict_of_lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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7.25, 7.775, 15.2458, 7.225, 7.775, 15.7417, 10.5, 6.75, 21.0, 21.0, 26.55, 21.0, 6.975, 7.75, 108.9, 7.25, 75.25, 13.0, 13.5, 35.5, 56.9292, 14.5, 13.0, 79.2, 10.5, 7.75, 0.0, 19.9667, 13.5, 16.1, 221.7792, 110.8833, 8.6625, 7.25, 7.75, 134.5, 23.0, 7.7958, 7.25, 13.0, 13.0, 42.4, 106.425, 33.0, 263.0, 7.775, 7.25, 7.75, 211.3375, 6.45, 39.0, 7.65, 7.4958, 14.4, 7.775, 7.775, 9.5, 15.5, 52.5542, 8.05, 135.6333, 10.4625, 13.0, 16.1, 31.275, 7.65, 10.5, 7.8792, 7.6292, 7.225, 7.05, 263.0, 136.7792, 211.3375, 10.5, 29.0, 79.65, 10.5, 10.5, 9.8375, 113.275, 7.2292, 13.0, 164.8667, 7.8958, 7.2292, 14.4542, 51.4792, 7.775, 7.775, 13.0, 49.5042, 26.55, 0.0, 22.025, 7.2292, 12.475, 26.55, 7.8542, 15.5, 7.775, 55.4417, 20.25, 7.8958, 76.2917, 7.75, 13.775, 24.0, 39.0, 7.7958, 60.0, 26.0, 263.0, 59.4, 35.5, 27.7208, 6.8583, 8.6625, 13.0, 134.5, 8.05, 32.5, 7.8292, 26.0, 39.6875, 153.4625, 10.5, 7.8958, 13.0, 27.7208, 7.8542, 8.05, 7.7958, 52.0, 7.8958, 20.575, 7.05, 8.05, 134.5, 8.3625, 8.05, 7.925, 21.0, 0.0, 13.0, 164.8667, 27.0, 60.0, 7.8958, 0.0, 7.225, 15.0458, 16.1, 7.75, 7.7333, 7.55, 52.0, 29.7, 49.5042, 30.5, 27.9, 81.8583, 25.5875, 57.9792, 16.1, 13.7917, 7.225, 8.1125, 65.0, 26.0, 61.175, 14.5, 7.925, 53.1, 227.525, 19.5, 18.0, 7.8958, 28.5, 30.0, 21.0, 7.25, 83.475, 7.8958, 8.05, 61.9792, 30.0708, 24.15, 26.0, 46.9, 9.225, 27.7208, 7.3125, 71.2833, 15.0458, 9.5, 31.3875, 29.0, 23.25, 134.5, 9.4833, 0.0, 13.0, 8.6625, 55.4417, 7.8958, 7.4958, 8.6625, 31.3875, 26.25, 7.8542, 7.75, 18.7875, 18.0, 8.85, 7.8958, 21.0, 9.5, 7.8958, 13.0, 57.0, 10.5, 13.5, 7.75, 7.7875, 26.55, 7.8875, 9.475, 8.05, 26.0, 23.0, 262.375, 26.25, 7.775, 7.775, 26.0, 14.4583, 7.5792, 151.55, 262.375, 23.0, 31.275, 21.6792, 7.75, 12.35, 135.6333, 40.125, 35.0, 90.0, 7.2292, 25.4667, 39.6875, 7.225, 77.9583, 83.475, 7.05, 7.8958, 39.6875, 7.7292, 8.05, 16.1, 7.0, 30.0, 10.5, 26.0, 12.2875, 8.05, 15.75, 7.75, 26.0, 31.0, 93.5, 7.7375, 8.05, 13.0, 13.9, 13.0, 14.4542, 10.1708, 13.5, 7.2292, 120.0, 39.0, 13.0, 7.25, 8.05, 7.2292, 7.2292, 7.225, 56.4958, 26.55, 14.4583, 15.5, 25.4667, 8.6625, 39.0, 69.55, 7.65, 6.95, 7.925, 29.125, 37.0042, 7.8958, 63.3583, 7.125, 26.55, 8.6625, 8.6625, 12.275, 15.1, 26.55, 65.0, 7.25, 7.2292, 9.0, 13.8583, 7.75, 22.3583, 9.5875, 11.5, 25.4667, 46.9, 36.75, 7.75, 7.775, 8.1375, 20.525, 27.75], 'home.dest': [b'Sarnia, ON', b'Ruotsinphytaa, Finland New York, NY', b'Spain', b'Lyndhurst, England', b'Unknown', b'New York, NY', b'Weston-Super-Mare / Moose Jaw, SK', b'Unknown', b'Ireland Chicago, IL', b'Unknown', b'Portugal', b'Brunswick, ME', b'West Hampstead, London / Neepawa, MB', b'Montreal, PQ', b'Skara, Sweden / Rockford, IL', b'Unknown', b'Unknown', b'New York, NY', b'New York, NY', b'Unknown', b'Cambridge, MA', b'Omaha, NE', b'Unknown', b'Plymouth, Devon / Detroit, MI', b'Altdorf, Switzerland', b'Unknown', b'Cornwall / Akron, OH', b'Sweden Winnipeg, MN', b'Tampico, MT', b'Unknown', b'Unknown', b'Unknown', b'Milford, NH', b'Sweden  Worcester, MA', b'Unknown', b'Walthamstow, England', b'New York, NY', b'Portland, OR', b'Syria Fredericksburg, VA', b'Bryn Mawr, PA', b'Unknown', b'Brooklyn, NY', b'London Brooklyn, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Vancouver, BC', b'Haverford, PA / Cooperstown, NY', b'New York, NY / Ithaca, NY', b'Unknown', b'England Albion, NY', b'Unknown', b'Unknown', b'St Louis, MO', b'Haverford, PA', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Ruotsinphyhtaa, Finland New York, NY', b'Montreal, PQ', b'New York, NY', b'Milwaukee, WI', b'Bishopstoke, Hants / Fayette Valley, ID', b'Unknown', b'Bulgaria Chicago, IL', b'Unknown', b'Unknown', b'Madrid, Spain', b'St Andrews, Guernsey', b'Unknown', b'New York, NY', b'Cornwall / Akron, OH', b'Unknown', b'Unknown', b'Unknown', b'Geneva, Switzerland / Radnor, PA', b'Syria New York, NY', b'Hudson, NY', b'Cornwall / Akron, OH', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Zurich, Switzerland', b'London, England', b'Germantown, Philadelphia, PA', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Unknown', b'Oslo, Norway Cameron, WI', b'Unknown', b'Rotherfield, Sussex, England Essex Co, MA', b'New York, NY', b'Birkdale, England Cleveland, Ohio', b'Syria New York, NY', b'Haverford, PA', b'Unknown', b'Bromsgrove, England / Montreal, PQ', b'Stockholm, Sweden New York', b'Unknown', b'New York, NY', b'Harrow-on-the-Hill, Middlesex', b'Dorchester, MA', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Montreal, PQ', b'Unknown', b'Youngstown, OH', b'Unknown', b'Guernsey / Elizabeth, NJ', b'England Oglesby, IL', b'Philadelphia, PA', b'Unknown', b'Unknown', b'Winnipeg, MN', b'Syria New York, NY', b'Unknown', b'Unknown', b'London /  East Orange, NJ', b'Unknown', b'Brooklyn, NY', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Berne, Switzerland / Central City, IA', b'Unknown', b'Unknown', b'Unknown', b'London / New York, NY', b'Denmark Hill, Surrey / Chicago', b'Medeltorp, Sweden Chicago, IL', b'Bromsgrove, England / Montreal, PQ', b'Paris / Haiti', b'Detroit, MI', b'Unknown', b'London Skanteales, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Broomfield, Chelmsford, England', b'Paris, France', b'Unknown', b'Guernsey / Elizabeth, NJ', b'Brookline, MA', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Unknown', b'Unknown', b'Gunnislake, England / Butte, MT', b'Unknown', b'Unknown', b'Unknown', b'Bournmouth, England', b'Unknown', b'New York, NY', b'Treherbert, Cardiff, Wales', b'Paris, France', b'Southington / Noank, CT', b'Cornwall / Akron, OH', b'Unknown', b'Unknown', b'Zurich, Switzerland', b'New York, NY', b'Unknown', b'Unknown', b'Norrlot, Sweden Chicago, IL', b'Cornwall / Clear Creek, CO', b'Buenos Aires, Argentina / New Jersey, NJ', b'Unknown', b'Unknown', b'Strood, Kent, England Detroit, MI', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Pennsylvania', b'Liverpool / Montreal, PQ', b'Bridgwater, Somerset, England', b'Plymouth, Dorset / Houghton, MI', b'St Ives, Cornwall / Calumet, MI', b'Unknown', b'Unknown', b'Unknown', b'Worcester, MA', b'Lexington, MA', b'Unknown', b'London', b'Pondersend, England / New Durham, NJ', b'Sweden Winnipeg, MN', b'Unknown', b'Belfast', b'Fond du Lac, WI', b'Kingston, Surrey', b'Lucca, Italy / California', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Greece', b'Strood, Kent, England Detroit, MI', b'Unknown', b'Unknown', b'Woodford County, KY', b'Paris', b'Unknown', b'Unknown', b'Unknown', b'London / Staten Island, NY', b'Unknown', b'Bristol, Avon / Jacksonville, FL', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Unknown', b'Hessle, Yorks', b'New York, NY', b'Stockholm, Sweden', b'Syria', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Stanton, IA', b'Rotherfield, Sussex, England Essex Co, MA', b'Sweden  Worcester, MA', b'Belfast', b'Unknown', b'Philadelphia, PA', b'Unknown', b'Bulgaria Coon Rapids, IA', b'Unknown', b'San Francisco, CA', b'London', b'Unknown', b'St Ives, Cornwall / Hancock, MI', b'Unknown', b'Unknown', b'Unknown', b'Argentina', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Italy Philadelphia, PA', b'Paris, France', b'Duluth, MN', b'Calgary, AB', b'Unknown', b'Unknown', b'London, England', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Lakewood, NJ', b'England Brooklyn, NY', b'London', b'London', b'Unknown', b'Glasgow / Bangor, ME', b'England / Sacramento, CA', b'Boston, MA', b'Unknown', b'Hong Kong New York, NY', b'Unknown', b'East Bridgewater, MA', b'Syria Kent, ON', b'Unknown', b'New Forest, England', b'Unknown', b'Unknown', b'Bergen, Norway', b'New York, NY', b'Brennes, Norway New York', b'Ireland New York, NY', b'Southington / Noank, CT', b'Unknown', b'Unknown', b'Montreal, PQ', b'Unknown', b'Ireland', b'Winnipeg, MB', b'Unknown', b'New York, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Nice, France', b'Unknown', b'Cornwall / Camden, NJ', b'Unknown', b'New York, NY', b'England / Bennington, VT', b'St Austall, Cornwall', b'Unknown', b'London / New York, NY', b'Cornwall / Camden, NJ', b'Hong Kong New York, NY', b'England / Hartford, CT', b'Unknown', b'Unknown', b'Plymouth, England', b'Unknown', b'Finland / Minneapolis, MN', b'Unknown', b'Ireland New York, NY', b'England Salt Lake City, Utah', b'New Britain, CT', b'Mt Airy, Philadelphia, PA', b'Unknown', b'Unknown', b'Dowagiac, MI', b'London / Winnipeg, MB', b'Spain / Havana, Cuba', b'Unknown', b'San Francisco, CA', b'Unknown', b'London', b'Dowagiac, MI', b'Sweden Worcester, MA', b'Unknown', b'Paris /  New York, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Somerset / Bernardsville, NJ', b'Unknown', b'Unknown', b'Brunswick, ME', b'Unknown', b'Unknown', b'England New York, NY', b'Unknown', b'Swindon, England', b'Unknown', b'Unknown', b'New York, NY', b'Dorking, Surrey, England', b'Unknown', b'Philadelphia, PA', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Cornwall / Akron, OH', b'Unknown', b'Unknown', b'Salo, Finland Astoria, OR', b'Unknown', b'Washington, DC', b'Unknown', b'Unknown', b'New York, NY', b'Greenport, NY', b'Guernsey / Elizabeth, NJ', b'Unknown', b'Unknown', b'Trenton, NJ', b'Unknown', b'Unknown', b'Unknown', b'Weston-Super-Mare, Somerset', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Bronx, NY', b'Aberdeen / Portland, OR', b'Manchester, England', b'Ascot, Berkshire / Rochester, NY', b'Devon, England Wichita, KS', b'Unknown', b'Unknown', b'St Leonards-on-Sea, England Ohio', b'Co Longford, Ireland New York, NY', b'Unknown', b'Ireland Chicago, IL', b'Isleworth, England', b'Lower Clapton, Middlesex or Erdington, Birmingham', b'Unknown', b'India / Rapid City, SD', b'Unknown', b'Unknown', b'New York, NY / Ithaca, NY', b'Unknown', b'Unknown', b'Montreal, PQ', b'Unknown', b'Unknown', b'Somerset / Bernardsville, NJ', b'Unknown', b'Frankfort, KY', b'Middleburg Heights, OH', b'New York, NY / Greenwich CT', b'Wimbledon Park, London / Hayling Island, Hants', b'Unknown', b'Ottawa, ON', b'Finland Sudbury, ON', b'Greenwich, CT', b'Unknown', b'Unknown', b'Paris', b'Cooperstown, NY', b'Unknown', b'Unknown', b'New York, NY', b'Sweden  Worcester, MA', b'Unknown', b'Unknown', b'Bishopstoke, Hants / Fayette Valley, ID', b'Liverpool / Montreal, PQ', b'New York, NY', b'Cape Town, South Africa / Seattle, WA', b'North Evington, England', b'Unknown', b'Unknown', b'Syria Kent, ON', b'Southampton / New York, NY', b'India / Pittsburgh, PA', b'Unknown', b'Unknown', b'Amenia, ND', b'West Kensington, London', b'Bangkok, Thailand / Roseville, IL', b'Southampton', b'Lexington, MA', b'Unknown', b'New York, NY', b'West Hampstead, London / Neepawa, MB', b'Unknown', b'Warwick, England', b'Paris, France / New York, NY', b'London / Birmingham', b'Unknown', b'Unknown', b'Paris, France', b'Liverpool', b'Unknown', b'Elkins Park, PA', b'Cornwall / Hancock, MI', b'Philadelphia, PA', b'Wiltshire, England Niagara Falls, NY', b'Unknown', b'Paris', b'Unknown', b'Youngstown, OH', b'Unknown', b'Unknown', b'Devon, England Wichita, KS', b'Unknown', b'Jacksonville, FL', b'Toronto, ON', b'Chicago, IL', b'East Orange, NJ', b'Unknown', b'Austria-Hungary / Germantown, Philadelphia, PA', b'Providence, RI', b'Unknown', b'Unknown', b'New York, NY', b'Lakewood, NJ', b'Glen Ridge, NJ', b'Ireland New York, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Kilmacowen, Co Sligo, Ireland New York, NY', b'England Albion, NY', b'London, England Norfolk, VA', b'London  Vancouver, BC', b'Southsea, Hants', b'Unknown', b'Nice, France', b'Unknown', b'Unknown', b'Columbus, OH', b'Unknown', b'Unknown', b'Cornwall / Houghton, MI', b'Unknown', b'England / San Francisco, CA', b'Unknown', b'Kingwilliamstown, Co Cork, Ireland New York, NY', b'Unknown', b'Unknown', b'Nice, France', b'East Providence, RI', b'Unknown', b'Unknown', b'Stoughton, MA', b'Minneapolis, MN', b'Unknown', b'Unknown', b'Hornsey, England', b\"St Anne's-on-Sea, Lancashire\", b'New York, NY', b'Washington, DC', b'Unknown', b'Fond du Lac, WI', b'Syria', b'Unknown', b'Brooklyn, NY', b'Lakewood, NJ', b'Mexico City, Mexico', b'Cornwall / Spokane, WA', b'Unknown', b'Unknown', b'Winnipeg, MB', b'Bath, England / Massachusetts', b'New York, NY', b'Detroit, MI', b'Sweden Winnipeg, MN', b'Ireland Chicago, IL', b'London', b'Unknown', b'Unknown', b'Unknown', b'Hong Kong New York, NY', b'Paris / Montreal, PQ', b'Merrill, WI', b'Unknown', b'Unknown', b'Unknown', b'Haverford, PA / Cooperstown, NY', b'Finland / Washington, DC', b'Kontiolahti, Finland / Detroit, MI', b'Unknown', b'Co Sligo, Ireland Hartford, CT', b'Minneapolis, MN', b'Montreal, PQ / Chesterville, ON', b'Ilford, Essex / Winnipeg, MB', b'Brighton, Sussex', b'Greece', b'Unknown', b'Basel, Switzerland', b'Ottawa, ON', b'Rotherfield, Sussex, England Essex Co, MA', b'Austria', b'Guernsey, England / Edgewood, RI', b'New York, NY', b'Unknown', b'New York, NY /  Stamford CT', b'Unknown', b'Unknown', b'Unknown', b'Stockholm, Sweden / Washington, DC', b'Co Longford, Ireland New York, NY', b'Vadsbro, Sweden Ministee, MI', b'Newark, NJ', b'Unknown', b'New York, NY', b'Unknown', b'New York, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Guernsey / Elizabeth, NJ', b'Devon, England Wichita, KS', b'Unknown', b'New York, NY', b'Unknown', b'Stanton, IA', b'Chicago, IL', b'Bournemouth, England', b'Unknown', b'St James, Long Island, NY', b'Barre, Co Washington, VT', b'Sweden Winnipeg, MN', b'Philadelphia, PA', b'Los Angeles, CA', b'Unknown', b'Wiltshire, England Niagara Falls, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Scituate, MA', b'Unknown', b'Belfast', b'Unknown', b'Co Cork, Ireland Roxbury, MA', b'New York, NY', b'St Ives, Cornwall / Calumet, MI', b'Zurich, Switzerland', b'Haverford, PA', b'Unknown', b'Indianapolis, IN', b'Unknown', b'Unknown', b'Unknown', b'Brockton, MA', b'Unknown', b'Unknown', b'Australia Fingal, ND', b'Kingwilliamstown, Co Cork, Ireland Glens Falls, NY', b'Halifax, NS', b'Unknown', b'Unknown', b'New York, NY', b'Belgium Detroit, MI', b'Bristol, England / New Britain, CT', b'Sweden Winnipeg, MN', b'Bristol, England Cleveland, OH', b'Syria Kent, ON', b'Austria Niagara Falls, NY', b'Weston-Super-Mare, Somerset', b'Unknown', b'Goteborg, Sweden Huntley, IL', b'London', b'Unknown', b'Unknown', b'Isleworth, England', b'Mt Airy, Philadelphia, PA', b'Harrisburg, PA', b'Seattle, WA', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'New York, NY /  Stamford CT', b'Hartford, Huntingdonshire', b'Wiltshire, England Niagara Falls, NY', b'Guernsey', b'Liverpool, England Bedford, OH', b'Unknown', b'Unknown', b'Tokyo, Japan', b'Bryn Mawr, PA', b'Unknown', b'Paris / Montreal, PQ', b'London', b'New York, NY', b'Unknown', b'Cape Town, South Africa / Seattle, WA', b'Unknown', b'New York, NY', b'Unknown', b'Montreal, PQ', b'Isle of Wight, England', b'Croatia', b'West Hoboken, NJ', b'Guntur, India / Benton Harbour, MI', b'St Leonards-on-Sea, England Ohio', b'Unknown', b'Bridgerule, Devon', b'Cornwall / Akron, OH', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Bennington, VT', b'Unknown', b'Unknown', b'Lima, Peru', b'London / Middlesex', b'Philadelphia, PA', b'Unknown', b'Unknown', b'Sweden Worcester, MA', b'Altdorf, Switzerland', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'London', b'Warwick, England', b'Unknown', b'Unknown', b'San Francisco, CA', b'Seattle, WA / Toledo, OH', b'Rochester, NY', b'Bryn Mawr, PA, USA', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Moscow / Bronx, NY', b'Unknown', b'England', b'Denmark / New York, NY', b'Elkins Park, PA', b'Unknown', b'London', b'Unknown', b'Unknown', b'Tranvik, Finland New York', b'Unknown', b'Unknown', b'Deer Lodge, MT', b'Barcelona, Spain / Havana, Cuba', b'New York, NY', b'Madrid, Spain', b'Unknown', b'Sweden / Arlington, NJ', b'Denver, CO', b'Unknown', b'Lake Arthur, Chavez County, NM', b'Unknown', b'Unknown', b'England Brooklyn, NY', b'Unknown', b'Unknown', b'Unknown', b'Milwaukee, WI', b'Streatham, Surrey', b'New York, NY', b'Unknown', b'Unknown', b'England / Sacramento, CA', b'Belfast', b'Rochester, NY', b'Unknown', b'St Louis, MO', b'Cornwall', b'Norwich / New York, NY', b'Unknown', b'Unknown', b'Newark, NJ', b'Swindon, England', b'Ruotsinphytaa, Finland New York, NY', b'Belfast', b'Elizabeth, NJ', b'Unknown', b'Unknown', b'Duluth, MN', b'Unknown', b'England / Detroit, MI', b'Syria Ottawa, ON', b'Janjgir, India / Pennsylvania', b'Unknown', b'Ireland Chicago, IL', b'Belfast', b'Unknown', b'Tuxedo Park, NY', b'New York, NY', b'Unknown', b'Unknown', b'Cincinatti, OH', b'Chelsea, London', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Montevideo, Uruguay', b'Unknown', b'New York, NY', b'New York, NY', b'Unknown', b'Barcelona, Spain / Havana, Cuba', b'Paris / Haiti', b'Unknown', b'Lucca, Italy / California', b'West Bromwich, England Pontiac, MI', b'Unknown', b'Unknown', b'Syria Ottawa, ON', b'Bournmouth, England', b'Wiltshire, England Niagara Falls, NY', b'Deephaven, MN / Cedar Rapids, IA', b'Unknown', b'Unknown', b'West Haven, CT', b'St Ives, Cornwall / Houghton, MI', b'Brighton, MA', b'Unknown', b'Paris / Haiti', b'New York, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Minneapolis, MN', b'Bulgaria Chicago, IL', b'Syria New York, NY', b'Unknown', b'Krakoryd, Sweden Bloomington, IL', b'Haddenfield, NJ', b'Unknown', b'England', b'Haverford, PA / Cooperstown, NY', b'Syria', b'England / Philadelphia, PA', b'Plymouth, England', b'Unknown', b'Montreal, PQ', b'Unknown', b'Bayside, Queens, NY', b'Moscow / Bronx, NY', b'Unknown', b'New York, NY', b'Tofta, Sweden Joliet, IL', b'Cornwall', b'Goteborg, Sweden / Rockford, IL', b'Ireland Brooklyn, NY', b'Unknown', b'Co Athlone, Ireland New York, NY', b'London / Paris', b'Unknown', b'Winnipeg, MB', b'Paris, France New York, NY', b'New York, NY', b'Unknown', b'St Ives, Cornwall / Hancock, MI', b'Geneva, Switzerland / Radnor, PA', b'Sweden Chicago, IL', b'Cologne, Germany', b'Unknown', b'Unknown', b'Elkins Park, PA', b'Unknown', b'Rotterdam, Netherlands', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Windsor, England New York, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Tampico, MT', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Albany, NY', b'Unknown', b'Unknown', b'Paris, France', b'Unknown', b'Bournmouth, England', b'Unknown', b'Portland, OR', b'Unknown', b'Minneapolis, MN', b'Unknown', b'St Denys, Southampton, Hants', b'Montevideo, Uruguay', b'London, England Norfolk, VA', b'Hudson, NY', b'London, England / Marietta, Ohio and Milwaukee, WI', b'Provo, UT', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Ottawa, ON', b'Unknown', b'Unknown', b'Russia New York, NY', b'Ilford, Essex / Winnipeg, MB', b'Bryn Mawr, PA', b'Guernsey / Montclair, NJ and/or Toledo, Ohio', b'Unknown', b'Paris, France / New York, NY', b'West Hampstead, London / Neepawa, MB', b'Unknown', b'Washington, DC', b'Winnipeg, MB', b'Haverford, PA', b'New York, NY', b'Greenport, NY', b'Unknown', b'Unknown', b'East Providence, RI', b'Unknown', b'New Forest, England', b'Cornwall / Akron, OH', b'Bridgerule, Devon', b'Unknown', b'Paris / Haiti', b'Montreal, PQ / Chesterville, ON', b'Wiltshire, England Niagara Falls, NY', b'Guernsey / Wilmington, DE', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Brooklyn, NY', b'Unknown', b'Unknown', b'Boston, MA', b'Unknown', b'Penzance, Cornwall / Akron, OH', b'Unknown', b'Unknown', b'London / Chicago, IL', b'Unknown', b'London / Montreal, PQ', b'Unknown', b'Ruotsinphytaa, Finland New York, NY', b'Ireland', b'Unknown', b'Rotherfield, Sussex, England Essex Co, MA', b'Unknown', b'Sweden Worcester, MA', b'Little Onn Hall, Staffs', b'Worcester, MA', b'Unknown', b'Unknown', b'Unknown', b'West Bromwich, England Pontiac, MI', b'Sydney, Australia', b'Liverpool', b'Harrow, England', b'Paris, France', b'Unknown', b'England Brooklyn, NY', b'Halesworth, England', b'Paris, France', b'Plymouth, Devon / Detroit, MI', b'Brooklyn, NY', b'New York, NY', b'Mamaroneck, NY', b'Wiltshire, England Niagara Falls, NY', b'Somerset / Bernardsville, NJ', b'Rotherfield, Sussex, England Essex Co, MA', b'Unknown', b'Unknown', b'Unknown', b'Upper Burma, India Pittsburgh, PA', b'Waukegan, Chicago, IL', b'Montreal, PQ', b'New York, NY', b'Montreal, PQ / Chesterville, ON', b'Unknown', b'Unknown', b'Chicago, IL', b'Unknown', b'Paris, France', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Buffalo, NY', b'Little Onn Hall, Staffs', b'Unknown', b'Trenton, NJ', b'Effington Rut, SD', b'Unknown', b'Unknown', b'Co Limerick, Ireland Sherbrooke, PQ', b'Krakudden, Sweden Moune, IL', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Guernsey', b'Co Cork, Ireland Charlestown, MA', b'Elizabeth, NJ', b'Ilfracombe, Devon', b'Duluth, MN', b'Mamaroneck, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Portland, OR', b'Sittingbourne, England / San Diego, CA', b'Glasgow', b'London, England', b'London / Paris', b'Unknown', b'Boston, MA', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Bronx, NY', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Cornwall / Akron, OH', b'Unknown', b'Asarum, Sweden Brooklyn, NY', b'Elmira, NY / Orange, NJ', b'Auburn, NY', b'Washington, DC', b'Deephaven, MN / Cedar Rapids, IA', b'Denmark Hill, Surrey / Chicago', b'Winnipeg, MB', b'Sweden Joliet, IL', b'Unknown', b'Co Clare, Ireland Washington, DC', b'St Louis, MO', b'Unknown', b'Guntur, India / Benton Harbour, MI', b'Unknown', b'Unknown', b'Stanton, IA', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'New York, NY / Briarcliff Manor NY', b'Unknown', b'Russia', b'Liverpool, England Bedford, OH', b'Sweden Winnipeg, MN', b'Norway Los Angeles, CA', b'London / Fort Byron, NY', b'Co Sligo, Ireland New York, NY', b'Ireland', b'Ottawa, ON', b'Unknown', b'Winnipeg, MB', b'Los Angeles, CA', b'St Louis, MO', b'Cornwall, England Houghton, MI', b'Bangkok, Thailand / Roseville, IL', b'New York, NY', b'Folkstone, Kent / New York, NY', b'Hamilton, ON', b'Unknown', b'Lexington, MA', b'Unknown', b'Vancouver, BC', b'Youngstown, OH', b'Austria', b'Syria Youngstown, OH', b'Unknown', b'Belmont, MA', b'Karberg, Sweden Jerome Junction, AZ', b'Hartford, CT', b'Oslo, Norway Bayonne, NJ', b'Montevideo, Uruguay', b'Westcliff-on-Sea, Essex', b'Belfast, NI', b'Unknown', b'Unknown', b'Union Hill, NJ', b'Milwaukee, WI', b'Unknown', b'Unknown', b'Unknown', b'Brooklyn, NY', b'East Providence, RI', b'Unknown', b'Philadelphia, PA', b'Unknown', b'Unknown', b'Russia New York, NY', b'Guntur, India / Benton Harbour, MI', b'Oskarshamn, Sweden Minneapolis, MN', b'Huntington, WV', b'Skara, Sweden / Rockford, IL', b'Winnipeg, MB', b'New York, NY', b'Basel, Switzerland', b'Stockholm, Sweden', b'Unknown', b'Austria-Hungary', b'Helsinki, Finland Ashtabula, Ohio', b'Unknown', b'London', b'Greenport, NY', b'Unknown', b'Plymouth, Devon / Detroit, MI', b'Unknown', b'Greenwich, CT', b'Indianapolis, IN', b'Bulgaria Chicago, IL', b'Southampton', b'Gallipolis, Ohio / ? Paris / New York', b'Unknown', b'Unknown', b'Dagsas, Sweden Fower, MN', b'London /  East Orange, NJ', b'Unknown', b'Devon, England Wichita, KS', b'Unknown', b'Unknown', b'Tuxedo Park, NY', b'Foresvik, Norway Portland, ND', b'London New York, NY', b'Ruotsinphytaa, Finland New York, NY', b'Bristol, England / New Britain, CT', b'Belfast', b'Portugal / Sau Paulo, Brazil', b'Youngstown, OH', b'London', b'Huntington, WV', b'Unknown', b'Liverpool, England / Belfast', b'Unknown', b'Paris / Montreal, PQ', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Montreal, PQ', b'Paris, France', b'Belgium  Montreal, PQ', b'Pomeroy, WA', b'Unknown', b'San Francisco, CA', b'Victoria, BC', b'Chicago, IL', b'Bournemouth, England Newark, NJ', b'New York, NY', b'Unknown', b'Unknown', b'Somerset / Bernardsville, NJ', b'London', b'Amenia, ND', b'Unknown', b'Unknown', b'Scituate, MA', b'New York, NY', b'England / Bennington, VT', b'Unknown', b'Unknown', b'Surbiton Hill, Surrey', b'London', b'Ilfracombe, Devon', b'Illinois, USA', b'New York, NY', b'Austria', b'Unknown', b'Providence, RI', b'New York, NY', b'West Bromwich, England Pontiac, MI', b'Norwich / New York, NY', b'Wiltshire, England Niagara Falls, NY', b'Unknown', b'?Havana, Cuba', b'Unknown', b'New York, NY', b'New York, NY', b'Unknown', b'Sweden Worcester, MA', b'Bangkok, Thailand / Roseville, IL', b'Unknown', b'Tuxedo Park, NY', b'Unknown', b'Unknown', b'Brooklyn, NY', b'Unknown', b'Newark, NJ', b'Bulgaria Chicago, IL', b'Bulgaria Chicago, IL', b'Unknown', b'Sweden Worcester, MA', b'Bishopstoke, Hants / Fayette Valley, ID', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Cornwall / Clear Creek, CO', b'Unknown', b'Unknown', b'Rochester, NY', b'Calgary, AB', b'Cornwall / Houghton, MI', b'Yoevil, England / Cottage Grove, OR', b'Unknown', b'Unknown', b'New York, NY', b'Unknown', b'Sweden Akeley, MN', b'Unknown', b'Worcester, England', b'Southampton', b'Haverford, PA / Cooperstown, NY', b'Ilford, Essex / Winnipeg, MB', b'Unknown', b'Unknown', b'Cornwall / Spokane, WA', b'Ottawa, ON', b'Unknown', b'Montreal, PQ / Chesterville, ON', b'Haverford, PA / Cooperstown, NY', b'Cornwall / Akron, OH', b'Sweden Winnipeg, MN', b'Unknown', b'Unknown', b'Unknown', b'New York, NY / Washington, DC', b'Winnipeg, MB', b'London, England', b'Green Bay, WI', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Hudson, NY', b'New York, NY', b'Portugal', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Bournemouth, England Newark, NJ', b'Unknown', b'Springfield, MA', b'Catford, Kent / Detroit, MI', b'Holley, NY', b'Unknown', b'Unknown', b'Aberdeen / Portland, OR', b'Ireland Philadelphia, PA', b'England / San Francisco, CA', b'Unknown', b'Montreal, PQ', b'Unknown', b'Unknown', b'Copenhagen, Denmark', b'West Haven, CT', b'Devonport, England', b'Syria Ottawa, ON', b'Austria', b'Sault St Marie, ON', b'Unknown', b'Bryn Mawr, PA', b'Cape Town, South Africa / Seattle, WA', b'Clevedon, England', b'Unknown', b'Unknown', b'Unknown', b'Greensburg, PA', b'Unknown', b'Unknown', b'Halifax, NS', b'Unknown', b'Unknown', b'Unknown', b'Unknown', b'Guntur, India / Benton Harbour, MI', b'Unknown', b'Perkins County, SD', b'Co Athlone, Ireland New York, NY', b'Taalintehdas, Finland Hoboken, NJ', b'Unknown', b'Paris / Montreal, PQ', b'Unknown', b'New York, NY', b'Unknown', b'Roachdale, IN', b'Unknown', b'Unknown', b'Philadelphia, PA', b'Unknown', b'Los Angeles, CA', b'Somerset / Bernardsville, NJ', b'Unknown', b'Unknown', b'Antwerp, Belgium / Stanton, OH', b'Spain / Havana, Cuba', b'Aughnacliff, Co Longford, Ireland New York, NY', b'Unknown', b'Unknown', b'Penzance, Cornwall / Akron, OH', b'Unknown', b'Wiltshire, England Niagara Falls, NY', b'Cornwall / Hancock, MI', b'Ireland New York, NY', b'Myren, Sweden New York, NY', b'Unknown', b'Strood, Kent, England Detroit, MI', b'Bournmouth, England'], 'name': [b'McCrie, Mr. James Matthew', b'Gustafsson, Mr. Anders Vilhelm', b'Reynaldo, Ms. Encarnacion', b'Davies, Mr. Charles Henry', b'Gheorgheff, Mr. Stanio', b'Smart, Mr. John Montgomery', b'Maybery, Mr. Frank Hubert', b'Hansen, Mrs. Claus Peter (Jennie L Howard)', b'Bourke, Mrs. John (Catherine)', b'Perreault, Miss. Anne', b'Coelho, Mr. Domingos Fernandeo', b'White, Mr. Richard Frasar', b'Hickman, Mr. Stanley George', b'Levy, Mr. Rene Jacques', b'Bryhl, Mr. Kurt Arnold Gottfrid', b\"Kelly, Miss. Anna Katherine 'Annie Kate'\", b'Morrow, Mr. Thomas Rowan', b'Rothschild, Mrs. Martin (Elizabeth L. Barrett)', b\"Rosenshine, Mr. George ('Mr George Thorne')\", b'Webber, Mr. James', b'Myles, Mr. Thomas Francis', b'Brandeis, Mr. Emil', b'Moran, Mr. James', b'Quick, Mrs. Frederick Charles (Jane Richards)', b'Arnold-Franchi, Mrs. Josef (Josefine Franchi)', b'Johnson, Miss. Eleanor Ileen', b\"Hocking, Miss. Ellen 'Nellie'\", b'Andersson, Miss. Sigrid Elisabeth', b'de Messemaeker, Mrs. Guillaume Joseph (Emma)', b\"Katavelas, Mr. Vassilios ('Catavelas Vassilios')\", b'Lundin, Miss. Olga Elida', b'Oreskovic, Mr. Luka', b'Portaluppi, Mr. Emilio Ilario Giuseppe', b'Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)', b'Saad, Mr. Amin', b'Harris, Mr. Walter', b'Goldschmidt, Mr. George B', b'Klaber, Mr. Herman', b'Albimona, Mr. Nassef Cassem', b'Carter, Miss. Lucile Polk', b'Jonsson, Mr. Nils Hilding', b'Harder, Mr. George Achilles', b\"Cohen, Mr. Gurshon 'Gus'\", b'Bissette, Miss. Amelia', b'Lefebre, Miss. Mathilde', b'Harrison, Mr. William', b'Mamee, Mr. Hanna', b'Taussig, Miss. Ruth', b'McCaffry, Mr. Thomas Francis', b'Ryerson, Mrs. Arthur Larned (Emily Maria Borie)', b'Chambers, Mr. Norman Campbell', b'Baccos, Mr. Raffull', b'Duquemin, Mr. Joseph', b'Peacock, Mrs. Benjamin (Edith Nile)', b'Lundahl, Mr. Johan Svensson', b'Silverthorne, Mr. Spencer Victor', b'Stephenson, Mrs. Walter Bertram (Martha Eustis)', b'Johnston, Mr. Andrew G', b'Heikkinen, Miss. Laina', b'Reed, Mr. James George', b'Van Impe, Miss. Catharina', b'Andersson, Miss. Erna Alexandra', b'Davidson, Mrs. Thornton (Orian Hays)', b'Taussig, Mr. Emil', b'Crosby, Capt. Edward Gifford', b'Collyer, Mrs. Harvey (Charlotte Annie Tate)', b'Pearce, Mr. Ernest', b'Dantcheff, Mr. Ristiu', b'Leinonen, Mr. Antti Gustaf', b'Sage, Mr. George John Jr', b'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)', b'Parker, Mr. Clifford Richard', b'Olsson, Mr. Oscar Wilhelm', b'Mangiavacchi, Mr. Serafino Emilio', b'Richards, Master. William Rowe', b'Meanwell, Miss. (Marion Ogden)', b'Robbins, Mr. Victor', b\"O'Connor, Mr. Patrick\", b'Williams, Mr. Charles Duane', b'Baclini, Miss. Eugenie', b'Andrews, Miss. Kornelia Theodosia', b'Wells, Master. Ralph Lester', b\"Thomas, Mrs. Alexander (Thamine 'Thelma')\", b'Thomas, Mr. John', b'Hampe, Mr. Leon', b\"Leeni, Mr. Fahim ('Philip Zenni')\", b'Frolicher, Miss. Hedwig Margaritha', b'Wilhelms, Mr. Charles', b'Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)', b'Zakarian, Mr. Mapriededer', b\"O'Sullivan, Miss. Bridget Mary\", b'Calderhead, Mr. Edward Pennington', b'Makinen, Mr. Kalle Edvard', b'Ilmakangas, Miss. Pieta Sofia', b'Olsvigen, Mr. Thor Anderson', b'Harrington, Mr. Charles H', b\"Ford, Miss. Robina Maggie 'Ruby'\", b'Barry, Miss. Julia', b'Bonnell, Miss. Elizabeth', b'Baclini, Miss. Marie Catherine', b'Thayer, Mr. John Borland', b'Lennon, Miss. Mary', b'Weisz, Mr. Leopold', b'Bjorklund, Mr. Ernst Herbert', b'Ivanoff, Mr. Kanio', b\"Romaine, Mr. Charles Hallace ('Mr C Rolmane')\", b'Meyer, Mr. August', b'McCarthy, Mr. Timothy J', b'Rosblom, Mr. Viktor Richard', b'Myhrman, Mr. Pehr Fabian Oliver Malkolm', b'Lithman, Mr. Simon', b'Karlsson, Mr. Julius Konrad Eugen', b'Baxter, Mr. Quigg Edmond', b'Patchett, Mr. George', b'Wick, Mrs. George Dennick (Mary Hitchcock)', b'Jansson, Mr. Carl Olof', b'Jefferys, Mr. Ernest Wilfred', b'Dorking, Mr. Edward Arthur', b'Brewe, Dr. Arthur Jackson', b\"Hegarty, Miss. Hanora 'Nora'\", b'Strom, Miss. Telma Matilda', b'Beattie, Mr. Thomson', b'Baclini, Mrs. Solomon (Latifa Qurban)', b'Samaan, Mr. Hanna', b'Panula, Master. Urho Abraham', b'Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)', b'Rekic, Mr. Tido', b'Van der hoef, Mr. Wyckoff', b\"O'Donoghue, Ms. Bridget\", b'Svensson, Mr. Johan', b'Ware, Mr. Frederick', b'Meyer, Mrs. Edgar Joseph (Leila Saks)', b'Odahl, Mr. Nils Martin', b'Lehmann, Miss. Bertha', b'Moen, Mr. Sigurd Hansen', b'Nasr, Mr. Mustafa', b'Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)', b'Loring, Mr. Joseph Holland', b\"Harper, Miss. Annie Jessie 'Nina'\", b'Braf, Miss. Elin Ester Maria', b'Weisz, Mrs. Leopold (Mathilde Francoise Pede)', b'Laroche, Miss. Louise', b'Hamalainen, Master. Viljo', b'Betros, Master. Seman', b'Badman, Miss. Emily Louisa', b'McNamee, Mr. Neal', b'Kalvik, Mr. Johannes Halvorsen', b'Sivola, Mr. Antti Wilhelm', b'Strilic, Mr. Ivan', b'Nysveen, Mr. Johan Hansen', b'Franklin, Mr. Charles (Charles Fardon)', b'Brocklebank, Mr. William Alfred', b'Aubart, Mme. Leontine Pauline', b'Novel, Mr. Mansouer', b'Gavey, Mr. Lawrence', b'Eustis, Miss. Elizabeth Mussey', b'Landergren, Miss. Aurora Adelia', b'Maenpaa, Mr. Matti Alexanteri', b'Newsom, Miss. Helen Monypeny', b'Rosblom, Mrs. Viktor (Helena Wilhelmina)', b'Calic, Mr. Petar', b'Ilmakangas, Miss. Ida Livija', b'Pengelly, Mr. Frederick William', b'Lulic, Mr. Nikola', b'Skoog, Miss. Margit Elizabeth', b'Olsen, Master. Artur Karl', b'West, Mrs. Edwy Arthur (Ada Mary Worth)', b'Vander Cruyssen, Mr. Victor', b'Evans, Miss. Edith Corse', b\"Bowen, Mr. David John 'Dai'\", b'Lines, Mrs. Ernest H (Elizabeth Lindsey James)', b'Kenyon, Mr. Frederick R', b'Richards, Master. George Sibley', b'Todoroff, Mr. Lalio', b'Simmons, Mr. John', b'Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)', b'Holverson, Mrs. Alexander Oskar (Mary Aline Towner)', b'Lingane, Mr. John', b'Garfirth, Mr. John', b'Dahlberg, Miss. Gerda Ulrika', b'Gale, Mr. Harry', b'Andrew, Mr. Edgardo Samuel', b'Panula, Master. Juha Niilo', b'Yousseff, Mr. Gerious', b\"Goldsmith, Master. Frank John William 'Frankie'\", b'Lyntakoff, Mr. Stanko', b'Wiklund, Mr. Karl Johan', b'Jermyn, Miss. Annie', b'Mock, Mr. Philipp Edmund', b'Cook, Mrs. (Selena Rogers)', b'Gaskell, Mr. Alfred', b'Ford, Mr. Arthur', b'Banfield, Mr. Frederick James', b'Carbines, Mr. William', b'Jonsson, Mr. Carl', b\"Andersson, Mr. August Edvard ('Wennerstrom')\", b'Sunderland, Mr. Victor Francis', b'Porter, Mr. Walter Chamberlain', b'Newell, Mr. Arthur Webster', b\"Moubarek, Master. Halim Gonios ('William George')\", b'Byles, Rev. Thomas Roussel Davids', b'Oxenham, Mr. Percy Thomas', b'Andersson, Miss. Ebba Iris Alfrida', b'Shellard, Mr. Frederick William', b'Knight, Mr. Robert J', b'Minahan, Dr. William Edward', b'Hawksford, Mr. Walter James', b'del Carlo, Mrs. Sebastiano (Argenia Genovesi)', b'Kraeff, Mr. Theodor', b'Dennis, Mr. Samuel', b'Harmer, Mr. Abraham (David Lishin)', b'Gibson, Mrs. Leonard (Pauline C Boeson)', b'Johansson, Mr. Gustaf Joel', b'Chronopoulos, Mr. Apostolos', b'Goldsmith, Mr. Frank John', b\"Willer, Mr. Aaron ('Abi Weller')\", b'Balkic, Mr. Cerin', b'Parrish, Mrs. (Lutie Davis)', b'Pulbaum, Mr. Franz', b'Hagland, Mr. Konrad Mathias Reiersen', b'Moutal, Mr. Rahamin Haim', b'Sage, Mr. Douglas Bullen', b'Davis, Miss. Mary', b'Skoog, Master. Karl Thorsten', b'Ball, Mrs. (Ada E Hall)', b'Mannion, Miss. Margareth', b'Palsson, Master. Paul Folke', b'Harper, Mr. Henry Sleeper', b'Vande Walle, Mr. Nestor Cyriel', b'Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)', b'Barkworth, Mr. Algernon Henry Wilson', b'Harper, Mrs. Henry Sleeper (Myna Haxtun)', b\"Lindeberg-Lind, Mr. Erik Gustaf ('Mr Edward Lingrey')\", b'Elias, Mr. Tannous', b'Sandstrom, Miss. Marguerite Rut', b'Thomas, Mr. Charles P', b'Lindblom, Miss. Augusta Charlotta', b'Kink-Heilmann, Mrs. Anton (Luise Heilmann)', b'Skoog, Mr. Wilhelm', b'Danbom, Master. Gilbert Sigvard Emanuel', b\"Ford, Miss. Doolina Margaret 'Daisy'\", b'Asplund, Mr. Carl Oscar Vilhelm Gustafsson', b\"Frost, Mr. Anthony Wood 'Archie'\", b\"O'Brien, Mr. Timothy\", b'Conlon, Mr. Thomas Henry', b'Karlsson, Mr. Einar Gervasius', b'Denkoff, Mr. Mitto', b'Rasmussen, Mrs. (Lena Jacobsen Solvang)', b'Birnbaum, Mr. Jakob', b'Harris, Mr. George', b'Thomson, Mr. Alexander Morrison', b'Davies, Mrs. John Morgan (Elizabeth Agnes Mary White)', b'Henriksson, Miss. Jenny Lovisa', b'Persson, Mr. Ernst Ulrik', b'Cacic, Miss. Manda', b'Ali, Mr. William', b'Ringhini, Mr. Sante', b'Smith, Mr. Thomas', b'Pedersen, Mr. Olaf', b'Greenfield, Mrs. Leo David (Blanche Strouse)', b'Finoli, Mr. Luigi', b'Lines, Miss. Mary Conover', b'Silvey, Mr. William Baird', b'Dick, Mr. Albert Adrian', b'Spinner, Mr. Henry John', b\"Keane, Mr. Andrew 'Andy'\", b'Cherry, Miss. Gladys', b'Osen, Mr. Olaf Elon', b'Hanna, Mr. Mansour', b'Skoog, Miss. Mabel', b'Sagesser, Mlle. Emma', b'Lesurer, Mr. Gustave J', b'Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)', b'Compton, Mr. Alexander Taylor Jr', b\"Coutts, Master. Eden Leslie 'Neville'\", b'Julian, Mr. Henry Forbes', b'Christy, Miss. Julie Rachel', b'Mangan, Miss. Mary', b'Kirkland, Rev. Charles Leonard', b'Hold, Mrs. Stephen (Annie Margaret Hill)', b'Kimball, Mr. Edwin Nelson Jr', b'Saether, Mr. Simon Sivertsen', b'Bing, Mr. Lee', b'Kink-Heilmann, Miss. Luise Gretchen', b'Millet, Mr. Francis Davis', b'Boulos, Mrs. Joseph (Sultana)', b'Demetri, Mr. Marinko', b'Hood, Mr. Ambrose Jr', b'Kiernan, Mr. John', b'Stanley, Miss. Amy Zillah Elsie', b'Andersen, Mr. Albert Karvin', b'Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)', b'Birkeland, Mr. Hans Martin Monsen', b'Charters, Mr. David', b'Kenyon, Mrs. Frederick R (Marion)', b\"O'Connell, Mr. Patrick D\", b'Peltomaki, Mr. Nikolai Johannes', b'Molson, Mr. Harry Markland', b'Theobald, Mr. Thomas Leonard', b'Foley, Mr. William', b'Fortune, Mr. Charles Alexander', b\"McGowan, Miss. Anna 'Annie'\", b'Foreman, Mr. Benjamin Laventall', b'McGovern, Miss. Mary', b'Staneff, Mr. Ivan', b'Samaan, Mr. Elias', b\"O'Brien, Mrs. Thomas (Johanna 'Hannah' Godfrey)\", b'Hirvonen, Miss. Hildur E', b'Sage, Miss. Ada', b'Navratil, Master. Edmond Roger', b'Bazzani, Miss. Albina', b'Giles, Mr. Edgar', b\"Madigan, Miss. Margaret 'Maggie'\", b'Anderson, Mr. Harry', b'Mellinger, Mrs. (Elizabeth Anne Maidment)', b'Matthews, Mr. William John', b'Panula, Mrs. Juha (Maria Emilia Ojala)', b\"Kelly, Mrs. Florence 'Fannie'\", b'Giles, Mr. Frederick Edward', b'Foo, Mr. Choong', b'Webber, Miss. Susan', b'Olsen, Mr. Henry Margido', b'Harknett, Miss. Alice Phoebe', b'Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)', b'Vendel, Mr. Olof Edvin', b'Silven, Miss. Lyyli Karoliina', b\"Johnston, Miss. Catherine Helen 'Carrie'\", b'Doyle, Miss. Elizabeth', b'Weir, Col. John', b'Sloper, Mr. William Thompson', b'Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)', b'Jussila, Miss. Mari Aina', b'Lester, Mr. James', b'Bishop, Mrs. Dickinson H (Helen Walton)', b'Borebank, Mr. John James', b'Padro y Manent, Mr. Julian', b'Kassem, Mr. Fared', b'Dodge, Master. Washington', b'Torfa, Mr. Assad', b'Carter, Mrs. Ernest Courtenay (Lilian Hughes)', b'Bishop, Mr. Dickinson H', b'Brobeck, Mr. Karl Rudolf', b'McGowan, Miss. Katherine', b'Rheims, Mr. George Alexander Lucien', b'Rice, Master. Eric', b'Sundman, Mr. Johan Julian', b'Fleming, Miss. Honora', b'Karlsson, Mr. Nils August', b'Dika, Mr. Mirko', b'Mionoff, Mr. Stoytcho', b'Horgan, Mr. John', b'Herman, Miss. Kate', b'Johnson, Master. Harold Theodor', b'Ohman, Miss. Velin', b'White, Mr. Percival Wayland', b'Ware, Mr. William Jeffery', b'Robins, Mr. Alexander A', b'Barton, Mr. David John', b'Lindell, Mr. Edvard Bengtsson', b'Howard, Mrs. Benjamin (Ellen Truelove Arman)', b'Palsson, Miss. Torborg Danira', b'Slocovski, Mr. Selman Francis', b'Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)', b'Green, Mr. George Henry', b'Sawyer, Mr. Frederick Charles', b'Goldsmith, Mr. Nathan', b'Lobb, Mrs. William Arthur (Cordelia K Stanlick)', b'Nankoff, Mr. Minko', b'Salomon, Mr. Abraham L', b'Hakkarainen, Mr. Pekka Pietari', b'Hocking, Mr. Richard George', b'Ali, Mr. Ahmed', b'Wiseman, Mr. Phillippe', b'Alhomaki, Mr. Ilmari Rudolf', b'Peacock, Master. Alfred Edward', b'Candee, Mrs. Edward (Helen Churchill Hungerford)', b'Guest, Mr. Robert', b'Peter, Master. Michael J', b'Straus, Mrs. Isidor (Rosalie Ida Blun)', b'Drew, Master. Marshall Brines', b'Denbury, Mr. Herbert', b\"O'Keefe, Mr. Patrick\", b'Laitinen, Miss. Kristina Sofia', b'Roebling, Mr. Washington Augustus II', b'Smith, Miss. Marion Elsie', b'Moran, Miss. Bertha', b'Leonard, Mr. Lionel', b'Louch, Mr. Charles Alexander', b'Ward, Miss. Anna', b'Paulner, Mr. Uscher', b'Sage, Miss. Stella Anna', b'Connors, Mr. Patrick', b'Rommetvedt, Mr. Knud Paust', b'Greenberg, Mr. Samuel', b\"Watt, Mrs. James (Elizabeth 'Bessie' Inglis Milne)\", b'Saalfeld, Mr. Adolphe', b'Case, Mr. Howard Brown', b'Dean, Mrs. Bertram (Eva Georgetta Light)', b'Hansen, Mr. Henrik Juul', b'Toufik, Mr. Nakli', b'Bowerman, Miss. Elsie Edith', b\"Gilnagh, Miss. Katherine 'Katie'\", b'Swane, Mr. George', b'Bourke, Mr. John', b'Pears, Mrs. Thomas (Edith Wearne)', b'Allen, Mr. William Henry', b'Barah, Mr. Hanna Assi', b'Hewlett, Mrs. (Mary D Kingcome)', b'Lahoud, Mr. Sarkis', b'Givard, Mr. Hans Kristensen', b'Chambers, Mrs. Norman Campbell (Bertha Griggs)', b'Shorney, Mr. Charles Joseph', b'Hansen, Mr. Claus Peter', b'Hays, Mr. Charles Melville', b\"Johnston, Mrs. Andrew G (Elizabeth 'Lily' Watson)\", b'Sap, Mr. Julius', b'Herman, Mr. Samuel', b\"Barber, Miss. Ellen 'Nellie'\", b'Moraweck, Dr. Ernest', b'Otter, Mr. Richard', b'Shutes, Miss. Elizabeth W', b'Stead, Mr. William Thomas', b'Olsson, Mr. Nils Johan Goransson', b'Caram, Mrs. Joseph (Maria Elias)', b'Nirva, Mr. Iisakki Antino Aijo', b'Graham, Miss. Margaret Edith', b\"Moubarek, Mrs. George (Omine 'Amenia' Alexander)\", b'Reynolds, Mr. Harold J', b'Malachard, Mr. Noel', b'Bowen, Miss. Grace Scott', b'Tenglin, Mr. Gunnar Isidor', b'Lurette, Miss. Elise', b'Baumann, Mr. John D', b'Asplund, Master. Carl Edgar', b'Minkoff, Mr. Lazar', b'Willey, Mr. Edward', b'Collyer, Mr. Harvey', b'Fynney, Mr. Joseph J', b'Bystrom, Mrs. (Karolina)', b'Brown, Miss. Edith Eileen', b'Jarvis, Mr. John Denzil', b'Humblen, Mr. Adolf Mathias Nicolai Olsen', b'Icard, Miss. Amelie', b'Boulos, Master. Akar', b'Mack, Mrs. (Mary)', b'Karnes, Mrs. J Frank (Claire Bennett)', b'Panula, Mr. Jaako Arnold', b'Rogers, Mr. Reginald Harry', b'Chaffee, Mr. Herbert Fuller', b'Giles, Mr. Ralph', b'Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)', b'Doling, Mrs. John T (Ada Julia Bone)', b'Newell, Miss. Madeleine', b'Ryan, Mr. Patrick', b'Leader, Dr. Alice (Farnham)', b'Hickman, Mr. Leonard Mark', b\"O'Driscoll, Miss. Bridget\", b'Angle, Mr. William A', b'Goldenberg, Mrs. Samuel L (Edwiga Grabowska)', b'Hipkins, Mr. William Edward', b'Moubarek, Master. Gerios', b'Nieminen, Miss. Manta Josefina', b'Spencer, Mrs. William Augustus (Marie Eugenie)', b'Ismay, Mr. Joseph Bruce', b'Klasen, Mrs. (Hulda Kristina Eugenia Lofqvist)', b'Widener, Mr. George Dunton', b'Sincock, Miss. Maude', b'Daniel, Mr. Robert Williams', b'Goodwin, Miss. Jessie Allis', b'Slabenoff, Mr. Petco', b\"Yrois, Miss. Henriette ('Mrs Harbeck')\", b'Peruschitz, Rev. Joseph Maria', b'Bonnell, Miss. Caroline', b'Stranden, Mr. Juho', b'Cook, Mr. Jacob', b'Dean, Master. Bertram Vere', b\"Morley, Mr. Henry Samuel ('Mr Henry Marshall')\", b'Bateman, Rev. Robert James', b'Peuchen, Major. Arthur Godfrey', b'Hippach, Mrs. Louis Albert (Ida Sophia Fischer)', b'Walker, Mr. William Anderson', b'Karun, Mr. Franz', b'Cardeza, Mr. Thomas Drake Martinez', b'Ostby, Mr. Engelhart Cornelius', b'Storey, Mr. Thomas', b'Spector, Mr. Woolf', b'Nasser, Mr. Nicholas', b'Compton, Miss. Sara Rebecca', b'Blank, Mr. Henry', b'Fox, Mr. Patrick', b'Niskanen, Mr. Juha', b'Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)', b'Tobin, Mr. Roger', b'Roth, Miss. Sarah A', b'Olsen, Mr. Karl Siegwart Andreas', b'Soholt, Mr. Peter Andreas Lauritz Andersen', b'Devaney, Miss. Margaret Delia', b'Alexander, Mr. William', b'Aks, Mrs. Sam (Leah Rosen)', b'Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)', b'Butler, Mr. Reginald Fenton', b'Cleaver, Miss. Alice', b\"Navratil, Mr. Michel ('Louis M Hoffman')\", b'Osman, Mrs. Mara', b\"Murphy, Miss. Katherine 'Kate'\", b'Trout, Mrs. William H (Jessie L)', b'Abbing, Mr. Anthony', b'Lindell, Mrs. Edvard Bengtsson (Elin Gerda Persson)', b'Sobey, Mr. Samuel James Hayden', b'Delalic, Mr. Redjo', b'Clarke, Mr. Charles Valentine', b'Stanley, Mr. Edward Roland', b'Buckley, Mr. Daniel', b'Keeping, Mr. Edwin', b'Moor, Master. Meier', b'Navratil, Master. Michel M', b'Abbott, Mrs. Stanton (Rosa Hunt)', b'Kink, Miss. Maria', b'Daniels, Miss. Sarah', b'Clifford, Mr. George Quincy', b'Snyder, Mr. John Pillsbury', b'Laleff, Mr. Kristo', b'Ryan, Mr. Edward', b'Sharp, Mr. Percival James R', b'Gee, Mr. Arthur H', b'Beckwith, Mrs. Richard Leonard (Sallie Monypeny)', b'Butt, Major. Archibald Willingham', b'Lockyer, Mr. Edward', b'Minahan, Mrs. William Edward (Lillian E Thorpe)', b'Boulos, Mr. Hanna', b'Sdycoff, Mr. Todor', b'Natsch, Mr. Charles H', b'Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)', b'Uruchurtu, Don. Manuel E', b'Chapman, Mrs. John Henry (Sara Elizabeth Lawry)', b'Mahon, Miss. Bridget Delia', b'Petersen, Mr. Marius', b'Graham, Mr. George Edward', b\"Troutt, Miss. Edwina Celia 'Winnie'\", b'Hays, Miss. Margaret Bechstein', b'Hamalainen, Mrs. William (Anna)', b'Andersson, Mr. Anders Johan', b'Bourke, Miss. Mary', b'Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)', b'Sage, Master. William Henry', b'Morley, Mr. William', b'Sage, Miss. Constance Gladys', b'Chip, Mr. Chang', b'Mallet, Mrs. Albert (Antoinette Magnin)', b'Coxon, Mr. Daniel', b'Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)', b'Jardin, Mr. Jose Neto', b'Moran, Mr. Daniel J', b'Ryerson, Miss. Emily Borie', b'Sinkkonen, Miss. Anna', b'Hiltunen, Miss. Marta', b'Sirota, Mr. Maurice', b'Carr, Miss. Jeannie', b'Lahtinen, Mrs. William (Anna Sylfven)', b'Allison, Mrs. Hudson J C (Bessie Waldo Daniels)', b'Hart, Mrs. Benjamin (Esther Ada Bloomfield)', b'Reeves, Mr. David', b'Chronopoulos, Mr. Demetrios', b'Kiernan, Mr. Philip', b'Stahelin-Maeglin, Dr. Max', b'Attalah, Mr. Sleiman', b'Ford, Mr. Edward Watson', b'Cor, Mr. Liudevit', b'Wheadon, Mr. Edward H', b'Thorne, Mrs. Gertrude Maybelle', b'Naughton, Miss. Hannah', b'Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)', b'Palsson, Mrs. Nils (Alma Cornelia Berglund)', b\"Sage, Miss. Dorothy Edith 'Dolly'\", b'Heininen, Miss. Wendla Maria', b'Bjornstrom-Steffansson, Mr. Mauritz Hakan', b\"Carr, Miss. Helen 'Ellen'\", b'Andersson, Miss. Ida Augusta Margareta', b'Schmidt, Mr. August', b'Turja, Miss. Anna Sofia', b'Cumings, Mr. John Bradley', b'McEvoy, Mr. Michael', b'Frauenthal, Mr. Isaac Gerald', b'Honkanen, Miss. Eliina', b'Asim, Mr. Adola', b'Wenzel, Mr. Linhart', b'van Melkebeke, Mr. Philemon', b'Jefferys, Mr. Clifford Thomas', b'Dean, Mr. Bertram Frank', b'Lefebre, Mrs. Frank (Frances)', b'Holverson, Mr. Alexander Oskar', b'Kelly, Mr. James', b'Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)', b'Lemore, Mrs. (Amelia Milley)', b'Adams, Mr. John', b'Pettersson, Miss. Ellen Natalia', b'Smith, Mr. James Clinch', b'Veal, Mr. James', b'Andersson, Miss. Ellis Anna Maria', b'Dulles, Mr. William Crothers', b'Clark, Mr. Walter Miller', b'Deacon, Mr. Percy William', b'Goodwin, Mrs. Frederick (Augusta Tyler)', b'Saundercock, Mr. William Henry', b'Johnson, Mr. William Cahoone Jr', b'Thomas, Master. Assad Alexander', b'LeRoy, Miss. Bertha', b'Mahon, Mr. John', b'Futrelle, Mrs. Jacques Heath (Lily May Peel)', b'Vande Velde, Mr. Johannes Joseph', b\"Parkes, Mr. Francis 'Frank'\", b'Widegren, Mr. Carl/Charles Peter', b'Buckley, Miss. Katherine', b'Guggenheim, Mr. Benjamin', b'Berriman, Mr. William John', b'Frolicher-Stehli, Mr. Maxmillian', b'Thayer, Mrs. John Borland (Marian Longstreth Morris)', b\"O'Connor, Mr. Maurice\", b\"Homer, Mr. Harry ('Mr E Haven')\", b'Plotcharsky, Mr. Vasil', b'Saad, Mr. Khalil', b'Nosworthy, Mr. Richard Cater', b'Maguire, Mr. John Edward', b'Elias, Mr. Joseph Jr', b'Karun, Miss. Manca', b'Dahl, Mr. Karl Edwart', b'Bradley, Miss. Bridget Delia', b'Slayter, Miss. Hilda Mary', b'Skoog, Master. Harald', b'Doharr, Mr. Tannous', b'Astor, Col. John Jacob', b'de Mulder, Mr. Theodore', b'Ware, Mr. John James', b'Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)', b'Crease, Mr. Ernest James', b'Boulos, Miss. Nourelain', b'Drazenoic, Mr. Jozef', b'Louch, Mrs. Charles Alexander (Alice Adelaide Slow)', b'Wirz, Mr. Albert', b'Carlsson, Mr. Carl Robert', b'Bowenur, Mr. Solomon', b'Rice, Master. Eugene', b'Somerton, Mr. Francis William', b'Pears, Mr. Thomas Clinton', b'Earnshaw, Mrs. Boulton (Olive Potter)', b'Keane, Miss. Nora A', b'Rood, Mr. Hugh Roscoe', b'Lemberopolous, Mr. Peter L', b'Williams, Mr. Leslie', b'Serepeca, Miss. Augusta', b\"Nakid, Mrs. Said (Waika 'Mary' Mowad)\", b'Hoyt, Mr. Frederick Maxfield', b'Coleridge, Mr. Reginald Charles', b'Goodwin, Master. Harold Victor', b'Ilett, Miss. Bertha', b'Davison, Mr. Thomas Henry', b'Pasic, Mr. Jakob', b'Midtsjo, Mr. Karl Albert', b'Hosono, Mr. Masabumi', b'Carter, Master. William Thornton II', b'Peter, Mrs. Catherine (Catherine Rizk)', b'Mallet, Mr. Albert', b'Corn, Mr. Harry', b'Marvin, Mr. Daniel Warner', b'Christmann, Mr. Emil', b'Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)', b'Elias, Mr. Dibo', b'Behr, Mr. Karl Howell', b'Risien, Mrs. Samuel (Emma)', b'Baxter, Mrs. James (Helene DeLaudeniere Chaput)', b'Nicholson, Mr. Arthur Ernest', b'Cacic, Mr. Luka', b'Ashby, Mr. John', b'Becker, Master. Richard F', b'Chibnall, Mrs. (Edith Martha Bowerman)', b\"Williams, Mr. Howard Hugh 'Harry'\", b'Braund, Mr. Owen Harris', b'Richards, Mrs. Sidney (Emily Hocking)', b'Moor, Mrs. (Beila)', b'Salonen, Mr. Johan Werner', b'Pavlovic, Mr. Stefo', b\"McCarthy, Miss. Catherine 'Katie'\", b'Frauenthal, Mrs. Henry William (Clara Heinsheimer)', b'Madsen, Mr. Fridtjof Arne', b'Jones, Mr. Charles Cresson', b'Vestrom, Miss. Hulda Amanda Adolfina', b'Maioni, Miss. Roberta', b'Daly, Mr. Peter Denis', b'Head, Mr. Christopher', b'McGough, Mr. James Robert', b'Zakarian, Mr. Ortin', b'Johansson, Mr. Erik', b'Asplund, Master. Clarence Gustaf Hugo', b'Arnold-Franchi, Mr. Josef', b'Nenkoff, Mr. Christo', b'Vander Planke, Mr. Julius', b'Scanlan, Mr. James', b'Rosblom, Miss. Salli Helena', b'Rowe, Mr. Alfred G', b\"Angle, Mrs. William A (Florence 'Mary' Agnes Hughes)\", b'Palsson, Miss. Stina Viola', b'Linehan, Mr. Michael', b'Dodge, Mrs. Washington (Ruth Vidaver)', b'Harbeck, Mr. William H', b'Bentham, Miss. Lilian W', b'Aldworth, Mr. Charles Augustus', b'Nicola-Yarred, Master. Elias', b'Robins, Mrs. Alexander A (Grace Charity Laury)', b\"Mockler, Miss. Helen Mary 'Ellie'\", b'Lam, Mr. Len', b'Moore, Mr. Leonard Charles', b'Kantor, Mr. Sinai', b'Lobb, Mr. William Arthur', b'Beauchamp, Mr. Henry James', b'Ponesell, Mr. Martin', b'Widener, Mrs. George Dunton (Eleanor Elkins)', b'Ilieff, Mr. Ylio', b'Beesley, Mr. Lawrence', b'Miles, Mr. Frank', b'Peduzzi, Mr. Joseph', b'Berglund, Mr. Karl Ivar Sven', b'Markoff, Mr. Marin', b'Touma, Miss. Maria Youssef', b'Shelley, Mrs. William (Imanita Parrish Hall)', b'Duran y More, Miss. Asuncion', b'Flegenheim, Mrs. Alfred (Antoinette)', b'Penasco y Castellana, Mr. Victor de Satode', b\"Mullens, Miss. Katherine 'Katie'\", b'Kvillner, Mr. Johan Henrik Johannesson', b'Brown, Mrs. James Joseph (Margaret Tobin)', b'Sadowitz, Mr. Harry', b'Bracken, Mr. James H', b'Nilsson, Mr. August Ferdinand', b'Dimic, Mr. Jovan', b\"Coutts, Master. William Loch 'William'\", b\"Healy, Miss. Hanora 'Nora'\", b'Coleff, Mr. Satio', b'Jalsevac, Mr. Ivan', b'Crosby, Miss. Harriet R', b'Smith, Mr. Richard William', b'Endres, Miss. Caroline Louise', b\"O'Dwyer, Miss. Ellen 'Nellie'\", b'Palsson, Master. Gosta Leonard', b'Hold, Mr. Stephen', b'Campbell, Mr. William', b'McKane, Mr. Peter David', b'McNeill, Miss. Bridget', b'Allen, Miss. Elisabeth Walton', b'Gilbert, Mr. William', b'Beane, Mr. Edward', b'Youseff, Mr. Gerious', b'Jussila, Mr. Eiriik', b'Stengel, Mrs. Charles Emil Henry (Annie May Morris)', b'Howard, Mr. Benjamin', b'Backstrom, Mr. Karl Alfred', b'Watson, Mr. Ennis Hastings', b'Renouf, Mrs. Peter Henry (Lillian Jefferys)', b'Lindahl, Miss. Agda Thorilda Viktoria', b'Cacic, Mr. Jego Grga', b'Silvey, Mrs. William Baird (Alice Munger)', b'Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)', b'Eitemiller, Mr. George Floyd', b'Barbara, Mrs. (Catherine David)', b'Funk, Miss. Annie Clemmer', b'Johansson, Mr. Nils', b'Foley, Mr. Joseph', b'Parr, Mr. William Henry Marsh', b'Mitkoff, Mr. Mito', b'Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)', b'Hoyt, Mr. William Fisher', b'Kelly, Mr. James', b'Hansen, Mr. Henry Damsgaard', b'Stone, Mrs. George Nelson (Martha Evelyn)', b'Mellors, Mr. William John', b'Wiklund, Mr. Jakob Alfred', b'Rouse, Mr. Richard Henry', b'Farthing, Mr. John', b'Fischer, Mr. Eberhard Thelander', b'Carrau, Mr. Francisco M', b'Jussila, Miss. Katriina', b'Frauenthal, Dr. Henry William', b'Straus, Mr. Isidor', b'Pokrnic, Mr. Mate', b'Duran y More, Miss. Florentina', b'Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)', b'Risien, Mr. Samuel Beard', b'del Carlo, Mr. Sebastiano', b'Davies, Mr. Joseph', b'Lamb, Mr. John Joseph', b'Flynn, Mr. John', b'Elias, Mr. Joseph', b'West, Miss. Constance Mirium', b'Goodwin, Master. Sidney Leonard', b'Douglas, Mrs. Walter Donald (Mahala Dutton)', b'McCoy, Mr. Bernard', b'Geiger, Miss. Amalie', b'Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)', b'Jenkin, Mr. Stephen Curnow', b'Hilliard, Mr. Herbert Henry', b'Meek, Mrs. Thomas (Annie Louise Rowley)', b'Laroche, Miss. Simonne Marie Anne Andree', b'Emanuel, Miss. Virginia Ethel', b\"Riordan, Miss. Johanna 'Hannah'\", b'de Pelsmaeker, Mr. Alfons', b'Rice, Master. Albert', b'Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)', b'Lahtinen, Rev. William', b'Angheloff, Mr. Minko', b'Baclini, Miss. Helene Barbara', b'Lam, Mr. Ali', b'Augustsson, Mr. Albert', b'Sutton, Mr. Frederick', b'Klasen, Miss. Gertrud Emilia', b'Beavan, Mr. William Thomas', b'Ryerson, Mr. Arthur Larned', b'Betros, Mr. Tannous', b'Faunthorpe, Mr. Harry', b'Turpin, Mr. William John Robert', b'Rintamaki, Mr. Matti', b'Douglas, Mrs. Frederick Charles (Mary Helene Baxter)', b'Jensen, Mr. Hans Peder', b'Appleton, Mrs. Edward Dale (Charlotte Lamson)', b'Kantor, Mrs. Sinai (Miriam Sternin)', b'Yasbeck, Mr. Antoni', b'Schabert, Mrs. Paul (Emma Mock)', b'Edvardsson, Mr. Gustaf Hjalmar', b'Slemen, Mr. Richard James', b'Enander, Mr. Ingvar', b'Connaghton, Mr. Michael', b'Hellstrom, Miss. Hilda Maria', b'Daly, Mr. Eugene Patrick', b\"Duff Gordon, Lady. (Lucille Christiana Sutherland) ('Mrs Morgan')\", b'Jensen, Mr. Svend Lauritz', b'Fortune, Miss. Mabel Helen', b'Isham, Miss. Ann Elizabeth', b'Rothschild, Mr. Martin', b'Larsson, Mr. Bengt Edvin', b'Davies, Master. John Morgan Jr', b'Williams, Mr. Richard Norris II', b'Andreasson, Mr. Paul Edvin', b\"Nourney, Mr. Alfred ('Baron von Drachstedt')\", b'Rice, Mrs. William (Margaret Norton)', b'Pokrnic, Mr. Tome', b'Widener, Mr. Harry Elkins', b'Vanden Steen, Mr. Leo Peter', b'Reuchlin, Jonkheer. John George', b'Tornquist, Mr. William Henry', b'Saade, Mr. Jean Nassr', b\"O'Brien, Mr. Thomas\", b'Salander, Mr. Karl Johan', b'McMahon, Mr. Martin', b'Allum, Mr. Owen George', b'Zabour, Miss. Thamine', b'Ling, Mr. Lee', b'Pernot, Mr. Rene', b'Van Impe, Mr. Jean Baptiste', b'Sage, Mr. John George', b'de Messemaeker, Mr. Guillaume Joseph', b'Jonkoff, Mr. Lalio', b'Smyth, Miss. Julia', b'Troupiansky, Mr. Moses Aaron', b'Sandstrom, Miss. Beatrice Irene', b'Lindqvist, Mr. Eino William', b'Tucker, Mr. Gilbert Milligan Jr', b'Zimmerman, Mr. Leo', b'Henry, Miss. Delia', b'Spencer, Mr. William Augustus', b'Emir, Mr. Farred Chehab', b'West, Miss. Barbara J', b'Chaudanson, Miss. Victorine', b'Warren, Mrs. Frank Manley (Anna Sophia Atkinson)', b'Mulvihill, Miss. Bertha E', b'Snyder, Mrs. John Pillsbury (Nelle Stevenson)', b'Bidois, Miss. Rosalie', b'Carver, Mr. Alfred John', b'Carrau, Mr. Jose Pedro', b'Aks, Master. Philip Frank', b'Hogeboom, Mrs. John C (Anna Andrews)', b'Ridsdale, Miss. Lucy', b'Corbett, Mrs. Walter H (Irene Colvin)', b'Sage, Mr. Frederick', b'Hassab, Mr. Hammad', b'Matinoff, Mr. Nicola', b'Sirayanian, Mr. Orsen', b'Assaf, Mr. Gerios', b'Nicola-Yarred, Miss. Jamila', b'Jensen, Mr. Niels Peder', b'Abelson, Mr. Samuel', b'Hart, Mr. Benjamin', b'Carter, Mr. William Ernest', b'Mitchell, Mr. Henry Michael', b'Maisner, Mr. Simon', b'Goldenberg, Mr. Samuel L', b'Hickman, Mr. Lewis', b'Cacic, Miss. Marija', b'Gracie, Col. Archibald IV', b'Fortune, Mrs. Mark (Mary McDougald)', b'Thayer, Mr. John Borland Jr', b'Gallagher, Mr. Martin', b'Drew, Mr. James Vivian', b'Niklasson, Mr. Samuel', b'Touma, Mrs. Darwis (Hanne Youssef Razi)', b'Abbott, Master. Eugene Joseph', b'Johanson, Mr. Jakob Alfred', b'Dibden, Mr. William', b\"Wells, Mrs. Arthur Henry ('Addie' Dart Trevaskis)\", b'Braund, Mr. Lewis Richard', b'Vovk, Mr. Janko', b'Laroche, Mr. Joseph Philippe Lemercier', b'Allison, Mr. Hudson Joshua Creighton', b'Goodwin, Mr. Charles Frederick', b'Rugg, Miss. Emily', b'Turcin, Mr. Stjepan', b'MacKay, Mr. George William', b'Kreuchen, Miss. Emilie', b'Tikkanen, Mr. Juho', b'Holthen, Mr. Johan Martin', b'Swift, Mrs. Frederick Joel (Margaret Welles Barron)', b'Rice, Master. Arthur', b\"Johnston, Master. William Arthur 'Willie'\", b'Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)', b'Francatelli, Miss. Laura Mabel', b'Bailey, Mr. Percy Andrew', b'Davies, Mr. Evan', b\"Khalil, Mrs. Betros (Zahie 'Maria' Elias)\", b'Leitch, Miss. Jessie Wills', b'Masselmani, Mrs. Fatima', b\"Brown, Miss. Amelia 'Mildred'\", b'Lang, Mr. Fang', b'Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)', b'Connolly, Miss. Kate', b'Mineff, Mr. Ivan', b'Ford, Mrs. Edward (Margaret Ann Watson)', b'Hendekovic, Mr. Ignjac', b'Asplund, Master. Edvin Rojj Felix', b'Cavendish, Mr. Tyrell William', b'Montvila, Rev. Juozas', b'Klasen, Mr. Klas Albin', b'Kelly, Miss. Mary', b\"Wheeler, Mr. Edwin 'Frederick'\", b'Davies, Mr. Alfred J', b'McCrae, Mr. Arthur Gordon', b'Sedgwick, Mr. Charles Frederick Waddington', b'Williams, Mr. Charles Eugene', b'Omont, Mr. Alfred Fernand', b'Naidenoff, Mr. Penko', b\"Coutts, Mrs. William (Winnie 'Minnie' Treanor)\", b'Mudd, Mr. Thomas Charles', b'Marechal, Mr. Pierre', b'Quick, Miss. Winifred Vera', b\"Flynn, Mr. John Irwin ('Irving')\", b'Carlsson, Mr. Frans Olof', b'Cameron, Miss. Clear Annie', b'Goodwin, Master. William Frederick', b'Sweet, Mr. George Frederick', b'Ford, Mr. William Neal', b'Yasbeck, Mrs. Antoni (Selini Alexander)', b'McCoy, Miss. Agnes', b'Johansson Palmquist, Mr. Oskar Leander', b'Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)', b'Gustafsson, Mr. Alfred Ossian', b'Hays, Mrs. Charles Melville (Clara Jennings Gregg)', b'Meyer, Mr. Edgar Joseph', b'Allison, Master. Hudson Trevor', b'Markun, Mr. Johann', b\"McNamee, Mrs. Neal (Eileen O'Leary)\", b'Lewy, Mr. Ervin G', b'Nancarrow, Mr. William Henry', b'Rosenbaum, Miss. Edith Louise', b'Shine, Miss. Ellen Natalia', b'van Billiard, Mr. Austin Blyler', b'Sage, Master. Thomas Henry', b'Zabour, Miss. Hileni', b'McCormack, Mr. Thomas Joseph', b'Kent, Mr. Edward Austin', b'Cavendish, Mrs. Tyrell William (Julia Florence Siegel)', b'Vartanian, Mr. David', b'Blackwell, Mr. Stephen Weart', b'Ekstrom, Mr. Johan', b\"Nakid, Miss. Maria ('Mary')\", b'Cann, Mr. Ernest Charles', b'Colbert, Mr. Patrick', b'Bengtsson, Mr. John Viktor', b'Touma, Master. Georges Youssef', b'Daher, Mr. Shedid', b'Johnson, Mr. Malkolm Joackim', b'Nakid, Mr. Sahid', b'Baimbrigge, Mr. Charles Robert', b'Burke, Mr. Jeremiah', b'Renouf, Mr. Peter Henry', b'Phillips, Mr. Escott Robert', b'Willard, Miss. Constance', b'Walcroft, Miss. Nellie', b'Hedman, Mr. Oskar Arvid', b'Shaughnessy, Mr. Patrick', b'Oliva y Ocana, Dona. Fermina', b\"Lovell, Mr. John Hall ('Henry')\", b'Warren, Mr. Frank Manley', b'Buss, Miss. Kate', b'Norman, Mr. Robert Douglas', b'Woolner, Mr. Hugh', b\"Duff Gordon, Sir. Cosmo Edmund ('Mr Morgan')\", b'van Billiard, Master. Walter John', b'Nesson, Mr. Israel', b'Giglio, Mr. Victor', b'Leyson, Mr. Robert William Norman', b'Kennedy, Mr. John', b'Johnson, Mr. Alfred', b'Hagland, Mr. Ingvald Olai Olsen', b'Chapman, Mr. Charles Henry', b'Thorneycroft, Mr. Percival', b'Bird, Miss. Ellen', b'Fleming, Miss. Margaret', b'Oreskovic, Miss. Marija', b'Keefe, Mr. Arthur', b'Duane, Mr. Frank', b'Burns, Miss. Elizabeth Margaret', b'Wells, Miss. Joan', b'Svensson, Mr. Olof', b'Adahl, Mr. Mauritz Nils Martin', b'Botsford, Mr. William Hull', b'Hale, Mr. Reginald', b'Moore, Mr. Clarence Bloomfield', b'Douglas, Mr. Walter Donald', b'Harper, Rev. John', b'Fortune, Miss. Alice Elizabeth', b'Aronsson, Mr. Ernst Axel Algot', b'Dennis, Mr. William', b'Glynn, Miss. Mary Agatha', b'Madill, Miss. Georgette Alexandra', b'Holm, Mr. John Fredrik Alexander', b'Becker, Miss. Marion Louise', b'Salkjelsvik, Miss. Anna Kristine', b'Sjoblom, Miss. Anna Sofia', b'Danbom, Mr. Ernst Gilbert', b'Larsson-Rondberg, Mr. Edvard A', b'Petterson, Mr. Johan Emil', b'Wittevrongel, Mr. Camille', b'Murphy, Miss. Nora', b'Beckwith, Mr. Richard Leonard', b'Torber, Mr. Ernst William', b'White, Mrs. John Stuart (Ella Holmes)', b'Strom, Mrs. Wilhelm (Elna Matilda Persson)', b'Pinsky, Mrs. (Rosa)', b'Davison, Mrs. Thomas Henry (Mary E Finck)', b'Andersson, Master. Sigvard Harald Elias', b'Abelseth, Miss. Karen Marie', b'Collett, Mr. Sidney C Stuart', b'Burns, Miss. Mary Delia', b'Connolly, Miss. Kate', b\"Assaf Khalil, Mrs. Mariana ('Miriam')\", b'Sutehall, Mr. Henry Jr', b'Fortune, Miss. Ethel Flora', b'Clark, Mrs. Walter Miller (Virginia McDowell)', b'Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)', b'Andrew, Mr. Frank Thomas', b'Caldwell, Mr. Albert Francis', b'Taussig, Mrs. Emil (Tillie Mandelbaum)', b'Nye, Mrs. (Elizabeth Ramell)', b'Pain, Dr. Alfred', b'Strandberg, Miss. Ida Sofia', b'Newell, Miss. Marjorie', b'Ibrahim Shawah, Mr. Yousseff', b'Gillespie, Mr. William Henry', b'Wick, Mr. George Dennick', b'Cor, Mr. Bartol', b'Ayoub, Miss. Banoura', b'Khalil, Mr. Betros', b'Brown, Mrs. John Murray (Caroline Lane Lamson)', b'Eklund, Mr. Hans Linus', b'Andersson, Mr. Johan Samuel', b'Fahlstrom, Mr. Arne Jonas', b'Artagaveytia, Mr. Ramon', b'Franklin, Mr. Thomas Parham', b'Andrews, Mr. Thomas Jr', b'Kink-Heilmann, Mr. Anton', b'Krekorian, Mr. Neshan', b'Dowdell, Miss. Elizabeth', b'Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)', b'Olsson, Miss. Elina', b'Murphy, Miss. Margaret Jane', b'Nilsson, Miss. Berta Olivia', b'Harder, Mrs. George Achilles (Dorothy Annan)', b'Abbott, Mr. Rossmore Edward', b'Sivic, Mr. Husein', b'Bucknell, Mrs. William Robert (Emma Eliza Ward)', b'Nysten, Miss. Anna Sofia', b'Peacock, Miss. Treasteall', b'Abelson, Mrs. Samuel (Hannah Wizosky)', b'Becker, Miss. Ruth Elizabeth', b'Asplund, Mr. Johan Charles', b'Smith, Mr. Lucien Philip', b'Bryhl, Miss. Dagmar Jenny Ingeborg', b'Fortune, Mr. Mark', b'Gibson, Miss. Dorothy Winifred', b'Simonius-Blumer, Col. Oberst Alfons', b'Lindstrom, Mrs. Carl Johan (Sigrid Posse)', b'Hart, Mr. Henry', b'Culumovic, Mr. Jeso', b'Collander, Mr. Erik Gustaf', b'Wilson, Miss. Helen Alice', b'Celotti, Mr. Francesco', b'Drew, Mrs. James Vivian (Lulu Thorne Christian)', b\"O'Leary, Miss. Hanora 'Norah'\", b'Quick, Miss. Phyllis May', b'Panula, Master. Eino Viljami', b'Graham, Mrs. William Thompson (Edith Junkins)', b'Toomey, Miss. Ellen', b'Bostandyeff, Mr. Guentcho', b'Hodges, Mr. Henry Price', b'Stewart, Mr. Albert A', b'Nilsson, Miss. Helmina Josefina', b'Rush, Mr. Alfred George John', b'Carlsson, Mr. August Sigfrid', b'Taylor, Mr. Elmer Zebley', b'Petranec, Miss. Matilda', b\"Dean, Miss. Elizabeth Gladys 'Millvina'\", b'Assam, Mr. Ali', b'Tomlin, Mr. Ernest Portage', b'Spedden, Mr. Frederic Oakley', b'Gronnestad, Mr. Daniel Danielsen', b'Drapkin, Miss. Jennie', b'Gustafsson, Mr. Johan Birger', b'Ware, Mrs. John James (Florence Louise Long)', b'Cunningham, Mr. Alfred Fleming', b'de Brito, Mr. Jose Joaquim', b'Wick, Miss. Mary Natalie', b'Jacobsohn, Mr. Sidney Samuel', b'Smith, Mrs. Lucien Philip (Mary Eloise Hughes)', b\"Petroff, Mr. Pastcho ('Pentcho')\", b'Chisholm, Mr. Roderick Robert Crispin', b\"Najib, Miss. Adele Kiamie 'Jane'\", b'Richard, Mr. Emile', b'Thorneycroft, Mrs. Percival (Florence Kate White)', b'Canavan, Miss. 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Bertha J', b'Canavan, Mr. Patrick', b'Clarke, Mrs. Charles V (Ada Maria Winfield)', b'Cairns, Mr. Alexander', b'Payne, Mr. Vivian Ponsonby', b'Kilgannon, Mr. Thomas J', b'Howard, Miss. May Elizabeth', b'Milling, Mr. Jacob Christian', b'Dyker, Mr. Adolf Fredrik', b'Hocking, Mr. Samuel James Metcalfe', b'Barbara, Miss. Saiide', b'Dakic, Mr. Branko', b'Sjostedt, Mr. Ernst Adolf', b'Mardirosian, Mr. Sarkis', b'Carter, Mrs. William Ernest (Lucile Polk)', b'Brown, Mr. Thomas William Solomon', b'Gill, Mr. John William', b'Perkin, Mr. John Henry', b'Rogers, Mr. William John', b'Moussa, Mrs. (Mantoura Boulos)', b'Abrahim, Mrs. Joseph (Sophie Halaut Easu)', b'Thomas, Mr. Tannous', b'Hee, Mr. Ling', b'Wright, Mr. George', b'Attalah, Miss. Malake', b'Lennon, Mr. Denis', b'Lefebre, Miss. Ida', b'Oreskovic, Miss. Jelka', b'Becker, Mrs. Allen Oliver (Nellie E Baumgardner)', b'Sage, Mrs. John (Annie Bullen)', b'Abelseth, Mr. Olaus Jorgensen', b\"Daly, Miss. 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b'345773', b'CA. 2343', b'345572', b'349204', b'335432', b'233639', b'PP 9549', b'STON/O 2. 3101285', b'2543', b'315082', b'382649', b'PC 17569', b'2631', b'C.A. 34651', b'PC 17608', b'110813', b'382653', b'21228', b'PC 17757', b'392095', b'113059', b'392091', b'13502', b'W./C. 14258', b'237249', b'CA. 2343', b'PC 17572', b'349255', b'2669', b'2692', b'2651', b'350047', b'P/PP 3381', b'F.C.C. 13529', b'113760', b'C.A. 24580', b'A/S 2816', b'17453', b'S.O.C. 14879', b'315084', b'113780', b'19950', b'17421', b'36864', b'28220', b'363611', b'2650', b'C.A. 2673', b'3101264', b'S.O.C. 14879', b'29103', b'3460', b'349252', b'SC/Paris 2123', b'113781', b'CA 2144', b'C.A. 31026', b'349247', b'C.A. 42795', b'24160', b'STON/O 2. 3101293', b'C 4001', b'17466', b'382652', b'W./C. 6607', b'11753', b'PC 17485', b'29108', b'SC/A4 23568', b'2660', b'248727', b'2649', b'248733', b'1601', b'3101278', b'370373', b'349233', b'W./C. 6608', b'349243', b'347077', b'19877', b'211536', b'350404', b'14312', b'SC/PARIS 2159', b'A/4 48871', b'237216', b'244361', b'244373', b'F.C. 12998', b'349206', b'C.A. 37671', b'S.O./P.P. 3', b'11774', b'26360', b'PC 17474', b'695', b'F.C.C. 13528', b'CA 2144', b'220845', b'W./C. 6608', b'2659', b'367226', b'347070', b'F.C.C. 13534', b'7534', b'12749', b'PC 17604', b'113781', b'349257', b'376566', b'PC 17612', b'A./5. 3338', b'PC 17613', b'330968', b'A/5. 851', b'CA. 2343', b'2665', b'367228', b'11771', b'19877', b'2658', b'113784', b'347061', b'2653', b'A./5. 2152', b'371109', b'347068', b'2650', b'2698', b'347062', b'2653', b'C.A. 31030', b'365222', b'31027', b'S.O./P.P. 2', b'113795', b'F.C.C. 13528', b'347089', b'370374', b'PC 17758', b'A/5 21173', b'110813', b'27849', b'218629', b'19947', b'PC 17485', b'A/5. 851', b'244368', b'PC 17593', b'C.A. 29566', b'368783', b'LINE', b'65303', b'248731', b'376564', b'PC 17483', b'17421', b'315096', b'323592', b'336439', b'16966', b'29103', b'350035', b'C 7076', b'237670', b'250653', b'113796', b'PC 17761', b'248727', b'19950', b'349911', b'A/5 21175', b'335677', b'24160', b'C 7075', b'230136', b'343120', b'3101265', b'347080', b'347065', b'347076', b'345771', b'36568', b'11751', b'364511', b'PC 17760', b'347054', b'234604', b'386525', b'347082', b'348125', b'28034', b'330963', b'330972', b'2696', b'SOTON/OQ 392076', b'19950', b'13508', b'24160', b'C.A. 34050', b'248738', b'110413', b'C.A. 29395', b'244278', b'7553', b'35273', b'2685', b'12233', b'36928', b'349230', b'2687', b'2660', b'11769', b'347074', b'347075', b'236171', b'PC 17609', b'113778', b'112050', b'315153', b'2654', b'364516', b'112901', b'350407', b'367230', b'347066', b'11765', b'C.A. 2673', b'349251', b'11813', b'347081', b'SOTON/O.Q. 3101315', b'P/PP 3381', b'230136', b'350054', b'13695', b'236853', b'19950', b'112378', b'13213', b'112377', b'394140', b'315090', b'248740', b'16966', b'343275', b'28220', b'330919', b'26360', b'3101295', b'PC 17582', b'F.C.C. 13531', b'349224', b'250643', b'PC 17605', b'347470', b'A/4. 20589', b'350042', b'19996', b'349245', b'C.A. 2315', b'SOTON/O.Q. 3101309', b'364499', b'16966', b'8471', b'SOTON/OQ 392083', b'3101277', b'CA 31352', b'239853', b'244360', b'36928', b'243847', b'13695', b'349215', b'112051', b'2667', b'SC/PARIS 2133', b'376564', b'364846', b'AQ/3. 30631', b'C.A. 49867', b'F.C. 12750', b'PC 17594', b'PC 17482', b'113054', b'347088', b'33638', b'5727', b'111361', b'371362', b'SC/AH Basle 541', b'2647', b'65306', b'220845', b'244252', b'W.E.P. 5734', b'A/5. 851', b'STON/O 2. 3101294', b'113803', b'PC 17757', b'250644', b'345764', b'349212', b'113043', b'237789', b'S.O./P.P. 2', b'A/5 3902', b'36973', b'349229', b'A.5. 11206', b'113509', b'237736', b'A/4 48871', b'2908', b'CA 2144', b'7538', b'PC 17562', b'Fa 265302', b'PC 17599', b'237734', b'345779', b'347077', b'248738', b'367226', b'16966', b'7545', b'112058', b'243880', b'315093', b'11778', b'349219', b'349210', b'315037', b'347077', b'C.A. 31921', b'350046', b'364851', b'2699', b'345764', b'349236', b'349205', b'28664', b'345781', b'349246', b'229236', b'17474', b'C.A. 15185', b'220844', b'368703', b'330932', b'113794', b'3470', b'7546', b'SOTON/O.Q. 392078', b'250655', b'231919', b'PC 17608', b'F.C.C. 13529', b'312991', b'347063', b'SC/AH 29037', b'2689', b'350403', b'113781', b'PC 17608', b'29105', b'347082', b'2662', b'7935', b'233734', b'PC 17760', b'13049', b'113510', b'19928', b'2688', b'4133', b'3101295', b'2623', b'13502', b'36973', b'SOTON/O.Q. 3101306', b'349223', b'3101295', b'367655', b'14973', b'371362', b'363272', b'113501', b'F.C.C. 13540', b'28403', b'3101298', b'A./5. 3235', b'C.A. 33595', b'364858', b'2003', b'113798', b'12749', b'36865', b'A. 2. 39186', b'234360', b'347072', b'242963', b'2691', b'349228', b'237442', b'2655', b'113760', b'29750', b'233866', b'A/5 21174', b'S.C./A.4. 23567', b'2626', b'2657', b'2684', b'1601', b'113807', b'2627', b'370371', b'4133', b'315085', b'230136', b'CA. 2343', b'348122', b'382650', b'SOTON/O2 3101284', b'382652', b'S.C./PARIS 2079', b'349226', b'PC 17759', b'STON/O 2. 3101274', b'113791', b'315151', b'349239', b'SCO/W 1585', b'C.A. 6212', b'111427', b'220845', b'SOTON/OQ 3101317', b'2629', b'345767', b'SC/PARIS 2147', b'367232', b'2668', b'4134', b'29107', b'4133', b'CA 2144', b'C.A. 33112', b'370376', b'347069', b'330935', b'363291', b'C.A. 34651'], 'survived': [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 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1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0]}\n"
     ]
    }
   ],
   "source": [
    "for elem in tfds.as_numpy(ds):\n",
    "    dict_of_lists['survived'].append(elem['survived'])\n",
    "    for key in elem['features']:\n",
    "        dict_of_lists[key].append(elem['features'][key])\n",
    "print(dict_of_lists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "      <th>fare</th>\n",
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       "      <th>sex</th>\n",
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       "      <td>b'Ruotsinphytaa, Finland New York, NY'</td>\n",
       "      <td>b'Gustafsson, Mr. Anders Vilhelm'</td>\n",
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       "      <td>b'3101276'</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>b'9'</td>\n",
       "      <td>-1</td>\n",
       "      <td>b'Unknown'</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>b'Spain'</td>\n",
       "      <td>b'Reynaldo, Ms. Encarnacion'</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>b'230434'</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>b'Unknown'</td>\n",
       "      <td>-1</td>\n",
       "      <td>b'Unknown'</td>\n",
       "      <td>2</td>\n",
       "      <td>73.5000</td>\n",
       "      <td>b'Lyndhurst, England'</td>\n",
       "      <td>b'Davies, Mr. Charles Henry'</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>b'S.O.C. 14879'</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>b'Unknown'</td>\n",
       "      <td>-1</td>\n",
       "      <td>b'Unknown'</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>b'Unknown'</td>\n",
       "      <td>b'Gheorgheff, Mr. Stanio'</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>b'349254'</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age        boat  body       cabin  embarked     fare  \\\n",
       "0  30.0  b'Unknown'    -1  b'Unknown'         2  13.0000   \n",
       "1  37.0  b'Unknown'    98  b'Unknown'         2   7.9250   \n",
       "2  28.0        b'9'    -1  b'Unknown'         2  13.0000   \n",
       "3  18.0  b'Unknown'    -1  b'Unknown'         2  73.5000   \n",
       "4  -1.0  b'Unknown'    -1  b'Unknown'         0   7.8958   \n",
       "\n",
       "                                home.dest                               name  \\\n",
       "0                           b'Sarnia, ON'       b'McCrie, Mr. James Matthew'   \n",
       "1  b'Ruotsinphytaa, Finland New York, NY'  b'Gustafsson, Mr. Anders Vilhelm'   \n",
       "2                                b'Spain'       b'Reynaldo, Ms. Encarnacion'   \n",
       "3                   b'Lyndhurst, England'       b'Davies, Mr. Charles Henry'   \n",
       "4                              b'Unknown'          b'Gheorgheff, Mr. Stanio'   \n",
       "\n",
       "   parch  pclass  sex  sibsp           ticket  survived  \n",
       "0      0       1    0      0        b'233478'         0  \n",
       "1      0       2    0      2       b'3101276'         0  \n",
       "2      0       1    1      0        b'230434'         1  \n",
       "3      0       1    0      0  b'S.O.C. 14879'         0  \n",
       "4      0       2    0      0        b'349254'         0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw = pd.DataFrame(data=dict_of_lists)\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing\n",
    "\n",
    "## 1: Drop columns that will not be used further\n",
    "The column 'cabin' has mostly unknown values. 'boat' is known almost exclusively for survivors. The others don't hold useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>73.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  embarked     fare  parch  pclass  sex  sibsp  survived\n",
       "0  30.0         2  13.0000      0       1    0      0         0\n",
       "1  37.0         2   7.9250      0       2    0      2         0\n",
       "2  28.0         2  13.0000      0       1    1      0         1\n",
       "3  18.0         2  73.5000      0       1    0      0         0\n",
       "4  -1.0         0   7.8958      0       2    0      0         0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop = df_raw.drop(columns=['boat', 'body', 'cabin', 'home.dest', 'name', 'ticket'])\n",
    "df_drop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2: Clean the remaining features.\n",
    "\n",
    "### embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "embarked\n",
       "0    270\n",
       "1    123\n",
       "2    914\n",
       "3      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop.pivot(columns='embarked', values='survived').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "embarked category 3 corresponds to missing values, of which there are two. They can be replaced with the most frequent port of embarkation (2, corresponding to Southampton)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "embarked\n",
       "0    270\n",
       "1    123\n",
       "2    916\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embarked = df_drop.copy()\n",
    "df_embarked['embarked'] = df_drop.embarked.apply(lambda x: min(2,x))\n",
    "df_embarked.pivot(columns='embarked', values='survived').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, `embarked` is finally one-hot-encoded. It is not necessary to keep all three embarkation ports, because they are linearly dependent with the \"mean column\" or column of 1s that is equivalent to adding a bias term in the linear model, which is done in net_fn."
   ]
  },
  {
   "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",
       "    }\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>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>73.5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     fare  parch  pclass  sex  sibsp  survived  embarked_0  embarked_1\n",
       "0  30.0  13.0000      0       1    0      0         0           0           0\n",
       "1  37.0   7.9250      0       2    0      2         0           0           0\n",
       "2  28.0  13.0000      0       1    1      0         1           0           0\n",
       "3  18.0  73.5000      0       1    0      0         0           0           0\n",
       "4  -1.0   7.8958      0       2    0      0         0           1           0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embarked_1hot = pd.get_dummies(df_embarked, columns=['embarked']).drop(columns=['embarked_2'])\n",
    "df_embarked_1hot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4240db2d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_embarked_1hot.plot(y='fare', kind='density')#, bins=[-1]+list(range(0,550,50)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(df_embarked_1hot['fare'].min())\n",
    "print((df_embarked_1hot['fare'] < 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We simlmpy standardize the `fare` feature, ignoring the one passenger with a negative fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.309000e+03\n",
       "mean    -8.709399e-17\n",
       "std      1.000000e+00\n",
       "min     -6.622395e-01\n",
       "25%     -4.903319e-01\n",
       "50%     -3.635935e-01\n",
       "75%     -3.853863e-02\n",
       "max      9.257633e+00\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fare = df_embarked_1hot.copy()\n",
    "df_fare['fare'] = (df_fare['fare'] - df_fare['fare'].mean()) / df_fare['fare'].std()\n",
    "df_fare['fare'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## parch and sibsp\n",
    "These columns are combined into a binary variable indicating whether the passenger travelled alone:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.489768</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      fare  parch  pclass  sex  sibsp  survived  embarked_0  \\\n",
       "0  30.0 -0.391695      0       1    0      0         0           0   \n",
       "1  37.0 -0.489768      0       2    0      2         0           0   \n",
       "2  28.0 -0.391695      0       1    1      0         1           0   \n",
       "3  18.0  0.777442      0       1    0      0         0           0   \n",
       "4  -1.0 -0.490332      0       2    0      0         0           1   \n",
       "\n",
       "   embarked_1  travel_alone  \n",
       "0           0             1  \n",
       "1           0             0  \n",
       "2           0             1  \n",
       "3           0             1  \n",
       "4           0             1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_parch = df_fare.copy()\n",
    "df_parch['travel_alone']=np.where((df_parch[\"sibsp\"]+df_parch[\"parch\"])>0, 0, 1)\n",
    "df_parch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>fare</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
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       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      fare  pclass  sex  survived  embarked_0  embarked_1  travel_alone\n",
       "0  30.0 -0.391695       1    0         0           0           0             1\n",
       "1  37.0 -0.489768       2    0         0           0           0             0\n",
       "2  28.0 -0.391695       1    1         1           0           0             1\n",
       "3  18.0  0.777442       1    0         0           0           0             1\n",
       "4  -1.0 -0.490332       2    0         0           1           0             1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_parch = df_parch.drop(columns=['sibsp', 'parch'])\n",
    "df_parch.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passenger class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass\n",
       "0    323\n",
       "1    277\n",
       "2    709\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_parch.pivot(columns='pclass', values='survived').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pclass is one-hot encoded. It is not necessary to keep all three passenger classes, because they are linearly dependent with the \"mean column\" or column of 1s that is equivalent to adding a bias term in the linear model, which is done in net_fn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.489768</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      fare  sex  survived  embarked_0  embarked_1  travel_alone  \\\n",
       "0  30.0 -0.391695    0         0           0           0             1   \n",
       "1  37.0 -0.489768    0         0           0           0             0   \n",
       "2  28.0 -0.391695    1         1           0           0             1   \n",
       "3  18.0  0.777442    0         0           0           0             1   \n",
       "4  -1.0 -0.490332    0         0           1           0             1   \n",
       "\n",
       "   pclass_0  pclass_1  \n",
       "0         0         1  \n",
       "1         0         0  \n",
       "2         0         1  \n",
       "3         0         1  \n",
       "4         0         0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pclass = pd.get_dummies(df_parch, columns=['pclass']).drop(columns=['pclass_2'])\n",
    "df_pclass.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "0    843\n",
       "1    466\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pclass.pivot(columns='sex', values='survived').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sex` feature is good to go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20091673032849502"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion_missing = np.sum(df_pclass['age'] < 0) / df_pclass.shape[0]\n",
    "proportion_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc30c2034d0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pclass.plot(y='age', kind='hist', bins=[-1]+list(range(0,101,5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc3443e1450>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_age = df_pclass.copy()\n",
    "df_age['age_group'] = pd.cut(df_age['age'], 16)\n",
    "df_age.groupby(by='age_group').mean().plot(y='survived', rot=60, kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20.1% of `age` values are missing. Since the age distribution is skewed, we impute them with the column median instaed of the mean, before \n",
    "We also create a new feature `IsMinor` for children under the age of 16, following MNassri (cf. link at the top of the notebook), even though we do not find a surprising difference in survival rates between minors and adults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>age_group</th>\n",
       "      <th>age_missing</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.391695</td>\n",
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       "      <td>False</td>\n",
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       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
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       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
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       "      <td>(-1.081, 4.062]</td>\n",
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       "      <td>56.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(54.688, 59.75]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.333722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>(39.5, 44.562]</td>\n",
       "      <td>False</td>\n",
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       "      <td>45.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "    age      fare  sex  survived  embarked_0  embarked_1  travel_alone  \\\n",
       "0  30.0 -0.391695    0         0           0           0             1   \n",
       "1  37.0 -0.489768    0         0           0           0             0   \n",
       "2  28.0 -0.391695    1         1           0           0             1   \n",
       "3  18.0  0.777442    0         0           0           0             1   \n",
       "4  -1.0 -0.490332    0         0           1           0             1   \n",
       "5  56.0 -0.129847    0         0           0           0             1   \n",
       "6  40.0 -0.333722    0         0           0           0             1   \n",
       "7  45.0 -0.370278    1         1           0           0             0   \n",
       "8  32.0 -0.343384    1         0           0           1             0   \n",
       "9  30.0  1.163933    1         1           0           0             1   \n",
       "\n",
       "   pclass_0  pclass_1         age_group  age_missing  \n",
       "0         0         1  (29.375, 34.438]        False  \n",
       "1         0         0    (34.438, 39.5]        False  \n",
       "2         0         1  (24.312, 29.375]        False  \n",
       "3         0         1   (14.188, 19.25]        False  \n",
       "4         0         0   (-1.081, 4.062]         True  \n",
       "5         1         0   (54.688, 59.75]        False  \n",
       "6         0         1    (39.5, 44.562]        False  \n",
       "7         0         0  (44.562, 49.625]        False  \n",
       "8         0         0  (29.375, 34.438]        False  \n",
       "9         1         0  (29.375, 34.438]        False  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age['age_missing'] = df_age['age'] < 0\n",
    "df_age.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       30.0\n",
       "1       37.0\n",
       "2       28.0\n",
       "3       18.0\n",
       "5       56.0\n",
       "        ... \n",
       "1303    19.0\n",
       "1304    32.0\n",
       "1305    19.0\n",
       "1307    31.0\n",
       "1308    36.0\n",
       "Name: age, Length: 1046, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_known_series = df_age.loc[~df_age['age_missing'], 'age']\n",
    "age_known_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median: 28.0 mean: 29.881134512433587 std: 14.41349969991273\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>age_group</th>\n",
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       "      <th>age_imputed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>0</td>\n",
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       "      <td>(29.375, 34.438]</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.489768</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>37.0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>-0.129847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(54.688, 59.75]</td>\n",
       "      <td>False</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.333722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(39.5, 44.562]</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-0.370278</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>45.0</td>\n",
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       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-0.343384</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      fare  sex  survived  embarked_0  embarked_1  travel_alone  \\\n",
       "0  30.0 -0.391695    0         0           0           0             1   \n",
       "1  37.0 -0.489768    0         0           0           0             0   \n",
       "2  28.0 -0.391695    1         1           0           0             1   \n",
       "3  18.0  0.777442    0         0           0           0             1   \n",
       "4  -1.0 -0.490332    0         0           1           0             1   \n",
       "5  56.0 -0.129847    0         0           0           0             1   \n",
       "6  40.0 -0.333722    0         0           0           0             1   \n",
       "7  45.0 -0.370278    1         1           0           0             0   \n",
       "8  32.0 -0.343384    1         0           0           1             0   \n",
       "9  30.0  1.163933    1         1           0           0             1   \n",
       "\n",
       "   pclass_0  pclass_1         age_group  age_missing  age_imputed  \n",
       "0         0         1  (29.375, 34.438]        False         30.0  \n",
       "1         0         0    (34.438, 39.5]        False         37.0  \n",
       "2         0         1  (24.312, 29.375]        False         28.0  \n",
       "3         0         1   (14.188, 19.25]        False         18.0  \n",
       "4         0         0   (-1.081, 4.062]         True         28.0  \n",
       "5         1         0   (54.688, 59.75]        False         56.0  \n",
       "6         0         1    (39.5, 44.562]        False         40.0  \n",
       "7         0         0  (44.562, 49.625]        False         45.0  \n",
       "8         0         0  (29.375, 34.438]        False         32.0  \n",
       "9         1         0  (29.375, 34.438]        False         30.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the median for missing values\n",
    "age_median = age_known_series.median()\n",
    "age_mean   = age_known_series.mean()\n",
    "age_std    = age_known_series.std()\n",
    "print('median:', age_median,'mean:', age_mean, 'std:', age_std)\n",
    "df_age['age_imputed'] = df_age['age']\n",
    "df_age.loc[df_age['age_missing'], 'age_imputed'] = age_median\n",
    "df_age.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc30c4aba10>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_age.plot(y='age_imputed', kind='hist', bins = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>age_group</th>\n",
       "      <th>age_missing</th>\n",
       "      <th>age_imputed</th>\n",
       "      <th>age_standardized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.489768</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.493903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>-0.129847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(54.688, 59.75]</td>\n",
       "      <td>False</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.812111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.333722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(39.5, 44.562]</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.702041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-0.370278</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.048938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-0.343384</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.147006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.163933</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      fare  sex  survived  embarked_0  embarked_1  travel_alone  \\\n",
       "0  30.0 -0.391695    0         0           0           0             1   \n",
       "1  37.0 -0.489768    0         0           0           0             0   \n",
       "2  28.0 -0.391695    1         1           0           0             1   \n",
       "3  18.0  0.777442    0         0           0           0             1   \n",
       "4  -1.0 -0.490332    0         0           1           0             1   \n",
       "5  56.0 -0.129847    0         0           0           0             1   \n",
       "6  40.0 -0.333722    0         0           0           0             1   \n",
       "7  45.0 -0.370278    1         1           0           0             0   \n",
       "8  32.0 -0.343384    1         0           0           1             0   \n",
       "9  30.0  1.163933    1         1           0           0             1   \n",
       "\n",
       "   pclass_0  pclass_1         age_group  age_missing  age_imputed  \\\n",
       "0         0         1  (29.375, 34.438]        False         30.0   \n",
       "1         0         0    (34.438, 39.5]        False         37.0   \n",
       "2         0         1  (24.312, 29.375]        False         28.0   \n",
       "3         0         1   (14.188, 19.25]        False         18.0   \n",
       "4         0         0   (-1.081, 4.062]         True         28.0   \n",
       "5         1         0   (54.688, 59.75]        False         56.0   \n",
       "6         0         1    (39.5, 44.562]        False         40.0   \n",
       "7         0         0  (44.562, 49.625]        False         45.0   \n",
       "8         0         0  (29.375, 34.438]        False         32.0   \n",
       "9         1         0  (29.375, 34.438]        False         30.0   \n",
       "\n",
       "   age_standardized  \n",
       "0          0.008247  \n",
       "1          0.493903  \n",
       "2         -0.130512  \n",
       "3         -0.824306  \n",
       "4         -0.130512  \n",
       "5          1.812111  \n",
       "6          0.702041  \n",
       "7          1.048938  \n",
       "8          0.147006  \n",
       "9          0.008247  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age['age_standardized'] = (df_age['age_imputed'] - age_mean) / age_std\n",
    "df_age.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
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       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
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       "      <th>age_missing</th>\n",
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       "      <th>age_standardized</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.391695</td>\n",
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       "      <td>(29.375, 34.438]</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.489768</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.493903</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>-0.129847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(54.688, 59.75]</td>\n",
       "      <td>False</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.812111</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.333722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(39.5, 44.562]</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.702041</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-0.370278</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.048938</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-0.343384</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.147006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.163933</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.506677</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.850634</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.616168</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.616168</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-0.394111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.424523</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.140476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.338650</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.493149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.493149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.504965</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(49.625, 54.688]</td>\n",
       "      <td>False</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.673352</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.887592</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.118317</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.487352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>62.0</td>\n",
       "      <td>-0.455708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(59.75, 64.812]</td>\n",
       "      <td>False</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.228388</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.332895</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.257076</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.479462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-0.140476</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.216385</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.298937</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.427769</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.003756</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.198450</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-0.038539</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(9.125, 14.188]</td>\n",
       "      <td>False</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-1.309962</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-0.306667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.424523</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>18.5</td>\n",
       "      <td>-0.503214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.5</td>\n",
       "      <td>-0.789616</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-0.491136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-0.477409</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.475516</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.396768</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.036365</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.563282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.503214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.440007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.026763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(69.875, 74.938]</td>\n",
       "      <td>False</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.852802</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.129847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.279854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.269271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.676035</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(9.125, 14.188]</td>\n",
       "      <td>False</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-1.101824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-0.491136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-0.199891</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.428473</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.338650</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.487352</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.978142</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.355144</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.150782</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.642915</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(39.5, 44.562]</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.702041</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.503214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.896288</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.811099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.118317</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.427372</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.257076</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.383220</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-0.199891</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.503295</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.497014</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.408030</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.376719</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.269271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-0.506595</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(49.625, 54.688]</td>\n",
       "      <td>False</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.465214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age      fare  sex  survived  embarked_0  embarked_1  travel_alone  \\\n",
       "0   30.0 -0.391695    0         0           0           0             1   \n",
       "1   37.0 -0.489768    0         0           0           0             0   \n",
       "2   28.0 -0.391695    1         1           0           0             1   \n",
       "3   18.0  0.777442    0         0           0           0             1   \n",
       "4   -1.0 -0.490332    0         0           1           0             1   \n",
       "5   56.0 -0.129847    0         0           0           0             1   \n",
       "6   40.0 -0.333722    0         0           0           0             1   \n",
       "7   45.0 -0.370278    1         1           0           0             0   \n",
       "8   32.0 -0.343384    1         0           0           1             0   \n",
       "9   30.0  1.163933    1         1           0           0             1   \n",
       "10  20.0 -0.506677    0         0           0           0             1   \n",
       "11  21.0  0.850634    0         0           0           0             0   \n",
       "12  21.0  0.777442    0         0           0           0             0   \n",
       "13  36.0 -0.394111    0         0           1           0             1   \n",
       "14  25.0 -0.140476    0         0           0           0             0   \n",
       "15  -1.0 -0.493149    1         1           0           1             1   \n",
       "16  -1.0 -0.493149    0         0           0           1             1   \n",
       "17  54.0  0.504965    1         1           1           0             0   \n",
       "18  46.0  0.887592    0         0           1           0             1   \n",
       "19  -1.0 -0.487352    0         0           0           0             1   \n",
       "20  62.0 -0.455708    0         0           0           1             1   \n",
       "21  48.0  0.332895    0         0           1           0             1   \n",
       "22  -1.0 -0.479462    0         0           0           1             1   \n",
       "23  33.0 -0.140476    1         1           0           0             0   \n",
       "24  18.0 -0.298937    1         0           0           0             0   \n",
       "25   1.0 -0.427769    1         1           0           0             0   \n",
       "26  20.0 -0.198450    1         1           0           0             0   \n",
       "27  11.0 -0.038539    1         0           0           0             0   \n",
       "28  36.0 -0.306667    1         1           0           0             0   \n",
       "29  18.5 -0.503214    0         0           1           0             1   \n",
       "30  23.0 -0.491136    1         1           0           0             1   \n",
       "31  20.0 -0.475516    0         0           0           0             1   \n",
       "32  30.0 -0.396768    0         1           1           0             1   \n",
       "33  38.0 -0.036365    1         1           0           0             0   \n",
       "34  -1.0 -0.503214    0         0           1           0             1   \n",
       "35  30.0 -0.440007    0         0           0           0             1   \n",
       "36  71.0  0.026763    0         0           1           0             1   \n",
       "37  -1.0 -0.129847    0         0           0           0             1   \n",
       "38  26.0 -0.279854    0         1           1           0             1   \n",
       "39  14.0  1.676035    1         1           0           0             0   \n",
       "40  27.0 -0.491136    0         0           0           0             1   \n",
       "41  25.0  0.428473    0         1           1           0             0   \n",
       "42  18.0 -0.487352    0         1           0           0             1   \n",
       "43  35.0  1.978142    1         1           0           0             1   \n",
       "44  -1.0 -0.150782    1         0           0           0             0   \n",
       "45  40.0 -0.642915    0         0           0           0             1   \n",
       "46  -1.0 -0.503214    0         1           1           0             1   \n",
       "47  18.0  0.896288    1         1           0           0             0   \n",
       "48  46.0  0.811099    0         0           1           0             1   \n",
       "49  48.0  4.427372    1         1           1           0             0   \n",
       "50  27.0  0.383220    0         1           0           0             0   \n",
       "51  20.0 -0.503295    0         0           1           0             1   \n",
       "52  24.0 -0.497014    0         1           0           0             1   \n",
       "53  26.0 -0.376719    1         0           0           0             0   \n",
       "54  51.0 -0.506595    0         0           0           0             1   \n",
       "\n",
       "    pclass_0  pclass_1         age_group  age_missing  age_imputed  \\\n",
       "0          0         1  (29.375, 34.438]        False         30.0   \n",
       "1          0         0    (34.438, 39.5]        False         37.0   \n",
       "2          0         1  (24.312, 29.375]        False         28.0   \n",
       "3          0         1   (14.188, 19.25]        False         18.0   \n",
       "4          0         0   (-1.081, 4.062]         True         28.0   \n",
       "5          1         0   (54.688, 59.75]        False         56.0   \n",
       "6          0         1    (39.5, 44.562]        False         40.0   \n",
       "7          0         0  (44.562, 49.625]        False         45.0   \n",
       "8          0         0  (29.375, 34.438]        False         32.0   \n",
       "9          1         0  (29.375, 34.438]        False         30.0   \n",
       "10         0         0   (19.25, 24.312]        False         20.0   \n",
       "11         1         0   (19.25, 24.312]        False         21.0   \n",
       "12         0         1   (19.25, 24.312]        False         21.0   \n",
       "13         0         1    (34.438, 39.5]        False         36.0   \n",
       "14         0         1  (24.312, 29.375]        False         25.0   \n",
       "15         0         0   (-1.081, 4.062]         True         28.0   \n",
       "16         0         0   (-1.081, 4.062]         True         28.0   \n",
       "17         1         0  (49.625, 54.688]        False         54.0   \n",
       "18         1         0  (44.562, 49.625]        False         46.0   \n",
       "19         0         0   (-1.081, 4.062]         True         28.0   \n",
       "20         0         1   (59.75, 64.812]        False         62.0   \n",
       "21         1         0  (44.562, 49.625]        False         48.0   \n",
       "22         0         0   (-1.081, 4.062]         True         28.0   \n",
       "23         0         1  (29.375, 34.438]        False         33.0   \n",
       "24         0         0   (14.188, 19.25]        False         18.0   \n",
       "25         0         0   (-1.081, 4.062]        False          1.0   \n",
       "26         0         1   (19.25, 24.312]        False         20.0   \n",
       "27         0         0   (9.125, 14.188]        False         11.0   \n",
       "28         0         0    (34.438, 39.5]        False         36.0   \n",
       "29         0         0   (14.188, 19.25]        False         18.5   \n",
       "30         0         0   (19.25, 24.312]        False         23.0   \n",
       "31         0         0   (19.25, 24.312]        False         20.0   \n",
       "32         0         1  (29.375, 34.438]        False         30.0   \n",
       "33         0         0    (34.438, 39.5]        False         38.0   \n",
       "34         0         0   (-1.081, 4.062]         True         28.0   \n",
       "35         0         1  (29.375, 34.438]        False         30.0   \n",
       "36         1         0  (69.875, 74.938]        False         71.0   \n",
       "37         1         0   (-1.081, 4.062]         True         28.0   \n",
       "38         0         0  (24.312, 29.375]        False         26.0   \n",
       "39         1         0   (9.125, 14.188]        False         14.0   \n",
       "40         0         0  (24.312, 29.375]        False         27.0   \n",
       "41         1         0  (24.312, 29.375]        False         25.0   \n",
       "42         0         0   (14.188, 19.25]        False         18.0   \n",
       "43         1         0    (34.438, 39.5]        False         35.0   \n",
       "44         0         0   (-1.081, 4.062]         True         28.0   \n",
       "45         1         0    (39.5, 44.562]        False         40.0   \n",
       "46         0         0   (-1.081, 4.062]         True         28.0   \n",
       "47         1         0   (14.188, 19.25]        False         18.0   \n",
       "48         1         0  (44.562, 49.625]        False         46.0   \n",
       "49         1         0  (44.562, 49.625]        False         48.0   \n",
       "50         1         0  (24.312, 29.375]        False         27.0   \n",
       "51         0         0   (19.25, 24.312]        False         20.0   \n",
       "52         0         0   (19.25, 24.312]        False         24.0   \n",
       "53         0         0  (24.312, 29.375]        False         26.0   \n",
       "54         0         0  (49.625, 54.688]        False         51.0   \n",
       "\n",
       "    age_standardized  is_minor  \n",
       "0           0.008247         0  \n",
       "1           0.493903         0  \n",
       "2          -0.130512         0  \n",
       "3          -0.824306         0  \n",
       "4          -0.130512         1  \n",
       "5           1.812111         0  \n",
       "6           0.702041         0  \n",
       "7           1.048938         0  \n",
       "8           0.147006         0  \n",
       "9           0.008247         0  \n",
       "10         -0.685547         0  \n",
       "11         -0.616168         0  \n",
       "12         -0.616168         0  \n",
       "13          0.424523         0  \n",
       "14         -0.338650         0  \n",
       "15         -0.130512         1  \n",
       "16         -0.130512         1  \n",
       "17          1.673352         0  \n",
       "18          1.118317         0  \n",
       "19         -0.130512         1  \n",
       "20          2.228388         0  \n",
       "21          1.257076         0  \n",
       "22         -0.130512         1  \n",
       "23          0.216385         0  \n",
       "24         -0.824306         0  \n",
       "25         -2.003756         1  \n",
       "26         -0.685547         0  \n",
       "27         -1.309962         1  \n",
       "28          0.424523         0  \n",
       "29         -0.789616         0  \n",
       "30         -0.477409         0  \n",
       "31         -0.685547         0  \n",
       "32          0.008247         0  \n",
       "33          0.563282         0  \n",
       "34         -0.130512         1  \n",
       "35          0.008247         0  \n",
       "36          2.852802         0  \n",
       "37         -0.130512         1  \n",
       "38         -0.269271         0  \n",
       "39         -1.101824         1  \n",
       "40         -0.199891         0  \n",
       "41         -0.338650         0  \n",
       "42         -0.824306         0  \n",
       "43          0.355144         0  \n",
       "44         -0.130512         1  \n",
       "45          0.702041         0  \n",
       "46         -0.130512         1  \n",
       "47         -0.824306         0  \n",
       "48          1.118317         0  \n",
       "49          1.257076         0  \n",
       "50         -0.199891         0  \n",
       "51         -0.685547         0  \n",
       "52         -0.408030         0  \n",
       "53         -0.269271         0  \n",
       "54          1.465214         0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age['is_minor'] = (df_age['age'] < 17).astype(int)\n",
    "df_age.head(55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>age_group</th>\n",
       "      <th>age_missing</th>\n",
       "      <th>age_imputed</th>\n",
       "      <th>age_standardized</th>\n",
       "      <th>is_minor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>(29.375, 34.438]</td>\n",
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       "      <td>0.008247</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>-0.489768</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.493903</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>-0.129847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(54.688, 59.75]</td>\n",
       "      <td>False</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.812111</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.333722</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(39.5, 44.562]</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.702041</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-0.370278</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1.048938</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-0.343384</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.147006</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.163933</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.506677</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.850634</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.616168</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-0.616168</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-0.394111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.424523</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.140476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.338650</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.493149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.493149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.504965</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(49.625, 54.688]</td>\n",
       "      <td>False</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1.673352</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.887592</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.118317</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.487352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>62.0</td>\n",
       "      <td>-0.455708</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(59.75, 64.812]</td>\n",
       "      <td>False</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.228388</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.332895</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.257076</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.479462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-0.140476</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.216385</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.298937</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.427769</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.003756</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.198450</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-0.038539</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(9.125, 14.188]</td>\n",
       "      <td>False</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-1.309962</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-0.306667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.424523</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>18.5</td>\n",
       "      <td>-0.503214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.5</td>\n",
       "      <td>-0.789616</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-0.491136</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-0.477409</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.475516</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.396768</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>38.0</td>\n",
       "      <td>-0.036365</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.563282</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.503214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>30.0</td>\n",
       "      <td>-0.440007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>(29.375, 34.438]</td>\n",
       "      <td>False</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.026763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(69.875, 74.938]</td>\n",
       "      <td>False</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.852802</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.129847</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.279854</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.269271</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.676035</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(9.125, 14.188]</td>\n",
       "      <td>False</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-1.101824</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-0.491136</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-0.199891</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.428473</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.338650</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.487352</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.978142</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(34.438, 39.5]</td>\n",
       "      <td>False</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.355144</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.150782</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-0.642915</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(39.5, 44.562]</td>\n",
       "      <td>False</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.702041</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.503214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(-1.081, 4.062]</td>\n",
       "      <td>True</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.896288</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(14.188, 19.25]</td>\n",
       "      <td>False</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.811099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.118317</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.427372</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(44.562, 49.625]</td>\n",
       "      <td>False</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1.257076</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.383220</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>27.0</td>\n",
       "      <td>-0.199891</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.503295</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.685547</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.497014</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(19.25, 24.312]</td>\n",
       "      <td>False</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-0.408030</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>53</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.376719</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(24.312, 29.375]</td>\n",
       "      <td>False</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.269271</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54</td>\n",
       "      <td>51.0</td>\n",
       "      <td>-0.506595</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>(49.625, 54.688]</td>\n",
       "      <td>False</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1.465214</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age      fare  sex  survived  embarked_0  embarked_1  travel_alone  \\\n",
       "0   30.0 -0.391695    0         0           0           0             1   \n",
       "1   37.0 -0.489768    0         0           0           0             0   \n",
       "2   28.0 -0.391695    1         1           0           0             1   \n",
       "3   18.0  0.777442    0         0           0           0             1   \n",
       "4   -1.0 -0.490332    0         0           1           0             1   \n",
       "5   56.0 -0.129847    0         0           0           0             1   \n",
       "6   40.0 -0.333722    0         0           0           0             1   \n",
       "7   45.0 -0.370278    1         1           0           0             0   \n",
       "8   32.0 -0.343384    1         0           0           1             0   \n",
       "9   30.0  1.163933    1         1           0           0             1   \n",
       "10  20.0 -0.506677    0         0           0           0             1   \n",
       "11  21.0  0.850634    0         0           0           0             0   \n",
       "12  21.0  0.777442    0         0           0           0             0   \n",
       "13  36.0 -0.394111    0         0           1           0             1   \n",
       "14  25.0 -0.140476    0         0           0           0             0   \n",
       "15  -1.0 -0.493149    1         1           0           1             1   \n",
       "16  -1.0 -0.493149    0         0           0           1             1   \n",
       "17  54.0  0.504965    1         1           1           0             0   \n",
       "18  46.0  0.887592    0         0           1           0             1   \n",
       "19  -1.0 -0.487352    0         0           0           0             1   \n",
       "20  62.0 -0.455708    0         0           0           1             1   \n",
       "21  48.0  0.332895    0         0           1           0             1   \n",
       "22  -1.0 -0.479462    0         0           0           1             1   \n",
       "23  33.0 -0.140476    1         1           0           0             0   \n",
       "24  18.0 -0.298937    1         0           0           0             0   \n",
       "25   1.0 -0.427769    1         1           0           0             0   \n",
       "26  20.0 -0.198450    1         1           0           0             0   \n",
       "27  11.0 -0.038539    1         0           0           0             0   \n",
       "28  36.0 -0.306667    1         1           0           0             0   \n",
       "29  18.5 -0.503214    0         0           1           0             1   \n",
       "30  23.0 -0.491136    1         1           0           0             1   \n",
       "31  20.0 -0.475516    0         0           0           0             1   \n",
       "32  30.0 -0.396768    0         1           1           0             1   \n",
       "33  38.0 -0.036365    1         1           0           0             0   \n",
       "34  -1.0 -0.503214    0         0           1           0             1   \n",
       "35  30.0 -0.440007    0         0           0           0             1   \n",
       "36  71.0  0.026763    0         0           1           0             1   \n",
       "37  -1.0 -0.129847    0         0           0           0             1   \n",
       "38  26.0 -0.279854    0         1           1           0             1   \n",
       "39  14.0  1.676035    1         1           0           0             0   \n",
       "40  27.0 -0.491136    0         0           0           0             1   \n",
       "41  25.0  0.428473    0         1           1           0             0   \n",
       "42  18.0 -0.487352    0         1           0           0             1   \n",
       "43  35.0  1.978142    1         1           0           0             1   \n",
       "44  -1.0 -0.150782    1         0           0           0             0   \n",
       "45  40.0 -0.642915    0         0           0           0             1   \n",
       "46  -1.0 -0.503214    0         1           1           0             1   \n",
       "47  18.0  0.896288    1         1           0           0             0   \n",
       "48  46.0  0.811099    0         0           1           0             1   \n",
       "49  48.0  4.427372    1         1           1           0             0   \n",
       "50  27.0  0.383220    0         1           0           0             0   \n",
       "51  20.0 -0.503295    0         0           1           0             1   \n",
       "52  24.0 -0.497014    0         1           0           0             1   \n",
       "53  26.0 -0.376719    1         0           0           0             0   \n",
       "54  51.0 -0.506595    0         0           0           0             1   \n",
       "\n",
       "    pclass_0  pclass_1         age_group  age_missing  age_imputed  \\\n",
       "0          0         1  (29.375, 34.438]        False         30.0   \n",
       "1          0         0    (34.438, 39.5]        False         37.0   \n",
       "2          0         1  (24.312, 29.375]        False         28.0   \n",
       "3          0         1   (14.188, 19.25]        False         18.0   \n",
       "4          0         0   (-1.081, 4.062]         True         28.0   \n",
       "5          1         0   (54.688, 59.75]        False         56.0   \n",
       "6          0         1    (39.5, 44.562]        False         40.0   \n",
       "7          0         0  (44.562, 49.625]        False         45.0   \n",
       "8          0         0  (29.375, 34.438]        False         32.0   \n",
       "9          1         0  (29.375, 34.438]        False         30.0   \n",
       "10         0         0   (19.25, 24.312]        False         20.0   \n",
       "11         1         0   (19.25, 24.312]        False         21.0   \n",
       "12         0         1   (19.25, 24.312]        False         21.0   \n",
       "13         0         1    (34.438, 39.5]        False         36.0   \n",
       "14         0         1  (24.312, 29.375]        False         25.0   \n",
       "15         0         0   (-1.081, 4.062]         True         28.0   \n",
       "16         0         0   (-1.081, 4.062]         True         28.0   \n",
       "17         1         0  (49.625, 54.688]        False         54.0   \n",
       "18         1         0  (44.562, 49.625]        False         46.0   \n",
       "19         0         0   (-1.081, 4.062]         True         28.0   \n",
       "20         0         1   (59.75, 64.812]        False         62.0   \n",
       "21         1         0  (44.562, 49.625]        False         48.0   \n",
       "22         0         0   (-1.081, 4.062]         True         28.0   \n",
       "23         0         1  (29.375, 34.438]        False         33.0   \n",
       "24         0         0   (14.188, 19.25]        False         18.0   \n",
       "25         0         0   (-1.081, 4.062]        False          1.0   \n",
       "26         0         1   (19.25, 24.312]        False         20.0   \n",
       "27         0         0   (9.125, 14.188]        False         11.0   \n",
       "28         0         0    (34.438, 39.5]        False         36.0   \n",
       "29         0         0   (14.188, 19.25]        False         18.5   \n",
       "30         0         0   (19.25, 24.312]        False         23.0   \n",
       "31         0         0   (19.25, 24.312]        False         20.0   \n",
       "32         0         1  (29.375, 34.438]        False         30.0   \n",
       "33         0         0    (34.438, 39.5]        False         38.0   \n",
       "34         0         0   (-1.081, 4.062]         True         28.0   \n",
       "35         0         1  (29.375, 34.438]        False         30.0   \n",
       "36         1         0  (69.875, 74.938]        False         71.0   \n",
       "37         1         0   (-1.081, 4.062]         True         28.0   \n",
       "38         0         0  (24.312, 29.375]        False         26.0   \n",
       "39         1         0   (9.125, 14.188]        False         14.0   \n",
       "40         0         0  (24.312, 29.375]        False         27.0   \n",
       "41         1         0  (24.312, 29.375]        False         25.0   \n",
       "42         0         0   (14.188, 19.25]        False         18.0   \n",
       "43         1         0    (34.438, 39.5]        False         35.0   \n",
       "44         0         0   (-1.081, 4.062]         True         28.0   \n",
       "45         1         0    (39.5, 44.562]        False         40.0   \n",
       "46         0         0   (-1.081, 4.062]         True         28.0   \n",
       "47         1         0   (14.188, 19.25]        False         18.0   \n",
       "48         1         0  (44.562, 49.625]        False         46.0   \n",
       "49         1         0  (44.562, 49.625]        False         48.0   \n",
       "50         1         0  (24.312, 29.375]        False         27.0   \n",
       "51         0         0   (19.25, 24.312]        False         20.0   \n",
       "52         0         0   (19.25, 24.312]        False         24.0   \n",
       "53         0         0  (24.312, 29.375]        False         26.0   \n",
       "54         0         0  (49.625, 54.688]        False         51.0   \n",
       "\n",
       "    age_standardized  is_minor  \n",
       "0           0.008247  0.000000  \n",
       "1           0.493903  0.000000  \n",
       "2          -0.130512  0.000000  \n",
       "3          -0.824306  0.000000  \n",
       "4          -0.130512  0.200917  \n",
       "5           1.812111  0.000000  \n",
       "6           0.702041  0.000000  \n",
       "7           1.048938  0.000000  \n",
       "8           0.147006  0.000000  \n",
       "9           0.008247  0.000000  \n",
       "10         -0.685547  0.000000  \n",
       "11         -0.616168  0.000000  \n",
       "12         -0.616168  0.000000  \n",
       "13          0.424523  0.000000  \n",
       "14         -0.338650  0.000000  \n",
       "15         -0.130512  0.200917  \n",
       "16         -0.130512  0.200917  \n",
       "17          1.673352  0.000000  \n",
       "18          1.118317  0.000000  \n",
       "19         -0.130512  0.200917  \n",
       "20          2.228388  0.000000  \n",
       "21          1.257076  0.000000  \n",
       "22         -0.130512  0.200917  \n",
       "23          0.216385  0.000000  \n",
       "24         -0.824306  0.000000  \n",
       "25         -2.003756  1.000000  \n",
       "26         -0.685547  0.000000  \n",
       "27         -1.309962  1.000000  \n",
       "28          0.424523  0.000000  \n",
       "29         -0.789616  0.000000  \n",
       "30         -0.477409  0.000000  \n",
       "31         -0.685547  0.000000  \n",
       "32          0.008247  0.000000  \n",
       "33          0.563282  0.000000  \n",
       "34         -0.130512  0.200917  \n",
       "35          0.008247  0.000000  \n",
       "36          2.852802  0.000000  \n",
       "37         -0.130512  0.200917  \n",
       "38         -0.269271  0.000000  \n",
       "39         -1.101824  1.000000  \n",
       "40         -0.199891  0.000000  \n",
       "41         -0.338650  0.000000  \n",
       "42         -0.824306  0.000000  \n",
       "43          0.355144  0.000000  \n",
       "44         -0.130512  0.200917  \n",
       "45          0.702041  0.000000  \n",
       "46         -0.130512  0.200917  \n",
       "47         -0.824306  0.000000  \n",
       "48          1.118317  0.000000  \n",
       "49          1.257076  0.000000  \n",
       "50         -0.199891  0.000000  \n",
       "51         -0.685547  0.000000  \n",
       "52         -0.408030  0.000000  \n",
       "53         -0.269271  0.000000  \n",
       "54          1.465214  0.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age.loc[df_age['age_missing'], 'is_minor'] = proportion_missing\n",
    "df_age.head(55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>survived</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fare  sex  survived  embarked_0  embarked_1  travel_alone  pclass_0  \\\n",
       "0 -0.391695    0         0           0           0             1         0   \n",
       "1 -0.489768    0         0           0           0             0         0   \n",
       "2 -0.391695    1         1           0           0             1         0   \n",
       "3  0.777442    0         0           0           0             1         0   \n",
       "4 -0.490332    0         0           1           0             1         0   \n",
       "\n",
       "   pclass_1  age_standardized  is_minor  \n",
       "0         1          0.008247  0.000000  \n",
       "1         0          0.493903  0.000000  \n",
       "2         1         -0.130512  0.000000  \n",
       "3         1         -0.824306  0.000000  \n",
       "4         0         -0.130512  0.200917  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_age.drop(columns=['age_missing', 'age_group', 'age_imputed', 'age'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../data/non-private/Titanic_preprocessed.csv\", index=False)\n",
    "df_age.to_csv(\"../data/non-private/Titanic_preprocessed_full.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create X, y, and age, by splitting off the `survived` column and copying `age`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/non-private/Titanic_preprocessed.csv')\n",
    "df_age = pd.read_csv('../data/non-private/Titanic_preprocessed_full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unknown age: 0.20091673032849502\n",
      "0-20 years: 0.19022154316271964\n",
      "21-35 years: 0.36287242169595113\n",
      "36+ years: 0.24598930481283424\n",
      "na values: 0.0\n"
     ]
    }
   ],
   "source": [
    "na = np.sum(df_age['age'] < 0) / len(df)\n",
    "print(\"unknown age:\", na)\n",
    "lower_21 = np.sum(df_age['age'] < 21) / len(df)\n",
    "print(\"0-20 years:\", lower_21 - na)\n",
    "lower_36 = np.sum(df_age['age'] < 36) / len(df)\n",
    "print(\"21-35 years:\", lower_36 - lower_21)\n",
    "print(\"36+ years:\", np.sum(df_age['age'] >=36) / len(df))\n",
    "print(\"na values:\", 1 - df_age['age'].count() / len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_original = df_age.loc[:, ['age']].copy()\n",
    "y = df.loc[:,['survived']].copy()\n",
    "X = df.drop(columns=['survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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      "text/plain": [
       "    age\n",
       "0  30.0\n",
       "1  37.0\n",
       "2  28.0\n",
       "3  18.0\n",
       "4  -1.0"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "age_original.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   survived\n",
       "0         0\n",
       "1         0\n",
       "2         1\n",
       "3         0\n",
       "4         0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>fare</th>\n",
       "      <th>sex</th>\n",
       "      <th>embarked_0</th>\n",
       "      <th>embarked_1</th>\n",
       "      <th>travel_alone</th>\n",
       "      <th>pclass_0</th>\n",
       "      <th>pclass_1</th>\n",
       "      <th>age_standardized</th>\n",
       "      <th>is_minor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-0.489768</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493903</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-0.391695</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.777442</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.824306</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.490332</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.130512</td>\n",
       "      <td>0.200917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fare  sex  embarked_0  embarked_1  travel_alone  pclass_0  pclass_1  \\\n",
       "0 -0.391695    0           0           0             1         0         1   \n",
       "1 -0.489768    0           0           0             0         0         0   \n",
       "2 -0.391695    1           0           0             1         0         1   \n",
       "3  0.777442    0           0           0             1         0         1   \n",
       "4 -0.490332    0           1           0             1         0         0   \n",
       "\n",
       "   age_standardized  is_minor  \n",
       "0          0.008247  0.000000  \n",
       "1          0.493903  0.000000  \n",
       "2         -0.130512  0.000000  \n",
       "3         -0.824306  0.000000  \n",
       "4         -0.130512  0.200917  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fare', 'sex', 'embarked_0', 'embarked_1', 'travel_alone', 'pclass_0',\n",
       "       'pclass_1', 'age_standardized', 'is_minor'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the feature matrix $\\mathbf{X}$, the target vector $\\mathbf{y}$, and the age (for reference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "save_dir = '../data/non-private/predict_titanic_survived'\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.mkdir(save_dir)\n",
    "X.to_csv(os.path.join(save_dir, 'X.csv'), index=False)\n",
    "y.to_csv(os.path.join(save_dir, 'y.csv'), index=False)\n",
    "age_original.to_csv(os.path.join(save_dir, 'age.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309,)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = np.loadtxt(os.path.join(save_dir, 'y.csv'), skiprows=1, delimiter=',')\n",
    "y2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['fare'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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