diff --git a/.ipynb_checkpoints/data_exploration-checkpoint.ipynb b/.ipynb_checkpoints/data_exploration-checkpoint.ipynb new file mode 100644 index 0000000..ce551ef --- /dev/null +++ b/.ipynb_checkpoints/data_exploration-checkpoint.ipynb @@ -0,0 +1,1568 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Warmup Project: Data Exploration\n", + "\n", + "This notebook contains data exploration for the warmup project for data science. In this project, I'm using the titanic dataset from Kaggle. The goal of this notebook is to just start to get a feel for the data, and start to understand what is actually in the dataset and how it might impact survival rates of the titanic sinking. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Everything" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading in the data\n", + "First, loading in the data. Doing this using Pandas' read_csv() function" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale2210A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female3810PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale2600STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123S
4503Allen, Mr. William Henrymale35003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale23134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female270234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female141023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale411PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale580011378326.5500C103S
121303Saundercock, Mr. William Henrymale2000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale391534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female550024870616.0000NaNS
161703Rice, Master. Eugenemale24138265229.1250NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female311034576318.0000NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.2250NaNC
202102Fynney, Mr. Joseph Jmale350023986526.0000NaNS
212212Beesley, Mr. Lawrencemale340024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale280011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale83134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female381534707731.3875NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.2250NaNC
272801Fortune, Mr. Charles Alexandermale193219950263.0000C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.8792NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.8958NaNS
.......................................
86186202Giles, Mr. Frederick Edwardmale21102813411.5000NaNS
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...female48001746625.9292D17S
86386403Sage, Miss. Dorothy Edith \"Dolly\"femaleNaN82CA. 234369.5500NaNS
86486502Gill, Mr. John Williammale240023386613.0000NaNS
86586612Bystrom, Mrs. (Karolina)female420023685213.0000NaNS
86686712Duran y More, Miss. Asuncionfemale2710SC/PARIS 214913.8583NaNC
86786801Roebling, Mr. Washington Augustus IImale3100PC 1759050.4958A24S
86886903van Melkebeke, Mr. PhilemonmaleNaN003457779.5000NaNS
86987013Johnson, Master. Harold Theodormale41134774211.1333NaNS
87087103Balkic, Mr. Cerinmale26003492487.8958NaNS
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47111175152.5542D35S
87287301Carlsson, Mr. Frans Olofmale33006955.0000B51 B53 B55S
87387403Vander Cruyssen, Mr. Victormale47003457659.0000NaNS
87487512Abelson, Mrs. Samuel (Hannah Wizosky)female2810P/PP 338124.0000NaNC
87587613Najib, Miss. Adele Kiamie \"Jane\"female150026677.2250NaNC
87687703Gustafsson, Mr. Alfred Ossianmale200075349.8458NaNS
87787803Petroff, Mr. Nedeliomale19003492127.8958NaNS
87887903Laleff, Mr. KristomaleNaN003492177.8958NaNS
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56011176783.1583C50C
88088112Shelley, Mrs. William (Imanita Parrish Hall)female250123043326.0000NaNS
88188203Markun, Mr. Johannmale33003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale2200755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale2800C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale2500SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female390538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale270021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale190011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale260011136930.0000C148C
89089103Dooley, Mr. Patrickmale32003703767.7500NaNQ
\n", + "

891 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "5 6 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "10 11 1 3 \n", + "11 12 1 1 \n", + "12 13 0 3 \n", + "13 14 0 3 \n", + "14 15 0 3 \n", + "15 16 1 2 \n", + "16 17 0 3 \n", + "17 18 1 2 \n", + "18 19 0 3 \n", + "19 20 1 3 \n", + "20 21 0 2 \n", + "21 22 1 2 \n", + "22 23 1 3 \n", + "23 24 1 1 \n", + "24 25 0 3 \n", + "25 26 1 3 \n", + "26 27 0 3 \n", + "27 28 0 1 \n", + "28 29 1 3 \n", + "29 30 0 3 \n", + ".. ... ... ... \n", + "861 862 0 2 \n", + "862 863 1 1 \n", + "863 864 0 3 \n", + "864 865 0 2 \n", + "865 866 1 2 \n", + "866 867 1 2 \n", + "867 868 0 1 \n", + "868 869 0 3 \n", + "869 870 1 3 \n", + "870 871 0 3 \n", + "871 872 1 1 \n", + "872 873 0 1 \n", + "873 874 0 3 \n", + "874 875 1 2 \n", + "875 876 1 3 \n", + "876 877 0 3 \n", + "877 878 0 3 \n", + "878 879 0 3 \n", + "879 880 1 1 \n", + "880 881 1 2 \n", + "881 882 0 3 \n", + "882 883 0 3 \n", + "883 884 0 2 \n", + "884 885 0 3 \n", + "885 886 0 3 \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", + "2 Heikkinen, Miss. Laina female 26 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", + "4 Allen, Mr. William Henry male 35 0 \n", + "5 Moran, Mr. James male NaN 0 \n", + "6 McCarthy, Mr. Timothy J male 54 0 \n", + "7 Palsson, Master. Gosta Leonard male 2 3 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 \n", + "10 Sandstrom, Miss. Marguerite Rut female 4 1 \n", + "11 Bonnell, Miss. Elizabeth female 58 0 \n", + "12 Saundercock, Mr. William Henry male 20 0 \n", + "13 Andersson, Mr. Anders Johan male 39 1 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) female 55 0 \n", + "16 Rice, Master. Eugene male 2 4 \n", + "17 Williams, Mr. Charles Eugene male NaN 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31 1 \n", + "19 Masselmani, Mrs. Fatima female NaN 0 \n", + "20 Fynney, Mr. Joseph J male 35 0 \n", + "21 Beesley, Mr. Lawrence male 34 0 \n", + "22 McGowan, Miss. Anna \"Annie\" female 15 0 \n", + "23 Sloper, Mr. William Thompson male 28 0 \n", + "24 Palsson, Miss. Torborg Danira female 8 3 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38 1 \n", + "26 Emir, Mr. Farred Chehab male NaN 0 \n", + "27 Fortune, Mr. Charles Alexander male 19 3 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 \n", + "29 Todoroff, Mr. Lalio male NaN 0 \n", + ".. ... ... ... ... \n", + "861 Giles, Mr. Frederick Edward male 21 1 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48 0 \n", + "863 Sage, Miss. Dorothy Edith \"Dolly\" female NaN 8 \n", + "864 Gill, Mr. John William male 24 0 \n", + "865 Bystrom, Mrs. (Karolina) female 42 0 \n", + "866 Duran y More, Miss. Asuncion female 27 1 \n", + "867 Roebling, Mr. Washington Augustus II male 31 0 \n", + "868 van Melkebeke, Mr. Philemon male NaN 0 \n", + "869 Johnson, Master. Harold Theodor male 4 1 \n", + "870 Balkic, Mr. Cerin male 26 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 \n", + "872 Carlsson, Mr. Frans Olof male 33 0 \n", + "873 Vander Cruyssen, Mr. Victor male 47 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" female 15 0 \n", + "876 Gustafsson, Mr. Alfred Ossian male 20 0 \n", + "877 Petroff, Mr. Nedelio male 19 0 \n", + "878 Laleff, Mr. Kristo male NaN 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 \n", + "881 Markun, Mr. Johann male 33 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika female 22 0 \n", + "883 Banfield, Mr. Frederick James male 28 0 \n", + "884 Sutehall, Mr. Henry Jr male 25 0 \n", + "885 Rice, Mrs. William (Margaret Norton) female 39 0 \n", + "886 Montvila, Rev. Juozas male 27 0 \n", + "887 Graham, Miss. Margaret Edith female 19 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26 0 \n", + "890 Dooley, Mr. Patrick male 32 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + "5 0 330877 8.4583 NaN Q \n", + "6 0 17463 51.8625 E46 S \n", + "7 1 349909 21.0750 NaN S \n", + "8 2 347742 11.1333 NaN S \n", + "9 0 237736 30.0708 NaN C \n", + "10 1 PP 9549 16.7000 G6 S \n", + "11 0 113783 26.5500 C103 S \n", + "12 0 A/5. 2151 8.0500 NaN S \n", + "13 5 347082 31.2750 NaN S \n", + "14 0 350406 7.8542 NaN S \n", + "15 0 248706 16.0000 NaN S \n", + "16 1 382652 29.1250 NaN Q \n", + "17 0 244373 13.0000 NaN S \n", + "18 0 345763 18.0000 NaN S \n", + "19 0 2649 7.2250 NaN C \n", + "20 0 239865 26.0000 NaN S \n", + "21 0 248698 13.0000 D56 S \n", + "22 0 330923 8.0292 NaN Q \n", + "23 0 113788 35.5000 A6 S \n", + "24 1 349909 21.0750 NaN S \n", + "25 5 347077 31.3875 NaN S \n", + "26 0 2631 7.2250 NaN C \n", + "27 2 19950 263.0000 C23 C25 C27 S \n", + "28 0 330959 7.8792 NaN Q \n", + "29 0 349216 7.8958 NaN S \n", + ".. ... ... ... ... ... \n", + "861 0 28134 11.5000 NaN S \n", + "862 0 17466 25.9292 D17 S \n", + "863 2 CA. 2343 69.5500 NaN S \n", + "864 0 233866 13.0000 NaN S \n", + "865 0 236852 13.0000 NaN S \n", + "866 0 SC/PARIS 2149 13.8583 NaN C \n", + "867 0 PC 17590 50.4958 A24 S \n", + "868 0 345777 9.5000 NaN S \n", + "869 1 347742 11.1333 NaN S \n", + "870 0 349248 7.8958 NaN S \n", + "871 1 11751 52.5542 D35 S \n", + "872 0 695 5.0000 B51 B53 B55 S \n", + "873 0 345765 9.0000 NaN S \n", + "874 0 P/PP 3381 24.0000 NaN C \n", + "875 0 2667 7.2250 NaN C \n", + "876 0 7534 9.8458 NaN S \n", + "877 0 349212 7.8958 NaN S \n", + "878 0 349217 7.8958 NaN S \n", + "879 1 11767 83.1583 C50 C \n", + "880 1 230433 26.0000 NaN S \n", + "881 0 349257 7.8958 NaN S \n", + "882 0 7552 10.5167 NaN S \n", + "883 0 C.A./SOTON 34068 10.5000 NaN S \n", + "884 0 SOTON/OQ 392076 7.0500 NaN S \n", + "885 5 382652 29.1250 NaN Q \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"./Data/train.csv\")\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to note is that in the data that loaded above, it seems like there are some tickets that are numbers, and some that are strings. I'll have to be careful of this if I want to visualize anything relating to ticket numbers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validating that the Data Loaded Correctly\n", + "I'm just going to do some basic checks that the data loaded correctly. \n", + "\n", + "For now, this will include just counting the null values, but I'll add more approaches as I think of them. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId 0\n", + "Survived 0\n", + "Pclass 0\n", + "Name 0\n", + "Sex 0\n", + "Age 177\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket 0\n", + "Fare 0\n", + "Cabin 687\n", + "Embarked 2\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hmmm, it seems that there are some null values. These seem to be mostly age and cabin values although a few of them seem to be whether the passengers actually got on the ship" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the Data\n", + "Here, I'll be plotting/displaying various parameters included in the dataset, just to get a feel for the data. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the things that I think most people think of when they want to predict who survived the titanic is \"Women and Children first\". Let's see if more women and children did survie than adult men. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#I'm not sure what people considered childhood back then and what people didn't, but this seems like a good guess. \n", + "ageOfAdulthood = 16\n", + "\n", + "#Split the dataframe into sections based on which data is about children and which is about adults\n", + "childData = data[data.Age < ageOfAdulthood]\n", + "adultData = data[data.Age >= ageOfAdulthood]\n", + "\n", + "#Okay, so now we have sex age, and whether or not they survived, but we want this in some sort of larger bucket by age\n", + "childSexGrouped = childData.groupby([\"Sex\", \"Survived\"]).count()\n", + "adultSexGrouped = adultData.groupby([\"Sex\", \"Survived\"]).count()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdPclassNameAgeSibSpParchTicketFareCabinEmbarked
SexSurvived
female01515151515151515215
12828282828282828428
male01919191919191919019
12121212121212121721
\n", + "
" + ], + "text/plain": [ + " PassengerId Pclass Name Age SibSp Parch Ticket Fare \\\n", + "Sex Survived \n", + "female 0 15 15 15 15 15 15 15 15 \n", + " 1 28 28 28 28 28 28 28 28 \n", + "male 0 19 19 19 19 19 19 19 19 \n", + " 1 21 21 21 21 21 21 21 21 \n", + "\n", + " Cabin Embarked \n", + "Sex Survived \n", + "female 0 2 15 \n", + " 1 4 28 \n", + "male 0 0 19 \n", + " 1 7 21 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "childSexGrouped" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdPclassNameAgeSibSpParchTicketFareCabinEmbarked
SexSurvived
female04949494949494949449
116916916916916916916916980167
male034134134134134134134134154341
172727272727272723472
\n", + "
" + ], + "text/plain": [ + " PassengerId Pclass Name Age SibSp Parch Ticket Fare \\\n", + "Sex Survived \n", + "female 0 49 49 49 49 49 49 49 49 \n", + " 1 169 169 169 169 169 169 169 169 \n", + "male 0 341 341 341 341 341 341 341 341 \n", + " 1 72 72 72 72 72 72 72 72 \n", + "\n", + " Cabin Embarked \n", + "Sex Survived \n", + "female 0 4 49 \n", + " 1 80 167 \n", + "male 0 54 341 \n", + " 1 34 72 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adultSexGrouped" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/.ipynb_checkpoints/model_iteration_1-checkpoint.ipynb b/.ipynb_checkpoints/model_iteration_1-checkpoint.ipynb new file mode 100644 index 0000000..286dcb3 --- /dev/null +++ b/.ipynb_checkpoints/model_iteration_1-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/.ipynb_checkpoints/model_iteration_2-checkpoint.ipynb b/.ipynb_checkpoints/model_iteration_2-checkpoint.ipynb new file mode 100644 index 0000000..8a570a1 --- /dev/null +++ b/.ipynb_checkpoints/model_iteration_2-checkpoint.ipynb @@ -0,0 +1,2573 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Source of Inspiration\n", + "In this notebook, I'm going to try to take inspiration from someone else's model implementation. I've been curious about random forests for quite a while, so I looked for scripts that implemented them with this dataset specifically.\n", + "\n", + "I'm going to be using this [Random Forest Script](https://www.kaggle.com/amoyakd/titanic/randomforest-method-v1-0) that I found when I was looking through the scripts section of the Kaggle competition. \n", + "\n", + "\n", + "In this script, the author implements several random forrest models in R, so I'm going to try to replicate much of their work in python first." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import re" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, they read in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train = pd.read_csv('./Data/train.csv')\n", + "test = pd.read_csv('./Data/test.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next is cleaning the data. In this particular script, the author mostly does something very similar to what we did in the previous tutorial for the sex and embarked and fare columns. . " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Recode Sex data\n", + "test.loc[test[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "test.loc[test[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "train.loc[train[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "train.loc[train[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "# Recode Embarked Data\n", + "test[\"Embarked\"] = test[\"Embarked\"].fillna(\"S\")\n", + "test.loc[test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "test.loc[test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "test.loc[test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "train[\"Embarked\"] = train[\"Embarked\"].fillna(\"S\")\n", + "train.loc[train[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "train.loc[train[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "train.loc[train[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, in this script, we also extract the title from the name of the person. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def extractTitle (name):\n", + " return re.split(r', \\w+.', name)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitle
0103Braund, Mr. Owen Harris02210A/5 211717.2500NaN0[Braund, Owen Harris]
1211Cumings, Mrs. John Bradley (Florence Briggs Th...13810PC 1759971.2833C851[Cumings, John Bradley (Florence Briggs Thayer)]
2313Heikkinen, Miss. Laina12600STON/O2. 31012827.9250NaN0[Heikkinen, Laina]
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)1351011380353.1000C1230[Futrelle, Jacques Heath (Lily May Peel)]
4503Allen, Mr. William Henry035003734508.0500NaN0[Allen, William Henry]
5603Moran, Mr. James0NaN003308778.4583NaN2[Moran, James]
6701McCarthy, Mr. Timothy J054001746351.8625E460[McCarthy, Timothy J]
7803Palsson, Master. Gosta Leonard023134990921.0750NaN0[Palsson, Gosta Leonard]
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)1270234774211.1333NaN0[Johnson, Oscar W (Elisabeth Vilhelmina Berg)]
91012Nasser, Mrs. Nicholas (Adele Achem)1141023773630.0708NaN1[Nasser, Nicholas (Adele Achem)]
101113Sandstrom, Miss. Marguerite Rut1411PP 954916.7000G60[Sandstrom, Marguerite Rut]
111211Bonnell, Miss. Elizabeth1580011378326.5500C1030[Bonnell, Elizabeth]
121303Saundercock, Mr. William Henry02000A/5. 21518.0500NaN0[Saundercock, William Henry]
131403Andersson, Mr. Anders Johan0391534708231.2750NaN0[Andersson, Anders Johan]
141503Vestrom, Miss. Hulda Amanda Adolfina114003504067.8542NaN0[Vestrom, Hulda Amanda Adolfina]
151612Hewlett, Mrs. (Mary D Kingcome)1550024870616.0000NaN0[Hewlett, (Mary D Kingcome) ]
161703Rice, Master. Eugene024138265229.1250NaN2[Rice, Eugene]
171812Williams, Mr. Charles Eugene0NaN0024437313.0000NaN0[Williams, Charles Eugene]
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...1311034576318.0000NaN0[Vander Planke, Julius (Emelia Maria Vandemoo...
192013Masselmani, Mrs. Fatima1NaN0026497.2250NaN1[Masselmani, Fatima]
202102Fynney, Mr. Joseph J0350023986526.0000NaN0[Fynney, Joseph J]
212212Beesley, Mr. Lawrence0340024869813.0000D560[Beesley, Lawrence]
222313McGowan, Miss. Anna \"Annie\"115003309238.0292NaN2[McGowan, Anna \"Annie\"]
232411Sloper, Mr. William Thompson0280011378835.5000A60[Sloper, William Thompson]
242503Palsson, Miss. Torborg Danira183134990921.0750NaN0[Palsson, Torborg Danira]
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...1381534707731.3875NaN0[Asplund, Carl Oscar (Selma Augusta Emilia Jo...
262703Emir, Mr. Farred Chehab0NaN0026317.2250NaN1[Emir, Farred Chehab]
272801Fortune, Mr. Charles Alexander0193219950263.0000C23 C25 C270[Fortune, Charles Alexander]
282913O'Dwyer, Miss. Ellen \"Nellie\"1NaN003309597.8792NaN2[O'Dwyer, Ellen \"Nellie\"]
293003Todoroff, Mr. Lalio0NaN003492167.8958NaN0[Todoroff, Lalio]
..........................................
86186202Giles, Mr. Frederick Edward021102813411.5000NaN0[Giles, Frederick Edward]
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...148001746625.9292D170[Swift, Frederick Joel (Margaret Welles Barron)]
86386403Sage, Miss. Dorothy Edith \"Dolly\"1NaN82CA. 234369.5500NaN0[Sage, Dorothy Edith \"Dolly\"]
86486502Gill, Mr. John William0240023386613.0000NaN0[Gill, John William]
86586612Bystrom, Mrs. (Karolina)1420023685213.0000NaN0[Bystrom, (Karolina)]
86686712Duran y More, Miss. Asuncion12710SC/PARIS 214913.8583NaN1[Duran y More, Asuncion]
86786801Roebling, Mr. Washington Augustus II03100PC 1759050.4958A240[Roebling, Washington Augustus II]
86886903van Melkebeke, Mr. Philemon0NaN003457779.5000NaN0[van Melkebeke, Philemon]
86987013Johnson, Master. Harold Theodor041134774211.1333NaN0[Johnson, Harold Theodor]
87087103Balkic, Mr. Cerin026003492487.8958NaN0[Balkic, Cerin]
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)147111175152.5542D350[Beckwith, Richard Leonard (Sallie Monypeny)]
87287301Carlsson, Mr. Frans Olof033006955.0000B51 B53 B550[Carlsson, Frans Olof]
87387403Vander Cruyssen, Mr. Victor047003457659.0000NaN0[Vander Cruyssen, Victor]
87487512Abelson, Mrs. Samuel (Hannah Wizosky)12810P/PP 338124.0000NaN1[Abelson, Samuel (Hannah Wizosky)]
87587613Najib, Miss. Adele Kiamie \"Jane\"1150026677.2250NaN1[Najib, Adele Kiamie \"Jane\"]
87687703Gustafsson, Mr. Alfred Ossian0200075349.8458NaN0[Gustafsson, Alfred Ossian]
87787803Petroff, Mr. Nedelio019003492127.8958NaN0[Petroff, Nedelio]
87887903Laleff, Mr. Kristo0NaN003492177.8958NaN0[Laleff, Kristo]
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)156011176783.1583C501[Potter, Thomas Jr (Lily Alexenia Wilson)]
88088112Shelley, Mrs. William (Imanita Parrish Hall)1250123043326.0000NaN0[Shelley, William (Imanita Parrish Hall)]
88188203Markun, Mr. Johann033003492577.8958NaN0[Markun, Johann]
88288303Dahlberg, Miss. Gerda Ulrika12200755210.5167NaN0[Dahlberg, Gerda Ulrika]
88388402Banfield, Mr. Frederick James02800C.A./SOTON 3406810.5000NaN0[Banfield, Frederick James]
88488503Sutehall, Mr. Henry Jr02500SOTON/OQ 3920767.0500NaN0[Sutehall, Henry Jr]
88588603Rice, Mrs. William (Margaret Norton)1390538265229.1250NaN2[Rice, William (Margaret Norton)]
88688702Montvila, Rev. Juozas0270021153613.0000NaN0[Montvila, Juozas]
88788811Graham, Miss. Margaret Edith1190011205330.0000B420[Graham, Margaret Edith]
88888903Johnston, Miss. Catherine Helen \"Carrie\"1NaN12W./C. 660723.4500NaN0[Johnston, Catherine Helen \"Carrie\"]
88989011Behr, Mr. Karl Howell0260011136930.0000C1481[Behr, Karl Howell]
89089103Dooley, Mr. Patrick032003703767.7500NaN2[Dooley, Patrick]
\n", + "

891 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "5 6 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "10 11 1 3 \n", + "11 12 1 1 \n", + "12 13 0 3 \n", + "13 14 0 3 \n", + "14 15 0 3 \n", + "15 16 1 2 \n", + "16 17 0 3 \n", + "17 18 1 2 \n", + "18 19 0 3 \n", + "19 20 1 3 \n", + "20 21 0 2 \n", + "21 22 1 2 \n", + "22 23 1 3 \n", + "23 24 1 1 \n", + "24 25 0 3 \n", + "25 26 1 3 \n", + "26 27 0 3 \n", + "27 28 0 1 \n", + "28 29 1 3 \n", + "29 30 0 3 \n", + ".. ... ... ... \n", + "861 862 0 2 \n", + "862 863 1 1 \n", + "863 864 0 3 \n", + "864 865 0 2 \n", + "865 866 1 2 \n", + "866 867 1 2 \n", + "867 868 0 1 \n", + "868 869 0 3 \n", + "869 870 1 3 \n", + "870 871 0 3 \n", + "871 872 1 1 \n", + "872 873 0 1 \n", + "873 874 0 3 \n", + "874 875 1 2 \n", + "875 876 1 3 \n", + "876 877 0 3 \n", + "877 878 0 3 \n", + "878 879 0 3 \n", + "879 880 1 1 \n", + "880 881 1 2 \n", + "881 882 0 3 \n", + "882 883 0 3 \n", + "883 884 0 2 \n", + "884 885 0 3 \n", + "885 886 0 3 \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "0 Braund, Mr. Owen Harris 0 22 1 0 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 1 38 1 0 \n", + "2 Heikkinen, Miss. Laina 1 26 0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 1 35 1 0 \n", + "4 Allen, Mr. William Henry 0 35 0 0 \n", + "5 Moran, Mr. James 0 NaN 0 0 \n", + "6 McCarthy, Mr. Timothy J 0 54 0 0 \n", + "7 Palsson, Master. Gosta Leonard 0 2 3 1 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 1 27 0 2 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) 1 14 1 0 \n", + "10 Sandstrom, Miss. Marguerite Rut 1 4 1 1 \n", + "11 Bonnell, Miss. Elizabeth 1 58 0 0 \n", + "12 Saundercock, Mr. William Henry 0 20 0 0 \n", + "13 Andersson, Mr. Anders Johan 0 39 1 5 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina 1 14 0 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) 1 55 0 0 \n", + "16 Rice, Master. Eugene 0 2 4 1 \n", + "17 Williams, Mr. Charles Eugene 0 NaN 0 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... 1 31 1 0 \n", + "19 Masselmani, Mrs. Fatima 1 NaN 0 0 \n", + "20 Fynney, Mr. Joseph J 0 35 0 0 \n", + "21 Beesley, Mr. Lawrence 0 34 0 0 \n", + "22 McGowan, Miss. Anna \"Annie\" 1 15 0 0 \n", + "23 Sloper, Mr. William Thompson 0 28 0 0 \n", + "24 Palsson, Miss. Torborg Danira 1 8 3 1 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 1 38 1 5 \n", + "26 Emir, Mr. Farred Chehab 0 NaN 0 0 \n", + "27 Fortune, Mr. Charles Alexander 0 19 3 2 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" 1 NaN 0 0 \n", + "29 Todoroff, Mr. Lalio 0 NaN 0 0 \n", + ".. ... .. ... ... ... \n", + "861 Giles, Mr. Frederick Edward 0 21 1 0 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... 1 48 0 0 \n", + "863 Sage, Miss. Dorothy Edith \"Dolly\" 1 NaN 8 2 \n", + "864 Gill, Mr. John William 0 24 0 0 \n", + "865 Bystrom, Mrs. (Karolina) 1 42 0 0 \n", + "866 Duran y More, Miss. Asuncion 1 27 1 0 \n", + "867 Roebling, Mr. Washington Augustus II 0 31 0 0 \n", + "868 van Melkebeke, Mr. Philemon 0 NaN 0 0 \n", + "869 Johnson, Master. Harold Theodor 0 4 1 1 \n", + "870 Balkic, Mr. Cerin 0 26 0 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 1 47 1 1 \n", + "872 Carlsson, Mr. Frans Olof 0 33 0 0 \n", + "873 Vander Cruyssen, Mr. Victor 0 47 0 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) 1 28 1 0 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" 1 15 0 0 \n", + "876 Gustafsson, Mr. Alfred Ossian 0 20 0 0 \n", + "877 Petroff, Mr. Nedelio 0 19 0 0 \n", + "878 Laleff, Mr. Kristo 0 NaN 0 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) 1 56 0 1 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) 1 25 0 1 \n", + "881 Markun, Mr. Johann 0 33 0 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika 1 22 0 0 \n", + "883 Banfield, Mr. Frederick James 0 28 0 0 \n", + "884 Sutehall, Mr. Henry Jr 0 25 0 0 \n", + "885 Rice, Mrs. William (Margaret Norton) 1 39 0 5 \n", + "886 Montvila, Rev. Juozas 0 27 0 0 \n", + "887 Graham, Miss. Margaret Edith 1 19 0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" 1 NaN 1 2 \n", + "889 Behr, Mr. Karl Howell 0 26 0 0 \n", + "890 Dooley, Mr. Patrick 0 32 0 0 \n", + "\n", + " Ticket Fare Cabin Embarked \\\n", + "0 A/5 21171 7.2500 NaN 0 \n", + "1 PC 17599 71.2833 C85 1 \n", + "2 STON/O2. 3101282 7.9250 NaN 0 \n", + "3 113803 53.1000 C123 0 \n", + "4 373450 8.0500 NaN 0 \n", + "5 330877 8.4583 NaN 2 \n", + "6 17463 51.8625 E46 0 \n", + "7 349909 21.0750 NaN 0 \n", + "8 347742 11.1333 NaN 0 \n", + "9 237736 30.0708 NaN 1 \n", + "10 PP 9549 16.7000 G6 0 \n", + "11 113783 26.5500 C103 0 \n", + "12 A/5. 2151 8.0500 NaN 0 \n", + "13 347082 31.2750 NaN 0 \n", + "14 350406 7.8542 NaN 0 \n", + "15 248706 16.0000 NaN 0 \n", + "16 382652 29.1250 NaN 2 \n", + "17 244373 13.0000 NaN 0 \n", + "18 345763 18.0000 NaN 0 \n", + "19 2649 7.2250 NaN 1 \n", + "20 239865 26.0000 NaN 0 \n", + "21 248698 13.0000 D56 0 \n", + "22 330923 8.0292 NaN 2 \n", + "23 113788 35.5000 A6 0 \n", + "24 349909 21.0750 NaN 0 \n", + "25 347077 31.3875 NaN 0 \n", + "26 2631 7.2250 NaN 1 \n", + "27 19950 263.0000 C23 C25 C27 0 \n", + "28 330959 7.8792 NaN 2 \n", + "29 349216 7.8958 NaN 0 \n", + ".. ... ... ... ... \n", + "861 28134 11.5000 NaN 0 \n", + "862 17466 25.9292 D17 0 \n", + "863 CA. 2343 69.5500 NaN 0 \n", + "864 233866 13.0000 NaN 0 \n", + "865 236852 13.0000 NaN 0 \n", + "866 SC/PARIS 2149 13.8583 NaN 1 \n", + "867 PC 17590 50.4958 A24 0 \n", + "868 345777 9.5000 NaN 0 \n", + "869 347742 11.1333 NaN 0 \n", + "870 349248 7.8958 NaN 0 \n", + "871 11751 52.5542 D35 0 \n", + "872 695 5.0000 B51 B53 B55 0 \n", + "873 345765 9.0000 NaN 0 \n", + "874 P/PP 3381 24.0000 NaN 1 \n", + "875 2667 7.2250 NaN 1 \n", + "876 7534 9.8458 NaN 0 \n", + "877 349212 7.8958 NaN 0 \n", + "878 349217 7.8958 NaN 0 \n", + "879 11767 83.1583 C50 1 \n", + "880 230433 26.0000 NaN 0 \n", + "881 349257 7.8958 NaN 0 \n", + "882 7552 10.5167 NaN 0 \n", + "883 C.A./SOTON 34068 10.5000 NaN 0 \n", + "884 SOTON/OQ 392076 7.0500 NaN 0 \n", + "885 382652 29.1250 NaN 2 \n", + "886 211536 13.0000 NaN 0 \n", + "887 112053 30.0000 B42 0 \n", + "888 W./C. 6607 23.4500 NaN 0 \n", + "889 111369 30.0000 C148 1 \n", + "890 370376 7.7500 NaN 2 \n", + "\n", + " Title \n", + "0 [Braund, Owen Harris] \n", + "1 [Cumings, John Bradley (Florence Briggs Thayer)] \n", + "2 [Heikkinen, Laina] \n", + "3 [Futrelle, Jacques Heath (Lily May Peel)] \n", + "4 [Allen, William Henry] \n", + "5 [Moran, James] \n", + "6 [McCarthy, Timothy J] \n", + "7 [Palsson, Gosta Leonard] \n", + "8 [Johnson, Oscar W (Elisabeth Vilhelmina Berg)] \n", + "9 [Nasser, Nicholas (Adele Achem)] \n", + "10 [Sandstrom, Marguerite Rut] \n", + "11 [Bonnell, Elizabeth] \n", + "12 [Saundercock, William Henry] \n", + "13 [Andersson, Anders Johan] \n", + "14 [Vestrom, Hulda Amanda Adolfina] \n", + "15 [Hewlett, (Mary D Kingcome) ] \n", + "16 [Rice, Eugene] \n", + "17 [Williams, Charles Eugene] \n", + "18 [Vander Planke, Julius (Emelia Maria Vandemoo... \n", + "19 [Masselmani, Fatima] \n", + "20 [Fynney, Joseph J] \n", + "21 [Beesley, Lawrence] \n", + "22 [McGowan, Anna \"Annie\"] \n", + "23 [Sloper, William Thompson] \n", + "24 [Palsson, Torborg Danira] \n", + "25 [Asplund, Carl Oscar (Selma Augusta Emilia Jo... \n", + "26 [Emir, Farred Chehab] \n", + "27 [Fortune, Charles Alexander] \n", + "28 [O'Dwyer, Ellen \"Nellie\"] \n", + "29 [Todoroff, Lalio] \n", + ".. ... \n", + "861 [Giles, Frederick Edward] \n", + "862 [Swift, Frederick Joel (Margaret Welles Barron)] \n", + "863 [Sage, Dorothy Edith \"Dolly\"] \n", + "864 [Gill, John William] \n", + "865 [Bystrom, (Karolina)] \n", + "866 [Duran y More, Asuncion] \n", + "867 [Roebling, Washington Augustus II] \n", + "868 [van Melkebeke, Philemon] \n", + "869 [Johnson, Harold Theodor] \n", + "870 [Balkic, Cerin] \n", + "871 [Beckwith, Richard Leonard (Sallie Monypeny)] \n", + "872 [Carlsson, Frans Olof] \n", + "873 [Vander Cruyssen, Victor] \n", + "874 [Abelson, Samuel (Hannah Wizosky)] \n", + "875 [Najib, Adele Kiamie \"Jane\"] \n", + "876 [Gustafsson, Alfred Ossian] \n", + "877 [Petroff, Nedelio] \n", + "878 [Laleff, Kristo] \n", + "879 [Potter, Thomas Jr (Lily Alexenia Wilson)] \n", + "880 [Shelley, William (Imanita Parrish Hall)] \n", + "881 [Markun, Johann] \n", + "882 [Dahlberg, Gerda Ulrika] \n", + "883 [Banfield, Frederick James] \n", + "884 [Sutehall, Henry Jr] \n", + "885 [Rice, William (Margaret Norton)] \n", + "886 [Montvila, Juozas] \n", + "887 [Graham, Margaret Edith] \n", + "888 [Johnston, Catherine Helen \"Carrie\"] \n", + "889 [Behr, Karl Howell] \n", + "890 [Dooley, Patrick] \n", + "\n", + "[891 rows x 13 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train[\"Title\"] = train['Name'].apply(lambda x: extractTitle(x))\n", + "train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Fill in missing age data\n", + "train[\"Age\"] = train[\"Age\"].fillna(train[\"Age\"].median())\n", + "test[\"Age\"] = test[\"Age\"].fillna(train[\"Age\"].median())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harris02210A/5 211717.2500NaN0
1211Cumings, Mrs. John Bradley (Florence Briggs Th...13810PC 1759971.2833C851
2313Heikkinen, Miss. Laina12600STON/O2. 31012827.9250NaN0
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)1351011380353.1000C1230
4503Allen, Mr. William Henry035003734508.0500NaN0
5603Moran, Mr. James0NaN003308778.4583NaN2
6701McCarthy, Mr. Timothy J054001746351.8625E460
7803Palsson, Master. Gosta Leonard023134990921.0750NaN0
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)1270234774211.1333NaN0
91012Nasser, Mrs. Nicholas (Adele Achem)1141023773630.0708NaN1
101113Sandstrom, Miss. Marguerite Rut1411PP 954916.7000G60
111211Bonnell, Miss. Elizabeth1580011378326.5500C1030
121303Saundercock, Mr. William Henry02000A/5. 21518.0500NaN0
131403Andersson, Mr. Anders Johan0391534708231.2750NaN0
141503Vestrom, Miss. Hulda Amanda Adolfina114003504067.8542NaN0
151612Hewlett, Mrs. (Mary D Kingcome)1550024870616.0000NaN0
161703Rice, Master. Eugene024138265229.1250NaN2
171812Williams, Mr. Charles Eugene0NaN0024437313.0000NaN0
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...1311034576318.0000NaN0
192013Masselmani, Mrs. Fatima1NaN0026497.2250NaN1
202102Fynney, Mr. Joseph J0350023986526.0000NaN0
212212Beesley, Mr. Lawrence0340024869813.0000D560
222313McGowan, Miss. Anna \"Annie\"115003309238.0292NaN2
232411Sloper, Mr. William Thompson0280011378835.5000A60
242503Palsson, Miss. Torborg Danira183134990921.0750NaN0
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...1381534707731.3875NaN0
262703Emir, Mr. Farred Chehab0NaN0026317.2250NaN1
272801Fortune, Mr. Charles Alexander0193219950263.0000C23 C25 C270
282913O'Dwyer, Miss. Ellen \"Nellie\"1NaN003309597.8792NaN2
293003Todoroff, Mr. Lalio0NaN003492167.8958NaN0
.......................................
86186202Giles, Mr. Frederick Edward021102813411.5000NaN0
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...148001746625.9292D170
86386403Sage, Miss. Dorothy Edith \"Dolly\"1NaN82CA. 234369.5500NaN0
86486502Gill, Mr. John William0240023386613.0000NaN0
86586612Bystrom, Mrs. (Karolina)1420023685213.0000NaN0
86686712Duran y More, Miss. Asuncion12710SC/PARIS 214913.8583NaN1
86786801Roebling, Mr. Washington Augustus II03100PC 1759050.4958A240
86886903van Melkebeke, Mr. Philemon0NaN003457779.5000NaN0
86987013Johnson, Master. Harold Theodor041134774211.1333NaN0
87087103Balkic, Mr. Cerin026003492487.8958NaN0
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)147111175152.5542D350
87287301Carlsson, Mr. Frans Olof033006955.0000B51 B53 B550
87387403Vander Cruyssen, Mr. Victor047003457659.0000NaN0
87487512Abelson, Mrs. Samuel (Hannah Wizosky)12810P/PP 338124.0000NaN1
87587613Najib, Miss. Adele Kiamie \"Jane\"1150026677.2250NaN1
87687703Gustafsson, Mr. Alfred Ossian0200075349.8458NaN0
87787803Petroff, Mr. Nedelio019003492127.8958NaN0
87887903Laleff, Mr. Kristo0NaN003492177.8958NaN0
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)156011176783.1583C501
88088112Shelley, Mrs. William (Imanita Parrish Hall)1250123043326.0000NaN0
88188203Markun, Mr. Johann033003492577.8958NaN0
88288303Dahlberg, Miss. Gerda Ulrika12200755210.5167NaN0
88388402Banfield, Mr. Frederick James02800C.A./SOTON 3406810.5000NaN0
88488503Sutehall, Mr. Henry Jr02500SOTON/OQ 3920767.0500NaN0
88588603Rice, Mrs. William (Margaret Norton)1390538265229.1250NaN2
88688702Montvila, Rev. Juozas0270021153613.0000NaN0
88788811Graham, Miss. Margaret Edith1190011205330.0000B420
88888903Johnston, Miss. Catherine Helen \"Carrie\"1NaN12W./C. 660723.4500NaN0
88989011Behr, Mr. Karl Howell0260011136930.0000C1481
89089103Dooley, Mr. Patrick032003703767.7500NaN2
\n", + "

891 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "5 6 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "10 11 1 3 \n", + "11 12 1 1 \n", + "12 13 0 3 \n", + "13 14 0 3 \n", + "14 15 0 3 \n", + "15 16 1 2 \n", + "16 17 0 3 \n", + "17 18 1 2 \n", + "18 19 0 3 \n", + "19 20 1 3 \n", + "20 21 0 2 \n", + "21 22 1 2 \n", + "22 23 1 3 \n", + "23 24 1 1 \n", + "24 25 0 3 \n", + "25 26 1 3 \n", + "26 27 0 3 \n", + "27 28 0 1 \n", + "28 29 1 3 \n", + "29 30 0 3 \n", + ".. ... ... ... \n", + "861 862 0 2 \n", + "862 863 1 1 \n", + "863 864 0 3 \n", + "864 865 0 2 \n", + "865 866 1 2 \n", + "866 867 1 2 \n", + "867 868 0 1 \n", + "868 869 0 3 \n", + "869 870 1 3 \n", + "870 871 0 3 \n", + "871 872 1 1 \n", + "872 873 0 1 \n", + "873 874 0 3 \n", + "874 875 1 2 \n", + "875 876 1 3 \n", + "876 877 0 3 \n", + "877 878 0 3 \n", + "878 879 0 3 \n", + "879 880 1 1 \n", + "880 881 1 2 \n", + "881 882 0 3 \n", + "882 883 0 3 \n", + "883 884 0 2 \n", + "884 885 0 3 \n", + "885 886 0 3 \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "0 Braund, Mr. Owen Harris 0 22 1 0 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 1 38 1 0 \n", + "2 Heikkinen, Miss. Laina 1 26 0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 1 35 1 0 \n", + "4 Allen, Mr. William Henry 0 35 0 0 \n", + "5 Moran, Mr. James 0 NaN 0 0 \n", + "6 McCarthy, Mr. Timothy J 0 54 0 0 \n", + "7 Palsson, Master. Gosta Leonard 0 2 3 1 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 1 27 0 2 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) 1 14 1 0 \n", + "10 Sandstrom, Miss. Marguerite Rut 1 4 1 1 \n", + "11 Bonnell, Miss. Elizabeth 1 58 0 0 \n", + "12 Saundercock, Mr. William Henry 0 20 0 0 \n", + "13 Andersson, Mr. Anders Johan 0 39 1 5 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina 1 14 0 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) 1 55 0 0 \n", + "16 Rice, Master. Eugene 0 2 4 1 \n", + "17 Williams, Mr. Charles Eugene 0 NaN 0 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... 1 31 1 0 \n", + "19 Masselmani, Mrs. Fatima 1 NaN 0 0 \n", + "20 Fynney, Mr. Joseph J 0 35 0 0 \n", + "21 Beesley, Mr. Lawrence 0 34 0 0 \n", + "22 McGowan, Miss. Anna \"Annie\" 1 15 0 0 \n", + "23 Sloper, Mr. William Thompson 0 28 0 0 \n", + "24 Palsson, Miss. Torborg Danira 1 8 3 1 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 1 38 1 5 \n", + "26 Emir, Mr. Farred Chehab 0 NaN 0 0 \n", + "27 Fortune, Mr. Charles Alexander 0 19 3 2 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" 1 NaN 0 0 \n", + "29 Todoroff, Mr. Lalio 0 NaN 0 0 \n", + ".. ... .. ... ... ... \n", + "861 Giles, Mr. Frederick Edward 0 21 1 0 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... 1 48 0 0 \n", + "863 Sage, Miss. Dorothy Edith \"Dolly\" 1 NaN 8 2 \n", + "864 Gill, Mr. John William 0 24 0 0 \n", + "865 Bystrom, Mrs. (Karolina) 1 42 0 0 \n", + "866 Duran y More, Miss. Asuncion 1 27 1 0 \n", + "867 Roebling, Mr. Washington Augustus II 0 31 0 0 \n", + "868 van Melkebeke, Mr. Philemon 0 NaN 0 0 \n", + "869 Johnson, Master. Harold Theodor 0 4 1 1 \n", + "870 Balkic, Mr. Cerin 0 26 0 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 1 47 1 1 \n", + "872 Carlsson, Mr. Frans Olof 0 33 0 0 \n", + "873 Vander Cruyssen, Mr. Victor 0 47 0 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) 1 28 1 0 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" 1 15 0 0 \n", + "876 Gustafsson, Mr. Alfred Ossian 0 20 0 0 \n", + "877 Petroff, Mr. Nedelio 0 19 0 0 \n", + "878 Laleff, Mr. Kristo 0 NaN 0 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) 1 56 0 1 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) 1 25 0 1 \n", + "881 Markun, Mr. Johann 0 33 0 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika 1 22 0 0 \n", + "883 Banfield, Mr. Frederick James 0 28 0 0 \n", + "884 Sutehall, Mr. Henry Jr 0 25 0 0 \n", + "885 Rice, Mrs. William (Margaret Norton) 1 39 0 5 \n", + "886 Montvila, Rev. Juozas 0 27 0 0 \n", + "887 Graham, Miss. Margaret Edith 1 19 0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" 1 NaN 1 2 \n", + "889 Behr, Mr. Karl Howell 0 26 0 0 \n", + "890 Dooley, Mr. Patrick 0 32 0 0 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "0 A/5 21171 7.2500 NaN 0 \n", + "1 PC 17599 71.2833 C85 1 \n", + "2 STON/O2. 3101282 7.9250 NaN 0 \n", + "3 113803 53.1000 C123 0 \n", + "4 373450 8.0500 NaN 0 \n", + "5 330877 8.4583 NaN 2 \n", + "6 17463 51.8625 E46 0 \n", + "7 349909 21.0750 NaN 0 \n", + "8 347742 11.1333 NaN 0 \n", + "9 237736 30.0708 NaN 1 \n", + "10 PP 9549 16.7000 G6 0 \n", + "11 113783 26.5500 C103 0 \n", + "12 A/5. 2151 8.0500 NaN 0 \n", + "13 347082 31.2750 NaN 0 \n", + "14 350406 7.8542 NaN 0 \n", + "15 248706 16.0000 NaN 0 \n", + "16 382652 29.1250 NaN 2 \n", + "17 244373 13.0000 NaN 0 \n", + "18 345763 18.0000 NaN 0 \n", + "19 2649 7.2250 NaN 1 \n", + "20 239865 26.0000 NaN 0 \n", + "21 248698 13.0000 D56 0 \n", + "22 330923 8.0292 NaN 2 \n", + "23 113788 35.5000 A6 0 \n", + "24 349909 21.0750 NaN 0 \n", + "25 347077 31.3875 NaN 0 \n", + "26 2631 7.2250 NaN 1 \n", + "27 19950 263.0000 C23 C25 C27 0 \n", + "28 330959 7.8792 NaN 2 \n", + "29 349216 7.8958 NaN 0 \n", + ".. ... ... ... ... \n", + "861 28134 11.5000 NaN 0 \n", + "862 17466 25.9292 D17 0 \n", + "863 CA. 2343 69.5500 NaN 0 \n", + "864 233866 13.0000 NaN 0 \n", + "865 236852 13.0000 NaN 0 \n", + "866 SC/PARIS 2149 13.8583 NaN 1 \n", + "867 PC 17590 50.4958 A24 0 \n", + "868 345777 9.5000 NaN 0 \n", + "869 347742 11.1333 NaN 0 \n", + "870 349248 7.8958 NaN 0 \n", + "871 11751 52.5542 D35 0 \n", + "872 695 5.0000 B51 B53 B55 0 \n", + "873 345765 9.0000 NaN 0 \n", + "874 P/PP 3381 24.0000 NaN 1 \n", + "875 2667 7.2250 NaN 1 \n", + "876 7534 9.8458 NaN 0 \n", + "877 349212 7.8958 NaN 0 \n", + "878 349217 7.8958 NaN 0 \n", + "879 11767 83.1583 C50 1 \n", + "880 230433 26.0000 NaN 0 \n", + "881 349257 7.8958 NaN 0 \n", + "882 7552 10.5167 NaN 0 \n", + "883 C.A./SOTON 34068 10.5000 NaN 0 \n", + "884 SOTON/OQ 392076 7.0500 NaN 0 \n", + "885 382652 29.1250 NaN 2 \n", + "886 211536 13.0000 NaN 0 \n", + "887 112053 30.0000 B42 0 \n", + "888 W./C. 6607 23.4500 NaN 0 \n", + "889 111369 30.0000 C148 1 \n", + "890 370376 7.7500 NaN 2 \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Data/test.csv b/Data/test.csv new file mode 100644 index 0000000..f705412 --- /dev/null +++ b/Data/test.csv @@ -0,0 +1,419 @@ +PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q +893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S +894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q +895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S +896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S +897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S +898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q +899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S +900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C +901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S +902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S +903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S +904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S +905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S +906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S +907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C +908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q +909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C +910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S +911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C +912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C +913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S +914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S +915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C +916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C +917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S +918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C +919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C +920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S +921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C +922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S +923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S +924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S +925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S +926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C +927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C +928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S +929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S +930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S +931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S +932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C +933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S +934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S +935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S +936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S +937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S +938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C +939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q +940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C +941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S +942,1,"Smith, Mr. Lucien Philip",male,24,1,0,13695,60,C31,S +943,2,"Pulbaum, Mr. Franz",male,27,0,0,SC/PARIS 2168,15.0333,,C +944,2,"Hocking, Miss. Ellen Nellie""""",female,20,2,1,29105,23,,S +945,1,"Fortune, Miss. Ethel Flora",female,28,3,2,19950,263,C23 C25 C27,S +946,2,"Mangiavacchi, Mr. Serafino Emilio",male,,0,0,SC/A.3 2861,15.5792,,C +947,3,"Rice, Master. Albert",male,10,4,1,382652,29.125,,Q +948,3,"Cor, Mr. Bartol",male,35,0,0,349230,7.8958,,S +949,3,"Abelseth, Mr. Olaus Jorgensen",male,25,0,0,348122,7.65,F G63,S +950,3,"Davison, Mr. Thomas Henry",male,,1,0,386525,16.1,,S +951,1,"Chaudanson, Miss. Victorine",female,36,0,0,PC 17608,262.375,B61,C +952,3,"Dika, Mr. Mirko",male,17,0,0,349232,7.8958,,S +953,2,"McCrae, Mr. Arthur Gordon",male,32,0,0,237216,13.5,,S +954,3,"Bjorklund, Mr. Ernst Herbert",male,18,0,0,347090,7.75,,S +955,3,"Bradley, Miss. Bridget Delia",female,22,0,0,334914,7.725,,Q +956,1,"Ryerson, Master. John Borie",male,13,2,2,PC 17608,262.375,B57 B59 B63 B66,C +957,2,"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)",female,,0,0,F.C.C. 13534,21,,S +958,3,"Burns, Miss. Mary Delia",female,18,0,0,330963,7.8792,,Q +959,1,"Moore, Mr. Clarence Bloomfield",male,47,0,0,113796,42.4,,S +960,1,"Tucker, Mr. Gilbert Milligan Jr",male,31,0,0,2543,28.5375,C53,C +961,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60,1,4,19950,263,C23 C25 C27,S +962,3,"Mulvihill, Miss. Bertha E",female,24,0,0,382653,7.75,,Q +963,3,"Minkoff, Mr. Lazar",male,21,0,0,349211,7.8958,,S +964,3,"Nieminen, Miss. Manta Josefina",female,29,0,0,3101297,7.925,,S +965,1,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C +966,1,"Geiger, Miss. Amalie",female,35,0,0,113503,211.5,C130,C +967,1,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C +968,3,"Miles, Mr. Frank",male,,0,0,359306,8.05,,S +969,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55,2,0,11770,25.7,C101,S +970,2,"Aldworth, Mr. Charles Augustus",male,30,0,0,248744,13,,S +971,3,"Doyle, Miss. Elizabeth",female,24,0,0,368702,7.75,,Q +972,3,"Boulos, Master. Akar",male,6,1,1,2678,15.2458,,C +973,1,"Straus, Mr. Isidor",male,67,1,0,PC 17483,221.7792,C55 C57,S +974,1,"Case, Mr. Howard Brown",male,49,0,0,19924,26,,S +975,3,"Demetri, Mr. Marinko",male,,0,0,349238,7.8958,,S +976,2,"Lamb, Mr. John Joseph",male,,0,0,240261,10.7083,,Q +977,3,"Khalil, Mr. Betros",male,,1,0,2660,14.4542,,C +978,3,"Barry, Miss. Julia",female,27,0,0,330844,7.8792,,Q +979,3,"Badman, Miss. Emily Louisa",female,18,0,0,A/4 31416,8.05,,S +980,3,"O'Donoghue, Ms. Bridget",female,,0,0,364856,7.75,,Q +981,2,"Wells, Master. Ralph Lester",male,2,1,1,29103,23,,S +982,3,"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)",female,22,1,0,347072,13.9,,S +983,3,"Pedersen, Mr. Olaf",male,,0,0,345498,7.775,,S +984,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27,1,2,F.C. 12750,52,B71,S +985,3,"Guest, Mr. Robert",male,,0,0,376563,8.05,,S +986,1,"Birnbaum, Mr. Jakob",male,25,0,0,13905,26,,C +987,3,"Tenglin, Mr. Gunnar Isidor",male,25,0,0,350033,7.7958,,S +988,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76,1,0,19877,78.85,C46,S +989,3,"Makinen, Mr. Kalle Edvard",male,29,0,0,STON/O 2. 3101268,7.925,,S +990,3,"Braf, Miss. Elin Ester Maria",female,20,0,0,347471,7.8542,,S +991,3,"Nancarrow, Mr. William Henry",male,33,0,0,A./5. 3338,8.05,,S +992,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43,1,0,11778,55.4417,C116,C +993,2,"Weisz, Mr. Leopold",male,27,1,0,228414,26,,S +994,3,"Foley, Mr. William",male,,0,0,365235,7.75,,Q +995,3,"Johansson Palmquist, Mr. Oskar Leander",male,26,0,0,347070,7.775,,S +996,3,"Thomas, Mrs. Alexander (Thamine Thelma"")""",female,16,1,1,2625,8.5167,,C +997,3,"Holthen, Mr. Johan Martin",male,28,0,0,C 4001,22.525,,S +998,3,"Buckley, Mr. Daniel",male,21,0,0,330920,7.8208,,Q +999,3,"Ryan, Mr. Edward",male,,0,0,383162,7.75,,Q +1000,3,"Willer, Mr. Aaron (Abi Weller"")""",male,,0,0,3410,8.7125,,S +1001,2,"Swane, Mr. George",male,18.5,0,0,248734,13,F,S +1002,2,"Stanton, Mr. Samuel Ward",male,41,0,0,237734,15.0458,,C +1003,3,"Shine, Miss. Ellen Natalia",female,,0,0,330968,7.7792,,Q +1004,1,"Evans, Miss. Edith Corse",female,36,0,0,PC 17531,31.6792,A29,C +1005,3,"Buckley, Miss. Katherine",female,18.5,0,0,329944,7.2833,,Q +1006,1,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63,1,0,PC 17483,221.7792,C55 C57,S +1007,3,"Chronopoulos, Mr. Demetrios",male,18,1,0,2680,14.4542,,C +1008,3,"Thomas, Mr. John",male,,0,0,2681,6.4375,,C +1009,3,"Sandstrom, Miss. Beatrice Irene",female,1,1,1,PP 9549,16.7,G6,S +1010,1,"Beattie, Mr. Thomson",male,36,0,0,13050,75.2417,C6,C +1011,2,"Chapman, Mrs. John Henry (Sara Elizabeth Lawry)",female,29,1,0,SC/AH 29037,26,,S +1012,2,"Watt, Miss. Bertha J",female,12,0,0,C.A. 33595,15.75,,S +1013,3,"Kiernan, Mr. John",male,,1,0,367227,7.75,,Q +1014,1,"Schabert, Mrs. Paul (Emma Mock)",female,35,1,0,13236,57.75,C28,C +1015,3,"Carver, Mr. Alfred John",male,28,0,0,392095,7.25,,S +1016,3,"Kennedy, Mr. John",male,,0,0,368783,7.75,,Q +1017,3,"Cribb, Miss. Laura Alice",female,17,0,1,371362,16.1,,S +1018,3,"Brobeck, Mr. Karl Rudolf",male,22,0,0,350045,7.7958,,S +1019,3,"McCoy, Miss. Alicia",female,,2,0,367226,23.25,,Q +1020,2,"Bowenur, Mr. Solomon",male,42,0,0,211535,13,,S +1021,3,"Petersen, Mr. Marius",male,24,0,0,342441,8.05,,S +1022,3,"Spinner, Mr. Henry John",male,32,0,0,STON/OQ. 369943,8.05,,S +1023,1,"Gracie, Col. Archibald IV",male,53,0,0,113780,28.5,C51,C +1024,3,"Lefebre, Mrs. Frank (Frances)",female,,0,4,4133,25.4667,,S +1025,3,"Thomas, Mr. Charles P",male,,1,0,2621,6.4375,,C +1026,3,"Dintcheff, Mr. Valtcho",male,43,0,0,349226,7.8958,,S +1027,3,"Carlsson, Mr. Carl Robert",male,24,0,0,350409,7.8542,,S +1028,3,"Zakarian, Mr. Mapriededer",male,26.5,0,0,2656,7.225,,C +1029,2,"Schmidt, Mr. August",male,26,0,0,248659,13,,S +1030,3,"Drapkin, Miss. Jennie",female,23,0,0,SOTON/OQ 392083,8.05,,S +1031,3,"Goodwin, Mr. Charles Frederick",male,40,1,6,CA 2144,46.9,,S +1032,3,"Goodwin, Miss. Jessie Allis",female,10,5,2,CA 2144,46.9,,S +1033,1,"Daniels, Miss. Sarah",female,33,0,0,113781,151.55,,S +1034,1,"Ryerson, Mr. Arthur Larned",male,61,1,3,PC 17608,262.375,B57 B59 B63 B66,C +1035,2,"Beauchamp, Mr. Henry James",male,28,0,0,244358,26,,S +1036,1,"Lindeberg-Lind, Mr. Erik Gustaf (Mr Edward Lingrey"")""",male,42,0,0,17475,26.55,,S +1037,3,"Vander Planke, Mr. Julius",male,31,3,0,345763,18,,S +1038,1,"Hilliard, Mr. Herbert Henry",male,,0,0,17463,51.8625,E46,S +1039,3,"Davies, Mr. Evan",male,22,0,0,SC/A4 23568,8.05,,S +1040,1,"Crafton, Mr. John Bertram",male,,0,0,113791,26.55,,S +1041,2,"Lahtinen, Rev. William",male,30,1,1,250651,26,,S +1042,1,"Earnshaw, Mrs. Boulton (Olive Potter)",female,23,0,1,11767,83.1583,C54,C +1043,3,"Matinoff, Mr. Nicola",male,,0,0,349255,7.8958,,C +1044,3,"Storey, Mr. Thomas",male,60.5,0,0,3701,,,S +1045,3,"Klasen, Mrs. (Hulda Kristina Eugenia Lofqvist)",female,36,0,2,350405,12.1833,,S +1046,3,"Asplund, Master. Filip Oscar",male,13,4,2,347077,31.3875,,S +1047,3,"Duquemin, Mr. Joseph",male,24,0,0,S.O./P.P. 752,7.55,,S +1048,1,"Bird, Miss. Ellen",female,29,0,0,PC 17483,221.7792,C97,S +1049,3,"Lundin, Miss. Olga Elida",female,23,0,0,347469,7.8542,,S +1050,1,"Borebank, Mr. John James",male,42,0,0,110489,26.55,D22,S +1051,3,"Peacock, Mrs. Benjamin (Edith Nile)",female,26,0,2,SOTON/O.Q. 3101315,13.775,,S +1052,3,"Smyth, Miss. Julia",female,,0,0,335432,7.7333,,Q +1053,3,"Touma, Master. Georges Youssef",male,7,1,1,2650,15.2458,,C +1054,2,"Wright, Miss. Marion",female,26,0,0,220844,13.5,,S +1055,3,"Pearce, Mr. Ernest",male,,0,0,343271,7,,S +1056,2,"Peruschitz, Rev. Joseph Maria",male,41,0,0,237393,13,,S +1057,3,"Kink-Heilmann, Mrs. Anton (Luise Heilmann)",female,26,1,1,315153,22.025,,S +1058,1,"Brandeis, Mr. Emil",male,48,0,0,PC 17591,50.4958,B10,C +1059,3,"Ford, Mr. Edward Watson",male,18,2,2,W./C. 6608,34.375,,S +1060,1,"Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)",female,,0,0,17770,27.7208,,C +1061,3,"Hellstrom, Miss. Hilda Maria",female,22,0,0,7548,8.9625,,S +1062,3,"Lithman, Mr. Simon",male,,0,0,S.O./P.P. 251,7.55,,S +1063,3,"Zakarian, Mr. Ortin",male,27,0,0,2670,7.225,,C +1064,3,"Dyker, Mr. Adolf Fredrik",male,23,1,0,347072,13.9,,S +1065,3,"Torfa, Mr. Assad",male,,0,0,2673,7.2292,,C +1066,3,"Asplund, Mr. Carl Oscar Vilhelm Gustafsson",male,40,1,5,347077,31.3875,,S +1067,2,"Brown, Miss. Edith Eileen",female,15,0,2,29750,39,,S +1068,2,"Sincock, Miss. Maude",female,20,0,0,C.A. 33112,36.75,,S +1069,1,"Stengel, Mr. Charles Emil Henry",male,54,1,0,11778,55.4417,C116,C +1070,2,"Becker, Mrs. Allen Oliver (Nellie E Baumgardner)",female,36,0,3,230136,39,F4,S +1071,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)",female,64,0,2,PC 17756,83.1583,E45,C +1072,2,"McCrie, Mr. James Matthew",male,30,0,0,233478,13,,S +1073,1,"Compton, Mr. Alexander Taylor Jr",male,37,1,1,PC 17756,83.1583,E52,C +1074,1,"Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)",female,18,1,0,113773,53.1,D30,S +1075,3,"Lane, Mr. Patrick",male,,0,0,7935,7.75,,Q +1076,1,"Douglas, Mrs. Frederick Charles (Mary Helene Baxter)",female,27,1,1,PC 17558,247.5208,B58 B60,C +1077,2,"Maybery, Mr. Frank Hubert",male,40,0,0,239059,16,,S +1078,2,"Phillips, Miss. Alice Frances Louisa",female,21,0,1,S.O./P.P. 2,21,,S +1079,3,"Davies, Mr. Joseph",male,17,2,0,A/4 48873,8.05,,S +1080,3,"Sage, Miss. Ada",female,,8,2,CA. 2343,69.55,,S +1081,2,"Veal, Mr. James",male,40,0,0,28221,13,,S +1082,2,"Angle, Mr. William A",male,34,1,0,226875,26,,S +1083,1,"Salomon, Mr. Abraham L",male,,0,0,111163,26,,S +1084,3,"van Billiard, Master. Walter John",male,11.5,1,1,A/5. 851,14.5,,S +1085,2,"Lingane, Mr. John",male,61,0,0,235509,12.35,,Q +1086,2,"Drew, Master. Marshall Brines",male,8,0,2,28220,32.5,,S +1087,3,"Karlsson, Mr. Julius Konrad Eugen",male,33,0,0,347465,7.8542,,S +1088,1,"Spedden, Master. Robert Douglas",male,6,0,2,16966,134.5,E34,C +1089,3,"Nilsson, Miss. Berta Olivia",female,18,0,0,347066,7.775,,S +1090,2,"Baimbrigge, Mr. Charles Robert",male,23,0,0,C.A. 31030,10.5,,S +1091,3,"Rasmussen, Mrs. (Lena Jacobsen Solvang)",female,,0,0,65305,8.1125,,S +1092,3,"Murphy, Miss. Nora",female,,0,0,36568,15.5,,Q +1093,3,"Danbom, Master. Gilbert Sigvard Emanuel",male,0.33,0,2,347080,14.4,,S +1094,1,"Astor, Col. John Jacob",male,47,1,0,PC 17757,227.525,C62 C64,C +1095,2,"Quick, Miss. Winifred Vera",female,8,1,1,26360,26,,S +1096,2,"Andrew, Mr. Frank Thomas",male,25,0,0,C.A. 34050,10.5,,S +1097,1,"Omont, Mr. Alfred Fernand",male,,0,0,F.C. 12998,25.7417,,C +1098,3,"McGowan, Miss. Katherine",female,35,0,0,9232,7.75,,Q +1099,2,"Collett, Mr. Sidney C Stuart",male,24,0,0,28034,10.5,,S +1100,1,"Rosenbaum, Miss. Edith Louise",female,33,0,0,PC 17613,27.7208,A11,C +1101,3,"Delalic, Mr. Redjo",male,25,0,0,349250,7.8958,,S +1102,3,"Andersen, Mr. Albert Karvin",male,32,0,0,C 4001,22.525,,S +1103,3,"Finoli, Mr. Luigi",male,,0,0,SOTON/O.Q. 3101308,7.05,,S +1104,2,"Deacon, Mr. Percy William",male,17,0,0,S.O.C. 14879,73.5,,S +1105,2,"Howard, Mrs. Benjamin (Ellen Truelove Arman)",female,60,1,0,24065,26,,S +1106,3,"Andersson, Miss. Ida Augusta Margareta",female,38,4,2,347091,7.775,,S +1107,1,"Head, Mr. Christopher",male,42,0,0,113038,42.5,B11,S +1108,3,"Mahon, Miss. Bridget Delia",female,,0,0,330924,7.8792,,Q +1109,1,"Wick, Mr. George Dennick",male,57,1,1,36928,164.8667,,S +1110,1,"Widener, Mrs. George Dunton (Eleanor Elkins)",female,50,1,1,113503,211.5,C80,C +1111,3,"Thomson, Mr. Alexander Morrison",male,,0,0,32302,8.05,,S +1112,2,"Duran y More, Miss. Florentina",female,30,1,0,SC/PARIS 2148,13.8583,,C +1113,3,"Reynolds, Mr. Harold J",male,21,0,0,342684,8.05,,S +1114,2,"Cook, Mrs. (Selena Rogers)",female,22,0,0,W./C. 14266,10.5,F33,S +1115,3,"Karlsson, Mr. Einar Gervasius",male,21,0,0,350053,7.7958,,S +1116,1,"Candee, Mrs. Edward (Helen Churchill Hungerford)",female,53,0,0,PC 17606,27.4458,,C +1117,3,"Moubarek, Mrs. George (Omine Amenia"" Alexander)""",female,,0,2,2661,15.2458,,C +1118,3,"Asplund, Mr. Johan Charles",male,23,0,0,350054,7.7958,,S +1119,3,"McNeill, Miss. Bridget",female,,0,0,370368,7.75,,Q +1120,3,"Everett, Mr. Thomas James",male,40.5,0,0,C.A. 6212,15.1,,S +1121,2,"Hocking, Mr. Samuel James Metcalfe",male,36,0,0,242963,13,,S +1122,2,"Sweet, Mr. George Frederick",male,14,0,0,220845,65,,S +1123,1,"Willard, Miss. Constance",female,21,0,0,113795,26.55,,S +1124,3,"Wiklund, Mr. Karl Johan",male,21,1,0,3101266,6.4958,,S +1125,3,"Linehan, Mr. Michael",male,,0,0,330971,7.8792,,Q +1126,1,"Cumings, Mr. John Bradley",male,39,1,0,PC 17599,71.2833,C85,C +1127,3,"Vendel, Mr. Olof Edvin",male,20,0,0,350416,7.8542,,S +1128,1,"Warren, Mr. Frank Manley",male,64,1,0,110813,75.25,D37,C +1129,3,"Baccos, Mr. Raffull",male,20,0,0,2679,7.225,,C +1130,2,"Hiltunen, Miss. Marta",female,18,1,1,250650,13,,S +1131,1,"Douglas, Mrs. Walter Donald (Mahala Dutton)",female,48,1,0,PC 17761,106.425,C86,C +1132,1,"Lindstrom, Mrs. Carl Johan (Sigrid Posse)",female,55,0,0,112377,27.7208,,C +1133,2,"Christy, Mrs. (Alice Frances)",female,45,0,2,237789,30,,S +1134,1,"Spedden, Mr. Frederic Oakley",male,45,1,1,16966,134.5,E34,C +1135,3,"Hyman, Mr. Abraham",male,,0,0,3470,7.8875,,S +1136,3,"Johnston, Master. William Arthur Willie""""",male,,1,2,W./C. 6607,23.45,,S +1137,1,"Kenyon, Mr. Frederick R",male,41,1,0,17464,51.8625,D21,S +1138,2,"Karnes, Mrs. J Frank (Claire Bennett)",female,22,0,0,F.C.C. 13534,21,,S +1139,2,"Drew, Mr. James Vivian",male,42,1,1,28220,32.5,,S +1140,2,"Hold, Mrs. Stephen (Annie Margaret Hill)",female,29,1,0,26707,26,,S +1141,3,"Khalil, Mrs. Betros (Zahie Maria"" Elias)""",female,,1,0,2660,14.4542,,C +1142,2,"West, Miss. Barbara J",female,0.92,1,2,C.A. 34651,27.75,,S +1143,3,"Abrahamsson, Mr. Abraham August Johannes",male,20,0,0,SOTON/O2 3101284,7.925,,S +1144,1,"Clark, Mr. Walter Miller",male,27,1,0,13508,136.7792,C89,C +1145,3,"Salander, Mr. Karl Johan",male,24,0,0,7266,9.325,,S +1146,3,"Wenzel, Mr. Linhart",male,32.5,0,0,345775,9.5,,S +1147,3,"MacKay, Mr. George William",male,,0,0,C.A. 42795,7.55,,S +1148,3,"Mahon, Mr. John",male,,0,0,AQ/4 3130,7.75,,Q +1149,3,"Niklasson, Mr. Samuel",male,28,0,0,363611,8.05,,S +1150,2,"Bentham, Miss. Lilian W",female,19,0,0,28404,13,,S +1151,3,"Midtsjo, Mr. Karl Albert",male,21,0,0,345501,7.775,,S +1152,3,"de Messemaeker, Mr. Guillaume Joseph",male,36.5,1,0,345572,17.4,,S +1153,3,"Nilsson, Mr. August Ferdinand",male,21,0,0,350410,7.8542,,S +1154,2,"Wells, Mrs. Arthur Henry (Addie"" Dart Trevaskis)""",female,29,0,2,29103,23,,S +1155,3,"Klasen, Miss. Gertrud Emilia",female,1,1,1,350405,12.1833,,S +1156,2,"Portaluppi, Mr. Emilio Ilario Giuseppe",male,30,0,0,C.A. 34644,12.7375,,C +1157,3,"Lyntakoff, Mr. Stanko",male,,0,0,349235,7.8958,,S +1158,1,"Chisholm, Mr. Roderick Robert Crispin",male,,0,0,112051,0,,S +1159,3,"Warren, Mr. Charles William",male,,0,0,C.A. 49867,7.55,,S +1160,3,"Howard, Miss. May Elizabeth",female,,0,0,A. 2. 39186,8.05,,S +1161,3,"Pokrnic, Mr. Mate",male,17,0,0,315095,8.6625,,S +1162,1,"McCaffry, Mr. Thomas Francis",male,46,0,0,13050,75.2417,C6,C +1163,3,"Fox, Mr. Patrick",male,,0,0,368573,7.75,,Q +1164,1,"Clark, Mrs. Walter Miller (Virginia McDowell)",female,26,1,0,13508,136.7792,C89,C +1165,3,"Lennon, Miss. Mary",female,,1,0,370371,15.5,,Q +1166,3,"Saade, Mr. Jean Nassr",male,,0,0,2676,7.225,,C +1167,2,"Bryhl, Miss. Dagmar Jenny Ingeborg ",female,20,1,0,236853,26,,S +1168,2,"Parker, Mr. Clifford Richard",male,28,0,0,SC 14888,10.5,,S +1169,2,"Faunthorpe, Mr. Harry",male,40,1,0,2926,26,,S +1170,2,"Ware, Mr. John James",male,30,1,0,CA 31352,21,,S +1171,2,"Oxenham, Mr. Percy Thomas",male,22,0,0,W./C. 14260,10.5,,S +1172,3,"Oreskovic, Miss. Jelka",female,23,0,0,315085,8.6625,,S +1173,3,"Peacock, Master. Alfred Edward",male,0.75,1,1,SOTON/O.Q. 3101315,13.775,,S +1174,3,"Fleming, Miss. Honora",female,,0,0,364859,7.75,,Q +1175,3,"Touma, Miss. Maria Youssef",female,9,1,1,2650,15.2458,,C +1176,3,"Rosblom, Miss. Salli Helena",female,2,1,1,370129,20.2125,,S +1177,3,"Dennis, Mr. William",male,36,0,0,A/5 21175,7.25,,S +1178,3,"Franklin, Mr. Charles (Charles Fardon)",male,,0,0,SOTON/O.Q. 3101314,7.25,,S +1179,1,"Snyder, Mr. John Pillsbury",male,24,1,0,21228,82.2667,B45,S +1180,3,"Mardirosian, Mr. Sarkis",male,,0,0,2655,7.2292,F E46,C +1181,3,"Ford, Mr. Arthur",male,,0,0,A/5 1478,8.05,,S +1182,1,"Rheims, Mr. George Alexander Lucien",male,,0,0,PC 17607,39.6,,S +1183,3,"Daly, Miss. Margaret Marcella Maggie""""",female,30,0,0,382650,6.95,,Q +1184,3,"Nasr, Mr. Mustafa",male,,0,0,2652,7.2292,,C +1185,1,"Dodge, Dr. Washington",male,53,1,1,33638,81.8583,A34,S +1186,3,"Wittevrongel, Mr. Camille",male,36,0,0,345771,9.5,,S +1187,3,"Angheloff, Mr. Minko",male,26,0,0,349202,7.8958,,S +1188,2,"Laroche, Miss. Louise",female,1,1,2,SC/Paris 2123,41.5792,,C +1189,3,"Samaan, Mr. Hanna",male,,2,0,2662,21.6792,,C +1190,1,"Loring, Mr. Joseph Holland",male,30,0,0,113801,45.5,,S +1191,3,"Johansson, Mr. Nils",male,29,0,0,347467,7.8542,,S +1192,3,"Olsson, Mr. Oscar Wilhelm",male,32,0,0,347079,7.775,,S +1193,2,"Malachard, Mr. Noel",male,,0,0,237735,15.0458,D,C +1194,2,"Phillips, Mr. Escott Robert",male,43,0,1,S.O./P.P. 2,21,,S +1195,3,"Pokrnic, Mr. Tome",male,24,0,0,315092,8.6625,,S +1196,3,"McCarthy, Miss. Catherine Katie""""",female,,0,0,383123,7.75,,Q +1197,1,"Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)",female,64,1,1,112901,26.55,B26,S +1198,1,"Allison, Mr. Hudson Joshua Creighton",male,30,1,2,113781,151.55,C22 C26,S +1199,3,"Aks, Master. Philip Frank",male,0.83,0,1,392091,9.35,,S +1200,1,"Hays, Mr. Charles Melville",male,55,1,1,12749,93.5,B69,S +1201,3,"Hansen, Mrs. Claus Peter (Jennie L Howard)",female,45,1,0,350026,14.1083,,S +1202,3,"Cacic, Mr. Jego Grga",male,18,0,0,315091,8.6625,,S +1203,3,"Vartanian, Mr. David",male,22,0,0,2658,7.225,,C +1204,3,"Sadowitz, Mr. Harry",male,,0,0,LP 1588,7.575,,S +1205,3,"Carr, Miss. Jeannie",female,37,0,0,368364,7.75,,Q +1206,1,"White, Mrs. John Stuart (Ella Holmes)",female,55,0,0,PC 17760,135.6333,C32,C +1207,3,"Hagardon, Miss. Kate",female,17,0,0,AQ/3. 30631,7.7333,,Q +1208,1,"Spencer, Mr. William Augustus",male,57,1,0,PC 17569,146.5208,B78,C +1209,2,"Rogers, Mr. Reginald Harry",male,19,0,0,28004,10.5,,S +1210,3,"Jonsson, Mr. Nils Hilding",male,27,0,0,350408,7.8542,,S +1211,2,"Jefferys, Mr. Ernest Wilfred",male,22,2,0,C.A. 31029,31.5,,S +1212,3,"Andersson, Mr. Johan Samuel",male,26,0,0,347075,7.775,,S +1213,3,"Krekorian, Mr. Neshan",male,25,0,0,2654,7.2292,F E57,C +1214,2,"Nesson, Mr. Israel",male,26,0,0,244368,13,F2,S +1215,1,"Rowe, Mr. Alfred G",male,33,0,0,113790,26.55,,S +1216,1,"Kreuchen, Miss. Emilie",female,39,0,0,24160,211.3375,,S +1217,3,"Assam, Mr. Ali",male,23,0,0,SOTON/O.Q. 3101309,7.05,,S +1218,2,"Becker, Miss. Ruth Elizabeth",female,12,2,1,230136,39,F4,S +1219,1,"Rosenshine, Mr. George (Mr George Thorne"")""",male,46,0,0,PC 17585,79.2,,C +1220,2,"Clarke, Mr. Charles Valentine",male,29,1,0,2003,26,,S +1221,2,"Enander, Mr. Ingvar",male,21,0,0,236854,13,,S +1222,2,"Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) ",female,48,0,2,C.A. 33112,36.75,,S +1223,1,"Dulles, Mr. William Crothers",male,39,0,0,PC 17580,29.7,A18,C +1224,3,"Thomas, Mr. Tannous",male,,0,0,2684,7.225,,C +1225,3,"Nakid, Mrs. Said (Waika Mary"" Mowad)""",female,19,1,1,2653,15.7417,,C +1226,3,"Cor, Mr. Ivan",male,27,0,0,349229,7.8958,,S +1227,1,"Maguire, Mr. John Edward",male,30,0,0,110469,26,C106,S +1228,2,"de Brito, Mr. Jose Joaquim",male,32,0,0,244360,13,,S +1229,3,"Elias, Mr. Joseph",male,39,0,2,2675,7.2292,,C +1230,2,"Denbury, Mr. Herbert",male,25,0,0,C.A. 31029,31.5,,S +1231,3,"Betros, Master. Seman",male,,0,0,2622,7.2292,,C +1232,2,"Fillbrook, Mr. Joseph Charles",male,18,0,0,C.A. 15185,10.5,,S +1233,3,"Lundstrom, Mr. Thure Edvin",male,32,0,0,350403,7.5792,,S +1234,3,"Sage, Mr. John George",male,,1,9,CA. 2343,69.55,,S +1235,1,"Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake)",female,58,0,1,PC 17755,512.3292,B51 B53 B55,C +1236,3,"van Billiard, Master. James William",male,,1,1,A/5. 851,14.5,,S +1237,3,"Abelseth, Miss. Karen Marie",female,16,0,0,348125,7.65,,S +1238,2,"Botsford, Mr. William Hull",male,26,0,0,237670,13,,S +1239,3,"Whabee, Mrs. George Joseph (Shawneene Abi-Saab)",female,38,0,0,2688,7.2292,,C +1240,2,"Giles, Mr. Ralph",male,24,0,0,248726,13.5,,S +1241,2,"Walcroft, Miss. Nellie",female,31,0,0,F.C.C. 13528,21,,S +1242,1,"Greenfield, Mrs. Leo David (Blanche Strouse)",female,45,0,1,PC 17759,63.3583,D10 D12,C +1243,2,"Stokes, Mr. Philip Joseph",male,25,0,0,F.C.C. 13540,10.5,,S +1244,2,"Dibden, Mr. William",male,18,0,0,S.O.C. 14879,73.5,,S +1245,2,"Herman, Mr. Samuel",male,49,1,2,220845,65,,S +1246,3,"Dean, Miss. Elizabeth Gladys Millvina""""",female,0.17,1,2,C.A. 2315,20.575,,S +1247,1,"Julian, Mr. Henry Forbes",male,50,0,0,113044,26,E60,S +1248,1,"Brown, Mrs. John Murray (Caroline Lane Lamson)",female,59,2,0,11769,51.4792,C101,S +1249,3,"Lockyer, Mr. Edward",male,,0,0,1222,7.8792,,S +1250,3,"O'Keefe, Mr. Patrick",male,,0,0,368402,7.75,,Q +1251,3,"Lindell, Mrs. Edvard Bengtsson (Elin Gerda Persson)",female,30,1,0,349910,15.55,,S +1252,3,"Sage, Master. William Henry",male,14.5,8,2,CA. 2343,69.55,,S +1253,2,"Mallet, Mrs. Albert (Antoinette Magnin)",female,24,1,1,S.C./PARIS 2079,37.0042,,C +1254,2,"Ware, Mrs. John James (Florence Louise Long)",female,31,0,0,CA 31352,21,,S +1255,3,"Strilic, Mr. Ivan",male,27,0,0,315083,8.6625,,S +1256,1,"Harder, Mrs. George Achilles (Dorothy Annan)",female,25,1,0,11765,55.4417,E50,C +1257,3,"Sage, Mrs. John (Annie Bullen)",female,,1,9,CA. 2343,69.55,,S +1258,3,"Caram, Mr. Joseph",male,,1,0,2689,14.4583,,C +1259,3,"Riihivouri, Miss. Susanna Juhantytar Sanni""""",female,22,0,0,3101295,39.6875,,S +1260,1,"Gibson, Mrs. Leonard (Pauline C Boeson)",female,45,0,1,112378,59.4,,C +1261,2,"Pallas y Castello, Mr. Emilio",male,29,0,0,SC/PARIS 2147,13.8583,,C +1262,2,"Giles, Mr. Edgar",male,21,1,0,28133,11.5,,S +1263,1,"Wilson, Miss. Helen Alice",female,31,0,0,16966,134.5,E39 E41,C +1264,1,"Ismay, Mr. Joseph Bruce",male,49,0,0,112058,0,B52 B54 B56,S +1265,2,"Harbeck, Mr. William H",male,44,0,0,248746,13,,S +1266,1,"Dodge, Mrs. Washington (Ruth Vidaver)",female,54,1,1,33638,81.8583,A34,S +1267,1,"Bowen, Miss. Grace Scott",female,45,0,0,PC 17608,262.375,,C +1268,3,"Kink, Miss. Maria",female,22,2,0,315152,8.6625,,S +1269,2,"Cotterill, Mr. Henry Harry""""",male,21,0,0,29107,11.5,,S +1270,1,"Hipkins, Mr. William Edward",male,55,0,0,680,50,C39,S +1271,3,"Asplund, Master. Carl Edgar",male,5,4,2,347077,31.3875,,S +1272,3,"O'Connor, Mr. Patrick",male,,0,0,366713,7.75,,Q +1273,3,"Foley, Mr. Joseph",male,26,0,0,330910,7.8792,,Q +1274,3,"Risien, Mrs. Samuel (Emma)",female,,0,0,364498,14.5,,S +1275,3,"McNamee, Mrs. Neal (Eileen O'Leary)",female,19,1,0,376566,16.1,,S +1276,2,"Wheeler, Mr. Edwin Frederick""""",male,,0,0,SC/PARIS 2159,12.875,,S +1277,2,"Herman, Miss. Kate",female,24,1,2,220845,65,,S +1278,3,"Aronsson, Mr. Ernst Axel Algot",male,24,0,0,349911,7.775,,S +1279,2,"Ashby, Mr. John",male,57,0,0,244346,13,,S +1280,3,"Canavan, Mr. Patrick",male,21,0,0,364858,7.75,,Q +1281,3,"Palsson, Master. Paul Folke",male,6,3,1,349909,21.075,,S +1282,1,"Payne, Mr. Vivian Ponsonby",male,23,0,0,12749,93.5,B24,S +1283,1,"Lines, Mrs. Ernest H (Elizabeth Lindsey James)",female,51,0,1,PC 17592,39.4,D28,S +1284,3,"Abbott, Master. Eugene Joseph",male,13,0,2,C.A. 2673,20.25,,S +1285,2,"Gilbert, Mr. William",male,47,0,0,C.A. 30769,10.5,,S +1286,3,"Kink-Heilmann, Mr. Anton",male,29,3,1,315153,22.025,,S +1287,1,"Smith, Mrs. Lucien Philip (Mary Eloise Hughes)",female,18,1,0,13695,60,C31,S +1288,3,"Colbert, Mr. Patrick",male,24,0,0,371109,7.25,,Q +1289,1,"Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli)",female,48,1,1,13567,79.2,B41,C +1290,3,"Larsson-Rondberg, Mr. Edvard A",male,22,0,0,347065,7.775,,S +1291,3,"Conlon, Mr. Thomas Henry",male,31,0,0,21332,7.7333,,Q +1292,1,"Bonnell, Miss. Caroline",female,30,0,0,36928,164.8667,C7,S +1293,2,"Gale, Mr. Harry",male,38,1,0,28664,21,,S +1294,1,"Gibson, Miss. Dorothy Winifred",female,22,0,1,112378,59.4,,C +1295,1,"Carrau, Mr. Jose Pedro",male,17,0,0,113059,47.1,,S +1296,1,"Frauenthal, Mr. Isaac Gerald",male,43,1,0,17765,27.7208,D40,C +1297,2,"Nourney, Mr. Alfred (Baron von Drachstedt"")""",male,20,0,0,SC/PARIS 2166,13.8625,D38,C +1298,2,"Ware, Mr. William Jeffery",male,23,1,0,28666,10.5,,S +1299,1,"Widener, Mr. George Dunton",male,50,1,1,113503,211.5,C80,C +1300,3,"Riordan, Miss. Johanna Hannah""""",female,,0,0,334915,7.7208,,Q +1301,3,"Peacock, Miss. Treasteall",female,3,1,1,SOTON/O.Q. 3101315,13.775,,S +1302,3,"Naughton, Miss. Hannah",female,,0,0,365237,7.75,,Q +1303,1,"Minahan, Mrs. William Edward (Lillian E Thorpe)",female,37,1,0,19928,90,C78,Q +1304,3,"Henriksson, Miss. Jenny Lovisa",female,28,0,0,347086,7.775,,S +1305,3,"Spector, Mr. Woolf",male,,0,0,A.5. 3236,8.05,,S +1306,1,"Oliva y Ocana, Dona. Fermina",female,39,0,0,PC 17758,108.9,C105,C +1307,3,"Saether, Mr. Simon Sivertsen",male,38.5,0,0,SOTON/O.Q. 3101262,7.25,,S +1308,3,"Ware, Mr. Frederick",male,,0,0,359309,8.05,,S +1309,3,"Peter, Master. Michael J",male,,1,1,2668,22.3583,,C diff --git a/Data/train.csv b/Data/train.csv new file mode 100644 index 0000000..63b68ab --- /dev/null +++ b/Data/train.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S +4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S +5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S +6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q +7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S +8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S +9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S +10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C +11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S +12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S +13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S +14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S +15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S +16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S +17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q +18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S +19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S +20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C +21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S +22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S +23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q +24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S +25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S +26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S +27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C +28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S +29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q +30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S +31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C +32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C +33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q +34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S +35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C +36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S +37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C +38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S +39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S +40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C +41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S +42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S +43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C +44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C +45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q +46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S +47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q +48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q +49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C +50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S +51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S +52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S +53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C +54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S +55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C +56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S +57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S +58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C +59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S +60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S +61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C +62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28, +63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S +64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S +65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C +66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C +67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S +68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S +69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S +70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S +71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S +72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S +73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S +74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C +75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S +76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S +77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S +78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S +79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S +80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S +81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S +82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S +83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q +84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S +85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S +86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S +87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S +88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S +89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S +90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S +91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S +92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S +93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S +94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S +95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S +96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S +97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C +98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C +99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S +100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S +101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S +102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S +103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S +104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S +105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S +106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S +107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S +108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S +109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S +110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q +111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S +112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C +113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S +114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S +115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C +116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S +117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q +118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S +119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C +120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S +121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S +122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S +123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C +124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S +125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S +126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C +127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q +128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S +129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C +130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S +131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C +132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S +133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S +134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S +135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S +136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C +137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S +138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S +139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S +140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C +141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C +142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S +143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S +144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q +145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S +146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S +147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S +148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S +149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S +150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S +151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S +152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S +153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S +154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S +155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S +156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C +157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q +158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S +159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S +160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S +161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S +162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S +163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S +164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S +165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S +166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S +167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S +168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S +169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S +170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S +171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S +172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q +173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S +174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S +175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C +176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S +177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S +178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C +179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S +180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S +181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S +182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C +183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S +184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S +185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S +186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S +187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q +188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S +189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q +190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S +191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S +192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S +193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S +194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S +195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C +196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C +197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q +198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S +199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q +200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S +201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S +202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S +203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S +204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C +205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S +206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S +207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S +208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C +209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q +210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C +211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S +212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S +213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S +214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S +215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q +216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C +217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S +218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S +219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C +220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S +221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S +222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S +223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S +224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S +225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S +226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S +227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S +228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S +229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S +230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S +231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S +232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S +233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S +234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S +235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S +236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S +237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S +238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S +239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S +240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S +241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C +242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q +243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S +244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S +245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C +246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q +247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S +248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S +249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S +250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S +251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S +252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S +253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S +254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S +255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S +256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C +257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C +258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S +259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C +260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S +261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q +262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S +263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S +264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S +265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q +266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S +267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S +268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S +269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S +270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S +271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S +272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S +273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S +274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C +275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q +276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S +277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S +278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S +279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q +280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S +281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q +282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S +283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S +284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S +285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S +286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C +287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S +288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S +289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S +290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q +291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S +292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C +293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C +294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S +295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S +296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C +297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C +298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S +299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S +300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C +301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q +302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q +303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S +304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q +305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S +306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S +307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C +308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C +309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C +310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C +311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C +312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C +313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S +314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S +315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S +316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S +317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S +318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S +319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S +320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C +321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S +322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S +323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q +324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S +325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S +326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C +327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S +328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S +329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S +330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C +331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q +332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S +333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S +334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S +335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S +336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S +337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S +338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C +339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S +340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S +341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S +342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S +343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S +344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S +345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S +346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S +347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S +348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S +349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S +350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S +351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S +352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S +353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C +354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S +355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C +356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S +357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S +358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S +359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q +360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q +361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S +362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C +363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C +364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S +365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q +366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S +367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C +368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C +369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q +370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C +371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C +372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S +373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S +374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C +375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S +376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C +377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S +378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C +379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C +380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S +381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C +382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C +383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S +384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S +385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S +386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S +387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S +388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S +389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q +390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C +391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S +392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S +393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S +394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C +395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S +396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S +397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S +398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S +399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S +400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S +401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S +402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S +403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S +404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S +405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S +406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S +407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S +408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S +409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S +410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S +411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S +412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q +413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q +414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S +415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S +416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S +417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S +418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S +419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S +420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S +421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C +422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q +423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S +424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S +425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S +426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S +427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S +428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S +429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q +430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S +431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S +432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S +433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S +434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S +435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S +436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S +437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S +438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S +439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S +440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S +441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S +442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S +443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S +444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S +445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S +446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S +447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S +448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S +449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C +450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S +451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S +452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S +453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C +454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C +455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S +456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C +457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S +458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S +459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S +460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q +461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S +462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S +463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S +464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S +465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S +466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S +467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S +468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S +469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q +470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C +471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S +472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S +473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S +474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C +475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S +476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S +477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S +478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S +479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S +480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S +481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S +482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S +483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S +484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S +485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C +486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S +487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S +488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C +489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S +490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S +491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S +492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S +493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S +494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C +495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S +496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C +497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C +498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S +499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S +500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S +501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S +502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q +503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q +504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S +505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S +506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C +507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S +508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S +509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S +510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S +511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q +512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S +513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S +514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C +515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S +516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S +517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S +518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q +519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S +520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S +521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S +522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S +523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C +524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C +525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C +526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q +527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S +528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S +529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S +530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S +531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S +532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C +533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C +534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C +535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S +536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S +537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S +538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C +539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S +540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C +541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S +542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S +543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S +544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S +545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C +546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S +547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S +548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C +549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S +550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S +551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C +552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S +553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q +554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C +555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S +556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S +557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C +558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C +559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S +560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S +561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q +562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S +563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S +564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S +565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S +566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S +567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S +568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S +569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C +570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S +571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S +572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S +573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S +574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q +575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S +576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S +577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S +578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S +579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C +580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S +581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S +582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C +583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S +584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C +585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C +586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S +587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S +588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C +589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S +590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S +591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S +592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C +593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S +594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q +595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S +596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S +597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S +598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S +599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C +600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C +601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S +602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S +603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S +604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S +605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C +606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S +607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S +608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S +609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C +610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S +611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S +612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S +613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q +614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q +615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S +616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S +617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S +618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S +619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S +620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S +621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C +622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S +623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C +624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S +625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S +626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S +627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q +628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S +629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S +630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q +631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S +632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S +633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C +634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S +635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S +636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S +637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S +638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S +639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S +640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S +641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S +642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C +643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S +644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S +645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C +646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C +647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S +648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C +649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S +650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S +651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S +652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S +653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S +654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q +655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q +656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S +657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S +658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q +659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S +660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C +661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S +662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C +663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S +664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S +665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S +666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S +667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S +668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S +669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S +670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S +671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S +672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S +673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S +674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S +675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S +676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S +677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S +678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S +679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S +680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C +681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q +682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C +683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S +684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S +685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S +686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C +687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S +688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S +689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S +690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S +691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S +692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C +693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S +694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C +695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S +696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S +697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S +698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q +699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C +700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S +701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C +702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S +703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C +704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q +705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S +706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S +707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S +708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S +709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S +710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C +711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C +712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S +713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S +714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S +715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S +716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S +717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C +718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S +719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q +720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S +721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S +722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S +723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S +724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S +725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S +726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S +727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S +728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q +729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S +730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S +731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S +732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C +733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S +734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S +735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S +736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S +737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S +738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C +739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S +740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S +741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S +742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S +743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C +744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S +745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S +746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S +747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S +748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S +749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S +750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q +751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S +752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S +753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S +754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S +755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S +756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S +757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S +758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S +759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S +760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S +761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S +762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S +763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C +764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S +765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S +766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S +767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C +768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q +769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q +770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S +771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S +772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S +773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S +774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C +775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S +776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S +777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q +778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S +779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q +780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S +781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C +782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S +783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S +784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S +785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S +786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S +787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S +788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q +789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S +790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C +791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q +792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S +793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S +794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C +795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S +796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S +797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S +798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S +799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C +800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S +801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S +802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S +803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S +804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C +805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S +806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S +807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S +808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S +809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S +810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S +811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S +812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S +813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S +814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S +815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S +816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S +817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S +818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C +819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S +820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S +821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S +822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S +823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S +824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S +825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S +826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q +827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S +828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C +829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q +830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28, +831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C +832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S +833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C +834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S +835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S +836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C +837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S +838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S +839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S +840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C +841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S +842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S +843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C +844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C +845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S +846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S +847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S +848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C +849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S +850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C +851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S +852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S +853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C +854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S +855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S +856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S +857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S +858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S +859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C +860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C +861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S +862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S +863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S +864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S +865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S +866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S +867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C +868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S +869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S +870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S +871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S +872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S +873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S +874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S +875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C +876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C +877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S +878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S +879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S +880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C +881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S +882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S +883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S +884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S +885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S +886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q +887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S +888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S +889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/data_exploration.ipynb b/data_exploration.ipynb new file mode 100644 index 0000000..6252352 --- /dev/null +++ b/data_exploration.ipynb @@ -0,0 +1,1646 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Warmup Project: Data Exploration\n", + "\n", + "This notebook contains data exploration for the warmup project for data science. In this project, I'm using the titanic dataset from Kaggle. The goal of this notebook is to just start to get a feel for the data, and start to understand what is actually in the dataset and how it might impact survival rates of the titanic sinking. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing Everything" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading in the data\n", + "First, loading in the data. Doing this using Pandas' read_csv() function" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale2210A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female3810PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale2600STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female351011380353.1000C123S
4503Allen, Mr. William Henrymale35003734508.0500NaNS
6701McCarthy, Mr. Timothy Jmale54001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale23134990921.0750NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female270234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female141023773630.0708NaNC
101113Sandstrom, Miss. Marguerite Rutfemale411PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale580011378326.5500C103S
121303Saundercock, Mr. William Henrymale2000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale391534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14003504067.8542NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female550024870616.0000NaNS
161703Rice, Master. Eugenemale24138265229.1250NaNQ
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female311034576318.0000NaNS
202102Fynney, Mr. Joseph Jmale350023986526.0000NaNS
212212Beesley, Mr. Lawrencemale340024869813.0000D56S
222313McGowan, Miss. Anna \"Annie\"female15003309238.0292NaNQ
232411Sloper, Mr. William Thompsonmale280011378835.5000A6S
242503Palsson, Miss. Torborg Danirafemale83134990921.0750NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female381534707731.3875NaNS
272801Fortune, Mr. Charles Alexandermale193219950263.0000C23 C25 C27S
303101Uruchurtu, Don. Manuel Emale4000PC 1760127.7208NaNC
333402Wheadon, Mr. Edward Hmale6600C.A. 2457910.5000NaNS
343501Meyer, Mr. Edgar Josephmale2810PC 1760482.1708NaNC
353601Holverson, Mr. Alexander Oskarmale421011378952.0000NaNS
373803Cann, Mr. Ernest Charlesmale2100A./5. 21528.0500NaNS
383903Vander Planke, Miss. Augusta Mariafemale182034576418.0000NaNS
.......................................
85685711Wick, Mrs. George Dennick (Mary Hitchcock)female451136928164.8667NaNS
85785811Daly, Mr. Peter Denismale510011305526.5500E17S
85885913Baclini, Mrs. Solomon (Latifa Qurban)female2403266619.2583NaNC
86086103Hansen, Mr. Claus Petermale412035002614.1083NaNS
86186202Giles, Mr. Frederick Edwardmale21102813411.5000NaNS
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...female48001746625.9292D17S
86486502Gill, Mr. John Williammale240023386613.0000NaNS
86586612Bystrom, Mrs. (Karolina)female420023685213.0000NaNS
86686712Duran y More, Miss. Asuncionfemale2710SC/PARIS 214913.8583NaNC
86786801Roebling, Mr. Washington Augustus IImale3100PC 1759050.4958A24S
86987013Johnson, Master. Harold Theodormale41134774211.1333NaNS
87087103Balkic, Mr. Cerinmale26003492487.8958NaNS
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47111175152.5542D35S
87287301Carlsson, Mr. Frans Olofmale33006955.0000B51 B53 B55S
87387403Vander Cruyssen, Mr. Victormale47003457659.0000NaNS
87487512Abelson, Mrs. Samuel (Hannah Wizosky)female2810P/PP 338124.0000NaNC
87587613Najib, Miss. Adele Kiamie \"Jane\"female150026677.2250NaNC
87687703Gustafsson, Mr. Alfred Ossianmale200075349.8458NaNS
87787803Petroff, Mr. Nedeliomale19003492127.8958NaNS
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56011176783.1583C50C
88088112Shelley, Mrs. William (Imanita Parrish Hall)female250123043326.0000NaNS
88188203Markun, Mr. Johannmale33003492577.8958NaNS
88288303Dahlberg, Miss. Gerda Ulrikafemale2200755210.5167NaNS
88388402Banfield, Mr. Frederick Jamesmale2800C.A./SOTON 3406810.5000NaNS
88488503Sutehall, Mr. Henry Jrmale2500SOTON/OQ 3920767.0500NaNS
88588603Rice, Mrs. William (Margaret Norton)female390538265229.1250NaNQ
88688702Montvila, Rev. Juozasmale270021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale190011205330.0000B42S
88989011Behr, Mr. Karl Howellmale260011136930.0000C148C
89089103Dooley, Mr. Patrickmale32003703767.7500NaNQ
\n", + "

714 rows × 12 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "10 11 1 3 \n", + "11 12 1 1 \n", + "12 13 0 3 \n", + "13 14 0 3 \n", + "14 15 0 3 \n", + "15 16 1 2 \n", + "16 17 0 3 \n", + "18 19 0 3 \n", + "20 21 0 2 \n", + "21 22 1 2 \n", + "22 23 1 3 \n", + "23 24 1 1 \n", + "24 25 0 3 \n", + "25 26 1 3 \n", + "27 28 0 1 \n", + "30 31 0 1 \n", + "33 34 0 2 \n", + "34 35 0 1 \n", + "35 36 0 1 \n", + "37 38 0 3 \n", + "38 39 0 3 \n", + ".. ... ... ... \n", + "856 857 1 1 \n", + "857 858 1 1 \n", + "858 859 1 3 \n", + "860 861 0 3 \n", + "861 862 0 2 \n", + "862 863 1 1 \n", + "864 865 0 2 \n", + "865 866 1 2 \n", + "866 867 1 2 \n", + "867 868 0 1 \n", + "869 870 1 3 \n", + "870 871 0 3 \n", + "871 872 1 1 \n", + "872 873 0 1 \n", + "873 874 0 3 \n", + "874 875 1 2 \n", + "875 876 1 3 \n", + "876 877 0 3 \n", + "877 878 0 3 \n", + "879 880 1 1 \n", + "880 881 1 2 \n", + "881 882 0 3 \n", + "882 883 0 3 \n", + "883 884 0 2 \n", + "884 885 0 3 \n", + "885 886 0 3 \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 \n", + "2 Heikkinen, Miss. Laina female 26 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 \n", + "4 Allen, Mr. William Henry male 35 0 \n", + "6 McCarthy, Mr. Timothy J male 54 0 \n", + "7 Palsson, Master. Gosta Leonard male 2 3 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 \n", + "10 Sandstrom, Miss. Marguerite Rut female 4 1 \n", + "11 Bonnell, Miss. Elizabeth female 58 0 \n", + "12 Saundercock, Mr. William Henry male 20 0 \n", + "13 Andersson, Mr. Anders Johan male 39 1 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) female 55 0 \n", + "16 Rice, Master. Eugene male 2 4 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31 1 \n", + "20 Fynney, Mr. Joseph J male 35 0 \n", + "21 Beesley, Mr. Lawrence male 34 0 \n", + "22 McGowan, Miss. Anna \"Annie\" female 15 0 \n", + "23 Sloper, Mr. William Thompson male 28 0 \n", + "24 Palsson, Miss. Torborg Danira female 8 3 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38 1 \n", + "27 Fortune, Mr. Charles Alexander male 19 3 \n", + "30 Uruchurtu, Don. Manuel E male 40 0 \n", + "33 Wheadon, Mr. Edward H male 66 0 \n", + "34 Meyer, Mr. Edgar Joseph male 28 1 \n", + "35 Holverson, Mr. Alexander Oskar male 42 1 \n", + "37 Cann, Mr. Ernest Charles male 21 0 \n", + "38 Vander Planke, Miss. Augusta Maria female 18 2 \n", + ".. ... ... ... ... \n", + "856 Wick, Mrs. George Dennick (Mary Hitchcock) female 45 1 \n", + "857 Daly, Mr. Peter Denis male 51 0 \n", + "858 Baclini, Mrs. Solomon (Latifa Qurban) female 24 0 \n", + "860 Hansen, Mr. Claus Peter male 41 2 \n", + "861 Giles, Mr. Frederick Edward male 21 1 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48 0 \n", + "864 Gill, Mr. John William male 24 0 \n", + "865 Bystrom, Mrs. (Karolina) female 42 0 \n", + "866 Duran y More, Miss. Asuncion female 27 1 \n", + "867 Roebling, Mr. Washington Augustus II male 31 0 \n", + "869 Johnson, Master. Harold Theodor male 4 1 \n", + "870 Balkic, Mr. Cerin male 26 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 \n", + "872 Carlsson, Mr. Frans Olof male 33 0 \n", + "873 Vander Cruyssen, Mr. Victor male 47 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" female 15 0 \n", + "876 Gustafsson, Mr. Alfred Ossian male 20 0 \n", + "877 Petroff, Mr. Nedelio male 19 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 \n", + "881 Markun, Mr. Johann male 33 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika female 22 0 \n", + "883 Banfield, Mr. Frederick James male 28 0 \n", + "884 Sutehall, Mr. Henry Jr male 25 0 \n", + "885 Rice, Mrs. William (Margaret Norton) female 39 0 \n", + "886 Montvila, Rev. Juozas male 27 0 \n", + "887 Graham, Miss. Margaret Edith female 19 0 \n", + "889 Behr, Mr. Karl Howell male 26 0 \n", + "890 Dooley, Mr. Patrick male 32 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + "6 0 17463 51.8625 E46 S \n", + "7 1 349909 21.0750 NaN S \n", + "8 2 347742 11.1333 NaN S \n", + "9 0 237736 30.0708 NaN C \n", + "10 1 PP 9549 16.7000 G6 S \n", + "11 0 113783 26.5500 C103 S \n", + "12 0 A/5. 2151 8.0500 NaN S \n", + "13 5 347082 31.2750 NaN S \n", + "14 0 350406 7.8542 NaN S \n", + "15 0 248706 16.0000 NaN S \n", + "16 1 382652 29.1250 NaN Q \n", + "18 0 345763 18.0000 NaN S \n", + "20 0 239865 26.0000 NaN S \n", + "21 0 248698 13.0000 D56 S \n", + "22 0 330923 8.0292 NaN Q \n", + "23 0 113788 35.5000 A6 S \n", + "24 1 349909 21.0750 NaN S \n", + "25 5 347077 31.3875 NaN S \n", + "27 2 19950 263.0000 C23 C25 C27 S \n", + "30 0 PC 17601 27.7208 NaN C \n", + "33 0 C.A. 24579 10.5000 NaN S \n", + "34 0 PC 17604 82.1708 NaN C \n", + "35 0 113789 52.0000 NaN S \n", + "37 0 A./5. 2152 8.0500 NaN S \n", + "38 0 345764 18.0000 NaN S \n", + ".. ... ... ... ... ... \n", + "856 1 36928 164.8667 NaN S \n", + "857 0 113055 26.5500 E17 S \n", + "858 3 2666 19.2583 NaN C \n", + "860 0 350026 14.1083 NaN S \n", + "861 0 28134 11.5000 NaN S \n", + "862 0 17466 25.9292 D17 S \n", + "864 0 233866 13.0000 NaN S \n", + "865 0 236852 13.0000 NaN S \n", + "866 0 SC/PARIS 2149 13.8583 NaN C \n", + "867 0 PC 17590 50.4958 A24 S \n", + "869 1 347742 11.1333 NaN S \n", + "870 0 349248 7.8958 NaN S \n", + "871 1 11751 52.5542 D35 S \n", + "872 0 695 5.0000 B51 B53 B55 S \n", + "873 0 345765 9.0000 NaN S \n", + "874 0 P/PP 3381 24.0000 NaN C \n", + "875 0 2667 7.2250 NaN C \n", + "876 0 7534 9.8458 NaN S \n", + "877 0 349212 7.8958 NaN S \n", + "879 1 11767 83.1583 C50 C \n", + "880 1 230433 26.0000 NaN S \n", + "881 0 349257 7.8958 NaN S \n", + "882 0 7552 10.5167 NaN S \n", + "883 0 C.A./SOTON 34068 10.5000 NaN S \n", + "884 0 SOTON/OQ 392076 7.0500 NaN S \n", + "885 5 382652 29.1250 NaN Q \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[714 rows x 12 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"./Data/train.csv\").dropna(subset = ['Survived', 'Age', 'Sex','Pclass'])\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One thing to note is that in the data that loaded above, it seems like there are some tickets that are numbers, and some that are strings. I'll have to be careful of this if I want to visualize anything relating to ticket numbers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validating that the Data Loaded Correctly\n", + "I'm just going to do some basic checks that the data loaded correctly. \n", + "\n", + "For now, this will include just counting the null values." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId 0\n", + "Survived 0\n", + "Pclass 0\n", + "Name 0\n", + "Sex 0\n", + "Age 0\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket 0\n", + "Fare 0\n", + "Cabin 529\n", + "Embarked 2\n", + "dtype: int64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hmmm, it seems that there are some null values. These seem to be mostly age and cabin values although a few of them seem to be whether the passengers actually got on the ship" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploring the Data\n", + "Here, I'll be plotting/displaying various parameters included in the dataset, just to get a feel for the data. \n", + "\n", + "To make my code more readable, I'm going to split the dataframe into two different dataframes: one for those who survived, and one for those who didn't. This should make my code more readable when I want to plot how things are related to survival status than always having to say data[data.Survived == 1]. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "survived = data[data.Survived == 1]\n", + "died = data[data.Survived == 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the things that I think most people think of when they want to predict who survived the titanic is \"Women and Children first\". Let's see if more women and children did survie than adult men. \n", + "\n", + "To do this, I'm first going to create a histogram of the number of people that died/survived for both sexes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEZCAYAAAB1mUk3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Histogram code borrowed from: http://stackoverflow.com/questions/25539195/multiple-histograms-in-pandas\n", + "fig, ax = plt.subplots()\n", + "\n", + "\n", + "femSurvivedHeights, femSurvivedBins = np.histogram(survived[survived.Sex == \"female\"][\"Age\"], bins = 10)\n", + "femDiedHeights, femDiedBins = np.histogram(died[died.Sex == \"female\"][\"Age\"], bins = femSurvivedBins)\n", + "malSurvivedHeights, malSurvivedBins = np.histogram(survived[survived.Sex == \"male\"][\"Age\"], bins = femSurvivedBins)\n", + "malDiedHeights, malDiedBins = np.histogram(died[died.Sex == \"male\"][\"Age\"], bins = femSurvivedBins)\n", + "\n", + "width = (femSurvivedBins[1] - femSurvivedBins[0])/5\n", + "\n", + "ax.bar(femSurvivedBins[:-1], femSurvivedHeights, width=width, facecolor='seagreen')\n", + "ax.bar(femDiedBins[:-1]+width, femDiedHeights, width=width, facecolor='r')\n", + "ax.bar(malSurvivedBins[:-1] + 2*width, malSurvivedHeights, width=width, facecolor='darkseagreen')\n", + "ax.bar(malDiedBins[:-1]+3*width, malDiedHeights, width=width, facecolor ='darkred')\n", + "\n", + "\n", + "ax.legend(['Female Survivors', 'Female Casualties', 'Male Surviviors', 'Male Casualties'])\n", + "ax.set_xlabel('Age')\n", + "ax.set_ylabel('Number of People')\n", + "ax.set_title(' Distribution of Survival Status Based on Gender')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, in looking at this histogram, it does appear that the number of women who survived is consistently grater than the number of women who died. This, however, is not true for men. Up unil the age of about 10, more male children survived than died, but after this, many more men died than survived. So, it is women and children first, it appears. \n", + "\n", + "Now, this graph isn't the easiest to read, so I think I'm going to just plot survival rates for each set of ages. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEZCAYAAACTsIJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "width = (femSurvivedBins[1] - femSurvivedBins[0])/3\n", + "\n", + "femSurvivalRatios = np.divide(femSurvivedHeights, np.add(femSurvivedHeights, femDiedHeights), dtype = float)\n", + "malSurvivalRatios = np.divide(malSurvivedHeights, np.add(malSurvivedHeights, malDiedHeights), dtype = float)\n", + "\n", + "ax.bar(femSurvivedBins[:-1], femSurvivalRatios, width=width, facecolor='paleturquoise')\n", + "ax.bar(malSurvivedBins[:-1]+width, malSurvivalRatios, width=width, facecolor='steelblue')\n", + "\n", + "ax.legend(['Female Survival Ratios', 'Male Survival Ratio'])\n", + "ax.set_xlabel('Age')\n", + "ax.set_ylabel('Percentage of people who survived')\n", + "ax.set_title(' Distribution of Survival Status Based on Gender')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, this graph tells us that for younger ages, male and female people were equally likely to survive, but as age increases, women are much more likely to survive than men. So, yes, I'd say that women and children were more likely to survive than men. There is, however, something interesting happening with survival rates around the age of 10 where both men and women experience a drop in survival rate. I'm not exactly sure why something like this would happen, but I'd immagine it might be because around this age, people are no longer thought of as children, but weren't quite able to fend for themselves yet either. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How does Passenger Class affect likelihood of survival?\n", + "Next, I'm interested in taking a look at whether or not the class of ticket that a passenger bought influences their chances of survival. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEmCAYAAACAtfxPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "ax.bar(np.add(range(1,4),-0.35),survived.groupby('Pclass').count()['PassengerId'], width = 0.35, facecolor='seagreen')\n", + "ax.bar(range(1,4) ,died.groupby('Pclass').count()['PassengerId'], width = 0.35, facecolor='darkred')\n", + "\n", + "ax.legend(['Survived', 'Died'])\n", + "\n", + "labels = ['Upper', 'Middle', 'Lower']\n", + "\n", + "plt.xticks(range(1,4), labels, rotation='vertical')\n", + "\n", + "plt.xlabel('Passenger\\'s Estimated Economic Class')\n", + "plt.ylabel('Number of Survivors')\n", + "\n", + "# survived.groupby('Pclass').count()['PassengerId'].plot(kind='bar');\n", + "# died.groupby('Pclass').count()['PassengerId'].plot(kind='bar', );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hmmm, so it does seem like people in first class were more likely to survive than they were to die. It seems like passengers in second class were about equally likely to survive as they were to die, and passengers in third class are much less likely to survive." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Does the deck and cost of fare predict survival?\n", + "\n", + "Now, I'm interested in seeing whether the cost of the fare or the deck (which is related to the cabin number, in that it appears to be the letter before the cabin number) that someone was in predicts their survival. I'm guessing that these are fairly related, as the cost of the fare would predict the cabin. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, I want to create a Cabin Recode column in both dataframes. To do this, I'll create a column that assigns numbers based on which cabin someone was staying in. Since the Cabins seem to be letter,number combinations, I'll be using the letters to distinguish between cabins.\n", + "\n", + "My mapping should be \n", + "\n", + "NaN -> 0\n", + "\n", + "A -> 1\n", + "\n", + "B -> 2\n", + "\n", + "C -> 3\n", + "\n", + "D -> 4\n", + "\n", + "E -> 5\n", + "\n", + "F -> 6 \n", + "\n", + "G -> 7\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def recodeCabinToDeck(cabin):\n", + " if isinstance(cabin, str):\n", + " if (cabin[0] == 'A'):\n", + " return 1\n", + " elif (cabin[0] == 'B'):\n", + " return 2\n", + " elif (cabin[0] == 'C'):\n", + " return 3\n", + " elif (cabin[0] == 'D'):\n", + " return 4\n", + " elif (cabin[0] == 'E'):\n", + " return 5\n", + " elif (cabin[0] == 'F'):\n", + " return 6\n", + " elif (cabin[0] == 'G'):\n", + " return 7\n", + " else:\n", + " return 8\n", + " else:\n", + " if (np.isnan):\n", + " return 0\n", + " else:\n", + " return 8\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that I've written a function to recode the cabin to a deck, I'm going to apply that function to my survived and died dataframes to just get the deck value for each passenger" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Sophia/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " if __name__ == '__main__':\n", + "/Users/Sophia/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " from ipykernel import kernelapp as app\n" + ] + } + ], + "source": [ + "survived['DeckRecode'] = survived.Cabin.apply(lambda x: recodeCabinToDeck(x))\n", + "died['DeckRecode'] = died.Cabin.apply(lambda x: recodeCabinToDeck(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAFMCAYAAAA6DZCHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSEJKFiIikpCShYiIJKRkISIiCf1/1ayJ5DY1YHoAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = survived.plot(kind='scatter', x='DeckRecode', y='Fare', color = 'seagreen');\n", + "died.plot(kind='scatter', x='DeckRecode', y='Fare', color = 'salmon', ax = ax)\n", + "ax.legend(['Survived', 'Died'])\n", + "\n", + "\n", + "labels = ['No listed deck', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'Other']\n", + "plt.xticks(range(0,9), labels, rotation='vertical')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow, this is not really a great way to see this data, but, it does appear that people who paid lower fairs regardless of deck were less likely to survive. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/kaggle.csv b/kaggle.csv new file mode 100644 index 0000000..485997c --- /dev/null +++ b/kaggle.csv @@ -0,0 +1,419 @@ +PassengerId,Survived +892,0 +893,0 +894,0 +895,0 +896,1 +897,0 +898,1 +899,0 +900,1 +901,0 +902,0 +903,0 +904,1 +905,0 +906,1 +907,1 +908,0 +909,0 +910,1 +911,1 +912,0 +913,0 +914,1 +915,1 +916,1 +917,0 +918,1 +919,0 +920,0 +921,0 +922,0 +923,0 +924,0 +925,1 +926,0 +927,0 +928,1 +929,1 +930,0 +931,0 +932,0 +933,0 +934,0 +935,1 +936,1 +937,0 +938,0 +939,0 +940,1 +941,1 +942,0 +943,0 +944,1 +945,1 +946,0 +947,0 +948,0 +949,0 +950,0 +951,1 +952,0 +953,0 +954,0 +955,1 +956,1 +957,1 +958,1 +959,0 +960,1 +961,1 +962,1 +963,0 +964,1 +965,1 +966,1 +967,1 +968,0 +969,1 +970,0 +971,1 +972,0 +973,0 +974,0 +975,0 +976,0 +977,0 +978,1 +979,1 +980,1 +981,0 +982,1 +983,0 +984,1 +985,0 +986,1 +987,0 +988,1 +989,0 +990,1 +991,0 +992,1 +993,0 +994,0 +995,0 +996,1 +997,0 +998,0 +999,0 +1000,0 +1001,0 +1002,0 +1003,1 +1004,1 +1005,1 +1006,1 +1007,0 +1008,0 +1009,1 +1010,0 +1011,1 +1012,1 +1013,0 +1014,1 +1015,0 +1016,0 +1017,1 +1018,0 +1019,1 +1020,0 +1021,0 +1022,0 +1023,0 +1024,1 +1025,0 +1026,0 +1027,0 +1028,0 +1029,0 +1030,1 +1031,0 +1032,0 +1033,1 +1034,0 +1035,0 +1036,0 +1037,0 +1038,0 +1039,0 +1040,0 +1041,0 +1042,1 +1043,0 +1044,0 +1045,1 +1046,0 +1047,0 +1048,1 +1049,1 +1050,0 +1051,1 +1052,1 +1053,0 +1054,1 +1055,0 +1056,0 +1057,1 +1058,0 +1059,0 +1060,1 +1061,1 +1062,0 +1063,0 +1064,0 +1065,0 +1066,0 +1067,1 +1068,1 +1069,0 +1070,1 +1071,1 +1072,0 +1073,0 +1074,1 +1075,0 +1076,1 +1077,0 +1078,1 +1079,0 +1080,0 +1081,0 +1082,0 +1083,0 +1084,0 +1085,0 +1086,0 +1087,0 +1088,1 +1089,1 +1090,0 +1091,1 +1092,1 +1093,0 +1094,0 +1095,1 +1096,0 +1097,1 +1098,1 +1099,0 +1100,1 +1101,0 +1102,0 +1103,0 +1104,0 +1105,1 +1106,0 +1107,0 +1108,1 +1109,0 +1110,1 +1111,0 +1112,1 +1113,0 +1114,1 +1115,0 +1116,1 +1117,1 +1118,0 +1119,1 +1120,0 +1121,0 +1122,0 +1123,1 +1124,0 +1125,0 +1126,0 +1127,0 +1128,0 +1129,0 +1130,1 +1131,1 +1132,1 +1133,1 +1134,0 +1135,0 +1136,0 +1137,0 +1138,1 +1139,0 +1140,1 +1141,1 +1142,1 +1143,0 +1144,1 +1145,0 +1146,0 +1147,0 +1148,0 +1149,0 +1150,1 +1151,0 +1152,0 +1153,0 +1154,1 +1155,1 +1156,0 +1157,0 +1158,0 +1159,0 +1160,1 +1161,0 +1162,0 +1163,0 +1164,1 +1165,1 +1166,0 +1167,1 +1168,0 +1169,0 +1170,0 +1171,0 +1172,1 +1173,0 +1174,1 +1175,1 +1176,1 +1177,0 +1178,0 +1179,0 +1180,0 +1181,0 +1182,0 +1183,1 +1184,0 +1185,0 +1186,0 +1187,0 +1188,1 +1189,0 +1190,0 +1191,0 +1192,0 +1193,0 +1194,0 +1195,0 +1196,1 +1197,1 +1198,0 +1199,0 +1200,0 +1201,0 +1202,0 +1203,0 +1204,0 +1205,1 +1206,1 +1207,1 +1208,0 +1209,0 +1210,0 +1211,0 +1212,0 +1213,0 +1214,0 +1215,0 +1216,1 +1217,0 +1218,1 +1219,0 +1220,0 +1221,0 +1222,1 +1223,0 +1224,0 +1225,1 +1226,0 +1227,0 +1228,0 +1229,0 +1230,0 +1231,0 +1232,0 +1233,0 +1234,0 +1235,1 +1236,0 +1237,1 +1238,0 +1239,1 +1240,0 +1241,1 +1242,1 +1243,0 +1244,0 +1245,0 +1246,1 +1247,0 +1248,1 +1249,0 +1250,0 +1251,1 +1252,0 +1253,1 +1254,1 +1255,0 +1256,1 +1257,0 +1258,0 +1259,1 +1260,1 +1261,0 +1262,0 +1263,1 +1264,0 +1265,0 +1266,1 +1267,1 +1268,1 +1269,0 +1270,0 +1271,0 +1272,0 +1273,0 +1274,1 +1275,1 +1276,0 +1277,1 +1278,0 +1279,0 +1280,0 +1281,0 +1282,1 +1283,1 +1284,0 +1285,0 +1286,0 +1287,1 +1288,0 +1289,1 +1290,0 +1291,0 +1292,1 +1293,0 +1294,1 +1295,1 +1296,0 +1297,0 +1298,0 +1299,0 +1300,1 +1301,1 +1302,1 +1303,1 +1304,1 +1305,0 +1306,1 +1307,0 +1308,0 +1309,0 diff --git a/kaggle_it1.csv b/kaggle_it1.csv new file mode 100644 index 0000000..323fbca --- /dev/null +++ b/kaggle_it1.csv @@ -0,0 +1,419 @@ +PassengerId,Survived +892,0 +893,0 +894,0 +895,0 +896,1 +897,0 +898,1 +899,0 +900,1 +901,0 +902,0 +903,0 +904,1 +905,0 +906,1 +907,1 +908,0 +909,0 +910,1 +911,1 +912,0 +913,0 +914,1 +915,1 +916,1 +917,0 +918,1 +919,0 +920,0 +921,0 +922,0 +923,0 +924,0 +925,1 +926,0 +927,0 +928,1 +929,1 +930,0 +931,0 +932,0 +933,0 +934,0 +935,1 +936,1 +937,0 +938,0 +939,0 +940,1 +941,1 +942,0 +943,0 +944,1 +945,1 +946,0 +947,0 +948,0 +949,0 +950,0 +951,1 +952,0 +953,0 +954,0 +955,1 +956,1 +957,1 +958,1 +959,0 +960,1 +961,1 +962,1 +963,0 +964,1 +965,1 +966,1 +967,1 +968,0 +969,1 +970,0 +971,1 +972,0 +973,0 +974,0 +975,0 +976,0 +977,0 +978,1 +979,1 +980,1 +981,0 +982,1 +983,0 +984,1 +985,0 +986,1 +987,0 +988,1 +989,0 +990,1 +991,0 +992,1 +993,0 +994,0 +995,0 +996,1 +997,0 +998,0 +999,0 +1000,0 +1001,0 +1002,0 +1003,1 +1004,1 +1005,1 +1006,1 +1007,0 +1008,0 +1009,1 +1010,1 +1011,1 +1012,1 +1013,0 +1014,1 +1015,0 +1016,0 +1017,1 +1018,0 +1019,1 +1020,0 +1021,0 +1022,0 +1023,0 +1024,1 +1025,0 +1026,0 +1027,0 +1028,0 +1029,0 +1030,1 +1031,0 +1032,0 +1033,1 +1034,0 +1035,0 +1036,0 +1037,0 +1038,0 +1039,0 +1040,0 +1041,0 +1042,1 +1043,0 +1044,0 +1045,1 +1046,0 +1047,0 +1048,1 +1049,1 +1050,0 +1051,1 +1052,1 +1053,0 +1054,1 +1055,0 +1056,0 +1057,1 +1058,0 +1059,0 +1060,1 +1061,1 +1062,0 +1063,0 +1064,0 +1065,0 +1066,0 +1067,1 +1068,1 +1069,0 +1070,1 +1071,1 +1072,0 +1073,0 +1074,1 +1075,0 +1076,1 +1077,0 +1078,1 +1079,0 +1080,0 +1081,0 +1082,0 +1083,0 +1084,0 +1085,0 +1086,0 +1087,0 +1088,1 +1089,1 +1090,0 +1091,1 +1092,1 +1093,0 +1094,0 +1095,1 +1096,0 +1097,1 +1098,1 +1099,0 +1100,1 +1101,0 +1102,0 +1103,0 +1104,0 +1105,1 +1106,0 +1107,0 +1108,1 +1109,0 +1110,1 +1111,0 +1112,1 +1113,0 +1114,1 +1115,0 +1116,1 +1117,1 +1118,0 +1119,1 +1120,0 +1121,0 +1122,0 +1123,1 +1124,0 +1125,0 +1126,0 +1127,0 +1128,0 +1129,0 +1130,1 +1131,1 +1132,1 +1133,1 +1134,0 +1135,0 +1136,0 +1137,0 +1138,1 +1139,0 +1140,1 +1141,1 +1142,1 +1143,0 +1144,1 +1145,0 +1146,0 +1147,0 +1148,0 +1149,0 +1150,1 +1151,0 +1152,0 +1153,0 +1154,1 +1155,1 +1156,0 +1157,0 +1158,0 +1159,0 +1160,1 +1161,0 +1162,0 +1163,0 +1164,1 +1165,1 +1166,0 +1167,1 +1168,0 +1169,0 +1170,0 +1171,0 +1172,1 +1173,0 +1174,1 +1175,1 +1176,1 +1177,0 +1178,0 +1179,0 +1180,0 +1181,0 +1182,0 +1183,1 +1184,0 +1185,0 +1186,0 +1187,0 +1188,1 +1189,0 +1190,0 +1191,0 +1192,0 +1193,0 +1194,0 +1195,0 +1196,1 +1197,1 +1198,0 +1199,0 +1200,0 +1201,0 +1202,0 +1203,0 +1204,0 +1205,1 +1206,1 +1207,1 +1208,0 +1209,0 +1210,0 +1211,0 +1212,0 +1213,0 +1214,0 +1215,0 +1216,1 +1217,0 +1218,1 +1219,0 +1220,0 +1221,0 +1222,1 +1223,0 +1224,0 +1225,1 +1226,0 +1227,0 +1228,0 +1229,0 +1230,0 +1231,0 +1232,0 +1233,0 +1234,0 +1235,1 +1236,0 +1237,1 +1238,0 +1239,1 +1240,0 +1241,1 +1242,1 +1243,0 +1244,0 +1245,0 +1246,1 +1247,0 +1248,1 +1249,0 +1250,0 +1251,1 +1252,0 +1253,1 +1254,1 +1255,0 +1256,1 +1257,0 +1258,0 +1259,1 +1260,1 +1261,0 +1262,0 +1263,1 +1264,0 +1265,0 +1266,1 +1267,1 +1268,1 +1269,0 +1270,0 +1271,0 +1272,0 +1273,0 +1274,1 +1275,1 +1276,0 +1277,1 +1278,0 +1279,0 +1280,0 +1281,0 +1282,1 +1283,1 +1284,0 +1285,0 +1286,0 +1287,1 +1288,0 +1289,1 +1290,0 +1291,0 +1292,1 +1293,0 +1294,1 +1295,1 +1296,0 +1297,0 +1298,0 +1299,0 +1300,1 +1301,1 +1302,1 +1303,1 +1304,1 +1305,0 +1306,1 +1307,0 +1308,0 +1309,0 diff --git a/kaggle_it1_5.csv b/kaggle_it1_5.csv new file mode 100644 index 0000000..323fbca --- /dev/null +++ b/kaggle_it1_5.csv @@ -0,0 +1,419 @@ +PassengerId,Survived +892,0 +893,0 +894,0 +895,0 +896,1 +897,0 +898,1 +899,0 +900,1 +901,0 +902,0 +903,0 +904,1 +905,0 +906,1 +907,1 +908,0 +909,0 +910,1 +911,1 +912,0 +913,0 +914,1 +915,1 +916,1 +917,0 +918,1 +919,0 +920,0 +921,0 +922,0 +923,0 +924,0 +925,1 +926,0 +927,0 +928,1 +929,1 +930,0 +931,0 +932,0 +933,0 +934,0 +935,1 +936,1 +937,0 +938,0 +939,0 +940,1 +941,1 +942,0 +943,0 +944,1 +945,1 +946,0 +947,0 +948,0 +949,0 +950,0 +951,1 +952,0 +953,0 +954,0 +955,1 +956,1 +957,1 +958,1 +959,0 +960,1 +961,1 +962,1 +963,0 +964,1 +965,1 +966,1 +967,1 +968,0 +969,1 +970,0 +971,1 +972,0 +973,0 +974,0 +975,0 +976,0 +977,0 +978,1 +979,1 +980,1 +981,0 +982,1 +983,0 +984,1 +985,0 +986,1 +987,0 +988,1 +989,0 +990,1 +991,0 +992,1 +993,0 +994,0 +995,0 +996,1 +997,0 +998,0 +999,0 +1000,0 +1001,0 +1002,0 +1003,1 +1004,1 +1005,1 +1006,1 +1007,0 +1008,0 +1009,1 +1010,1 +1011,1 +1012,1 +1013,0 +1014,1 +1015,0 +1016,0 +1017,1 +1018,0 +1019,1 +1020,0 +1021,0 +1022,0 +1023,0 +1024,1 +1025,0 +1026,0 +1027,0 +1028,0 +1029,0 +1030,1 +1031,0 +1032,0 +1033,1 +1034,0 +1035,0 +1036,0 +1037,0 +1038,0 +1039,0 +1040,0 +1041,0 +1042,1 +1043,0 +1044,0 +1045,1 +1046,0 +1047,0 +1048,1 +1049,1 +1050,0 +1051,1 +1052,1 +1053,0 +1054,1 +1055,0 +1056,0 +1057,1 +1058,0 +1059,0 +1060,1 +1061,1 +1062,0 +1063,0 +1064,0 +1065,0 +1066,0 +1067,1 +1068,1 +1069,0 +1070,1 +1071,1 +1072,0 +1073,0 +1074,1 +1075,0 +1076,1 +1077,0 +1078,1 +1079,0 +1080,0 +1081,0 +1082,0 +1083,0 +1084,0 +1085,0 +1086,0 +1087,0 +1088,1 +1089,1 +1090,0 +1091,1 +1092,1 +1093,0 +1094,0 +1095,1 +1096,0 +1097,1 +1098,1 +1099,0 +1100,1 +1101,0 +1102,0 +1103,0 +1104,0 +1105,1 +1106,0 +1107,0 +1108,1 +1109,0 +1110,1 +1111,0 +1112,1 +1113,0 +1114,1 +1115,0 +1116,1 +1117,1 +1118,0 +1119,1 +1120,0 +1121,0 +1122,0 +1123,1 +1124,0 +1125,0 +1126,0 +1127,0 +1128,0 +1129,0 +1130,1 +1131,1 +1132,1 +1133,1 +1134,0 +1135,0 +1136,0 +1137,0 +1138,1 +1139,0 +1140,1 +1141,1 +1142,1 +1143,0 +1144,1 +1145,0 +1146,0 +1147,0 +1148,0 +1149,0 +1150,1 +1151,0 +1152,0 +1153,0 +1154,1 +1155,1 +1156,0 +1157,0 +1158,0 +1159,0 +1160,1 +1161,0 +1162,0 +1163,0 +1164,1 +1165,1 +1166,0 +1167,1 +1168,0 +1169,0 +1170,0 +1171,0 +1172,1 +1173,0 +1174,1 +1175,1 +1176,1 +1177,0 +1178,0 +1179,0 +1180,0 +1181,0 +1182,0 +1183,1 +1184,0 +1185,0 +1186,0 +1187,0 +1188,1 +1189,0 +1190,0 +1191,0 +1192,0 +1193,0 +1194,0 +1195,0 +1196,1 +1197,1 +1198,0 +1199,0 +1200,0 +1201,0 +1202,0 +1203,0 +1204,0 +1205,1 +1206,1 +1207,1 +1208,0 +1209,0 +1210,0 +1211,0 +1212,0 +1213,0 +1214,0 +1215,0 +1216,1 +1217,0 +1218,1 +1219,0 +1220,0 +1221,0 +1222,1 +1223,0 +1224,0 +1225,1 +1226,0 +1227,0 +1228,0 +1229,0 +1230,0 +1231,0 +1232,0 +1233,0 +1234,0 +1235,1 +1236,0 +1237,1 +1238,0 +1239,1 +1240,0 +1241,1 +1242,1 +1243,0 +1244,0 +1245,0 +1246,1 +1247,0 +1248,1 +1249,0 +1250,0 +1251,1 +1252,0 +1253,1 +1254,1 +1255,0 +1256,1 +1257,0 +1258,0 +1259,1 +1260,1 +1261,0 +1262,0 +1263,1 +1264,0 +1265,0 +1266,1 +1267,1 +1268,1 +1269,0 +1270,0 +1271,0 +1272,0 +1273,0 +1274,1 +1275,1 +1276,0 +1277,1 +1278,0 +1279,0 +1280,0 +1281,0 +1282,1 +1283,1 +1284,0 +1285,0 +1286,0 +1287,1 +1288,0 +1289,1 +1290,0 +1291,0 +1292,1 +1293,0 +1294,1 +1295,1 +1296,0 +1297,0 +1298,0 +1299,0 +1300,1 +1301,1 +1302,1 +1303,1 +1304,1 +1305,0 +1306,1 +1307,0 +1308,0 +1309,0 diff --git a/kaggle_it2_0.csv b/kaggle_it2_0.csv new file mode 100644 index 0000000..d41bfcc --- /dev/null +++ b/kaggle_it2_0.csv @@ -0,0 +1,419 @@ +PassengerId,Survived +892,0 +893,1 +894,0 +895,0 +896,1 +897,0 +898,1 +899,0 +900,1 +901,0 +903,0 +904,1 +905,0 +906,1 +907,1 +908,0 +909,0 +910,1 +911,1 +912,0 +913,1 +915,0 +916,1 +917,0 +918,1 +919,0 +920,0 +922,0 +923,0 +924,1 +926,0 +927,0 +929,1 +930,0 +932,0 +934,0 +935,1 +936,1 +937,0 +938,0 +940,1 +941,1 +942,0 +943,0 +944,1 +945,1 +947,0 +948,0 +949,0 +951,1 +952,0 +953,0 +954,0 +955,1 +956,1 +958,1 +959,0 +960,0 +961,1 +962,1 +963,0 +964,1 +965,1 +966,1 +967,1 +969,1 +970,0 +971,1 +972,1 +973,0 +974,0 +978,1 +979,1 +981,1 +982,1 +984,1 +986,0 +987,0 +988,1 +989,0 +990,1 +991,0 +992,1 +993,0 +995,0 +996,1 +997,0 +998,0 +1001,0 +1002,0 +1004,1 +1005,1 +1006,1 +1007,0 +1009,1 +1010,0 +1011,1 +1012,1 +1014,1 +1015,0 +1017,1 +1018,0 +1020,0 +1021,0 +1022,0 +1023,1 +1026,0 +1027,0 +1028,0 +1029,0 +1030,1 +1031,0 +1032,0 +1033,1 +1034,0 +1035,0 +1036,0 +1037,0 +1039,0 +1041,0 +1042,1 +1044,0 +1045,1 +1046,0 +1047,0 +1048,1 +1049,1 +1050,0 +1051,1 +1053,1 +1054,1 +1056,0 +1057,1 +1058,0 +1059,0 +1061,1 +1063,0 +1064,0 +1066,0 +1067,1 +1068,1 +1069,0 +1070,1 +1071,1 +1072,0 +1073,0 +1074,1 +1076,1 +1077,0 +1078,1 +1079,0 +1081,0 +1082,0 +1084,1 +1085,0 +1086,1 +1087,0 +1088,1 +1089,1 +1090,0 +1093,1 +1094,1 +1095,1 +1096,0 +1098,1 +1099,0 +1100,1 +1101,0 +1102,0 +1104,0 +1105,1 +1106,0 +1107,0 +1109,0 +1110,1 +1112,1 +1113,0 +1114,1 +1115,0 +1116,1 +1118,0 +1120,0 +1121,0 +1122,0 +1123,1 +1124,0 +1126,0 +1127,0 +1128,0 +1129,0 +1130,1 +1131,1 +1132,1 +1133,1 +1134,0 +1137,0 +1138,1 +1139,0 +1140,1 +1142,1 +1143,0 +1144,0 +1145,0 +1146,0 +1149,0 +1150,1 +1151,0 +1152,0 +1153,0 +1154,1 +1155,1 +1156,0 +1161,0 +1162,0 +1164,1 +1167,1 +1168,0 +1169,0 +1170,0 +1171,0 +1172,1 +1173,1 +1175,1 +1176,1 +1177,0 +1179,0 +1183,1 +1185,0 +1186,0 +1187,0 +1188,1 +1190,0 +1191,0 +1192,0 +1194,0 +1195,0 +1197,1 +1198,0 +1199,1 +1200,0 +1201,1 +1202,0 +1203,0 +1205,1 +1206,1 +1207,1 +1208,0 +1209,0 +1210,0 +1211,0 +1212,0 +1213,0 +1214,0 +1215,0 +1216,1 +1217,0 +1218,1 +1219,0 +1220,0 +1221,0 +1222,1 +1223,0 +1225,1 +1226,0 +1227,0 +1228,0 +1229,0 +1230,0 +1232,0 +1233,0 +1235,1 +1237,1 +1238,0 +1239,1 +1240,0 +1241,1 +1242,1 +1243,0 +1244,0 +1245,0 +1246,1 +1247,0 +1248,1 +1251,1 +1252,0 +1253,1 +1254,1 +1255,0 +1256,1 +1259,1 +1260,1 +1261,0 +1262,0 +1263,1 +1264,0 +1265,0 +1266,1 +1267,1 +1268,0 +1269,0 +1270,0 +1271,0 +1273,0 +1275,1 +1277,1 +1278,0 +1279,0 +1280,0 +1281,0 +1282,0 +1283,1 +1284,1 +1285,0 +1286,0 +1287,1 +1288,0 +1289,1 +1290,0 +1291,0 +1292,1 +1293,0 +1294,1 +1295,0 +1296,0 +1297,0 +1298,0 +1299,0 +1301,1 +1303,1 +1304,1 +1306,1 +1307,0 +902,0 +914,1 +921,0 +925,1 +928,1 +931,0 +933,0 +939,0 +946,0 +950,0 +957,1 +968,0 +975,0 +976,0 +977,0 +980,1 +983,0 +985,0 +994,0 +999,0 +1000,0 +1003,1 +1008,0 +1013,0 +1016,0 +1019,1 +1024,1 +1025,0 +1038,0 +1040,0 +1043,0 +1052,1 +1055,0 +1060,1 +1062,0 +1065,0 +1075,0 +1080,0 +1083,0 +1091,1 +1092,1 +1097,0 +1103,0 +1108,1 +1111,0 +1117,1 +1119,1 +1125,0 +1135,0 +1136,1 +1141,1 +1147,0 +1148,0 +1157,0 +1158,0 +1159,0 +1160,1 +1163,0 +1165,1 +1166,0 +1174,1 +1178,0 +1180,0 +1181,0 +1182,0 +1184,0 +1189,0 +1193,0 +1196,1 +1204,0 +1224,0 +1231,1 +1234,0 +1236,1 +1249,0 +1250,0 +1257,0 +1258,0 +1272,0 +1274,1 +1276,0 +1300,1 +1302,1 +1305,0 +1308,0 +1309,1 diff --git a/model_iteration_1.ipynb b/model_iteration_1.ipynb new file mode 100644 index 0000000..09fb9a4 --- /dev/null +++ b/model_iteration_1.ipynb @@ -0,0 +1,1961 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Importing all the things!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Data Quest Tutorial\n", + " These next few cells are me following the data quest tutorial for the titanic dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# We can use the pandas library in python to read in the csv file.\n", + "# This creates a pandas dataframe and assigns it to the titanic variable.\n", + "titanic = pd.read_csv(\"./data/train.csv\")\n", + "\n", + "# Print the first 5 rows of the dataframe.\n", + "print(titanic.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Now, we're filling in missing data by just replacing all the missing data with the mean value. Is this really something that you can do? Is this okay? It feels wrong to me, because you'd be skewing the data even more heavily to the median. If we have to fill in the data, wouldn't it be better to fill it in with something that contains noise?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The titanic variable is available here.\n", + "titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to recode the sex column so that it's a number, and not a string. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Find all the unique genders -- the column appears to contain only male and female.\n", + "print(titanic[\"Sex\"].unique())\n", + "\n", + "# Replace all the occurences of male with the number 0.\n", + "titanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "titanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Doing the same thing witht the embarked column. Again, I'm very unsure that it's \"okay\" to just replace missing values with the most common value. Couldn't that confuse our model?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Find all the unique values for \"Embarked\".\n", + "print(titanic[\"Embarked\"].unique())\n", + "\n", + "titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")\n", + "titanic.loc[titanic[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic.loc[titanic[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic.loc[titanic[\"Embarked\"] == \"Q\", \"Embarked\"] = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've cleaned up our data, let's move on to machine learning!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import the linear regression class\n", + "from sklearn.linear_model import LinearRegression\n", + "# Sklearn also has a helper that makes it easy to do cross validation\n", + "from sklearn.cross_validation import KFold\n", + "\n", + "# The columns we'll use to predict the target\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "\n", + "# Initialize our algorithm class\n", + "alg = LinearRegression()\n", + "# Generate cross validation folds for the titanic dataset. It return the row indices corresponding to train and test.\n", + "# We set random_state to ensure we get the same splits every time we run this.\n", + "kf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n", + "\n", + "predictions = []\n", + "for train, test in kf:\n", + " # The predictors we're using the train the algorithm. Note how we only take the rows in the train folds.\n", + " train_predictors = (titanic[predictors].iloc[train,:])\n", + " # The target we're using to train the algorithm.\n", + " train_target = titanic[\"Survived\"].iloc[train]\n", + " # Training the algorithm using the predictors and target.\n", + " alg.fit(train_predictors, train_target)\n", + " # We can now make predictions on the test fold\n", + " test_predictions = alg.predict(titanic[predictors].iloc[test,:])\n", + " predictions.append(test_predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we've made some predictions, we want to evaluate them!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# The predictions are in three separate numpy arrays. Concatenate them into one. \n", + "# We concatenate them on axis 0, as they only have one axis.\n", + "predictions = np.concatenate(predictions, axis=0)\n", + "\n", + "# Map predictions to outcomes (only possible outcomes are 1 and 0)\n", + "predictions[predictions > .5] = 1\n", + "predictions[predictions <=.5] = 0\n", + "\n", + "accuracy = sum([predictions[i] == titanic[\"Survived\"].tolist()[i] for i in range(len(predictions))])/float(len(predictions))\n", + "print \"accuracy: \" + str(accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, so now we're, as per the tutorial, trying logistic regression. I believe that this helps us actually classify things, instead of predicting on a scale from 0 to 1." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'titanic' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0malg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_validation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitanic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitanic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Survived\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;31m# Take the mean of the scores (because we have one for each fold)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'titanic' is not defined" + ] + } + ], + "source": [ + "from sklearn import cross_validation\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.cross_validation import KFold\n", + "\n", + "# Initialize our algorithm\n", + "alg = LogisticRegression(random_state=1)\n", + "# Compute the accuracy score for all the cross validation folds. (much simpler than what we did before!)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic[\"Survived\"], cv=3)\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print(scores.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we actually want to make predictions on the test data provided to us by Kaggle!\n", + "\n", + "First, let's load in and clean up the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "titanic_test = pd.read_csv(\"./data/test.csv\")\n", + "\n", + "#Set age values to the median age in the training set\n", + "titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic[\"Age\"].median())\n", + "\n", + "#Recode the sex\n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "titanic_test.loc[titanic_test[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "#Fill in the missing values are recode the point of embarkation\n", + "titanic_test[\"Embarked\"] = titanic_test[\"Embarked\"].fillna(\"S\")\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "titanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "#Fill in missing fare values\n", + "titanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic[\"Fare\"].median())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we are making a prediction! This will have accuracty of about 0.75, but we'll work on this in future iterations!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize the algorithm class\n", + "alg = LogisticRegression(random_state=1)\n", + "\n", + "# Train the algorithm using all the training data\n", + "alg.fit(titanic[predictors], titanic[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictions = alg.predict(titanic_test[predictors])\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": titanic_test[\"PassengerId\"],\n", + " \"Survived\": predictions\n", + " })\n", + "\n", + "submission.to_csv(\"kaggle.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indeed, my model had a score 0.75120 wen I uploaded it to Kaggle\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Model Iteration 1\n", + "For this iteration, I'll switch from away from encoding the port that people embarked on as numbers and increase (0 for S, 1 for Q and 2 for C). The reason that I am doing this is that we are finding weights for each column. If we encode things like the port where someone embarked as a number that increases, we might falsely give the model the impression that these numbers are increasing, and then embarking with number 2 means that someone embarked twice as much (whatever that means...) as someone that has embarked 1. To do this, I will transition to one-hot encoding. This means that I will add columns \"EmbarkedS\", \"EmbarkedC\", and \"EmbarkedQ\". These columns will have have a 1 in them if a person embarked at that respective port and 0 otherwise. \n", + "\n", + "(I tried two different variations of this model, one where I filled in the NaN values for Embarked and one where I didn't, so I commented out the fill na for the embarked column)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 \n" + ] + } + ], + "source": [ + "titanic_it1 = pd.read_csv(\"./data/train.csv\")\n", + "\n", + "print(titanic_it1.describe())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, I'm going to recode the Sex and Embarked columns." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# The titanic variable is available here.\n", + "titanic_it1[\"Age\"] = titanic_it1[\"Age\"].fillna(titanic_it1[\"Age\"].median())\n", + "\n", + "#Recode the sex\n", + "titanic_it1.loc[titanic_it1[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "titanic_it1.loc[titanic_it1[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "#Recode the Port where people Embarked\n", + "titanic_it1[\"Embarked\"] = titanic_it1[\"Embarked\"].fillna(\"S\")\n", + "titanic_it1['EmbarkedS'] = titanic_it1['Embarked'].apply(lambda x: int(x == 'S'))\n", + "titanic_it1['EmbarkedC'] = titanic_it1['Embarked'].apply(lambda x: int(x == 'C'))\n", + "titanic_it1['EmbarkedQ'] = titanic_it1['Embarked'].apply(lambda x: int(x == 'Q'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, I'm just checking to see that my recoding worked by showing the description of the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedEmbarkedSEmbarkedCEmbarkedQ
0103Braund, Mr. Owen Harris02210A/5 211717.2500NaNS100
1211Cumings, Mrs. John Bradley (Florence Briggs Th...13810PC 1759971.2833C85C010
2313Heikkinen, Miss. Laina12600STON/O2. 31012827.9250NaNS100
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)1351011380353.1000C123S100
4503Allen, Mr. William Henry035003734508.0500NaNS100
5603Moran, Mr. James028003308778.4583NaNQ001
6701McCarthy, Mr. Timothy J054001746351.8625E46S100
7803Palsson, Master. Gosta Leonard023134990921.0750NaNS100
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)1270234774211.1333NaNS100
91012Nasser, Mrs. Nicholas (Adele Achem)1141023773630.0708NaNC010
101113Sandstrom, Miss. Marguerite Rut1411PP 954916.7000G6S100
111211Bonnell, Miss. Elizabeth1580011378326.5500C103S100
121303Saundercock, Mr. William Henry02000A/5. 21518.0500NaNS100
131403Andersson, Mr. Anders Johan0391534708231.2750NaNS100
141503Vestrom, Miss. Hulda Amanda Adolfina114003504067.8542NaNS100
151612Hewlett, Mrs. (Mary D Kingcome)1550024870616.0000NaNS100
161703Rice, Master. Eugene024138265229.1250NaNQ001
171812Williams, Mr. Charles Eugene0280024437313.0000NaNS100
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...1311034576318.0000NaNS100
192013Masselmani, Mrs. Fatima1280026497.2250NaNC010
202102Fynney, Mr. Joseph J0350023986526.0000NaNS100
212212Beesley, Mr. Lawrence0340024869813.0000D56S100
222313McGowan, Miss. Anna \"Annie\"115003309238.0292NaNQ001
232411Sloper, Mr. William Thompson0280011378835.5000A6S100
242503Palsson, Miss. Torborg Danira183134990921.0750NaNS100
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...1381534707731.3875NaNS100
262703Emir, Mr. Farred Chehab0280026317.2250NaNC010
272801Fortune, Mr. Charles Alexander0193219950263.0000C23 C25 C27S100
282913O'Dwyer, Miss. Ellen \"Nellie\"128003309597.8792NaNQ001
293003Todoroff, Mr. Lalio028003492167.8958NaNS100
................................................
86186202Giles, Mr. Frederick Edward021102813411.5000NaNS100
86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...148001746625.9292D17S100
86386403Sage, Miss. Dorothy Edith \"Dolly\"12882CA. 234369.5500NaNS100
86486502Gill, Mr. John William0240023386613.0000NaNS100
86586612Bystrom, Mrs. (Karolina)1420023685213.0000NaNS100
86686712Duran y More, Miss. Asuncion12710SC/PARIS 214913.8583NaNC010
86786801Roebling, Mr. Washington Augustus II03100PC 1759050.4958A24S100
86886903van Melkebeke, Mr. Philemon028003457779.5000NaNS100
86987013Johnson, Master. Harold Theodor041134774211.1333NaNS100
87087103Balkic, Mr. Cerin026003492487.8958NaNS100
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)147111175152.5542D35S100
87287301Carlsson, Mr. Frans Olof033006955.0000B51 B53 B55S100
87387403Vander Cruyssen, Mr. Victor047003457659.0000NaNS100
87487512Abelson, Mrs. Samuel (Hannah Wizosky)12810P/PP 338124.0000NaNC010
87587613Najib, Miss. Adele Kiamie \"Jane\"1150026677.2250NaNC010
87687703Gustafsson, Mr. Alfred Ossian0200075349.8458NaNS100
87787803Petroff, Mr. Nedelio019003492127.8958NaNS100
87887903Laleff, Mr. Kristo028003492177.8958NaNS100
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)156011176783.1583C50C010
88088112Shelley, Mrs. William (Imanita Parrish Hall)1250123043326.0000NaNS100
88188203Markun, Mr. Johann033003492577.8958NaNS100
88288303Dahlberg, Miss. Gerda Ulrika12200755210.5167NaNS100
88388402Banfield, Mr. Frederick James02800C.A./SOTON 3406810.5000NaNS100
88488503Sutehall, Mr. Henry Jr02500SOTON/OQ 3920767.0500NaNS100
88588603Rice, Mrs. William (Margaret Norton)1390538265229.1250NaNQ001
88688702Montvila, Rev. Juozas0270021153613.0000NaNS100
88788811Graham, Miss. Margaret Edith1190011205330.0000B42S100
88888903Johnston, Miss. Catherine Helen \"Carrie\"12812W./C. 660723.4500NaNS100
88989011Behr, Mr. Karl Howell0260011136930.0000C148C010
89089103Dooley, Mr. Patrick032003703767.7500NaNQ001
\n", + "

891 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "5 6 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "10 11 1 3 \n", + "11 12 1 1 \n", + "12 13 0 3 \n", + "13 14 0 3 \n", + "14 15 0 3 \n", + "15 16 1 2 \n", + "16 17 0 3 \n", + "17 18 1 2 \n", + "18 19 0 3 \n", + "19 20 1 3 \n", + "20 21 0 2 \n", + "21 22 1 2 \n", + "22 23 1 3 \n", + "23 24 1 1 \n", + "24 25 0 3 \n", + "25 26 1 3 \n", + "26 27 0 3 \n", + "27 28 0 1 \n", + "28 29 1 3 \n", + "29 30 0 3 \n", + ".. ... ... ... \n", + "861 862 0 2 \n", + "862 863 1 1 \n", + "863 864 0 3 \n", + "864 865 0 2 \n", + "865 866 1 2 \n", + "866 867 1 2 \n", + "867 868 0 1 \n", + "868 869 0 3 \n", + "869 870 1 3 \n", + "870 871 0 3 \n", + "871 872 1 1 \n", + "872 873 0 1 \n", + "873 874 0 3 \n", + "874 875 1 2 \n", + "875 876 1 3 \n", + "876 877 0 3 \n", + "877 878 0 3 \n", + "878 879 0 3 \n", + "879 880 1 1 \n", + "880 881 1 2 \n", + "881 882 0 3 \n", + "882 883 0 3 \n", + "883 884 0 2 \n", + "884 885 0 3 \n", + "885 886 0 3 \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "0 Braund, Mr. Owen Harris 0 22 1 0 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... 1 38 1 0 \n", + "2 Heikkinen, Miss. Laina 1 26 0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 1 35 1 0 \n", + "4 Allen, Mr. William Henry 0 35 0 0 \n", + "5 Moran, Mr. James 0 28 0 0 \n", + "6 McCarthy, Mr. Timothy J 0 54 0 0 \n", + "7 Palsson, Master. Gosta Leonard 0 2 3 1 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 1 27 0 2 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) 1 14 1 0 \n", + "10 Sandstrom, Miss. Marguerite Rut 1 4 1 1 \n", + "11 Bonnell, Miss. Elizabeth 1 58 0 0 \n", + "12 Saundercock, Mr. William Henry 0 20 0 0 \n", + "13 Andersson, Mr. Anders Johan 0 39 1 5 \n", + "14 Vestrom, Miss. Hulda Amanda Adolfina 1 14 0 0 \n", + "15 Hewlett, Mrs. (Mary D Kingcome) 1 55 0 0 \n", + "16 Rice, Master. Eugene 0 2 4 1 \n", + "17 Williams, Mr. Charles Eugene 0 28 0 0 \n", + "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... 1 31 1 0 \n", + "19 Masselmani, Mrs. Fatima 1 28 0 0 \n", + "20 Fynney, Mr. Joseph J 0 35 0 0 \n", + "21 Beesley, Mr. Lawrence 0 34 0 0 \n", + "22 McGowan, Miss. Anna \"Annie\" 1 15 0 0 \n", + "23 Sloper, Mr. William Thompson 0 28 0 0 \n", + "24 Palsson, Miss. Torborg Danira 1 8 3 1 \n", + "25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... 1 38 1 5 \n", + "26 Emir, Mr. Farred Chehab 0 28 0 0 \n", + "27 Fortune, Mr. Charles Alexander 0 19 3 2 \n", + "28 O'Dwyer, Miss. Ellen \"Nellie\" 1 28 0 0 \n", + "29 Todoroff, Mr. Lalio 0 28 0 0 \n", + ".. ... .. ... ... ... \n", + "861 Giles, Mr. Frederick Edward 0 21 1 0 \n", + "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... 1 48 0 0 \n", + "863 Sage, Miss. Dorothy Edith \"Dolly\" 1 28 8 2 \n", + "864 Gill, Mr. John William 0 24 0 0 \n", + "865 Bystrom, Mrs. (Karolina) 1 42 0 0 \n", + "866 Duran y More, Miss. Asuncion 1 27 1 0 \n", + "867 Roebling, Mr. Washington Augustus II 0 31 0 0 \n", + "868 van Melkebeke, Mr. Philemon 0 28 0 0 \n", + "869 Johnson, Master. Harold Theodor 0 4 1 1 \n", + "870 Balkic, Mr. Cerin 0 26 0 0 \n", + "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 1 47 1 1 \n", + "872 Carlsson, Mr. Frans Olof 0 33 0 0 \n", + "873 Vander Cruyssen, Mr. Victor 0 47 0 0 \n", + "874 Abelson, Mrs. Samuel (Hannah Wizosky) 1 28 1 0 \n", + "875 Najib, Miss. Adele Kiamie \"Jane\" 1 15 0 0 \n", + "876 Gustafsson, Mr. Alfred Ossian 0 20 0 0 \n", + "877 Petroff, Mr. Nedelio 0 19 0 0 \n", + "878 Laleff, Mr. Kristo 0 28 0 0 \n", + "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) 1 56 0 1 \n", + "880 Shelley, Mrs. William (Imanita Parrish Hall) 1 25 0 1 \n", + "881 Markun, Mr. Johann 0 33 0 0 \n", + "882 Dahlberg, Miss. Gerda Ulrika 1 22 0 0 \n", + "883 Banfield, Mr. Frederick James 0 28 0 0 \n", + "884 Sutehall, Mr. Henry Jr 0 25 0 0 \n", + "885 Rice, Mrs. William (Margaret Norton) 1 39 0 5 \n", + "886 Montvila, Rev. Juozas 0 27 0 0 \n", + "887 Graham, Miss. Margaret Edith 1 19 0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" 1 28 1 2 \n", + "889 Behr, Mr. Karl Howell 0 26 0 0 \n", + "890 Dooley, Mr. Patrick 0 32 0 0 \n", + "\n", + " Ticket Fare Cabin Embarked EmbarkedS EmbarkedC \\\n", + "0 A/5 21171 7.2500 NaN S 1 0 \n", + "1 PC 17599 71.2833 C85 C 0 1 \n", + "2 STON/O2. 3101282 7.9250 NaN S 1 0 \n", + "3 113803 53.1000 C123 S 1 0 \n", + "4 373450 8.0500 NaN S 1 0 \n", + "5 330877 8.4583 NaN Q 0 0 \n", + "6 17463 51.8625 E46 S 1 0 \n", + "7 349909 21.0750 NaN S 1 0 \n", + "8 347742 11.1333 NaN S 1 0 \n", + "9 237736 30.0708 NaN C 0 1 \n", + "10 PP 9549 16.7000 G6 S 1 0 \n", + "11 113783 26.5500 C103 S 1 0 \n", + "12 A/5. 2151 8.0500 NaN S 1 0 \n", + "13 347082 31.2750 NaN S 1 0 \n", + "14 350406 7.8542 NaN S 1 0 \n", + "15 248706 16.0000 NaN S 1 0 \n", + "16 382652 29.1250 NaN Q 0 0 \n", + "17 244373 13.0000 NaN S 1 0 \n", + "18 345763 18.0000 NaN S 1 0 \n", + "19 2649 7.2250 NaN C 0 1 \n", + "20 239865 26.0000 NaN S 1 0 \n", + "21 248698 13.0000 D56 S 1 0 \n", + "22 330923 8.0292 NaN Q 0 0 \n", + "23 113788 35.5000 A6 S 1 0 \n", + "24 349909 21.0750 NaN S 1 0 \n", + "25 347077 31.3875 NaN S 1 0 \n", + "26 2631 7.2250 NaN C 0 1 \n", + "27 19950 263.0000 C23 C25 C27 S 1 0 \n", + "28 330959 7.8792 NaN Q 0 0 \n", + "29 349216 7.8958 NaN S 1 0 \n", + ".. ... ... ... ... ... ... \n", + "861 28134 11.5000 NaN S 1 0 \n", + "862 17466 25.9292 D17 S 1 0 \n", + "863 CA. 2343 69.5500 NaN S 1 0 \n", + "864 233866 13.0000 NaN S 1 0 \n", + "865 236852 13.0000 NaN S 1 0 \n", + "866 SC/PARIS 2149 13.8583 NaN C 0 1 \n", + "867 PC 17590 50.4958 A24 S 1 0 \n", + "868 345777 9.5000 NaN S 1 0 \n", + "869 347742 11.1333 NaN S 1 0 \n", + "870 349248 7.8958 NaN S 1 0 \n", + "871 11751 52.5542 D35 S 1 0 \n", + "872 695 5.0000 B51 B53 B55 S 1 0 \n", + "873 345765 9.0000 NaN S 1 0 \n", + "874 P/PP 3381 24.0000 NaN C 0 1 \n", + "875 2667 7.2250 NaN C 0 1 \n", + "876 7534 9.8458 NaN S 1 0 \n", + "877 349212 7.8958 NaN S 1 0 \n", + "878 349217 7.8958 NaN S 1 0 \n", + "879 11767 83.1583 C50 C 0 1 \n", + "880 230433 26.0000 NaN S 1 0 \n", + "881 349257 7.8958 NaN S 1 0 \n", + "882 7552 10.5167 NaN S 1 0 \n", + "883 C.A./SOTON 34068 10.5000 NaN S 1 0 \n", + "884 SOTON/OQ 392076 7.0500 NaN S 1 0 \n", + "885 382652 29.1250 NaN Q 0 0 \n", + "886 211536 13.0000 NaN S 1 0 \n", + "887 112053 30.0000 B42 S 1 0 \n", + "888 W./C. 6607 23.4500 NaN S 1 0 \n", + "889 111369 30.0000 C148 C 0 1 \n", + "890 370376 7.7500 NaN Q 0 0 \n", + "\n", + " EmbarkedQ \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 1 \n", + "6 0 \n", + "7 0 \n", + "8 0 \n", + "9 0 \n", + "10 0 \n", + "11 0 \n", + "12 0 \n", + "13 0 \n", + "14 0 \n", + "15 0 \n", + "16 1 \n", + "17 0 \n", + "18 0 \n", + "19 0 \n", + "20 0 \n", + "21 0 \n", + "22 1 \n", + "23 0 \n", + "24 0 \n", + "25 0 \n", + "26 0 \n", + "27 0 \n", + "28 1 \n", + "29 0 \n", + ".. ... \n", + "861 0 \n", + "862 0 \n", + "863 0 \n", + "864 0 \n", + "865 0 \n", + "866 0 \n", + "867 0 \n", + "868 0 \n", + "869 0 \n", + "870 0 \n", + "871 0 \n", + "872 0 \n", + "873 0 \n", + "874 0 \n", + "875 0 \n", + "876 0 \n", + "877 0 \n", + "878 0 \n", + "879 0 \n", + "880 0 \n", + "881 0 \n", + "882 0 \n", + "883 0 \n", + "884 0 \n", + "885 1 \n", + "886 0 \n", + "887 0 \n", + "888 0 \n", + "889 0 \n", + "890 1 \n", + "\n", + "[891 rows x 15 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_it1" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.786756453423\n" + ] + } + ], + "source": [ + "from sklearn import cross_validation\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.cross_validation import KFold\n", + "import numpy as np\n", + "\n", + "# The columns we'll use to predict the target\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"EmbarkedS\", \"EmbarkedC\", \"EmbarkedQ\"]\n", + "\n", + "# Initialize our algorithm class\n", + "alg = LogisticRegression()\n", + "# Generate cross validation folds for the titanic dataset. It return the row indices corresponding to train and test.\n", + "# We set random_state to ensure we get the same splits every time we run this.\n", + "kf = KFold(titanic_it1.shape[0], n_folds=3, random_state=1)\n", + "\n", + "predictions = []\n", + "for train, test in kf:\n", + " # The predictors we're using the train the algorithm. Note how we only take the rows in the train folds.\n", + " train_predictors = (titanic_it1[predictors].iloc[train,:])\n", + " # The target we're using to train the algorithm.\n", + " train_target = titanic_it1[\"Survived\"].iloc[train]\n", + " # Training the algorithm using the predictors and target.\n", + " alg.fit(train_predictors, train_target)\n", + " # We can now make predictions on the test fold\n", + " test_predictions = alg.predict(titanic_it1[predictors].iloc[test,:])\n", + " predictions.append(test_predictions)\n", + " \n", + "# The predictions are in three separate numpy arrays. Concatenate them into one. \n", + "# We concatenate them on axis 0, as they only have one axis.\n", + "predictions = np.concatenate(predictions, axis=0)\n", + " \n", + "accuracy = sum([predictions[i] == titanic_it1[\"Survived\"].tolist()[i] for i in range(len(predictions))])/float(len(predictions))\n", + "print \"accuracy: \" + str(accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Okay, so my training accuracy here is a little better then before -- now I get around 78.7 percent when I test on the training set, so I'm going to try another submission to Kaggle. \n", + "\n", + "Below, I'll generate my submission file for Kaggle\n", + "\n", + "First, I need to recode the data in the same way. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "titanic_test_it1 = pd.read_csv(\"./data/test.csv\")\n", + "\n", + "#Set age values to the median age in the training set\n", + "titanic_test_it1[\"Age\"] = titanic_test_it1[\"Age\"].fillna(titanic_it1[\"Age\"].median())\n", + "\n", + "#Recode the sex\n", + "titanic_test_it1.loc[titanic_test_it1[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "titanic_test_it1.loc[titanic_test_it1[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "#Fill in the missing values are recode the point of embarkation\n", + "titanic_test_it1['EmbarkedS'] = titanic_test_it1['Embarked'].apply(lambda x: int(x == 'S'))\n", + "titanic_test_it1['EmbarkedC'] = titanic_test_it1['Embarked'].apply(lambda x: int(x == 'C'))\n", + "titanic_test_it1['EmbarkedQ'] = titanic_test_it1['Embarked'].apply(lambda x: int(x == 'Q'))\n", + "\n", + "#Fill in missing fare values\n", + "titanic_test_it1[\"Fare\"] = titanic_test_it1[\"Fare\"].fillna(titanic_it1[\"Fare\"].median())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Initialize the algorithm class\n", + "alg = LogisticRegression(random_state=1)\n", + "\n", + "# Train the algorithm using all the training data\n", + "alg.fit(titanic_it1[predictors], titanic_it1[\"Survived\"])\n", + "\n", + "# Make predictions using the test set.\n", + "predictions = alg.predict(titanic_test_it1[predictors])\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": titanic_test[\"PassengerId\"],\n", + " \"Survived\": predictions\n", + " })\n", + "\n", + "submission.to_csv(\"kaggle_it1_5.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When I submitted this model the first time, I got a score of 0.74641. When I then didn't filter out the NaNs for the embarked port, I got the same score again. Intrestingly enough, which is worse than I did with the DataQuest model. Maybe this has something to do with the fact that I pulled out more columns for something that isn't all that important, and the model got confused. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Interesting things from class:\n", + "\n", + "Crew members column?\n", + "\n", + "Titles?\n", + "\n", + "Is child flag? Male child flag?\n", + "\n", + "Use other more sensitive error metrics, like ones like the log loss of the model. \n", + "\n", + "Aggregating data like port, class, and fare\n", + "\n", + "Better predicting age via other factors. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/model_iteration_2.ipynb b/model_iteration_2.ipynb new file mode 100644 index 0000000..730a455 --- /dev/null +++ b/model_iteration_2.ipynb @@ -0,0 +1,490 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Source of Inspiration\n", + "For this iteration of my project, I was intersted in different ways that others pre-processed the data. My intuition in the first iteration of the project didn't actually lead to an improvement of performance for my model. As a result, I wanted to look at what other people had done in terms of pre-processing the data and adding new features. \n", + "\n", + "One script that I found in the Kaggle scripts section was this [Random Forest Script](https://www.kaggle.com/amoyakd/titanic/randomforest-method-v1-0). In this script, the author does quite a bit of pre-processing. This script, however, was written in R, so I'm going to try to replicate this pre-processing work in Python. \n", + "\n", + "Although they used random forrests in this script to do the learning, I wanted to use linear regression. The reason that I'm doing this is that I didn't want to use a model that I didn't quite understand -- this wouldn't tell me if my score improvements (or declines) were a result of the model or the new features I created. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "% matplotlib inline\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import re\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.cross_validation import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, they read in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train = pd.read_csv('./Data/train.csv')\n", + "test = pd.read_csv('./Data/test.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next is cleaning the data. In this particular script, the author mostly does something very similar to what we did in the previous tutorial for the sex and embarked and fare columns." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Recode Sex data\n", + "test.loc[test[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "test.loc[test[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "train.loc[train[\"Sex\"] == \"male\", \"Sex\"] = 0\n", + "train.loc[train[\"Sex\"] == \"female\", \"Sex\"] = 1\n", + "\n", + "# Recode Embarked Data\n", + "test[\"Embarked\"] = test[\"Embarked\"].fillna(\"S\")\n", + "test.loc[test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "test.loc[test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "test.loc[test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "train[\"Embarked\"] = train[\"Embarked\"].fillna(\"S\")\n", + "train.loc[train[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", + "train.loc[train[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", + "train.loc[train[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", + "\n", + "#Replace one missing fare data\n", + "test[\"Fare\"] = test[\"Fare\"].fillna(train[\"Fare\"].median())\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, in this script, we also extract the title from the name of the person. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def extractTitle (name):\n", + " title = re.findall(r', \\w+\\s?\\w*\\.', name)[0][2:-1]\n", + " if (title in ['Don','Lady','the Countess', 'Jonkheer']):\n", + " return 'Lady'\n", + " elif (title in ['Capt', 'Don', 'Major', 'Sir']):\n", + " return 'Sir'\n", + " \n", + " return title " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train[\"Title\"] = train['Name'].apply(lambda x: extractTitle(x))\n", + "test[\"Title\"] = test['Name'].apply(lambda x: extractTitle(x))\n", + "\n", + "titles = train[\"Title\"].unique()\n", + "\n", + "titleColumns = []\n", + "for title in titles:\n", + " train[\"Title\" + title] = train[\"Title\"].apply(lambda x: int(x == title))\n", + " test[\"Title\" + title] = test[\"Title\"].apply(lambda x: int(x == title))\n", + " titleColumns.append(\"Title\" + title)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, using the title, class, sex, sibsp, parch, and fare data, we run a regression to predict the age. I thought that this was a particularly interesting method of filling in missing age values. We had briefly talked about other methods for filling in missing age values other than just adding in the median value for age. I was really curious about other methods, so I'm very interested in trying this method. \n", + "\n", + "In this particular script, they used both the training and the testing data to fit a regression for age. I wasn't sure about whether it's okay to use the testing set to also fit a regression for age. Is that okay? To be safe, I just used the training set. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Sophia/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:15: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "/Users/Sophia/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:17: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" + ] + } + ], + "source": [ + "#First, we want to select all of the non-null training values so that we can run our regression\n", + "ageFilledTrain = train.dropna(subset = titleColumns + ['Age', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Fare'])\n", + "ageXTrain = ageFilledTrain[titleColumns + ['Pclass', 'Sex', 'SibSp', 'Parch', 'Fare']]\n", + "ageYTrain = ageFilledTrain['Age']\n", + "\n", + "#Also extract the filled test data (we'll use this for later)\n", + "ageFilledTest = test.dropna(subset = titleColumns + ['Age', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Fare'])\n", + "\n", + "#Now, we fit a linear regression\n", + "regr = LinearRegression()\n", + "regr.fit(ageXTrain, ageYTrain)\n", + "\n", + "#Now, we want to predict the missing ages\n", + "nullAgesTrain = train[train['Age'].isnull()]\n", + "nullAgesTrain['Age'] = regr.predict(nullAgesTrain[titleColumns + ['Pclass', 'Sex', 'SibSp', 'Parch', 'Fare']])\n", + "nullAgesTest = test[test['Age'].isnull()]\n", + "nullAgesTest['Age'] = regr.predict(nullAgesTest[titleColumns + ['Pclass', 'Sex', 'SibSp', 'Parch', 'Fare']])\n", + "\n", + "\n", + "#Now, we want to add these back in to the dataframe. (I'm sure that there is a better way to do this!)\n", + "#I am not proud of concatening these back together... How do I do this better?\n", + "train = pd.concat([ageFilledTrain, nullAgesTrain])\n", + "test = pd.concat([ageFilledTest, nullAgesTest])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, to continue cleaning the data and recoding fields, we also add a family size and a mother/child field." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#Family size\n", + "train['FamilySize'] = train['SibSp'] + train['Parch'] + 1\n", + "test['FamilySize'] = test['SibSp'] + test['Parch'] + 1\n", + "\n", + "#Adding a mother flag\n", + "train['Mother'] = ((train['Sex'] == 1) \n", + " & (train['Parch'] > 0) \n", + " & (train['Age'] > 18)\n", + " & (train['Title'] != 'Miss'))\n", + "test['Mother'] = ((test['Sex'] == 1) \n", + " & (test['Parch'] > 0) \n", + " & (test['Age'] > 18)\n", + " & (test['Title'] != 'Miss'))\n", + "\n", + "#Adding a child flag\n", + "train['Child'] = ((train['Parch'] > 0) \n", + " & (train['Age'] < 18))\n", + "\n", + "test['Child'] = ((test['Parch'] > 0) \n", + " & (test['Age'] < 18))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This model also created a family ID field. I wasn't sure about this -- it seems like this field would divide the data up too much, and it doesn't really make sense to have the id encoded as something linear. Also, I wonder how much of this information can be encoded within the fields that have the number of family memebers on board. As a result, I didn't replicate this field in my iteration two. \n", + "\n", + "Next, this author also extracted the cabin number as well as deck from the data. I will do that as well. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def recodeCabinToDeck(cabin):\n", + " if isinstance(cabin, str):\n", + " if (cabin[0] == 'A'):\n", + " return 1\n", + " elif (cabin[0] == 'B'):\n", + " return 2\n", + " elif (cabin[0] == 'C'):\n", + " return 3\n", + " elif (cabin[0] == 'D'):\n", + " return 4\n", + " elif (cabin[0] == 'E'):\n", + " return 5\n", + " elif (cabin[0] == 'F'):\n", + " return 6\n", + " elif (cabin[0] == 'G'):\n", + " return 7\n", + " else:\n", + " return 0\n", + " else:\n", + " return 0\n", + " \n", + "\n", + "def extractCabinNumber(cabin):\n", + " if isinstance(cabin, str):\n", + " cabinNum = re.findall(r'\\d+',cabin)\n", + " if len(cabinNum) > 0:\n", + " return int(cabinNum[0])\n", + " else:\n", + " return 0\n", + " else:\n", + " return 0\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train['DeckRecode'] = train.Cabin.apply(lambda x: recodeCabinToDeck(x))\n", + "test['DeckRecode'] = test.Cabin.apply(lambda x: recodeCabinToDeck(x))\n", + "\n", + "train['CabinNumber'] = train.Cabin.apply(lambda x: extractCabinNumber(x))\n", + "test['CabinNumber'] = test.Cabin.apply(lambda x: extractCabinNumber(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we need to split our train data into a testing/training set. " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "useColumns = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Mother', 'Child', \n", + " 'FamilySize', 'DeckRecode', 'CabinNumber', 'Embarked'] + titleColumns\n", + "X_train, X_test, y_train, y_test = train_test_split(train[useColumns], train['Survived'], test_size = 0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we want to create our linear regression. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.854260089686\n" + ] + } + ], + "source": [ + "model = LinearRegression()\n", + "model.fit(X_train, y_train)\n", + "\n", + "y_predict = model.predict(X_test)\n", + "y_predict[y_predict > .5] = 1\n", + "y_predict[y_predict <=.5] = 0\n", + "\n", + "accuracy = sum([y_predict[i] == y_test.tolist()[i] for i in range(len(y_predict))])/float(len(y_predict))\n", + "print accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hey, that's not so bad! I'm going to use this to generate another submission to Kaggle" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "submissionModel = LinearRegression()\n", + "submissionModel.fit(train[useColumns], train['Survived'])\n", + "\n", + "predictions = submissionModel.predict(test[useColumns])\n", + "\n", + "predictions[predictions > .5] = 1\n", + "predictions[predictions <=.5] = 0\n", + "\n", + "# Create a new dataframe with only the columns Kaggle wants from the dataset.\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": test[\"PassengerId\"],\n", + " \"Survived\": predictions.astype(int)\n", + " })\n", + "\n", + "submission.to_csv(\"kaggle_it2_0.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this model, I scored a 0.78947, which improved my best score! Yay!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What actually happened? Why is this model better?\n", + "So, it does seem that this method of adding new features is an improvment. Now, I'm curious -- which features are the most predicive, and which just serve to confuse the model further?\n", + "\n", + "First, let's just plot the weights of the features" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1, 25)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABIYAAAKJCAYAAADOaSLvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "coefs = submissionModel.coef_\n", + "plt.figure(figsize=(20,10))\n", + "plt.bar([x - 0.5 for x in range(len(coefs))],coefs, width = 1)\n", + "plt.xticks(range(len(coefs)), useColumns, rotation='vertical')\n", + "plt.xlim(-1, len(coefs) + 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interesting... So, it seems like sex is, unsurprisingly, thge biggest predictor of survival. But, after this, I was surprised by the weights that the model learned. I was expecting age to receive a fairly high magnitude weight, and it does not appear that that actually happened. \n", + "\n", + "In fact, it appears that the 'Title Master' column is the next best predictor of survival. I looked more into the title itself, and according to [wikipedia](https://en.wikipedia.org/wiki/Master_(form_of_address)), \"Master is an English honorific for boys and young men.\". So, it appears that the model didn't pick up on age, but did pick up that boys and young men were more likely to survive. Similarly, it appears that younger girls (who have the title \"Miss\") were less likely to survive. It is interesting to me that this model appears to pick up age (and to some extent class) more in the titles than it does in the actual passenger age and class columns. This is interesting to me because I wasn't actually expecting to have the titles really affect the model that much. \n", + "\n", + "It appears, however, that the deck recode and the cabin number don't have that much affect. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Future Work\n", + "\n", + "If I were going to improve this model, I'd go through and pull out only the most informative features, and then run the model again, seeing if this improved the score. The idea is that we likely have some features that are distracting the model so that we should use only the most informative features for this model, maybe by selecting only the features that are above some threshold. \n", + "\n", + "I also would be curious whether implementing the family id field actually boosts performance. \n", + "\n", + "One thing that I've wondered about for datasets like this: does it ever really make sense to split the dataset and learn two separate models. For example, I imagine that you might be able to learn a separate model, with different weights for women and men. Is this ever beneficial, or is it always better to have as much data as possible?\n", + "\n", + "I'm also curious: how much does using a different kind of model (logistic vs linear regresssion vs random forrest) affect the score? Are there certain models that are particularly good at solving certain problems. (Why?) So, another thing tha" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}