diff --git a/.ipynb_checkpoints/DataExploration-checkpoint.ipynb b/.ipynb_checkpoints/DataExploration-checkpoint.ipynb new file mode 100644 index 0000000..5d5c676 --- /dev/null +++ b/.ipynb_checkpoints/DataExploration-checkpoint.ipynb @@ -0,0 +1,3028 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shivali Chandra\n", + "Data Exploration\n", + "1/25/16" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", + " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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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
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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
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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": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Import needed libraries and get data\n", + "%matplotlib inline\n", + "\n", + "import thinkstats2\n", + "import thinkplot\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "titanic_df = pd.read_csv('train.csv')\n", + "titanic_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Looking at the above data, a few factors immediately stand out to me to visualize. These include pclass, sex, age, and fare. I also thought it could be potentially useful or illuminating to create a visualization of passenger survival rate by cabin number, but there does not seem to be enough data to be able to gain any information. However, for each of the factors I will be analyzing I would like to keep the data size the same, so I will filter out those passengers who have NaN in any of the columns I chose to analyze. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = titanic_df.dropna(subset=['Survived','Pclass','Sex','Age','Fare'])\n", + "titanic_df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total males: 453 num survived: 93 survival rate: % 20.5298013245\n", + "total females: 261 num survived: 197 survival rate: % 75.4789272031\n" + ] + } + ], + "source": [ + "males = titanic_df[titanic_df.Sex == 'male']\n", + "females = titanic_df[titanic_df.Sex == 'female']\n", + "\n", + "male_sur = (males[males.Survived == 1])\n", + "female_sur = (females[females.Survived == 1])\n", + "\n", + "males_survived_perc = float(len(male_sur)) / float(len(males)) * 100\n", + "females_survived_perc = float(len(female_sur)) / float(len(females)) * 100 \n", + "\n", + "print 'total males: ', len(males), ' num survived: ', len(male_sur), ' survival rate: %', males_survived_perc\n", + "print 'total females: ', len(females), ' num survived: ', len(female_sur), ' survival rate: %', females_survived_perc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear from these survival rates that a higher percentage of females survived; however, there are nearly twice as many men as women, so the distribution is not proportionally represented by survival rate. Therefore, it will be helpful to create a graphic representation. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(males.Survived)\n", + "hist1 = thinkstats2.Hist(females.Survived)\n", + "thinkplot.PrePlot(2)\n", + "thinkplot.Hist(hist, align='left', width=0.5, label='males')\n", + "thinkplot.Hist(hist1, align='right', width=0.5, label='females')\n", + "thinkplot.Show(xlabel='survived', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, it is easier to see the relationship between survival rate and sex. Overall, females had a higher chance of surviving, which makes sense because when the Titanic was being evacuated there was an emphasis placed on getting the women and children on lifeboats first. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(male_sur.Age)\n", + "thinkplot.Hist(hist, label='males', width=.8)\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this visualization of surviving males, it can be seen that the majority of males who survived were between the ages of mid-20's to mid-30's. Interestingly, there are also 2 children under the age of 10, supporting the theory that children were a higher priority. However, this conclusion cannot be made without visualizing the ages of males who unfortunately did not survive. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "male_not = males[males.Survived == 0]\n", + "hist = thinkstats2.Hist(male_not.Age)\n", + "thinkplot.Hist(hist, label='males', width=.8)\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, there is a higher distribution of males who did not survive from the ages of the teens to early 30s, with a spike around the late 30's. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(female_sur.Age)\n", + "thinkplot.Hist(hist, label='females', width=.8)\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For women, the highest survival is around the ages of 20's to 30's, with a spike around age 5. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "female_not = females[females.Survived == 0]\n", + "hist = thinkstats2.Hist(female_not.Age)\n", + "thinkplot.Hist(hist, label='females')\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first thing that I notice about this graph is that the y-axis range is noticeably smaller than the survival y-axis range, corroborating the fact that %70 of women in this data set survived. This highest numbers here were very small children, possibly because of weaker immune systems, and between the ages of 15 to 30. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The other area that potentially holds useful information is visualizing survival rate by passenger class and fare, to see if there was a higher priority for first class or higher paying passengers, or if it didn't make a huge difference overall. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZpXJYmJlZKoeFmZmlcliYmVkqh4WZmaX6/4zO+YAcs2bUAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(titanic_df[titanic_df.Pclass == 1].Survived)\n", + "hist1 = thinkstats2.Hist(titanic_df[titanic_df.Pclass == 2].Survived)\n", + "hist2 = thinkstats2.Hist(titanic_df[titanic_df.Pclass == 3].Survived)\n", + "thinkplot.PrePlot(3)\n", + "thinkplot.Hist(hist, align='left', width=0.3, label='Pclass 1')\n", + "thinkplot.Hist(hist1, align='center', width=0.3, label='Pclass 2')\n", + "thinkplot.Hist(hist2, align='right', width=0.3, label='Pclass 3')\n", + "thinkplot.Show(xlabel='survived', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, two things can be seen. The first, that Pclass 1 is the smallest quantity of passengers, and that the majority were in class 3. However, the second piece of information that is seen is that the majority who survived were from Pclass 1. This indicates that either there was a higher priority placed on these passengers due to the money they had paid, or that there is a higher distribution of females and passengers between 25-35 in Pclass 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 40)\n", + "(2, 22)\n", + "(3, 16)\n", + "(4, 86)\n", + "(5, 114)\n", + "(6, 106)\n", + "(7, 95)\n", + "(8, 235)\n" + ] + }, + { + "data": { + "image/png": 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WdyIii5cikizuREQ2XolIsrgTEcWIF5FsP+SuiCSLOxFRjHgRyW/+uBvdLopI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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.arange(0, 40, 5)\n", + "indices = np.digitize(titanic_df.Age, bins)\n", + "groups = titanic_df.groupby(indices)\n", + "\n", + "for i, group in groups:\n", + " print(i, len(group))\n", + " age = [group.Age.mean() for i, group in groups]\n", + " cdfs = [thinkstats2.Cdf(group.Pclass) for i, group in groups]\n", + " \n", + "for percent in [75, 50, 25]:\n", + " Pclasses = [cdf.Percentile(percent) for cdf in cdfs]\n", + " label = '%dth' % percent\n", + " thinkplot.Plot(age, Pclasses, label=label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Very surprisingly, it appears that the majority of passengers in class 1 appear to be older. Thus, we will look at distribution of gender in each class. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 23)\n", + "(2, 9)\n", + "(3, 7)\n", + "(4, 50)\n", + "(5, 72)\n", + "(6, 76)\n", + "(7, 62)\n", + "(8, 154)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.arange(0, 40, 5)\n", + "indices = np.digitize(males.Age, bins)\n", + "groups = males.groupby(indices)\n", + "\n", + "for i, group in groups:\n", + " print(i, len(group))\n", + " age = [group.Age.mean() for i, group in groups]\n", + " cdfs = [thinkstats2.Cdf(group.Pclass) for i, group in groups]\n", + " \n", + "for percent in [75, 50, 25]:\n", + " Pclasses = [cdf.Percentile(percent) for cdf in cdfs]\n", + " label = '%dth' % percent\n", + " thinkplot.Plot(age, Pclasses, label=label)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 17)\n", + "(2, 13)\n", + "(3, 9)\n", + "(4, 36)\n", + "(5, 42)\n", + "(6, 30)\n", + "(7, 33)\n", + "(8, 81)\n" + ] + }, + { + "data": { + "image/png": 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NU/LFBGtzV55ZsiS8JPLkSWhosFuPUvGSm5UfGRPc3cappkNJFROszV15JjfXWfMepGfv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\nDj5ffH7U3CubNm9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.arange(0, 40, 5)\n", + "indices = np.digitize(females.Age, bins)\n", + "groups = females.groupby(indices)\n", + "\n", + "for i, group in groups:\n", + " print(i, len(group))\n", + " age = [group.Age.mean() for i, group in groups]\n", + " cdfs = [thinkstats2.Cdf(group.Pclass) for i, group in groups]\n", + " \n", + "for percent in [75, 50, 25]:\n", + " Pclasses = [cdf.Percentile(percent) for cdf in cdfs]\n", + " label = '%dth' % percent\n", + " thinkplot.Plot(age, Pclasses, label=label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these plots, it seems that my hypothesis was correct - more females are among classes 1 and 2, particularly among 1. There are very few women among the third class, potentially because of the ages - many of the women could be unmarried, and would probably be less likely to travel alone in class 3. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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oyH6T+Xnv836OHrXfzbXF+94H/OxnJpeIBfD995Gq3Zuus+J+Z9fL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\nIaAxIIQQAhoDQgghoDEghBAC4P8DOqqUeTVxGSkAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s1 = thinkplot.Scatter(females.Age, females.Fare)\n", + "thinkplot.Show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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FX0TaReR5EfmZiLwqIp/ObK8RkWdF5JKIfEtEqgLfeURErojIayLyvp2WgRxO4vHVbhzn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\nRCfyTE2p5VZd7f3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s1 = thinkplot.Scatter(males.Age, males.Fare)\n", + "thinkplot.Show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is definitely clear that the majority of males were younger and spent less on their tickets, while there is much more variation for females. It does seem that more females spent more in tickets, regardless of age, so it seems likely that there are more females in pclass 1. " + ] + } + ], + "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..d49c375 --- /dev/null +++ b/.ipynb_checkpoints/model_iteration_1-checkpoint.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shivali Chandra
\n", + "First iteration of model for Titanic Kaggle dataset.
\n", + "1/27/16
\n", + "Initial Score: 0.75120
\n", + "Random forests Score: 0.7535
\n", + "Adding more columns Score: 0.7799" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First steps: load libraries used, read from training file and show basic statistics of file" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassAgeSibSpParchFare
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import cross_validation\n", + "import numpy as np\n", + "\n", + "titanic = pd.read_csv(\"train.csv\")\n", + "titanic.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cleaning code, filling in NaN values and replacing text values with number codes: " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())\n", + "\n", + "titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0\n", + "titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1\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": [ + "Defining columns used to predict target, generating cross validation folds for the dataset (with random state set to ensure splits are the same every time), initializing predictors and target, training algorithm, and making predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.78787878787878773" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test = pd.read_csv('test.csv')\n", + "test['Age'] = test['Age'].fillna(titanic['Age'].median())\n", + "\n", + "test.loc[test['Sex'] == 'male', 'Sex'] = 0\n", + "test.loc[test['Sex'] == 'female', 'Sex'] = 1\n", + "\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", + "test['Fare'] = test['Fare'].fillna(titanic['Fare'].median())\n", + "\n", + "alg = LogisticRegression(random_state=1)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)\n", + "scores.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.819304152637\n" + ] + } + ], + "source": [ + "from sklearn import cross_validation\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']\n", + "titanic['NameLength'] = titanic['Name'].apply(lambda x: len(x))\n", + "test['FamilySize'] = test['SibSp'] + test['Parch']\n", + "test['NameLength'] = test['Name'].apply(lambda x: len(x))\n", + "\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\"]\n", + "\n", + "# Initialize our algorithm with the default paramters\n", + "# n_estimators is the number of trees we want to make\n", + "# min_samples_split is the minimum number of rows we need to make a split\n", + "# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=10, min_samples_leaf=5)\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", + "\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print(scores.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n", + "\n", + "alg = LinearRegression()\n", + "kf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)\n", + "scores.mean()\n", + "\n", + "predictions = []\n", + "for train, test in kf:\n", + " train_predictors = (titanic[predictors].iloc[train,:])\n", + " train_target = titanic['Survived'].iloc[train]\n", + " alg.fit(train_predictors, train_target)\n", + " test_predictions = alg.predict(titanic[predictors].iloc[test,:])\n", + " predictions.append(test_predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Concatenating three prediction np arrays into one, and mapping the predictions to outcomes. Then, calculating the accuracy: " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "predictions = np.concatenate(predictions, axis=0)\n", + "\n", + "predictions[predictions > 0.5] = 1\n", + "predictions[predictions <= 0.5] = 0\n", + "\n", + "accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computing accuracy score for all cross validation folds, and taking mean of scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cleaning test data. Filling missing NaN values and replacing text values with number codes: " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generating submission for competition - training algorithm, making predictions, and creating dataframe with the columns needed: " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "alg.fit(titanic[predictors], titanic['Survived'])\n", + "\n", + "predictions = alg.predict(test[predictors])\n", + "\n", + "submission = pd.DataFrame({\n", + " 'PassengerId': test['PassengerId'],\n", + " 'Survived': predictions\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "submission.to_csv('kaggle.csv', index=False)" + ] + } + ], + "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/DataExploration.ipynb b/DataExploration.ipynb new file mode 100644 index 0000000..9aa1ad4 --- /dev/null +++ b/DataExploration.ipynb @@ -0,0 +1,3028 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shivali Chandra\n", + "Data Exploration\n", + "1/25/16" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", + " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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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": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Import needed libraries and get data\n", + "%matplotlib inline\n", + "\n", + "import thinkstats2\n", + "import thinkplot\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "titanic_df = pd.read_csv('train.csv')\n", + "titanic_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n", + "
" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Looking at the above data, a few factors immediately stand out to me to visualize. These include pclass, sex, age, and fare. I also thought it could be potentially useful or illuminating to create a visualization of passenger survival rate by cabin number, but there does not seem to be enough data to be able to gain any information. However, for each of the factors I will be analyzing I would like to keep the data size the same, so I will filter out those passengers who have NaN in any of the columns I chose to analyze. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = titanic_df.dropna(subset=['Survived','Pclass','Sex','Age','Fare'])\n", + "titanic_df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total males: 453 num survived: 93 survival rate: % 20.5298013245\n", + "total females: 261 num survived: 197 survival rate: % 75.4789272031\n" + ] + } + ], + "source": [ + "males = titanic_df[titanic_df.Sex == 'male']\n", + "females = titanic_df[titanic_df.Sex == 'female']\n", + "\n", + "male_sur = (males[males.Survived == 1])\n", + "female_sur = (females[females.Survived == 1])\n", + "\n", + "males_survived_perc = float(len(male_sur)) / float(len(males)) * 100\n", + "females_survived_perc = float(len(female_sur)) / float(len(females)) * 100 \n", + "\n", + "print 'total males: ', len(males), ' num survived: ', len(male_sur), ' survival rate: %', males_survived_perc\n", + "print 'total females: ', len(females), ' num survived: ', len(female_sur), ' survival rate: %', females_survived_perc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is clear from these survival rates that a higher percentage of females survived; however, there are nearly twice as many men as women, so the distribution is not proportionally represented by survival rate. Therefore, it will be helpful to create a graphic representation. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(males.Survived)\n", + "hist1 = thinkstats2.Hist(females.Survived)\n", + "thinkplot.PrePlot(2)\n", + "thinkplot.Hist(hist, align='left', width=0.5, label='males')\n", + "thinkplot.Hist(hist1, align='right', width=0.5, label='females')\n", + "thinkplot.Show(xlabel='survived', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, it is easier to see the relationship between survival rate and sex. Overall, females had a higher chance of surviving, which makes sense because when the Titanic was being evacuated there was an emphasis placed on getting the women and children on lifeboats first. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(male_sur.Age)\n", + "thinkplot.Hist(hist, label='males', width=.8)\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this visualization of surviving males, it can be seen that the majority of males who survived were between the ages of mid-20's to mid-30's. Interestingly, there are also 2 children under the age of 10, supporting the theory that children were a higher priority. However, this conclusion cannot be made without visualizing the ages of males who unfortunately did not survive. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "male_not = males[males.Survived == 0]\n", + "hist = thinkstats2.Hist(male_not.Age)\n", + "thinkplot.Hist(hist, label='males', width=.8)\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, there is a higher distribution of males who did not survive from the ages of the teens to early 30s, with a spike around the late 30's. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(female_sur.Age)\n", + "thinkplot.Hist(hist, label='females', width=.8)\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For women, the highest survival is around the ages of 20's to 30's, with a spike around age 5. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "female_not = females[females.Survived == 0]\n", + "hist = thinkstats2.Hist(female_not.Age)\n", + "thinkplot.Hist(hist, label='females')\n", + "thinkplot.Show(xlabel='age', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first thing that I notice about this graph is that the y-axis range is noticeably smaller than the survival y-axis range, corroborating the fact that %70 of women in this data set survived. This highest numbers here were very small children, possibly because of weaker immune systems, and between the ages of 15 to 30. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The other area that potentially holds useful information is visualizing survival rate by passenger class and fare, to see if there was a higher priority for first class or higher paying passengers, or if it didn't make a huge difference overall. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZpXJYmJlZKoeFmZmlcliYmVkqh4WZmaX6/4zO+YAcs2bUAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(titanic_df[titanic_df.Pclass == 1].Survived)\n", + "hist1 = thinkstats2.Hist(titanic_df[titanic_df.Pclass == 2].Survived)\n", + "hist2 = thinkstats2.Hist(titanic_df[titanic_df.Pclass == 3].Survived)\n", + "thinkplot.PrePlot(3)\n", + "thinkplot.Hist(hist, align='left', width=0.3, label='Pclass 1')\n", + "thinkplot.Hist(hist1, align='center', width=0.3, label='Pclass 2')\n", + "thinkplot.Hist(hist2, align='right', width=0.3, label='Pclass 3')\n", + "thinkplot.Show(xlabel='survived', ylabel='amount')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, two things can be seen. The first, that Pclass 1 is the smallest quantity of passengers, and that the majority were in class 3. However, the second piece of information that is seen is that the majority who survived were from Pclass 1. This indicates that either there was a higher priority placed on these passengers due to the money they had paid, or that there is a higher distribution of females and passengers between 25-35 in Pclass 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 40)\n", + "(2, 22)\n", + "(3, 16)\n", + "(4, 86)\n", + "(5, 114)\n", + "(6, 106)\n", + "(7, 95)\n", + "(8, 235)\n" + ] + }, + { + "data": { + "image/png": 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WdyIii5cikizuREQ2XolIsrgTEcWIF5FsP+SuiCSLOxFRjHgRyW/+uBvdLopI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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.arange(0, 40, 5)\n", + "indices = np.digitize(titanic_df.Age, bins)\n", + "groups = titanic_df.groupby(indices)\n", + "\n", + "for i, group in groups:\n", + " print(i, len(group))\n", + " age = [group.Age.mean() for i, group in groups]\n", + " cdfs = [thinkstats2.Cdf(group.Pclass) for i, group in groups]\n", + " \n", + "for percent in [75, 50, 25]:\n", + " Pclasses = [cdf.Percentile(percent) for cdf in cdfs]\n", + " label = '%dth' % percent\n", + " thinkplot.Plot(age, Pclasses, label=label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Very surprisingly, it appears that the majority of passengers in class 1 appear to be older. Thus, we will look at distribution of gender in each class. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 23)\n", + "(2, 9)\n", + "(3, 7)\n", + "(4, 50)\n", + "(5, 72)\n", + "(6, 76)\n", + "(7, 62)\n", + "(8, 154)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.arange(0, 40, 5)\n", + "indices = np.digitize(males.Age, bins)\n", + "groups = males.groupby(indices)\n", + "\n", + "for i, group in groups:\n", + " print(i, len(group))\n", + " age = [group.Age.mean() for i, group in groups]\n", + " cdfs = [thinkstats2.Cdf(group.Pclass) for i, group in groups]\n", + " \n", + "for percent in [75, 50, 25]:\n", + " Pclasses = [cdf.Percentile(percent) for cdf in cdfs]\n", + " label = '%dth' % percent\n", + " thinkplot.Plot(age, Pclasses, label=label)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 17)\n", + "(2, 13)\n", + "(3, 9)\n", + "(4, 36)\n", + "(5, 42)\n", + "(6, 30)\n", + "(7, 33)\n", + "(8, 81)\n" + ] + }, + { + "data": { + "image/png": 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NU/LFBGtzV55ZsiS8JPLkSWhosFuPUvGSm5UfGRPc3cappkNJFROszV15JjfXWfMepGfv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\nDj5ffH7U3CubNm9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bins = np.arange(0, 40, 5)\n", + "indices = np.digitize(females.Age, bins)\n", + "groups = females.groupby(indices)\n", + "\n", + "for i, group in groups:\n", + " print(i, len(group))\n", + " age = [group.Age.mean() for i, group in groups]\n", + " cdfs = [thinkstats2.Cdf(group.Pclass) for i, group in groups]\n", + " \n", + "for percent in [75, 50, 25]:\n", + " Pclasses = [cdf.Percentile(percent) for cdf in cdfs]\n", + " label = '%dth' % percent\n", + " thinkplot.Plot(age, Pclasses, label=label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these plots, it seems that my hypothesis was correct - more females are among classes 1 and 2, particularly among 1. There are very few women among the third class, potentially because of the ages - many of the women could be unmarried, and would probably be less likely to travel alone in class 3. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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oyH6T+Xnv836OHrXfzbXF+94H/OxnJpeIBfD995Gq3Zuus+J+Z9fL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\nIaAxIIQQAhoDQgghoDEghBAC4P8DOqqUeTVxGSkAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s1 = thinkplot.Scatter(females.Age, females.Fare)\n", + "thinkplot.Show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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FX0TaReR5EfmZiLwqIp/ObK8RkWdF5JKIfEtEqgLfeURErojIayLyvp2WgRxO4vHVbhzn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\nG7MAslN4XLqk57DFamzWdCy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s1 = thinkplot.Scatter(males.Age, males.Fare)\n", + "thinkplot.Show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is definitely clear that the majority of males were younger and spent less on their tickets, while there is much more variation for females. It does seem that more females spent more in tickets, regardless of age, so it seems likely that there are more females in pclass 1. " + ] + } + ], + "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..d258dc3 --- /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 +910,0 +911,1 +912,1 +913,1 +914,1 +915,0 +916,1 +917,0 +918,1 +919,0 +920,0 +921,0 +922,0 +923,0 +924,0 +925,0 +926,0 +927,0 +928,0 +929,0 +930,0 +931,0 +932,0 +933,0 +934,0 +935,1 +936,1 +937,0 +938,0 +939,0 +940,1 +941,0 +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 +960,0 +961,1 +962,1 +963,0 +964,0 +965,0 +966,1 +967,0 +968,0 +969,1 +970,0 +971,1 +972,1 +973,0 +974,0 +975,0 +976,0 +977,0 +978,1 +979,1 +980,1 +981,1 +982,0 +983,0 +984,1 +985,0 +986,0 +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 +1024,0 +1025,0 +1026,0 +1027,0 +1028,0 +1029,0 +1030,0 +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 +1045,0 +1046,0 +1047,0 +1048,1 +1049,1 +1050,0 +1051,0 +1052,1 +1053,1 +1054,1 +1055,0 +1056,0 +1057,0 +1058,0 +1059,0 +1060,1 +1061,0 +1062,0 +1063,0 +1064,0 +1065,0 +1066,0 +1067,1 +1068,1 +1069,1 +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 +1086,1 +1087,0 +1088,1 +1089,1 +1090,0 +1091,0 +1092,1 +1093,1 +1094,1 +1095,1 +1096,0 +1097,0 +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 +1126,1 +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 +1141,0 +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 +1160,0 +1161,0 +1162,0 +1163,0 +1164,1 +1165,1 +1166,0 +1167,1 +1168,0 +1169,0 +1170,0 +1171,0 +1172,0 +1173,1 +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 +1199,1 +1200,0 +1201,0 +1202,0 +1203,0 +1204,0 +1205,1 +1206,1 +1207,1 +1208,1 +1209,0 +1210,0 +1211,0 +1212,0 +1213,0 +1214,0 +1215,1 +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 +1259,0 +1260,1 +1261,0 +1262,0 +1263,1 +1264,0 +1265,0 +1266,1 +1267,1 +1268,0 +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 +1282,0 +1283,1 +1284,0 +1285,0 +1286,0 +1287,1 +1288,0 +1289,1 +1290,0 +1291,0 +1292,1 +1293,0 +1294,1 +1295,0 +1296,0 +1297,0 +1298,0 +1299,0 +1300,1 +1301,1 +1302,1 +1303,1 +1304,0 +1305,0 +1306,1 +1307,0 +1308,0 +1309,0 diff --git a/model_iteration_1.ipynb b/model_iteration_1.ipynb new file mode 100644 index 0000000..87dd29e --- /dev/null +++ b/model_iteration_1.ipynb @@ -0,0 +1,408 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shivali Chandra
\n", + "First iteration of model for Titanic Kaggle dataset.
\n", + "1/27/16
\n", + "Initial Score: 0.75120
\n", + "Random forests Score: 0.7535
\n", + "Adding more columns Score: 0.7799" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First steps: load libraries used, read from training file and show basic statistics of file" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import cross_validation\n", + "import numpy as np\n", + "\n", + "titanic = pd.read_csv(\"train.csv\")\n", + "titanic.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cleaning code, filling in NaN values and replacing text values with number codes: " + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())\n", + "\n", + "titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0\n", + "titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1\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": [ + "Defining columns used to predict target:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "test = pd.read_csv('test.csv')\n", + "test['Age'] = test['Age'].fillna(titanic['Age'].median())\n", + "\n", + "test.loc[test['Sex'] == 'male', 'Sex'] = 0\n", + "test.loc[test['Sex'] == 'female', 'Sex'] = 1\n", + "\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", + "test['Fare'] = test['Fare'].fillna(titanic['Fare'].median())\n", + "\n", + "#alg = LogisticRegression(random_state=1)\n", + "#scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)\n", + "#scores.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Adding new columns and using the random forest classifier algorithm to get scores and the mean; generating cross validation folds for the dataset (with random state set to ensure splits are the same every time), initializing predictors and target, training algorithm, and making predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.821548821549\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']\n", + "titanic['NameLength'] = titanic['Name'].apply(lambda x: len(x))\n", + "test['FamilySize'] = test['SibSp'] + test['Parch']\n", + "test['NameLength'] = test['Name'].apply(lambda x: len(x))\n", + "\n", + "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Parch\", \"Fare\", \"Embarked\", \"NameLength\", \"FamilySize\"]\n", + "\n", + "# Initialize our algorithm with the default paramters\n", + "# n_estimators is the number of trees we want to make\n", + "# min_samples_split is the minimum number of rows we need to make a split\n", + "# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=10, min_samples_leaf=5)\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", + "\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print(scores.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This model gives an accuracy of 82% on the test data, which is an improvement. Below is the code for the previous columns and model used: " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\\n\\nalg = LinearRegression()\\nkf = KFold(titanic.shape[0], n_folds=3, random_state=1)\\nscores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)\\nscores.mean()\\n\\npredictions = []\\nfor train, test in kf:\\n train_predictors = (titanic[predictors].iloc[train,:])\\n train_target = titanic['Survived'].iloc[train]\\n alg.fit(train_predictors, train_target)\\n test_predictions = alg.predict(titanic[predictors].iloc[test,:])\\n predictions.append(test_predictions)\"" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n", + "\n", + "alg = LinearRegression()\n", + "kf = KFold(titanic.shape[0], n_folds=3, random_state=1)\n", + "scores = cross_validation.cross_val_score(alg, titanic[predictors], titanic['Survived'], cv=3)\n", + "scores.mean()\n", + "\n", + "predictions = []\n", + "for train, test in kf:\n", + " train_predictors = (titanic[predictors].iloc[train,:])\n", + " train_target = titanic['Survived'].iloc[train]\n", + " alg.fit(train_predictors, train_target)\n", + " test_predictions = alg.predict(titanic[predictors].iloc[test,:])\n", + " predictions.append(test_predictions)\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Concatenating three prediction np arrays into one, and mapping the predictions to outcomes. Then, calculating the accuracy: " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"predictions = np.concatenate(predictions, axis=0)\\n\\npredictions[predictions > 0.5] = 1\\npredictions[predictions <= 0.5] = 0\\n\\naccuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)\"" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"predictions = np.concatenate(predictions, axis=0)\n", + "\n", + "predictions[predictions > 0.5] = 1\n", + "predictions[predictions <= 0.5] = 0\n", + "\n", + "accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generating submission for competition - training algorithm, making predictions, and creating dataframe with the columns needed: " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "alg.fit(titanic[predictors], titanic['Survived'])\n", + "\n", + "predictions = alg.predict(test[predictors])\n", + "\n", + "submission = pd.DataFrame({\n", + " 'PassengerId': test['PassengerId'],\n", + " 'Survived': predictions\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "submission.to_csv('kaggle.csv', index=False)" + ] + } + ], + "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..d42fc5d --- /dev/null +++ b/model_iteration_2.ipynb @@ -0,0 +1,1088 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shivali Chandra
\n", + "1/30/16
\n", + "Warmup Project Iteration 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspiration 1: http://elenacuoco.altervista.org/blog/archives/1195?doing_wp_cron=1454278621.7234199047088623046875, provides a great overview of how the author figured out which values would be relevant
\n", + "Inspiration 2: https://triangleinequality.wordpress.com/2013/09/05/a-complete-guide-to-getting-0-79903-in-kaggles-titanic-competition-with-python/, good overview as to how to structure the code and model as decision trees. This was a really interesting approach, because it basically weighted different factors as deciding factors or questions leading to more factors (for example, starting with a male, checking the passenger class and moving to different factors based on the result). I did not implement this entirely, but read through it and the links it pointed to in order to get a better sense of how to modify my model and understand how the random classifier model actually works. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", + " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import train_test_split,StratifiedShuffleSplit,StratifiedKFold, KFold\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.feature_selection import SelectKBest, f_classif\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt \n", + "import thinkplot\n", + "import thinkstats2\n", + "import string\n", + "from patsy import dmatrices,dmatrix\n", + "from sklearn.ensemble import GradientBoostingClassifier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I incorporated my own ideas by going through and seeing how the ideas in the two articles could be combined, especially with respect to different variables. Some of the variables I used were different, because I am interested in seeing the difference that they make, not necessarily just in achieving a higher score each time. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train = pd.read_csv('train.csv')\n", + "test = pd.read_csv('test.csv')\n", + "\n", + "train.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The biggest impact that this blog had on my model was the fact that it parsed for title and used that to assign age, rather than using the same mean. This actually seems pretty significant because there are over 200 ages missing, and with about 900 total people that is a large portion, so trying to get the age in a closer range is important. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#code from link to first blog; finds title in each name if exists and places it in a new column, called title. The titles are normalized. \n", + "def substrings_in_string(big_string, substrings):\n", + " for substring in substrings:\n", + " if string.find(big_string, substring) != -1:\n", + " return substring\n", + " print big_string\n", + " return np.nan\n", + "\n", + "title_list=['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n", + " 'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n", + " 'Don', 'Jonkheer']\n", + "train['Title']=train['Name'].map(lambda x: substrings_in_string(x, title_list))\n", + "test['Title']=test['Name'].map(lambda x: substrings_in_string(x, title_list))\n", + "\n", + "#replacing all titles with mr, mrs, miss, master\n", + "def replace_titles(x):\n", + " title=x['Title']\n", + " if title in ['Mr','Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:\n", + " return 'Mr'\n", + " elif title in ['Master']:\n", + " return 'Master'\n", + " elif title in ['Countess', 'Mme','Mrs']:\n", + " return 'Mrs'\n", + " elif title in ['Mlle', 'Ms','Miss']:\n", + " return 'Miss'\n", + " elif title =='Dr':\n", + " if x['Sex']=='Male':\n", + " return 'Mr'\n", + " else:\n", + " return 'Mrs'\n", + " elif title =='':\n", + " if x['Sex']=='Male':\n", + " return 'Master'\n", + " else:\n", + " return 'Miss'\n", + " else:\n", + " return title\n", + "\n", + "train['Title']=train.apply(replace_titles, axis=1)\n", + "test['Title']=test.apply(replace_titles, axis=1)\n", + "\n", + "train['AgeFill']=train['Age']\n", + "test['AgeFill']=test['Age']\n", + "mean_ages = np.zeros(4)\n", + "mean_ages[0]=np.average(train[train['Title'] == 'Miss']['Age'].dropna())\n", + "mean_ages[1]=np.average(train[train['Title'] == 'Mrs']['Age'].dropna())\n", + "mean_ages[2]=np.average(train[train['Title'] == 'Mr']['Age'].dropna())\n", + "mean_ages[3]=np.average(train[train['Title'] == 'Master']['Age'].dropna())\n", + "train.loc[ (train.Age.isnull()) & (train.Title == 'Miss') ,'AgeFill'] = mean_ages[0]\n", + "train.loc[ (train.Age.isnull()) & (train.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]\n", + "train.loc[ (train.Age.isnull()) & (train.Title == 'Mr') ,'AgeFill'] = mean_ages[2]\n", + "train.loc[ (train.Age.isnull()) & (train.Title == 'Master') ,'AgeFill'] = mean_ages[3]\n", + "test.loc[ (test.Age.isnull()) & (test.Title == 'Miss') ,'AgeFill'] = mean_ages[0]\n", + "test.loc[ (test.Age.isnull()) & (test.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]\n", + "test.loc[ (test.Age.isnull()) & (test.Title == 'Mr') ,'AgeFill'] = mean_ages[2]\n", + "test.loc[ (test.Age.isnull()) & (test.Title == 'Master') ,'AgeFill'] = mean_ages[3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The effort that the blog put into trying to ensure the age was accurate led me to see if I should implement a more complex way of filling in missing Embarked values. However, when I run: " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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", + "Title 0\n", + "AgeFill 0\n", + "dtype: int64" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I see that there are actually only 2 passengers for whom the starting point is unknown. Thus, it seems safe to simply fill in those values with 'S', the most common location.\n", + "I will continue cleaning the data as I did earlier, dropping the columns which no longer matter and replacing the Embarked and Sex values with integers. I will also combine the SibSp and Parch columns into one Family column. \n", + "Unlike the blogpost, I also converted the title column into integer values, to see if that makes any impact on the final survival predictions. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train['FamilySize'] = train['SibSp'] + train['Parch']\n", + "train['NameLength'] = train['Name'].apply(lambda x: len(x))\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", + "train.loc[train['Sex'] == 'male', 'Sex'] = 1\n", + "train.loc[train['Sex'] == 'female', 'Sex'] = 0\n", + "test['FamilySize'] = test['SibSp'] + test['Parch']\n", + "test['NameLength'] = test['Name'].apply(lambda x: len(x))\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", + "test.loc[test['Sex'] == 'male', 'Sex'] = 1\n", + "test.loc[test['Sex'] == 'female', 'Sex'] = 0\n", + "\n", + "test.loc[ test.Title == 'Miss','Title'] = 4\n", + "test.loc[ test.Title == 'Mrs','Title'] = 3\n", + "test.loc[ test.Title == 'Mr','Title'] = 1\n", + "test.loc[ test.Title == 'Master','Title'] = 2\n", + "train.loc[ train.Title == 'Miss','Title'] = 4\n", + "train.loc[ train.Title == 'Mrs','Title'] = 3\n", + "train.loc[ train.Title == 'Mr','Title'] = 1\n", + "train.loc[ train.Title == 'Master','Title'] = 2\n", + "\n", + "train = train.drop(['Name','Age','Cabin','Ticket','SibSp','Parch'], axis=1)\n", + "test = test.drop(['Name','Age','Cabin','Ticket','SibSp','Parch'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassFareAgeFillFamilySizeNameLength
count891.000000891.000000891.000000891.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864232.20420829.8191310.90460226.965208
std257.3538420.4865920.83607149.69342913.2854231.6134599.281607
min1.0000000.0000001.0000000.0000000.4200000.00000012.000000
25%223.5000000.0000002.0000007.91040021.8356160.00000020.000000
50%446.0000000.0000003.00000014.45420030.0000000.00000025.000000
75%668.5000001.0000003.00000031.00000035.8416671.00000030.000000
max891.0000001.0000003.000000512.32920080.00000010.00000082.000000
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Fare AgeFill \\\n", + "count 891.000000 891.000000 891.000000 891.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 32.204208 29.819131 \n", + "std 257.353842 0.486592 0.836071 49.693429 13.285423 \n", + "min 1.000000 0.000000 1.000000 0.000000 0.420000 \n", + "25% 223.500000 0.000000 2.000000 7.910400 21.835616 \n", + "50% 446.000000 0.000000 3.000000 14.454200 30.000000 \n", + "75% 668.500000 1.000000 3.000000 31.000000 35.841667 \n", + "max 891.000000 1.000000 3.000000 512.329200 80.000000 \n", + "\n", + " FamilySize NameLength \n", + "count 891.000000 891.000000 \n", + "mean 0.904602 26.965208 \n", + "std 1.613459 9.281607 \n", + "min 0.000000 12.000000 \n", + "25% 0.000000 20.000000 \n", + "50% 0.000000 25.000000 \n", + "75% 1.000000 30.000000 \n", + "max 10.000000 82.000000 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While all the values of the train data are filled in, I realize that it is possible that the fare may be unknown for a passenger. Thus, I will use the Pclass to predict a more accurate value for fare. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p1 = train[train.Pclass == 1]\n", + "p2 = train[train.Pclass == 2]\n", + "p3 = train[train.Pclass == 3]\n", + "train.loc[train.Fare.isnull() & (train.Pclass == 1), 'Fare'] = p1.Fare.mean()\n", + "train.loc[train.Fare.isnull() & (train.Pclass == 2), 'Fare'] = p2.Fare.mean()\n", + "train.loc[train.Fare.isnull() & (train.Pclass == 3), 'Fare'] = p3.Fare.mean()\n", + "test.loc[test.Fare.isnull() & (test.Pclass == 1), 'Fare'] = p1.Fare.mean()\n", + "test.loc[test.Fare.isnull() & (test.Pclass == 2), 'Fare'] = p2.Fare.mean()\n", + "test.loc[test.Fare.isnull() & (test.Pclass == 3), 'Fare'] = p3.Fare.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have cleaner data, of the first suggestions of the first blog was to make plots of age and other characteristics to see if there was any correlation: " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6iIiI1ORqYLqZNQXWAKMInDI708wuBQqAoTGMT0REJKWoWBcRERHc/X3g+Epm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\nbt26hrwklVKxLhGxa9cumjdvTkZGBrt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "female = train[train.Sex == 0]\n", + "male = train[train.Sex == 1]\n", + "fig = plt.figure(figsize=(20,6))\n", + " \n", + "plt.subplots_adjust(bottom=0.1, right=0.8, top=2) \n", + "\n", + "##gender and age\n", + "#female\n", + "ax1 = fig.add_subplot(5,4,12)\n", + "female_aged = female.Survived[female.AgeFill >= 60].value_counts()\n", + "female_aged.plot(kind='bar', color='green', label='female, aged')\n", + "plt.legend(loc='best')\n", + "\n", + "ax2 = fig.add_subplot(5,4,9)\n", + "female_child = female.Survived[female.AgeFill <= 10].value_counts()\n", + "female_child.plot(kind='bar', color='green', label='female, children')\n", + "plt.legend(loc='best')\n", + "\n", + "ax3 = fig.add_subplot(5,4,10)\n", + "female_midage = female.Survived[female.AgeFill>10][female.AgeFill<=30].value_counts()\n", + "female_midage.plot(kind='bar', color='green', label='female, 10-30')\n", + "plt.legend(loc='best')\n", + "\n", + "ax4 = fig.add_subplot(5,4,11)\n", + "female_middleage = female.Survived[female.AgeFill>30][female.AgeFill<60].value_counts()\n", + "female_middleage.plot(kind='bar', color='green', label='female, 30-60')\n", + "plt.legend(loc='best')\n", + " \n", + "#male\n", + " \n", + "ax5 = fig.add_subplot(5,4,16)\n", + "male_aged = male.Survived[male.AgeFill >= 60].value_counts()\n", + "male_aged.plot(kind='bar', label='male, aged')\n", + "plt.legend(loc='best')\n", + "\n", + "ax6 = fig.add_subplot(5,4,13)\n", + "male_child = male.Survived[male.AgeFill <= 10].value_counts()\n", + "male_child.plot(kind='bar', label='male, children')\n", + "plt.legend(loc='best')\n", + "\n", + "ax7 = fig.add_subplot(5,4,14)\n", + "male_midage = male.Survived[male.AgeFill>10][male.AgeFill<=30].value_counts()\n", + "male_midage.plot(kind='bar', label='male, 10-30')\n", + "plt.legend(loc='best') \n", + "\n", + "ax8 = fig.add_subplot(5,4,15)\n", + "male_middleage = male.Survived[male.AgeFill>30][male.AgeFill<60].value_counts()\n", + "male_middleage.plot(kind='bar', label='male, 30-60') \n", + "plt.legend(loc='best')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this we gain some very interesting and potentially useful information: \n", + "- the four aged females all survived\n", + "- female children had slightly under 2/3 chance of survival, not as high as I would have suspected. On the other hand, females between 10-30 had around a 3/4 chance, while females between 30-60 had about a 4/5 chance of survival\n", + "- male children had the best chance of surviving, with men between 30-60 next (about a 1/5 chance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, checking once again the passenger class and gender correlation: " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/pandas/core/frame.py:1997: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " \"DataFrame index.\", UserWarning)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(female[train.Pclass == 1].Survived)\n", + "hist1 = thinkstats2.Hist(female[train.Pclass == 2].Survived)\n", + "hist2 = thinkstats2.Hist(female[train.Pclass == 3].Survived)\n", + "thinkplot.PrePlot(3)\n", + "thinkplot.Hist(hist, align='left', width=0.3, label='1')\n", + "thinkplot.Hist(hist1, align='center', width=0.3, label='2')\n", + "thinkplot.Hist(hist2, align='right', width=0.3, label='3')\n", + "thinkplot.Show(xlabel='survived', ylabel='amount of women in class')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hist = thinkstats2.Hist(male[train.Pclass == 1].Survived)\n", + "hist1 = thinkstats2.Hist(male[train.Pclass == 2].Survived)\n", + "hist2 = thinkstats2.Hist(male[train.Pclass == 3].Survived)\n", + "thinkplot.PrePlot(3)\n", + "thinkplot.Hist(hist, align='left', width=0.3, label='class 1')\n", + "thinkplot.Hist(hist1, align='center', width=0.3, label='class 2')\n", + "thinkplot.Hist(hist2, align='right', width=0.3, label='class 3')\n", + "thinkplot.Show(xlabel='survived', ylabel='amount of men in class')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the females, it appears there is a stronger correlation between class and survival; nearly all high class and middle class females survived, while there was about a 50-50 split between the third class, which was given lower priority during the sinking.
\n", + "For the males there is certainly a higher ratio of high class and middle class males who survived, but it is not as significant.
\n", + "Because of this correlation between age, gender, and class I will try to incorporate that into my model. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.82379349046\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"AgeFill\", \"Fare\", \"Embarked\", \"NameLength\", \"FamilySize\", \"Title\"]\n", + "\n", + "# Initialize our algorithm with the default paramters\n", + "# n_estimators is the number of trees we want to make\n", + "# min_samples_split is the minimum number of rows we need to make a split\n", + "# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=10, min_samples_leaf=5)\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, train[predictors], train[\"Survived\"], cv=3)\n", + "\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print(scores.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before adding the new title column the mean was around 0.81, so it looks like my decision to add the column in was a good choice. Next, I will try adding columns to differentiate sex, age, and class, to see if that makes a difference. The weightings are based off of the probability of each section surviving based on the train results." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "train['SexClass'] = train['Sex']\n", + "\"\"\"train.loc[ (train.Sex == 0) & (train.Pclass == 1) & (train.AgeFill>30) & (train.AgeFill<60),'SexClass'] = 100\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 1) & ((train.AgeFill>10) & (train.AgeFill<30) | (train.AgeFill>60)),'SexClass'] = 25\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 1) & (train.AgeFill<10),'SexClass'] = 40\n", + "\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 2) & (train.AgeFill>30) & (train.AgeFill<60),'SexClass'] = 9\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 2) & (train.AgeFill>10) & (train.AgeFill<30),'SexClass'] = 8\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 2) & (train.AgeFill<10),'SexClass'] = 7\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 2) & (train.AgeFill>60),'SexClass'] = 6\n", + "\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 3) ,'SexClass'] = 5\n", + "\n", + "train.loc[ (train.Sex == 1) & (train.Pclass == 1) & (train.AgeFill>30) & (train.AgeFill<60),'SexClass'] = 4\n", + "train.loc[ (train.Sex == 1) & (train.Pclass == 1) & ((train.AgeFill>10) & (train.AgeFill<30) | (train.AgeFill>60)),'SexClass'] = 3\n", + "train.loc[ (train.Sex == 1) & (train.Pclass == 1) & (train.AgeFill<10),'SexClass'] = 20\"\"\"\n", + "\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 1) ,'SexClass'] = 5\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 2) ,'SexClass'] = 4\n", + "train.loc[ (train.Sex == 0) & (train.Pclass == 3) ,'SexClass'] = 3\n", + "train.loc[ (train.Sex == 1) & (train.Pclass == 1) ,'SexClass'] = 2\n", + "train.loc[ (train.Sex == 1) & (train.Pclass == 2) ,'SexClass'] = 1\n", + "train.loc[ (train.Sex == 1) & (train.Pclass == 3) ,'SexClass'] = 0\n", + "\n", + "test['SexClass'] = test['Sex']\n", + "\"\"\"test.loc[ (test.Sex == 0) & (test.Pclass == 1) & (test.AgeFill>30) & (test.AgeFill<60),'SexClass'] = 100\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 1) & ((test.AgeFill>10) & (test.AgeFill<30) | (test.AgeFill>60)),'SexClass'] = 25\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 1) & (test.AgeFill<10),'SexClass'] = 40\n", + "\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 2) & (test.AgeFill>30) & (test.AgeFill<60),'SexClass'] = 9\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 2) & (test.AgeFill>10) & (test.AgeFill<30),'SexClass'] = 8\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 2) & (test.AgeFill<10),'SexClass'] = 7\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 2) & (test.AgeFill>60),'SexClass'] = 6\n", + "\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 3) ,'SexClass'] = 5\n", + "\n", + "test.loc[ (test.Sex == 1) & (test.Pclass == 1) & (test.AgeFill>30) & (test.AgeFill<60),'SexClass'] = 4\n", + "test.loc[ (test.Sex == 1) & (test.Pclass == 1) & ((test.AgeFill>10) & (test.AgeFill<30) | (test.AgeFill>60)),'SexClass'] = 3\n", + "test.loc[ (test.Sex == 1) & (test.Pclass == 1) & (test.AgeFill<10),'SexClass'] = 20\"\"\"\n", + "\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 1) ,'SexClass'] = 5\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 2) ,'SexClass'] = 4\n", + "test.loc[ (test.Sex == 0) & (test.Pclass == 3) ,'SexClass'] = 3\n", + "test.loc[ (test.Sex == 1) & (test.Pclass == 1) ,'SexClass'] = 2\n", + "test.loc[ (test.Sex == 1) & (test.Pclass == 2) ,'SexClass'] = 1\n", + "test.loc[ (test.Sex == 1) & (test.Pclass == 3) ,'SexClass'] = 0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "I played around a bit to see how to achieve the best means of weighting the values in this column - one thing I did not have a chance to try was weighting by percentages of survival in the training data. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.835137254902\n" + ] + } + ], + "source": [ + "predictors = [\"Pclass\", \"Sex\", \"AgeFill\", \"Fare\", \"Embarked\", \"NameLength\", \"FamilySize\", \"Title\", \"SexClass\"]\n", + "\n", + "# Initialize our algorithm with the default paramters\n", + "# n_estimators is the number of trees we want to make\n", + "# min_samples_split is the minimum number of rows we need to make a split\n", + "# min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree)\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2, min_samples_leaf=5)\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, train[predictors], train[\"Survived\"], cv=25)\n", + "\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print(scores.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Overall, I saw values for the mean range from around 0.819 to 0.837, so there was a definite improvement. However, when actually testing this in Kaggle, I actually went down in accuracy, from 0.7799 to around 0.75120 and 0.7713. Thus, I think I will also try changing the model variables to see if that helps." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.836311858077\n" + ] + } + ], + "source": [ + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=4, min_samples_leaf=5)\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, train[predictors], train[\"Survived\"], cv=25)\n", + "\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print(scores.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In changing model variables, the mean improved at most by 0.02. However, the Kaggle score did not improve, so that suggests that I need to change my model to make it more accurate. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.840855275444\n" + ] + } + ], + "source": [ + "# Perform feature selection\n", + "selector = SelectKBest(f_classif, k='all')\n", + "selector.fit(train[predictors], train[\"Survived\"])\n", + "\n", + "# Get the raw p-values for each feature, and transform from p-values into scores\n", + "scores = -np.log10(selector.pvalues_)\n", + "\n", + "# Plot the scores. See how \"Pclass\", \"Sex\", \"Title\", and \"Fare\" are the best?\n", + "plt.bar(range(len(predictors)), scores)\n", + "plt.xticks(range(len(predictors)), predictors, rotation='vertical')\n", + "plt.show()\n", + "\n", + "# Pick only the best features.\n", + "#predictors = [\"Pclass\", \"Sex\", \"Fare\", \"Title\"]\n", + "predictors = [\"SexClass\", \"Title\", \"Sex\", \"Pclass\", \"Fare\"]\n", + "\n", + "alg = RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=5)\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, train[predictors], train[\"Survived\"], cv=25)\n", + "\n", + "# Take the mean of the scores (because we have one for each fold)\n", + "print(scores.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By seeing which factors had the largest effect, I was able to select only those to use and get a pretty high result with my train data (0.841). However, in Kaggle my score actually remained the same when I submitted it. So, the last thing I tried was to change the type of model by using three algorithms, gradient boosting and logistic regression along with random forest, and taking the average of the three. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.813692480359\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:35: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index\n" + ] + } + ], + "source": [ + "# The algorithms we want to ensemble.\n", + "# We're using the more linear predictors for the logistic regression, and everything with the gradient boosting classifier.\n", + "algorithms = [\n", + " [GradientBoostingClassifier(random_state=1, n_estimators=25, max_depth=3), [\"Sex\", \"NameLength\", \"SexClass\", \"Pclass\", \"Fare\", \"Embarked\", \"Title\", \"FamilySize\"]],\n", + " [RandomForestClassifier(random_state=1, n_estimators=150, min_samples_split=8, min_samples_leaf=5), [\"Sex\", \"NameLength\", \"SexClass\", \"Pclass\", \"Fare\", \"Embarked\", \"Title\", \"FamilySize\"]],\n", + " [LogisticRegression(random_state=1), [\"Sex\", \"NameLength\", \"SexClass\", \"Pclass\", \"Fare\", \"Embarked\", \"Title\", \"FamilySize\"]]\n", + "]\n", + "\n", + "# Initialize the cross validation folds\n", + "kf = KFold(train.shape[0], n_folds=3, random_state=1)\n", + "\n", + "predictions = []\n", + "for train1, test1 in kf:\n", + " train_target = train[\"Survived\"].iloc[train1]\n", + " full_test_predictions = []\n", + " # Make predictions for each algorithm on each fold\n", + " for alg, predictors in algorithms:\n", + " # Fit the algorithm on the training data.\n", + " alg.fit(train[predictors].iloc[train1,:], train_target)\n", + " # Select and predict on the test fold. \n", + " # The .astype(float) is necessary to convert the dataframe to all floats and avoid an sklearn error.\n", + " test_predictions = alg.predict_proba(train[predictors].iloc[test1,:].astype(float))[:,1]\n", + " full_test_predictions.append(test_predictions)\n", + " # Use a simple ensembling scheme -- just average the predictions to get the final classification.\n", + " test_predictions = (full_test_predictions[0] + full_test_predictions[1] + full_test_predictions[2]) / 3\n", + " # Any value over .5 is assumed to be a 1 prediction, and below .5 is a 0 prediction.\n", + " test_predictions[test_predictions <= .5] = 0\n", + " test_predictions[test_predictions > .5] = 1\n", + " predictions.append(test_predictions)\n", + "\n", + "# Put all the predictions together into one array.\n", + "predictions = np.concatenate(predictions, axis=0)\n", + "\n", + "# Compute accuracy by comparing to the training data.\n", + "accuracy = sum(predictions[predictions == train[\"Survived\"]]) / len(predictions)\n", + "print(accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "full_predictions = []\n", + "for alg, predictors in algorithms:\n", + " # Fit the algorithm using the full training data.\n", + " alg.fit(train[predictors], train[\"Survived\"])\n", + " # Predict using the test dataset. We have to convert all the columns to floats to avoid an error.\n", + " predictions = alg.predict_proba(test[predictors].astype(float))[:,1]\n", + " full_predictions.append(predictions)\n", + "\n", + "# The gradient boosting classifier generates better predictions, so we weight it higher.\n", + "predictions = (full_predictions[0] * 5 + full_predictions[1]) / 4\n", + "predictions[predictions <= .5] = 0\n", + "predictions[predictions > .5] = 1\n", + "predictions = predictions.astype(int)\n", + "submission = pd.DataFrame({\n", + " \"PassengerId\": test[\"PassengerId\"],\n", + " \"Survived\": predictions\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "submission.to_csv('kaggle.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "In the end, this also did not improve my score, but I learned much more about how the models actually work. I think that implementing my own column, 'SexClass', helped my model significantly, but I need to figure out a better method of weighting. Overall, I think that my score on Kaggle did not improve because right now the weighting is very tailored to the train data, and is not necessarily very adaptable. In order to increase my scores with this model from 0.77033 to beat my highest score of 0.7799 and improve it in general, the weighting of different factors and variables is definitely where I would start. " + ] + }, + { + "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/test.csv b/test.csv new file mode 100644 index 0000000..f705412 --- /dev/null +++ b/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. 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