Titanic¶
This example uses the titanic dataset to predict the survival of passengers on the Titanic.
We will follow the data science cycle (Data Exploration - Data Preparation - Data Modeling - Model Evaluation - Model Deployment) to solve this problem.
Initialization¶
This example uses the following version of vastorbit:
import vastorbit as vo
vo.__version__
Connect to VAST. This example uses an existing connection called VASTDSN .
For details on how to create a connection, see the Connection tutorial.
You can skip the below cell if you already have an established connection.
vo.connect("VASTDSN")
Let’s create a VastFrame of the dataset.
from vastorbit.datasets import load_titanic
titanic = load_titanic()
titanic.head(5)
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | Abc sexVarchar(20) | 123 ageDouble | 123 sibspInteger | 123 parchInteger | Abc ticketVarchar(36) | 123 fareDouble | Abc cabinVarchar(30) | Abc embarkedVarchar(20) | Abc boatVarchar(100) | 123 bodyInteger | Abc home.destVarchar(100) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | Allen, Miss. Elisabeth Walton | female | 29.0 | 0 | 0 | 24160 | 211.3375 | B5 | S | 2 | [null] | St Louis, MO |
| 2 | 1 | 1 | Allison, Master. Hudson Trevor | male | 0.92 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | 11 | [null] | Montreal, PQ / Chesterville, ON |
| 3 | 1 | 0 | Allison, Miss. Helen Loraine | female | 2.0 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | [null] | [null] | Montreal, PQ / Chesterville, ON |
| 4 | 1 | 0 | Allison, Mr. Hudson Joshua Creighton | male | 30.0 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | [null] | 135 | Montreal, PQ / Chesterville, ON |
| 5 | 1 | 0 | Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | female | 25.0 | 1 | 2 | 113781 | 151.55 | C22 C26 | S | [null] | [null] | Montreal, PQ / Chesterville, ON |
Data Exploration and Preparation¶
Let’s explore the data by displaying descriptive statistics of all the columns.
titanic.describe(method = "categorical", unique = True)
| dtype | count | top | top_percent | unique | |
|---|---|---|---|---|---|
| "pclass" | integer | 1309 | 3 | 54.163 | 3.0 |
| "survived" | integer | 1309 | 0 | 61.803 | 2.0 |
| "name" | varchar(164) | 1309 | Connolly, Miss. Kate | 0.153 | 1307.0 |
| "sex" | varchar(20) | 1309 | male | 64.4 | 2.0 |
| "age" | double | 1046 | [null] | 20.092 | 98.0 |
| "sibsp" | integer | 1309 | 0 | 68.067 | 7.0 |
| "parch" | integer | 1309 | 0 | 76.547 | 8.0 |
| "ticket" | varchar(36) | 1309 | CA. 2343 | 0.84 | 929.0 |
| "fare" | double | 1308 | 8.05 | 4.584 | 281.0 |
| "cabin" | varchar(30) | 295 | [null] | 77.464 | 186.0 |
| "embarked" | varchar(20) | 1307 | S | 69.824 | 3.0 |
| "boat" | varchar(100) | 486 | [null] | 62.872 | 27.0 |
| "body" | integer | 121 | [null] | 90.756 | 121.0 |
| "home.dest" | varchar(100) | 745 | [null] | 43.086 | 369.0 |
The columns body (passenger ID), home.dest (passenger origin/destination), embarked (origin port) and ticket (ticket ID) shouldn’t influence survival, so we can ignore these.
Let’s focus our analysis on the columns name and cabin. We’ll begin with the passengers’ names.
from vastorbit.machine_learning.vast import TfidfVectorizer
model = TfidfVectorizer()
model.fit(titanic, "ticket", x="Name")
model.transform(titanic, "ticket", x="Name")["word"].topk()
| count | percent | |
|---|---|---|
| mr | 696 | 14.926 |
| miss | 218 | 4.675 |
| mrs | 196 | 4.203 |
| william | 71 | 1.523 |
| john | 58 | 1.244 |
| master | 42 | 0.901 |
| henry | 40 | 0.858 |
| james | 35 | 0.751 |
| charles | 33 | 0.708 |
| thomas | 31 | 0.665 |
| george | 30 | 0.643 |
| mary | 28 | 0.6 |
| joseph | 27 | 0.579 |
| elizabeth | 25 | 0.536 |
| edward | 25 | 0.536 |
| johan | 21 | 0.45 |
| anna | 18 | 0.386 |
| maria | 18 | 0.386 |
| arthur | 17 | 0.365 |
| frederick | 17 | 0.365 |
| robert | 17 | 0.365 |
| alfred | 17 | 0.365 |
| alexander | 15 | 0.322 |
| richard | 15 | 0.322 |
| samuel | 15 | 0.322 |
| karl | 13 | 0.279 |
| jr | 13 | 0.279 |
| j | 13 | 0.279 |
| margaret | 13 | 0.279 |
| patrick | 12 | 0.257 |
| alice | 12 | 0.257 |
| frank | 11 | 0.236 |
| harry | 11 | 0.236 |
| annie | 11 | 0.236 |
| albert | 11 | 0.236 |
| h | 10 | 0.214 |
| helen | 10 | 0.214 |
| ellen | 10 | 0.214 |
| ernest | 10 | 0.214 |
| david | 9 | 0.193 |
| martin | 9 | 0.193 |
| kate | 9 | 0.193 |
| edith | 9 | 0.193 |
| leonard | 9 | 0.193 |
| peter | 9 | 0.193 |
| elias | 9 | 0.193 |
| dr | 8 | 0.172 |
| walter | 8 | 0.172 |
| e | 8 | 0.172 |
| francis | 8 | 0.172 |
| catherine | 8 | 0.172 |
| rev | 8 | 0.172 |
| august | 8 | 0.172 |
| bertha | 7 | 0.15 |
| nils | 7 | 0.15 |
| louise | 7 | 0.15 |
| victor | 7 | 0.15 |
| florence | 7 | 0.15 |
| daniel | 7 | 0.15 |
| de | 6 | 0.129 |
| johansson | 6 | 0.129 |
| brown | 6 | 0.129 |
| c | 6 | 0.129 |
| y | 6 | 0.129 |
| ivan | 6 | 0.129 |
| bridget | 6 | 0.129 |
| l | 6 | 0.129 |
| a | 6 | 0.129 |
| marie | 6 | 0.129 |
| gustafsson | 6 | 0.129 |
| andersson | 6 | 0.129 |
| delia | 6 | 0.129 |
| ada | 6 | 0.129 |
| olsen | 6 | 0.129 |
| augusta | 6 | 0.129 |
| gustaf | 6 | 0.129 |
| ida | 6 | 0.129 |
| katherine | 6 | 0.129 |
| ernst | 6 | 0.129 |
| sofia | 6 | 0.129 |
| benjamin | 5 | 0.107 |
| marion | 5 | 0.107 |
| emily | 5 | 0.107 |
| philip | 5 | 0.107 |
| davies | 5 | 0.107 |
| howard | 5 | 0.107 |
| hansen | 5 | 0.107 |
| douglas | 5 | 0.107 |
| smith | 5 | 0.107 |
| williams | 5 | 0.107 |
| edvin | 5 | 0.107 |
| anne | 5 | 0.107 |
| carl | 5 | 0.107 |
| andrew | 5 | 0.107 |
| emil | 5 | 0.107 |
| emma | 5 | 0.107 |
| edvard | 5 | 0.107 |
| bertram | 5 | 0.107 |
| michael | 5 | 0.107 |
| kelly | 5 | 0.107 |
| nellie | 5 | 0.107 |
| hanna | 5 | 0.107 |
| jane | 5 | 0.107 |
| abraham | 5 | 0.107 |
| ali | 4 | 0.086 |
| katie | 4 | 0.086 |
| karlsson | 4 | 0.086 |
| reginald | 4 | 0.086 |
| g | 4 | 0.086 |
| elisabeth | 4 | 0.086 |
| elsie | 4 | 0.086 |
| julia | 4 | 0.086 |
| martha | 4 | 0.086 |
| johannes | 4 | 0.086 |
| hugh | 4 | 0.086 |
| col | 4 | 0.086 |
| white | 4 | 0.086 |
| dorothy | 4 | 0.086 |
| herbert | 4 | 0.086 |
| hocking | 4 | 0.086 |
| sidney | 4 | 0.086 |
| cacic | 4 | 0.086 |
| van | 4 | 0.086 |
| lillian | 4 | 0.086 |
| may | 4 | 0.086 |
| olof | 4 | 0.086 |
| svensson | 4 | 0.086 |
| stanley | 4 | 0.086 |
| tannous | 4 | 0.086 |
| harris | 4 | 0.086 |
| frances | 4 | 0.086 |
| constance | 4 | 0.086 |
| paul | 4 | 0.086 |
| hans | 4 | 0.086 |
| jakob | 4 | 0.086 |
| hannah | 4 | 0.086 |
| leo | 4 | 0.086 |
| wilhelm | 4 | 0.086 |
| eugene | 4 | 0.086 |
| nora | 4 | 0.086 |
| ethel | 4 | 0.086 |
| agnes | 4 | 0.086 |
| percy | 4 | 0.086 |
| clifford | 4 | 0.086 |
| fleming | 3 | 0.064 |
| miriam | 3 | 0.064 |
| d | 3 | 0.064 |
| hall | 3 | 0.064 |
| ruth | 3 | 0.064 |
| emilia | 3 | 0.064 |
| rosalie | 3 | 0.064 |
| helene | 3 | 0.064 |
| rene | 3 | 0.064 |
| ward | 3 | 0.064 |
| allen | 3 | 0.064 |
| gordon | 3 | 0.064 |
| julius | 3 | 0.064 |
| amy | 3 | 0.064 |
| hughes | 3 | 0.064 |
| edwin | 3 | 0.064 |
| maggie | 3 | 0.064 |
| jessie | 3 | 0.064 |
| ii | 3 | 0.064 |
| betros | 3 | 0.064 |
| mabel | 3 | 0.064 |
| hanora | 3 | 0.064 |
| irene | 3 | 0.064 |
| davis | 3 | 0.064 |
| w | 3 | 0.064 |
| charlotte | 3 | 0.064 |
| daly | 3 | 0.064 |
| rogers | 3 | 0.064 |
| jacob | 3 | 0.064 |
| larsson | 3 | 0.064 |
| percival | 3 | 0.064 |
| sigrid | 3 | 0.064 |
| eleanor | 3 | 0.064 |
| vander | 3 | 0.064 |
| khalil | 3 | 0.064 |
| jean | 3 | 0.064 |
| bradley | 3 | 0.064 |
| lily | 3 | 0.064 |
| augustus | 3 | 0.064 |
| johanna | 3 | 0.064 |
| carlsson | 3 | 0.064 |
| persson | 3 | 0.064 |
| ann | 3 | 0.064 |
| hays | 3 | 0.064 |
| emilio | 3 | 0.064 |
| caroline | 3 | 0.064 |
| gladys | 3 | 0.064 |
| eliza | 3 | 0.064 |
| solomon | 3 | 0.064 |
| julian | 3 | 0.064 |
| adolf | 3 | 0.064 |
| giles | 3 | 0.064 |
| ware | 3 | 0.064 |
| matilda | 3 | 0.064 |
| gilbert | 3 | 0.064 |
| r | 3 | 0.064 |
| watson | 3 | 0.064 |
| jose | 3 | 0.064 |
| morgan | 3 | 0.064 |
| gertrude | 3 | 0.064 |
| boulos | 3 | 0.064 |
| madeleine | 3 | 0.064 |
| cor | 3 | 0.064 |
| jensen | 3 | 0.064 |
| oreskovic | 3 | 0.064 |
| nilsson | 3 | 0.064 |
| edgar | 3 | 0.064 |
| johnson | 3 | 0.064 |
| viktor | 3 | 0.064 |
| wright | 3 | 0.064 |
| graham | 3 | 0.064 |
| wilson | 3 | 0.064 |
| oscar | 3 | 0.064 |
| jussila | 3 | 0.064 |
| erik | 3 | 0.064 |
| bengtsson | 3 | 0.064 |
| olsson | 3 | 0.064 |
| oskar | 3 | 0.064 |
| keane | 3 | 0.064 |
| barbara | 3 | 0.064 |
| simon | 3 | 0.064 |
| flynn | 3 | 0.064 |
| stephen | 3 | 0.064 |
| ford | 3 | 0.064 |
| anderson | 3 | 0.064 |
| elin | 3 | 0.064 |
| norman | 3 | 0.064 |
| amelia | 3 | 0.064 |
| harold | 3 | 0.064 |
| grace | 2 | 0.043 |
| thamine | 2 | 0.043 |
| youssef | 2 | 0.043 |
| edmund | 2 | 0.043 |
| romaine | 2 | 0.043 |
| frederic | 2 | 0.043 |
| fox | 2 | 0.043 |
| mccarthy | 2 | 0.043 |
| harper | 2 | 0.043 |
| forbes | 2 | 0.043 |
| denis | 2 | 0.043 |
| assad | 2 | 0.043 |
| canavan | 2 | 0.043 |
| petroff | 2 | 0.043 |
| zakarian | 2 | 0.043 |
| anders | 2 | 0.043 |
| jefferys | 2 | 0.043 |
| isidor | 2 | 0.043 |
| mathilde | 2 | 0.043 |
| austin | 2 | 0.043 |
| calic | 2 | 0.043 |
| antoinette | 2 | 0.043 |
| walton | 2 | 0.043 |
| neal | 2 | 0.043 |
| archibald | 2 | 0.043 |
| thorne | 2 | 0.043 |
| duane | 2 | 0.043 |
| marjorie | 2 | 0.043 |
| henrik | 2 | 0.043 |
| clara | 2 | 0.043 |
| lester | 2 | 0.043 |
| ralph | 2 | 0.043 |
| thomson | 2 | 0.043 |
| morris | 2 | 0.043 |
| ilmakangas | 2 | 0.043 |
| brogren | 2 | 0.043 |
| konrad | 2 | 0.043 |
| washington | 2 | 0.043 |
| oconnor | 2 | 0.043 |
| arvid | 2 | 0.043 |
| sophia | 2 | 0.043 |
| noel | 2 | 0.043 |
| christian | 2 | 0.043 |
| scott | 2 | 0.043 |
| manuel | 2 | 0.043 |
| fredrik | 2 | 0.043 |
| christy | 2 | 0.043 |
| frauenthal | 2 | 0.043 |
| cook | 2 | 0.043 |
| juho | 2 | 0.043 |
| meyer | 2 | 0.043 |
| antti | 2 | 0.043 |
| moore | 2 | 0.043 |
| asplund | 2 | 0.043 |
| josefina | 2 | 0.043 |
| pauline | 2 | 0.043 |
| carr | 2 | 0.043 |
| gillespie | 2 | 0.043 |
| virginia | 2 | 0.043 |
| murphy | 2 | 0.043 |
| goldsmith | 2 | 0.043 |
| lucien | 2 | 0.043 |
| parker | 2 | 0.043 |
| oliver | 2 | 0.043 |
| lawrence | 2 | 0.043 |
| mark | 2 | 0.043 |
| edmond | 2 | 0.043 |
| m | 2 | 0.043 |
| taylor | 2 | 0.043 |
| mahon | 2 | 0.043 |
| lalio | 2 | 0.043 |
| rosa | 2 | 0.043 |
| laura | 2 | 0.043 |
| hugo | 2 | 0.043 |
| emanuel | 2 | 0.043 |
| lauritz | 2 | 0.043 |
| spencer | 2 | 0.043 |
| thompson | 2 | 0.043 |
| lewis | 2 | 0.043 |
| malkolm | 2 | 0.043 |
| duff | 2 | 0.043 |
| gerios | 2 | 0.043 |
| warren | 2 | 0.043 |
| bowen | 2 | 0.043 |
| needs | 2 | 0.043 |
| hunt | 2 | 0.043 |
| aaron | 2 | 0.043 |
| webber | 2 | 0.043 |
| abelseth | 2 | 0.043 |
| roger | 2 | 0.043 |
| arne | 2 | 0.043 |
| campbell | 2 | 0.043 |
| hart | 2 | 0.043 |
| tobin | 2 | 0.043 |
| kink | 2 | 0.043 |
| mathias | 2 | 0.043 |
| arnold | 2 | 0.043 |
| lane | 2 | 0.043 |
| anthony | 2 | 0.043 |
| bloomfield | 2 | 0.043 |
| buckley | 2 | 0.043 |
| ms | 2 | 0.043 |
| phillips | 2 | 0.043 |
| vande | 2 | 0.043 |
| gretchen | 2 | 0.043 |
| braund | 2 | 0.043 |
| marija | 2 | 0.043 |
| long | 2 | 0.043 |
| eva | 2 | 0.043 |
| andreas | 2 | 0.043 |
| sara | 2 | 0.043 |
| eileen | 2 | 0.043 |
| dennis | 2 | 0.043 |
| miller | 2 | 0.043 |
| gerda | 2 | 0.043 |
| andersen | 2 | 0.043 |
| connolly | 2 | 0.043 |
| coleff | 2 | 0.043 |
| gerious | 2 | 0.043 |
| hagland | 2 | 0.043 |
| franz | 2 | 0.043 |
| yousseff | 2 | 0.043 |
| sarah | 2 | 0.043 |
| hoyt | 2 | 0.043 |
| wiklund | 2 | 0.043 |
| fischer | 2 | 0.043 |
| assaf | 2 | 0.043 |
| godfrey | 2 | 0.043 |
| marshall | 2 | 0.043 |
| joel | 2 | 0.043 |
| burns | 2 | 0.043 |
| saad | 2 | 0.043 |
| planke | 2 | 0.043 |
| einar | 2 | 0.043 |
| harald | 2 | 0.043 |
| duran | 2 | 0.043 |
| pokrnic | 2 | 0.043 |
| crosby | 2 | 0.043 |
| gifford | 2 | 0.043 |
| jeannie | 2 | 0.043 |
| attalah | 2 | 0.043 |
| mlle | 2 | 0.043 |
| kristina | 2 | 0.043 |
| theodor | 2 | 0.043 |
| winnie | 2 | 0.043 |
| sarkis | 2 | 0.043 |
| louis | 2 | 0.043 |
| hubert | 2 | 0.043 |
| jenny | 2 | 0.043 |
| thayer | 2 | 0.043 |
| daisy | 2 | 0.043 |
| bonnell | 2 | 0.043 |
| hilda | 2 | 0.043 |
| clarence | 2 | 0.043 |
| stanton | 2 | 0.043 |
| lucy | 2 | 0.043 |
| carrie | 2 | 0.043 |
| carter | 2 | 0.043 |
| mcgowan | 2 | 0.043 |
| ryan | 2 | 0.043 |
| jacques | 2 | 0.043 |
| monypeny | 2 | 0.043 |
| more | 2 | 0.043 |
| jonsson | 2 | 0.043 |
| lindqvist | 2 | 0.043 |
| klasen | 2 | 0.043 |
| clarke | 2 | 0.043 |
| vivian | 2 | 0.043 |
| minko | 2 | 0.043 |
| luka | 2 | 0.043 |
| thornton | 2 | 0.043 |
| franklin | 2 | 0.043 |
| maurice | 2 | 0.043 |
| vilhelm | 2 | 0.043 |
| oleary | 2 | 0.043 |
| nikolai | 2 | 0.043 |
| phyllis | 2 | 0.043 |
| kiernan | 2 | 0.043 |
| hedwig | 2 | 0.043 |
| jennie | 2 | 0.043 |
| andrews | 2 | 0.043 |
| hjalmar | 2 | 0.043 |
| leslie | 2 | 0.043 |
| peder | 2 | 0.043 |
| eino | 2 | 0.043 |
| rowe | 2 | 0.043 |
| rudolf | 2 | 0.043 |
| hulda | 2 | 0.043 |
| vera | 2 | 0.043 |
| alfons | 2 | 0.043 |
| lamson | 2 | 0.043 |
| herman | 2 | 0.043 |
| amin | 2 | 0.043 |
| natalia | 2 | 0.043 |
| juha | 2 | 0.043 |
| matti | 2 | 0.043 |
| stone | 2 | 0.043 |
| bessie | 2 | 0.043 |
| owen | 2 | 0.043 |
| olaf | 2 | 0.043 |
| nelson | 2 | 0.043 |
| louisa | 2 | 0.043 |
| stuart | 2 | 0.043 |
| winifred | 2 | 0.043 |
| adele | 2 | 0.043 |
| chapman | 2 | 0.043 |
| eugenie | 2 | 0.043 |
| ingeborg | 2 | 0.043 |
| bourke | 2 | 0.043 |
| richards | 2 | 0.043 |
| sigvard | 2 | 0.043 |
| foley | 2 | 0.043 |
| major | 2 | 0.043 |
| moran | 2 | 0.043 |
| razi | 2 | 0.043 |
| alexandra | 2 | 0.043 |
| harbeck | 2 | 0.043 |
| mauritz | 2 | 0.043 |
| matthew | 2 | 0.043 |
| gertrud | 2 | 0.043 |
| charlotta | 2 | 0.043 |
| lilian | 2 | 0.043 |
| berglund | 2 | 0.043 |
| morley | 2 | 0.043 |
| susan | 2 | 0.043 |
| obrien | 2 | 0.043 |
| timothy | 2 | 0.043 |
| cruyssen | 1 | 0.021 |
| goodwin | 1 | 0.021 |
| warburton | 1 | 0.021 |
| kristine | 1 | 0.021 |
| farthing | 1 | 0.021 |
| countess | 1 | 0.021 |
| alhomaki | 1 | 0.021 |
| nelle | 1 | 0.021 |
| leander | 1 | 0.021 |
| swift | 1 | 0.021 |
| urho | 1 | 0.021 |
| bowenur | 1 | 0.021 |
| paust | 1 | 0.021 |
| mohamed | 1 | 0.021 |
| fosdick | 1 | 0.021 |
| farred | 1 | 0.021 |
| renouf | 1 | 0.021 |
| luise | 1 | 0.021 |
| malake | 1 | 0.021 |
| osman | 1 | 0.021 |
| dooley | 1 | 0.021 |
| sutherland | 1 | 0.021 |
| anton | 1 | 0.021 |
| omont | 1 | 0.021 |
| riordan | 1 | 0.021 |
| ragnhild | 1 | 0.021 |
| willey | 1 | 0.021 |
| fannie | 1 | 0.021 |
| kent | 1 | 0.021 |
| lehmann | 1 | 0.021 |
| hosono | 1 | 0.021 |
| niklasson | 1 | 0.021 |
| carver | 1 | 0.021 |
| bazzani | 1 | 0.021 |
| gilnagh | 1 | 0.021 |
| kantor | 1 | 0.021 |
| aime | 1 | 0.021 |
| turcin | 1 | 0.021 |
| maenpaa | 1 | 0.021 |
| mapriededer | 1 | 0.021 |
| kalle | 1 | 0.021 |
| engelhart | 1 | 0.021 |
| susanna | 1 | 0.021 |
| buss | 1 | 0.021 |
| beard | 1 | 0.021 |
| dibden | 1 | 0.021 |
| sivic | 1 | 0.021 |
| karvin | 1 | 0.021 |
| karoliina | 1 | 0.021 |
| andreasson | 1 | 0.021 |
| markun | 1 | 0.021 |
| danbom | 1 | 0.021 |
| philippe | 1 | 0.021 |
| torborg | 1 | 0.021 |
| ennis | 1 | 0.021 |
| paula | 1 | 0.021 |
| seman | 1 | 0.021 |
| silverthorne | 1 | 0.021 |
| markoff | 1 | 0.021 |
| mitto | 1 | 0.021 |
| pears | 1 | 0.021 |
| shorney | 1 | 0.021 |
| stevenson | 1 | 0.021 |
| milton | 1 | 0.021 |
| gavey | 1 | 0.021 |
| jorgensen | 1 | 0.021 |
| malachard | 1 | 0.021 |
| cassem | 1 | 0.021 |
| mcdowell | 1 | 0.021 |
| moutal | 1 | 0.021 |
| goldenberg | 1 | 0.021 |
| clemmer | 1 | 0.021 |
| carmichael | 1 | 0.021 |
| ivar | 1 | 0.021 |
| edwy | 1 | 0.021 |
| bostandyeff | 1 | 0.021 |
| dakic | 1 | 0.021 |
| henriksson | 1 | 0.021 |
| ibrahim | 1 | 0.021 |
| selman | 1 | 0.021 |
| livija | 1 | 0.021 |
| francisco | 1 | 0.021 |
| barber | 1 | 0.021 |
| encarnacion | 1 | 0.021 |
| sagesser | 1 | 0.021 |
| albina | 1 | 0.021 |
| aldworth | 1 | 0.021 |
| lines | 1 | 0.021 |
| andre | 1 | 0.021 |
| jarvis | 1 | 0.021 |
| fernand | 1 | 0.021 |
| beatrice | 1 | 0.021 |
| hodges | 1 | 0.021 |
| peel | 1 | 0.021 |
| demetrios | 1 | 0.021 |
| roussel | 1 | 0.021 |
| jovan | 1 | 0.021 |
| ahlin | 1 | 0.021 |
| mulvihill | 1 | 0.021 |
| fernandeo | 1 | 0.021 |
| icard | 1 | 0.021 |
| gallagher | 1 | 0.021 |
| newell | 1 | 0.021 |
| jalsevac | 1 | 0.021 |
| jerwan | 1 | 0.021 |
| tenglin | 1 | 0.021 |
| svend | 1 | 0.021 |
| gunnar | 1 | 0.021 |
| mccoy | 1 | 0.021 |
| vendel | 1 | 0.021 |
| linhart | 1 | 0.021 |
| wizosky | 1 | 0.021 |
| joshua | 1 | 0.021 |
| israel | 1 | 0.021 |
| assi | 1 | 0.021 |
| badt | 1 | 0.021 |
| mamee | 1 | 0.021 |
| oconnell | 1 | 0.021 |
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| dulles | 1 | 0.021 |
| frolicherstehli | 1 | 0.021 |
| hamalainen | 1 | 0.021 |
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| fridtjof | 1 | 0.021 |
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| raffull | 1 | 0.021 |
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| adolphe | 1 | 0.021 |
| mirium | 1 | 0.021 |
| s | 1 | 0.021 |
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| louch | 1 | 0.021 |
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| nassr | 1 | 0.021 |
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| abbott | 1 | 0.021 |
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| ellie | 1 | 0.021 |
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| laury | 1 | 0.021 |
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| rossmore | 1 | 0.021 |
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| carlo | 1 | 0.021 |
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| mangiavacchi | 1 | 0.021 |
| nicholas | 1 | 0.021 |
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| of | 1 | 0.021 |
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| jan | 1 | 0.021 |
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| haven | 1 | 0.021 |
| chamberlain | 1 | 0.021 |
| slayter | 1 | 0.021 |
| wilhelms | 1 | 0.021 |
| hedman | 1 | 0.021 |
| manca | 1 | 0.021 |
| lurette | 1 | 0.021 |
| manley | 1 | 0.021 |
| pallas | 1 | 0.021 |
| algot | 1 | 0.021 |
| joackim | 1 | 0.021 |
| fatima | 1 | 0.021 |
| ingvar | 1 | 0.021 |
| odonoghue | 1 | 0.021 |
| endres | 1 | 0.021 |
| margaritha | 1 | 0.021 |
| ella | 1 | 0.021 |
| widener | 1 | 0.021 |
| mccrie | 1 | 0.021 |
| mcmahon | 1 | 0.021 |
| kiamie | 1 | 0.021 |
| telma | 1 | 0.021 |
| west | 1 | 0.021 |
| connors | 1 | 0.021 |
| sutton | 1 | 0.021 |
| dagmar | 1 | 0.021 |
| asuncion | 1 | 0.021 |
| pengelly | 1 | 0.021 |
| abrahim | 1 | 0.021 |
| drapkin | 1 | 0.021 |
| cater | 1 | 0.021 |
| dale | 1 | 0.021 |
| leontine | 1 | 0.021 |
| drake | 1 | 0.021 |
| roebling | 1 | 0.021 |
| nirva | 1 | 0.021 |
| rekic | 1 | 0.021 |
| flora | 1 | 0.021 |
| leitch | 1 | 0.021 |
| nourney | 1 | 0.021 |
| grga | 1 | 0.021 |
| cann | 1 | 0.021 |
| juul | 1 | 0.021 |
| sirayanian | 1 | 0.021 |
| clark | 1 | 0.021 |
| mcdougald | 1 | 0.021 |
| satode | 1 | 0.021 |
| mae | 1 | 0.021 |
| myles | 1 | 0.021 |
| hayden | 1 | 0.021 |
| alfrida | 1 | 0.021 |
| meier | 1 | 0.021 |
| sundman | 1 | 0.021 |
| bruce | 1 | 0.021 |
| wearne | 1 | 0.021 |
| rothes | 1 | 0.021 |
| cummins | 1 | 0.021 |
| marian | 1 | 0.021 |
| maybelle | 1 | 0.021 |
| v | 1 | 0.021 |
| sweet | 1 | 0.021 |
| jardin | 1 | 0.021 |
| kennedy | 1 | 0.021 |
| georges | 1 | 0.021 |
| carlcharles | 1 | 0.021 |
| christopher | 1 | 0.021 |
| coxon | 1 | 0.021 |
| krekorian | 1 | 0.021 |
| fahim | 1 | 0.021 |
| wiseman | 1 | 0.021 |
| head | 1 | 0.021 |
Passengers’ titles might come in handy. We can extract these from their names.
Let’s move on to the cabins.
model = TfidfVectorizer()
model.fit(titanic, "ticket", x="cabin")
model.transform(titanic, "ticket", x="cabin")["word"].topk()
| count | percent | |
|---|---|---|
| f | 8 | 3.433 |
| d | 4 | 1.717 |
| f33 | 4 | 1.717 |
| e101 | 3 | 1.288 |
| e24 | 2 | 0.858 |
| c78 | 2 | 0.858 |
| d17 | 2 | 0.858 |
| e46 | 2 | 0.858 |
| b51 | 2 | 0.858 |
| g6 | 2 | 0.858 |
| b55 | 2 | 0.858 |
| c106 | 2 | 0.858 |
| g73 | 2 | 0.858 |
| e25 | 2 | 0.858 |
| c52 | 2 | 0.858 |
| g63 | 2 | 0.858 |
| b53 | 2 | 0.858 |
| c101 | 2 | 0.858 |
| f2 | 2 | 0.858 |
| c124 | 2 | 0.858 |
| c95 | 1 | 0.429 |
| b102 | 1 | 0.429 |
| e68 | 1 | 0.429 |
| e45 | 1 | 0.429 |
| e57 | 1 | 0.429 |
| b30 | 1 | 0.429 |
| a18 | 1 | 0.429 |
| c148 | 1 | 0.429 |
| b22 | 1 | 0.429 |
| d40 | 1 | 0.429 |
| a24 | 1 | 0.429 |
| e44 | 1 | 0.429 |
| b19 | 1 | 0.429 |
| b45 | 1 | 0.429 |
| c128 | 1 | 0.429 |
| e8 | 1 | 0.429 |
| d26 | 1 | 0.429 |
| c30 | 1 | 0.429 |
| d50 | 1 | 0.429 |
| t | 1 | 0.429 |
| d12 | 1 | 0.429 |
| d38 | 1 | 0.429 |
| d30 | 1 | 0.429 |
| d46 | 1 | 0.429 |
| c68 | 1 | 0.429 |
| d43 | 1 | 0.429 |
| e12 | 1 | 0.429 |
| b94 | 1 | 0.429 |
| c32 | 1 | 0.429 |
| d48 | 1 | 0.429 |
| b56 | 1 | 0.429 |
| c45 | 1 | 0.429 |
| c87 | 1 | 0.429 |
| a26 | 1 | 0.429 |
| c125 | 1 | 0.429 |
| b98 | 1 | 0.429 |
| b24 | 1 | 0.429 |
| c85 | 1 | 0.429 |
| c116 | 1 | 0.429 |
| b39 | 1 | 0.429 |
| a32 | 1 | 0.429 |
| e60 | 1 | 0.429 |
| d37 | 1 | 0.429 |
| d6 | 1 | 0.429 |
| e50 | 1 | 0.429 |
| c46 | 1 | 0.429 |
| c104 | 1 | 0.429 |
| b18 | 1 | 0.429 |
| b50 | 1 | 0.429 |
| c62 | 1 | 0.429 |
| b49 | 1 | 0.429 |
| d34 | 1 | 0.429 |
| b79 | 1 | 0.429 |
| c53 | 1 | 0.429 |
| e52 | 1 | 0.429 |
| c83 | 1 | 0.429 |
| b84 | 1 | 0.429 |
| b35 | 1 | 0.429 |
| b57 | 1 | 0.429 |
| b101 | 1 | 0.429 |
| a31 | 1 | 0.429 |
| c2 | 1 | 0.429 |
| c57 | 1 | 0.429 |
| c90 | 1 | 0.429 |
| b10 | 1 | 0.429 |
| c86 | 1 | 0.429 |
| e67 | 1 | 0.429 |
| b11 | 1 | 0.429 |
| e49 | 1 | 0.429 |
| a14 | 1 | 0.429 |
| b80 | 1 | 0.429 |
| c118 | 1 | 0.429 |
| e63 | 1 | 0.429 |
| c93 | 1 | 0.429 |
| c110 | 1 | 0.429 |
| f4 | 1 | 0.429 |
| c82 | 1 | 0.429 |
| e36 | 1 | 0.429 |
| e17 | 1 | 0.429 |
| e33 | 1 | 0.429 |
| c31 | 1 | 0.429 |
| a19 | 1 | 0.429 |
| e58 | 1 | 0.429 |
| e69 | 1 | 0.429 |
| d20 | 1 | 0.429 |
| d36 | 1 | 0.429 |
| b38 | 1 | 0.429 |
| b77 | 1 | 0.429 |
| a23 | 1 | 0.429 |
| c49 | 1 | 0.429 |
| d22 | 1 | 0.429 |
| b26 | 1 | 0.429 |
| b4 | 1 | 0.429 |
| c27 | 1 | 0.429 |
| a5 | 1 | 0.429 |
| a21 | 1 | 0.429 |
| b54 | 1 | 0.429 |
| d56 | 1 | 0.429 |
| b63 | 1 | 0.429 |
| c7 | 1 | 0.429 |
| e41 | 1 | 0.429 |
| c47 | 1 | 0.429 |
| b71 | 1 | 0.429 |
| b96 | 1 | 0.429 |
| d45 | 1 | 0.429 |
| c91 | 1 | 0.429 |
| e10 | 1 | 0.429 |
| d21 | 1 | 0.429 |
| c99 | 1 | 0.429 |
| a10 | 1 | 0.429 |
| d9 | 1 | 0.429 |
| a7 | 1 | 0.429 |
| c105 | 1 | 0.429 |
| c97 | 1 | 0.429 |
| c111 | 1 | 0.429 |
| e31 | 1 | 0.429 |
| a20 | 1 | 0.429 |
| c92 | 1 | 0.429 |
| b20 | 1 | 0.429 |
| c80 | 1 | 0.429 |
| c50 | 1 | 0.429 |
| c51 | 1 | 0.429 |
| c70 | 1 | 0.429 |
| e38 | 1 | 0.429 |
| b5 | 1 | 0.429 |
| a34 | 1 | 0.429 |
| b60 | 1 | 0.429 |
| c54 | 1 | 0.429 |
| d15 | 1 | 0.429 |
| b69 | 1 | 0.429 |
| d7 | 1 | 0.429 |
| a16 | 1 | 0.429 |
| c25 | 1 | 0.429 |
| b66 | 1 | 0.429 |
| f38 | 1 | 0.429 |
| c23 | 1 | 0.429 |
| c126 | 1 | 0.429 |
| d19 | 1 | 0.429 |
| b28 | 1 | 0.429 |
| c28 | 1 | 0.429 |
| a11 | 1 | 0.429 |
| a29 | 1 | 0.429 |
| a6 | 1 | 0.429 |
| d33 | 1 | 0.429 |
| b86 | 1 | 0.429 |
| c132 | 1 | 0.429 |
| c130 | 1 | 0.429 |
| c26 | 1 | 0.429 |
| d11 | 1 | 0.429 |
| b36 | 1 | 0.429 |
| c65 | 1 | 0.429 |
| d10 | 1 | 0.429 |
| e121 | 1 | 0.429 |
| b73 | 1 | 0.429 |
| e77 | 1 | 0.429 |
| c123 | 1 | 0.429 |
| b42 | 1 | 0.429 |
| b78 | 1 | 0.429 |
| c39 | 1 | 0.429 |
| c22 | 1 | 0.429 |
| b82 | 1 | 0.429 |
| b52 | 1 | 0.429 |
| c55 | 1 | 0.429 |
| b3 | 1 | 0.429 |
| a9 | 1 | 0.429 |
| b41 | 1 | 0.429 |
| b61 | 1 | 0.429 |
| e34 | 1 | 0.429 |
| e40 | 1 | 0.429 |
| e39 | 1 | 0.429 |
| c89 | 1 | 0.429 |
| d49 | 1 | 0.429 |
| d28 | 1 | 0.429 |
| c64 | 1 | 0.429 |
| d47 | 1 | 0.429 |
| c6 | 1 | 0.429 |
| b59 | 1 | 0.429 |
| c103 | 1 | 0.429 |
| b58 | 1 | 0.429 |
| b37 | 1 | 0.429 |
| a36 | 1 | 0.429 |
| d35 | 1 | 0.429 |
Here, we have the cabin IDs, the letter of which represents a certain position on the boat. Let’s see how often each cabin occurs in the dataset.
model = TfidfVectorizer()
model.fit(titanic, "ticket", x="cabin")
model.transform(titanic, "ticket", x="cabin")["word"].str_slice(1, 2).groupby(
columns = ["word"], expr = ["SUM(tf) AS occurences"]
).sort({"occurences": "desc"})
Abc wordVarchar(495) | 123 occurencesDouble | |
|---|---|---|
| 1 | c | 114.0 |
| 2 | b | 96.0 |
| 3 | d | 48.0 |
| 4 | e | 45.0 |
| 5 | a | 22.0 |
| 6 | f | 21.0 |
| 7 | g | 9.0 |
| 8 | t | 1.0 |
While NULL values for “boat” clearly represent passengers who have a dedicated “lifeboat”, we can’t be so sure about NULL values for “cabin”. We can guess that these might represent passengers without a cabin. If this is the case, then these are missing values not at random (MNAR).
We’ll revisit this problem later. For now, let’s drop the columns that don’t affect survival and then encode the rest.
titanic.drop(["body", "home.dest", "embarked", "ticket"])
titanic["cabin"].str_slice(1, 2)["name"].str_extract(
' ([A-Za-z]+)\\.')["boat"].fillna(
method = "0ifnull"
)["cabin"].fillna("No Cabin")["name"].one_hot_encode()
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | Abc sexVarchar(20) | 123 ageDouble | 123 sibspInteger | 123 parchInteger | 123 fareDouble | Abc cabinVarchar(30) | 123 boatBool | 123 name__Capt.Bool | 123 name__Col.Bool | 123 name__Countess.Bool | 123 name__Don.Bool | 123 name__Dona.Bool | 123 name__Dr.Bool | 123 name__Jonkheer.Bool | 123 name__Lady.Bool | 123 name__Major.Bool | 123 name__Master.Bool | 123 name__Miss.Bool | 123 name__Mlle.Bool | 123 name__Mme.Bool | 123 name__Mr.Bool | 123 name__Mrs.Bool | 123 name__Ms.Bool | 123 name__Rev.Bool | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | Miss. | female | 29.0 | 0 | 0 | 211.3375 | B | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1 | 1 | Master. | male | 0.92 | 1 | 2 | 151.55 | C | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1 | 0 | Miss. | female | 2.0 | 1 | 2 | 151.55 | C | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 0 | Mr. | male | 30.0 | 1 | 2 | 151.55 | C | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5 | 1 | 0 | Mrs. | female | 25.0 | 1 | 2 | 151.55 | C | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6 | 1 | 1 | Mr. | male | 48.0 | 0 | 0 | 26.55 | E | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 7 | 1 | 1 | Miss. | female | 63.0 | 1 | 0 | 77.9583 | D | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 1 | 0 | Mr. | male | 39.0 | 0 | 0 | 0.0 | A | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 9 | 1 | 1 | Mrs. | female | 53.0 | 2 | 0 | 51.4792 | C | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 10 | 1 | 0 | Mr. | male | 71.0 | 0 | 0 | 49.5042 | No Cabin | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 11 | 1 | 0 | Col. | male | 47.0 | 1 | 0 | 227.525 | C | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 1 | 1 | Mrs. | female | 18.0 | 1 | 0 | 227.525 | C | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 13 | 1 | 1 | Mme. | female | 24.0 | 0 | 0 | 69.3 | B | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 14 | 1 | 1 | Miss. | female | 26.0 | 0 | 0 | 78.85 | No Cabin | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 1 | 1 | Mr. | male | 80.0 | 0 | 0 | 30.0 | A | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16 | 1 | 0 | Mr. | male | [null] | 0 | 0 | 25.925 | No Cabin | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 17 | 1 | 0 | Mr. | male | 24.0 | 0 | 1 | 247.5208 | B | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 18 | 1 | 1 | Mrs. | female | 50.0 | 0 | 1 | 247.5208 | B | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 19 | 1 | 1 | Miss. | female | 32.0 | 0 | 0 | 76.2917 | D | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20 | 1 | 0 | Mr. | male | 36.0 | 0 | 0 | 75.2417 | C | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Looking at our data now, we can see that some first class passengers have a NULL value for their cabin, so we can safely say that our assumption about the meaning of a NULL value of “cabin” turned out to be incorrect. This means that the “cabin” column has far too many missing values at random (MAR). We’ll have to drop it.
titanic["cabin"].drop()
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | Abc sexVarchar(20) | 123 ageDouble | 123 sibspInteger | 123 parchInteger | 123 fareDouble | 123 boatBool | 123 name__Capt.Bool | 123 name__Col.Bool | 123 name__Countess.Bool | 123 name__Don.Bool | 123 name__Dona.Bool | 123 name__Dr.Bool | 123 name__Jonkheer.Bool | 123 name__Lady.Bool | 123 name__Major.Bool | 123 name__Master.Bool | 123 name__Miss.Bool | 123 name__Mlle.Bool | 123 name__Mme.Bool | 123 name__Mr.Bool | 123 name__Mrs.Bool | 123 name__Ms.Bool | 123 name__Rev.Bool | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | Miss. | female | 29.0 | 0 | 0 | 211.3375 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1 | 1 | Master. | male | 0.92 | 1 | 2 | 151.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 3 | 1 | 0 | Miss. | female | 2.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 1 | 0 | Mr. | male | 30.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 5 | 1 | 0 | Mrs. | female | 25.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 6 | 1 | 1 | Mr. | male | 48.0 | 0 | 0 | 26.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 7 | 1 | 1 | Miss. | female | 63.0 | 1 | 0 | 77.9583 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 8 | 1 | 0 | Mr. | male | 39.0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 9 | 1 | 1 | Mrs. | female | 53.0 | 2 | 0 | 51.4792 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 10 | 1 | 0 | Mr. | male | 71.0 | 0 | 0 | 49.5042 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 11 | 1 | 0 | Col. | male | 47.0 | 1 | 0 | 227.525 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 1 | 1 | Mrs. | female | 18.0 | 1 | 0 | 227.525 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 13 | 1 | 1 | Mme. | female | 24.0 | 0 | 0 | 69.3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 14 | 1 | 1 | Miss. | female | 26.0 | 0 | 0 | 78.85 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 1 | 1 | Mr. | male | 80.0 | 0 | 0 | 30.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 16 | 1 | 0 | Mr. | male | [null] | 0 | 0 | 25.925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 17 | 1 | 0 | Mr. | male | 24.0 | 0 | 1 | 247.5208 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 18 | 1 | 1 | Mrs. | female | 50.0 | 0 | 1 | 247.5208 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| 19 | 1 | 1 | Miss. | female | 32.0 | 0 | 0 | 76.2917 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20 | 1 | 0 | Mr. | male | 36.0 | 0 | 0 | 75.2417 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Let’s look at descriptive statistics of the entire VastFrame.
titanic.describe(method = "all")
| "pclass" | "survived" | "age" | "sibsp" | "parch" | "fare" | "boat" | "name__Capt." | "name__Col." | "name__Countess." | "name__Don." | "name__Dona." | "name__Dr." | "name__Jonkheer." | "name__Lady." | "name__Major." | "name__Master." | "name__Miss." | "name__Mlle." | "name__Mme." | "name__Mr." | "name__Mrs." | "name__Ms." | "name__Rev." | "name" | "sex" | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| dtype | integer | integer | double | integer | integer | double | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | bool | varchar(164) | varchar(20) |
| percent | 100 | 100 | 79.908 | 100 | 100 | 99.924 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
| count | 1309 | 1309 | 1046 | 1309 | 1309 | 1308 | 1309.0 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 | 1309 |
| top | 3 | 0 | [null] | 0 | 0 | 8.05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | Mr. | male |
| top_percent | 54.163 | 61.803 | 20.092 | 68.067 | 76.547 | 4.584 | 62.872 | 99.924 | 99.694 | 99.924 | 99.924 | 99.924 | 99.389 | 99.924 | 99.924 | 99.847 | 95.34 | 80.138 | 99.847 | 99.924 | 57.83 | 84.95 | 99.847 | 99.389 | 57.83 | 64.4 |
| avg | 2.294881588999236 | 0.3819709702062643 | 29.881137667304014 | 0.4988540870893812 | 0.3850267379679144 | 33.29547928134565 | 0.3712757830404889 | 0.0007639419404125286 | 0.0030557677616501145 | 0.0007639419404125286 | 0.0007639419404125286 | 0.0007639419404125286 | 0.006111535523300229 | 0.0007639419404125286 | 0.0007639419404125286 | 0.0015278838808250573 | 0.04660045836516425 | 0.19862490450725745 | 0.0015278838808250573 | 0.0007639419404125286 | 0.5783040488922842 | 0.15049656226126815 | 0.0015278838808250573 | 0.006111535523300229 | 4.766997708174179 | 4.711993888464477 |
| stddev | 0.8378360189701272 | 0.4860551708664832 | 14.413493211271327 | 1.0416583905961019 | 0.865560275349515 | 51.758668239174185 | 0.4833306728617509 | 0.027639499641139115 | 0.05521556954333583 | 0.027639499641139122 | 0.027639499641139115 | 0.027639499641139122 | 0.07796684249672724 | 0.027639499641139122 | 0.02763949964113912 | 0.03907321043736397 | 0.21086209393900127 | 0.39911745608577426 | 0.039073210437363975 | 0.027639499641139115 | 0.494019149138988 | 0.3576941285915185 | 0.039073210437363996 | 0.07796684249672722 | 1.0948637037600872 | 0.9579945668826566 |
| min | 1 | 0 | 0.17 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 |
| approx_25% | 2 | 0 | 20.90530267915083 | 0 | 0 | 7.920113391304348 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 |
| approx_50% | 3 | 0 | 28.1764738120901 | 0 | 0 | 14.517231666666666 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 | 4 |
| approx_75% | 3 | 1 | 38.57310924369748 | 1 | 0 | 31.24888996860731 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 6 | 6 |
| max | 3 | 1 | 80.0 | 8 | 9 | 512.3292 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 | 6 |
| range | 2 | 1 | 79.83 | 8 | 9 | 512.3292 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 6 | 2 |
| empty | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | [null] | 0 | 0 |
Descriptive statistics can give us valuable insights into our data. Notice, for example, that the column “fare” has many outliers (The maximum of 512.33 is much greater than the 9th decile of 79.13). Most passengers traveled in 3rd class (median of pclass = 3).
The “sibsp” column represents the number of siblings for each passenger, while the “parch” column represents the number of parents and children. We can use these to create a new feature: “family_size”.
titanic["family_size"] = titanic["parch"] + titanic["sibsp"] + 1
Let’s move on to outliers. We have several tools for locating outliers (LocalOutlierFactor, DBSCAN, KMeans…), but we’ll just use winsorization in this example. Again, “fare” has many outliers, so we’ll start there.
titanic["fare"].fill_outliers(
method = "winsorize",
alpha = 0.03,
)
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | Abc sexVarchar(20) | 123 ageDouble | 123 sibspInteger | 123 parchInteger | 123 fareDouble | 123 boatBool | 123 name__Capt.Bool | 123 name__Col.Bool | 123 name__Countess.Bool | 123 name__Don.Bool | 123 name__Dona.Bool | 123 name__Dr.Bool | 123 name__Jonkheer.Bool | 123 name__Lady.Bool | 123 name__Major.Bool | 123 name__Master.Bool | 123 name__Miss.Bool | 123 name__Mlle.Bool | 123 name__Mme.Bool | 123 name__Mr.Bool | 123 name__Mrs.Bool | 123 name__Ms.Bool | 123 name__Rev.Bool | 123 family_sizeInteger | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | Miss. | female | 29.0 | 0 | 0 | 211.3375 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 1 | 1 | Master. | male | 0.92 | 1 | 2 | 151.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| 3 | 1 | 0 | Miss. | female | 2.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| 4 | 1 | 0 | Mr. | male | 30.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
| 5 | 1 | 0 | Mrs. | female | 25.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
| 6 | 1 | 1 | Mr. | male | 48.0 | 0 | 0 | 26.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 7 | 1 | 1 | Miss. | female | 63.0 | 1 | 0 | 77.9583 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 8 | 1 | 0 | Mr. | male | 39.0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 9 | 1 | 1 | Mrs. | female | 53.0 | 2 | 0 | 51.4792 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
| 10 | 1 | 0 | Mr. | male | 71.0 | 0 | 0 | 49.5042 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 11 | 1 | 0 | Col. | male | 47.0 | 1 | 0 | 227.525 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 12 | 1 | 1 | Mrs. | female | 18.0 | 1 | 0 | 227.525 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 13 | 1 | 1 | Mme. | female | 24.0 | 0 | 0 | 69.3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 14 | 1 | 1 | Miss. | female | 26.0 | 0 | 0 | 78.85 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 15 | 1 | 1 | Mr. | male | 80.0 | 0 | 0 | 30.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 16 | 1 | 0 | Mr. | male | [null] | 0 | 0 | 25.925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 17 | 1 | 0 | Mr. | male | 24.0 | 0 | 1 | 240.3301522380424 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 |
| 18 | 1 | 1 | Mrs. | female | 50.0 | 0 | 1 | 240.3301522380424 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 19 | 1 | 1 | Miss. | female | 32.0 | 0 | 0 | 76.2917 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 20 | 1 | 0 | Mr. | male | 36.0 | 0 | 0 | 75.2417 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
Let’s encode the column sex so we can use it with numerical methods.
titanic["sex"].label_encode()
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | 123 sexInteger | 123 ageDouble | 123 sibspInteger | 123 parchInteger | 123 fareDouble | 123 boatBool | 123 name__Capt.Bool | 123 name__Col.Bool | 123 name__Countess.Bool | 123 name__Don.Bool | 123 name__Dona.Bool | 123 name__Dr.Bool | 123 name__Jonkheer.Bool | 123 name__Lady.Bool | 123 name__Major.Bool | 123 name__Master.Bool | 123 name__Miss.Bool | 123 name__Mlle.Bool | 123 name__Mme.Bool | 123 name__Mr.Bool | 123 name__Mrs.Bool | 123 name__Ms.Bool | 123 name__Rev.Bool | 123 family_sizeInteger | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | Miss. | 0 | 29.0 | 0 | 0 | 211.3375 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 1 | 1 | Master. | 1 | 0.92 | 1 | 2 | 151.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| 3 | 1 | 0 | Miss. | 0 | 2.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| 4 | 1 | 0 | Mr. | 1 | 30.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 4 |
| 5 | 1 | 0 | Mrs. | 0 | 25.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
| 6 | 1 | 1 | Mr. | 1 | 48.0 | 0 | 0 | 26.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 7 | 1 | 1 | Miss. | 0 | 63.0 | 1 | 0 | 77.9583 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 8 | 1 | 0 | Mr. | 1 | 39.0 | 0 | 0 | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 9 | 1 | 1 | Mrs. | 0 | 53.0 | 2 | 0 | 51.4792 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
| 10 | 1 | 0 | Mr. | 1 | 71.0 | 0 | 0 | 49.5042 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 11 | 1 | 0 | Col. | 1 | 47.0 | 1 | 0 | 227.525 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 12 | 1 | 1 | Mrs. | 0 | 18.0 | 1 | 0 | 227.525 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 13 | 1 | 1 | Mme. | 0 | 24.0 | 0 | 0 | 69.3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 14 | 1 | 1 | Miss. | 0 | 26.0 | 0 | 0 | 78.85 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 15 | 1 | 1 | Mr. | 1 | 80.0 | 0 | 0 | 30.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 16 | 1 | 0 | Mr. | 1 | [null] | 0 | 0 | 25.925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 17 | 1 | 0 | Mr. | 1 | 24.0 | 0 | 1 | 240.3301522380424 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 |
| 18 | 1 | 1 | Mrs. | 0 | 50.0 | 0 | 1 | 240.3301522380424 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 19 | 1 | 1 | Miss. | 0 | 32.0 | 0 | 0 | 76.2917 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 20 | 1 | 0 | Mr. | 1 | 36.0 | 0 | 0 | 75.2417 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
The column age has too many missing values and since most machine learning algorithms can’t handle missing values, we need to impute our data. Let’s fill the missing values using the average age of the passengers that have the same “pclass” and “sex”.
titanic["age"].fillna(method = "mean", by = ["pclass", "sex"])
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | 123 sexInteger | 123 ageReal | 123 sibspInteger | 123 parchInteger | 123 fareDouble | 123 boatBool | 123 name__Capt.Bool | 123 name__Col.Bool | 123 name__Countess.Bool | 123 name__Don.Bool | 123 name__Dona.Bool | 123 name__Dr.Bool | 123 name__Jonkheer.Bool | 123 name__Lady.Bool | 123 name__Major.Bool | 123 name__Master.Bool | 123 name__Miss.Bool | 123 name__Mlle.Bool | 123 name__Mme.Bool | 123 name__Mr.Bool | 123 name__Mrs.Bool | 123 name__Ms.Bool | 123 name__Rev.Bool | 123 family_sizeInteger | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | Miss. | 0 | 29.0 | 0 | 0 | 211.3375 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 1 | 0 | Miss. | 0 | 2.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| 3 | 1 | 0 | Mrs. | 0 | 25.0 | 1 | 2 | 151.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
| 4 | 1 | 1 | Miss. | 0 | 63.0 | 1 | 0 | 77.9583 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 5 | 1 | 1 | Mrs. | 0 | 53.0 | 2 | 0 | 51.4792 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
| 6 | 1 | 1 | Mrs. | 0 | 18.0 | 1 | 0 | 227.525 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 7 | 1 | 1 | Mme. | 0 | 24.0 | 0 | 0 | 69.3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 8 | 1 | 1 | Miss. | 0 | 26.0 | 0 | 0 | 78.85 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 9 | 1 | 1 | Mrs. | 0 | 50.0 | 0 | 1 | 240.3301522380424 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 10 | 1 | 1 | Miss. | 0 | 32.0 | 0 | 0 | 76.2917 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 11 | 1 | 1 | Mrs. | 0 | 47.0 | 1 | 1 | 52.5542 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 |
| 12 | 1 | 1 | Miss. | 0 | 42.0 | 0 | 0 | 227.525 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 13 | 1 | 1 | Miss. | 0 | 29.0 | 0 | 0 | 221.7792 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 14 | 1 | 1 | Mrs. | 0 | 19.0 | 1 | 0 | 91.0792 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 15 | 1 | 1 | Miss. | 0 | 35.0 | 0 | 0 | 135.6333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 16 | 1 | 1 | Miss. | 0 | 30.0 | 0 | 0 | 164.8667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 17 | 1 | 1 | Miss. | 0 | 58.0 | 0 | 0 | 26.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 18 | 1 | 1 | Miss. | 0 | 45.0 | 0 | 0 | 240.3301522380424 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 19 | 1 | 1 | Miss. | 0 | 22.0 | 0 | 1 | 55.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 20 | 1 | 1 | Mrs. | 0 | 44.0 | 0 | 0 | 27.7208 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
Let’s draw the correlation matrix to see the links between variables.
titanic.corr(method = "spearman")
Fare correlates strongly with family size. This is about what you would expect: a larger family means more tickets, and more tickets means a greater fare.
Survival correlates strongly with whether or not a passenger has a lifeboat (the boat variable). Still, to increase the generality of our model, we should avoid predictions based on just one variable. Let’s split the study into two use cases:
Passengers with a lifeboat
Passengers without a lifeboat
Before we move on: we did a lot of work to clean up this data, but we haven’t saved anything to our VAST DataBase! Let’s look at the modifications we’ve made to the VastFrame.
print(titanic.current_relation())
Let see what’s happening when we aggregate and turn on SQL generation.
vo.set_option("sql_on", True)
titanic_avg = titanic.avg()
vastorbit dynamically generates SQL code whenever you make modifications to your data. To avoid recomputation, it also stores previous aggregations. If we filter anything in our data, it will update the catalog with our modifications.
vo.set_option("sql_on", False)
print(titanic.info())
Let’s move on to modeling our data. Save the VastFrame to your VAST DataBase.
from vastorbit.sql import drop
# Titanic Boat
drop("titanic_boat", method = "view")
titanic_boat = titanic.search(titanic["boat"] == 1).to_db("titanic_boat", relation_type = "view", inplace = True)
# Titanic No Boat
drop("titanic_no_boat", method = "view")
titanic_no_boat = titanic.search(titanic["boat"] == 0).to_db("titanic_no_boat", relation_type = "view", inplace = True)
Machine Learning¶
Passengers with a lifeboat¶
First, let’s look at the number of survivors.
titanic_boat["survived"].describe()
| value | |
|---|---|
| name | "survived" |
| dtype | integer |
| unique | 2.0 |
| count | 486.0 |
| 1 | 477 |
| 0 | 9 |
We have nine deaths. Let’s try to understand why these passengers died.
titanic_boat.search(titanic_boat["survived"] == 0).head(10)
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | 123 sexInteger | 123 ageDouble | 123 sibspInteger | 123 parchInteger | 123 fareDouble | 123 boatInteger | 123 name__Capt.Integer | 123 name__Col.Integer | 123 name__Countess.Integer | 123 name__Don.Integer | 123 name__Dona.Integer | 123 name__Dr.Integer | 123 name__Jonkheer.Integer | 123 name__Lady.Integer | 123 name__Major.Integer | 123 name__Master.Integer | 123 name__Miss.Integer | 123 name__Mlle.Integer | 123 name__Mme.Integer | 123 name__Mr.Integer | 123 name__Mrs.Integer | 123 name__Ms.Integer | 123 name__Rev.Integer | 123 family_sizeInteger | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3 | 0 | Mr. | 1 | 27.0 | 1 | 0 | 14.4542 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 |
| 2 | 3 | 0 | Mr. | 1 | 32.0 | 1 | 0 | 15.85 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 |
| 3 | 3 | 0 | Mr. | 1 | 25.0 | 0 | 0 | 7.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 4 | 3 | 0 | Mr. | 1 | 25.962263610315187 | 0 | 0 | 7.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 5 | 3 | 0 | Mr. | 1 | 36.0 | 1 | 0 | 15.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 |
| 6 | 1 | 0 | Mr. | 1 | 36.0 | 0 | 0 | 75.2417 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 7 | 1 | 0 | Mr. | 1 | 41.02927152317881 | 0 | 0 | 30.6958 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 8 | 3 | 0 | Mrs. | 0 | 30.0 | 1 | 0 | 15.55 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 9 | 2 | 0 | Mr. | 1 | 34.0 | 1 | 0 | 21.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 |
Apart from a decent amount of these passengers being third-class passengers, it doesn’t seem like there are any clear predictors here for their deaths. Making a model from this would be unhelpful.
Passengers without a lifeboat¶
Let’s move on to passengers without a lifeboat.
titanic_no_boat["survived"].describe()
| value | |
|---|---|
| name | "survived" |
| dtype | integer |
| unique | 2.0 |
| count | 823.0 |
| 0 | 800 |
| 1 | 23 |
Only 23 survived. Let’s find out why.
titanic_no_boat.search(titanic_boat["survived"] == 1).head(20)
123 pclassInteger | 123 survivedInteger | Abc nameVarchar(164) | 123 sexInteger | 123 ageDouble | 123 sibspInteger | 123 parchInteger | 123 fareDouble | 123 boatInteger | 123 name__Capt.Integer | 123 name__Col.Integer | 123 name__Countess.Integer | 123 name__Don.Integer | 123 name__Dona.Integer | 123 name__Dr.Integer | 123 name__Jonkheer.Integer | 123 name__Lady.Integer | 123 name__Major.Integer | 123 name__Master.Integer | 123 name__Miss.Integer | 123 name__Mlle.Integer | 123 name__Mme.Integer | 123 name__Mr.Integer | 123 name__Mrs.Integer | 123 name__Ms.Integer | 123 name__Rev.Integer | 123 family_sizeInteger | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 3 | 1 | Miss. | 0 | 15.0 | 0 | 0 | 8.0292 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 3 | 1 | Mrs. | 0 | 22.185328947368422 | 0 | 0 | 7.2292 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 3 | 3 | 1 | Mrs. | 0 | 22.185328947368422 | 1 | 0 | 15.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 4 | 3 | 1 | Miss. | 0 | 22.185328947368422 | 0 | 0 | 7.8792 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 3 | 1 | Mrs. | 0 | 31.0 | 0 | 0 | 8.6833 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 6 | 3 | 1 | Miss. | 0 | 22.185328947368422 | 0 | 0 | 7.7792 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 7 | 3 | 1 | Mrs. | 0 | 47.0 | 1 | 0 | 7.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 8 | 3 | 1 | Mrs. | 0 | 15.0 | 1 | 0 | 14.4542 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 9 | 3 | 1 | Mrs. | 0 | 33.0 | 3 | 0 | 15.85 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
| 10 | 3 | 1 | Miss. | 0 | 23.0 | 0 | 0 | 8.05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 11 | 3 | 1 | Miss. | 0 | 26.0 | 0 | 0 | 7.925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 12 | 3 | 1 | Miss. | 0 | 27.0 | 0 | 0 | 7.925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 13 | 3 | 1 | Mr. | 1 | 25.962263610315187 | 0 | 0 | 7.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 14 | 3 | 1 | Mr. | 1 | 25.962263610315187 | 0 | 0 | 7.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 15 | 2 | 1 | Mrs. | 0 | 42.0 | 0 | 0 | 13.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 |
| 16 | 2 | 1 | Miss. | 0 | 18.0 | 0 | 1 | 23.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
| 17 | 2 | 1 | Mrs. | 0 | 34.0 | 0 | 1 | 23.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 18 | 2 | 1 | Miss. | 0 | 17.0 | 0 | 0 | 10.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 19 | 2 | 1 | Mrs. | 0 | 42.0 | 1 | 0 | 26.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 2 |
| 20 | 1 | 1 | Miss. | 0 | 58.0 | 0 | 0 | 146.5208 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Most survivors seem to be women. Let’s build a model with this in mind.
One of our predictors is categorical: the passenger title. Some of these predictors are corrleated, so it’d be best to work with a non-linear classifier that can handle that. In this case, a random forest classifier seems to be perfect. Let’s evaluate it with a cross-validation.
from vastorbit.machine_learning.vast import RandomForestClassifier
from vastorbit.machine_learning.model_selection import cross_validate
predictors = titanic.get_columns(exclude_columns = ["survived", "name", "fare", "boat"])
response = "survived"
model = RandomForestClassifier(
n_estimators = 7
)
cross_validate(model, titanic_no_boat, predictors, response)
| auc | prc_auc | accuracy | log_loss | precision | recall | f1_score | mcc | informedness | markedness | csi | time | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1-fold | 0.0 | 0.0 | 0.010752688172043012 | 0.01739377571215514 | 0.0 | 0.0 | 0.0 | 0.0 | -0.989247311827957 | 0.0 | 0.0 | 0.31421613693237305 |
| 2-fold | 0.0 | 0.0 | 0.01773049645390071 | 0.04119118627554796 | 0.0 | 0.0 | 0.0 | 0.0 | -0.9822695035460993 | 0.0 | 0.0 | 0.3452432155609131 |
| 3-fold | 0.0 | 0.0 | 0.0035842293906810036 | 0.02809261468379726 | 0.0 | 0.0 | 0.0 | 0.0 | -0.996415770609319 | 0.0 | 0.0 | 0.34694600105285645 |
| avg | 0.0 | 0.0 | 0.010689138005541575 | 0.028892525557166787 | 0.0 | 0.0 | 0.0 | 0.0 | -0.9893108619944585 | 0.0 | 0.0 | 0.33546845118204754 |
| std | 0.0 | 0.0 | 0.005775364168819744 | 0.00973170353140261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.005775364168819717 | 0.0 | 0.0 | 0.015043725498981978 |
This dataset is pretty unbalanced so we’ll use an AUC to evaluate it. Looking at our table, our model has an average AUC of more than 0.8, so our model is quite good.
We can now build a model with the entire dataset.
model.fit(titanic_no_boat, predictors, response)
Let’s look at the importance of each feature.
model.features_importance()
As expected, the passenger’s age and title are the most important predictors of survival.
Conclusion¶
We’ve solved our problem in a pandas-like way, all without ever loading data into memory!