Titanic¶

This example uses the 'titanic' dataset to predict the survival of passengers on the Titanic. You can download the Jupyter Notebook of the study here.

Initialization¶

This example uses the following version of VerticaPy:

In [1]:

import verticapy as vp
vp.__version__

Out[1]:

'0.9.0'

Connect to Vertica. This example uses an existing connection called "VerticaDSN." For details on how to create a connection, use see the connection tutorial.

In [2]:

vp.connect("VerticaDSN")

Use the following command to allow Matplotlib to display graphics.

In [3]:

%matplotlib inline

Let's load the dataset.

In [4]:

from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic)

	123 pclass Int	123 survived Int	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Int	123 parch Int	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Int	Abc home.dest Varchar(100)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	30.0	1	2	113781	151.55	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	39.0	0	0	112050	0.0	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22	Montevideo, Uruguay
6	1	0		male	47.0	1	0	PC 17757	227.525	C62 C64	C	[null]	124	New York, NY
7	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]	New York, NY
8	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]	Montreal, PQ
9	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]	Winnipeg, MN
10	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148	San Francisco, CA
11	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]	Trenton, NJ
12	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]	London / Winnipeg, MB
13	1	0		male	41.0	0	0	113054	30.5	A21	S	[null]	[null]	Pomeroy, WA
14	1	0		male	48.0	0	0	PC 17591	50.4958	B10	C	[null]	208	Omaha, NE
15	1	0		male	[null]	0	0	112379	39.6	[null]	C	[null]	[null]	Philadelphia, PA
16	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]	Washington, DC
17	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]	[null]
18	1	0		male	33.0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]	New York, NY
19	1	0		male	28.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
20	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
21	1	0		male	49.0	0	0	19924	26.0	[null]	S	[null]	[null]	Ascot, Berkshire / Rochester, NY
22	1	0		male	36.0	1	0	19877	78.85	C46	S	[null]	172	Little Onn Hall, Staffs
23	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]	Amenia, ND
24	1	0		male	[null]	0	0	112051	0.0	[null]	S	[null]	[null]	Liverpool, England / Belfast
25	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]	Los Angeles, CA
26	1	0		male	[null]	0	0	110465	52.0	A14	S	[null]	[null]	Stoughton, MA
27	1	0		male	47.0	0	0	5727	25.5875	E58	S	[null]	[null]	Victoria, BC
28	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]	Lakewood, NJ
29	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]	Roachdale, IN
30	1	0		male	70.0	1	1	WE/P 5735	71.0	B22	S	[null]	269	Milwaukee, WI
31	1	0		male	39.0	1	0	PC 17599	71.2833	C85	C	[null]	[null]	New York, NY
32	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]	Montreal, PQ
33	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62	Deephaven, MN / Cedar Rapids, IA
34	1	0		male	39.0	0	0	PC 17580	29.7	A18	C	[null]	133	Philadelphia, PA
35	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]	New York, NY
36	1	0		male	[null]	0	0	PC 17483	221.7792	C95	S	[null]	[null]	[null]
37	1	0		male	30.0	0	0	113051	27.75	C111	C	[null]	[null]	New York, NY
38	1	0		male	19.0	3	2	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
39	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
40	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]	Westcliff-on-Sea, Essex
41	1	0		male	[null]	0	0	112058	0.0	B102	S	[null]	[null]	[null]
42	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]	Scituate, MA
43	1	0		male	47.0	0	0	111320	38.5	E63	S	[null]	275	St Anne's-on-Sea, Lancashire
44	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]	[null]
45	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]	New York, NY
46	1	0		male	38.0	0	1	PC 17582	153.4625	C91	S	[null]	147	Winnipeg, MB
47	1	0		male	46.0	0	0	PC 17593	79.2	B82 B84	C	[null]	[null]	New York, NY
48	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]	[null]
49	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]	New York, NY
50	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110	[null]
51	1	0		male	55.0	1	1	12749	93.5	B69	S	[null]	307	Montreal, PQ
52	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]	London / Middlesex
53	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]	Brighton, MA
54	1	0		male	55.0	0	0	680	50.0	C39	S	[null]	[null]	London / Birmingham
55	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38	New York, NY
56	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]	New York, NY
57	1	0		female	50.0	0	0	PC 17595	28.7125	C49	C	[null]	[null]	Paris, France New York, NY
58	1	0		male	46.0	0	0	694	26.0	[null]	S	[null]	80	Bennington, VT
59	1	0		male	50.0	0	0	113044	26.0	E60	S	[null]	[null]	London
60	1	0		male	32.5	0	0	113503	211.5	C132	C	[null]	45	[null]
61	1	0		male	58.0	0	0	11771	29.7	B37	C	[null]	258	Buffalo, NY
62	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]	Southington / Noank, CT
63	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]	Portland, OR
64	1	0		male	[null]	0	0	PC 17612	27.7208	[null]	C	[null]	[null]	Chicago, IL
65	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126	Springfield, MA
66	1	0		male	30.0	0	0	113801	45.5	[null]	S	[null]	[null]	London / New York, NY
67	1	0		male	30.0	0	0	110469	26.0	C106	S	[null]	[null]	Brockton, MA
68	1	0		male	19.0	1	0	113773	53.1	D30	S	[null]	[null]	New York, NY
69	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292	Vancouver, BC
70	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175	Dorchester, MA
71	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]	New York, NY
72	1	0		male	65.0	0	0	13509	26.55	E38	S	[null]	249	East Bridgewater, MA
73	1	0		male	44.0	2	0	19928	90.0	C78	Q	[null]	230	Fond du Lac, WI
74	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]	Montreal, PQ
75	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]	Washington, DC
76	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]	Brooklyn, NY
77	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122	Lexington, MA
78	1	0		male	64.0	0	0	693	26.0	[null]	S	[null]	263	Isle of Wight, England
79	1	0		male	65.0	0	1	113509	61.9792	B30	C	[null]	234	Providence, RI
80	1	0		male	28.5	0	0	PC 17562	27.7208	D43	C	[null]	189	?Havana, Cuba
81	1	0		male	[null]	0	0	112052	0.0	[null]	S	[null]	[null]	Belfast
82	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166	Surbiton Hill, Surrey
83	1	0		male	23.0	0	0	12749	93.5	B24	S	[null]	[null]	Montreal, PQ
84	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]	Isleworth, England
85	1	0		male	18.0	1	0	PC 17758	108.9	C65	C	[null]	[null]	Madrid, Spain
86	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207	Worcester, MA
87	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]	Rotterdam, Netherlands
88	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232	[null]
89	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]	[null]
90	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]	Trenton, NJ
91	1	0		male	[null]	0	0	113767	50.0	A32	S	[null]	[null]	Seattle, WA
92	1	0		male	36.0	0	0	13049	40.125	A10	C	[null]	[null]	Winnipeg, MB
93	1	0		male	55.0	1	0	PC 17603	59.4	[null]	C	[null]	[null]	New York, NY
94	1	0		male	33.0	0	0	113790	26.55	[null]	S	[null]	109	London
95	1	0		male	61.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	[null]	[null]	Haverford, PA / Cooperstown, NY
96	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]	Duluth, MN
97	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]	New York, NY
98	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]	St James, Long Island, NY
99	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]	Huntington, WV
100	1	0		male	[null]	0	0	113056	26.0	A19	S	[null]	[null]	Streatham, Surrey

Rows: 1-100 | Columns: 14

Data Exploration and Preparation¶

Let's explore the data by displaying descriptive statistics of all the columns.

In [5]:

titanic.describe(method = "categorical", unique = True)

Out[5]:

	dtype	count	top	top_percent	unique
"pclass"	int	1234	3	53.728	3.0
"survived"	int	1234	0	63.533	2.0
"name"	varchar(164)	1234	Kelly, Mr. James	0.162	1232.0
"sex"	varchar(20)	1234	male	65.964	2.0
"age"	numeric(6,3)	997	[null]	19.206	96.0
"sibsp"	int	1234	0	67.747	7.0
"parch"	int	1234	0	76.904	8.0
"ticket"	varchar(36)	1234	CA. 2343	0.81	887.0
"fare"	numeric(10,5)	1233	8.05	4.7	277.0
"cabin"	varchar(30)	286	[null]	76.823	182.0
"embarked"	varchar(20)	1232	S	70.746	3.0
"boat"	varchar(100)	439	[null]	64.425	26.0
"body"	int	118	[null]	90.438	118.0
"home.dest"	varchar(100)	706	[null]	42.788	359.0

Rows: 1-14 | Columns: 6

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.

In [6]:

from verticapy.learn.preprocessing import CountVectorizer
model = CountVectorizer("name_voc")
model.fit(titanic, ["Name"]).transform()

Out[6]:

	Abc token Varchar(128)	123 df Numeric(36,18)	123 cnt Integer	123 rnk Integer
1	mr	0.14816310052482842	734	1
2	miss	0.046023415421881306	228	2
3	mrs	0.03734356075898264	185	3
4	william	0.016148566814695196	80	4
5	john	0.013726281792490917	68	5
6	master	0.011303996770286637	56	6
7	henry	0.008881711748082359	44	7
8	james	0.007468712151796528	37	8
9	charles	0.007468712151796528	37	8
10	thomas	0.006863140896245458	34	10
11	joseph	0.006257569640694388	31	11
12	george	0.006257569640694388	31	11
13	edward	0.005450141299959629	27	13
14	mary	0.005248284214775939	26	14
15	johan	0.004440855874041179	22	15
16	elizabeth	0.004440855874041179	22	15
17	frederick	0.004037141703673799	20	17
18	samuel	0.003835284618490109	19	18
19	robert	0.003633427533306419	18	19
20	alexander	0.003633427533306419	18	19
21	arthur	0.003431570448122729	17	21
22	alfred	0.003431570448122729	17	21
23	richard	0.003229713362939039	16	23
24	maria	0.003229713362939039	16	23
25	anna	0.003229713362939039	16	23
26	karl	0.002825999192571659	14	26
27	jr	0.002825999192571659	14	26
28	albert	0.002825999192571659	14	26
29	peter	0.002624142107387969	13	29
30	j	0.002624142107387969	13	29
31	patrick	0.002422285022204279	12	31
32	frank	0.002422285022204279	12	31
33	ernest	0.002422285022204279	12	31
34	alice	0.002422285022204279	12	31
35	margaret	0.002220427937020589	11	35
36	h	0.002220427937020589	11	35
37	andersson	0.002220427937020589	11	35
38	walter	0.002018570851836899	10	38
39	sage	0.002018570851836899	10	38
40	martin	0.002018570851836899	10	38
41	leonard	0.002018570851836899	10	38
42	edith	0.00181671376665321	9	42
43	e	0.00181671376665321	9	42
44	y	0.00161485668146952	8	44
45	victor	0.00161485668146952	8	44
46	van	0.00161485668146952	8	44
47	rev	0.00161485668146952	8	44
48	harry	0.00161485668146952	8	44
49	goodwin	0.00161485668146952	8	44
50	francis	0.00161485668146952	8	44
51	elias	0.00161485668146952	8	44
52	dr	0.00161485668146952	8	44
53	david	0.00161485668146952	8	44
54	daniel	0.00161485668146952	8	44
55	asplund	0.00161485668146952	8	44
56	annie	0.00161485668146952	8	44
57	nils	0.00141299959628583	7	57
58	marie	0.00141299959628583	7	57
59	louise	0.00141299959628583	7	57
60	l	0.00141299959628583	7	57
61	helen	0.00141299959628583	7	57
62	ernst	0.00141299959628583	7	57
63	emil	0.00141299959628583	7	57
64	de	0.00141299959628583	7	57
65	davies	0.00141299959628583	7	57
66	carl	0.00141299959628583	7	57
67	brown	0.00141299959628583	7	57
68	bertram	0.00141299959628583	7	57
69	bertha	0.00141299959628583	7	57
70	benjamin	0.00141299959628583	7	57
71	august	0.00141299959628583	7	57
72	a	0.00141299959628583	7	57
73	sofia	0.00121114251110214	6	73
74	smith	0.00121114251110214	6	73
75	skoog	0.00121114251110214	6	73
76	rice	0.00121114251110214	6	73
77	panula	0.00121114251110214	6	73
78	olsen	0.00121114251110214	6	73
79	kate	0.00121114251110214	6	73
80	johnson	0.00121114251110214	6	73
81	johansson	0.00121114251110214	6	73
82	ivan	0.00121114251110214	6	73
83	ida	0.00121114251110214	6	73
84	hansen	0.00121114251110214	6	73
85	gustafsson	0.00121114251110214	6	73
86	fortune	0.00121114251110214	6	73
87	emily	0.00121114251110214	6	73
88	ellen	0.00121114251110214	6	73
89	douglas	0.00121114251110214	6	73
90	delia	0.00121114251110214	6	73
91	catherine	0.00121114251110214	6	73
92	carter	0.00121114251110214	6	73
93	bridget	0.00121114251110214	6	73
94	augusta	0.00121114251110214	6	73
95	ada	0.00121114251110214	6	73
96	williams	0.00100928542591845	5	96
97	white	0.00100928542591845	5	96
98	vander	0.00100928542591845	5	96
99	sidney	0.00100928542591845	5	96
100	philip	0.00100928542591845	5	96

Rows: 1-100 | Columns: 4

Passengers' titles might come in handy. We can extract these from their names.

Let's move on to the cabins.

In [7]:

model = CountVectorizer("cabin_voc")
model.fit("titanic", ["cabin"]).transform()

Out[7]:

	Abc token Varchar(128)	123 df Numeric(36,18)	123 cnt Integer	123 rnk Integer
1	[null]	0.7337461300309598	948	1
2	f	0.006191950464396285	8	2
3	c27	0.004643962848297214	6	3
4	c25	0.004643962848297214	6	3
5	c23	0.004643962848297214	6	3
6	g6	0.003869969040247678	5	6
7	f4	0.003095975232198142	4	7
8	d	0.003095975232198142	4	7
9	c78	0.003095975232198142	4	7
10	c26	0.003095975232198142	4	7
11	c22	0.003095975232198142	4	7
12	b98	0.003095975232198142	4	7
13	b96	0.003095975232198142	4	7
14	b66	0.003095975232198142	4	7
15	b63	0.003095975232198142	4	7
16	b59	0.003095975232198142	4	7
17	b57	0.003095975232198142	4	7
18	f33	0.002321981424148607	3	18
19	f2	0.002321981424148607	3	18
20	e46	0.002321981424148607	3	18
21	e34	0.002321981424148607	3	18
22	c101	0.002321981424148607	3	18
23	b60	0.002321981424148607	3	18
24	b58	0.002321981424148607	3	18
25	b55	0.002321981424148607	3	18
26	b53	0.002321981424148607	3	18
27	b51	0.002321981424148607	3	18
28	a34	0.002321981424148607	3	18
29	g73	0.001547987616099071	2	29
30	g63	0.001547987616099071	2	29
31	e8	0.001547987616099071	2	29
32	e67	0.001547987616099071	2	29
33	e50	0.001547987616099071	2	29
34	e44	0.001547987616099071	2	29
35	e33	0.001547987616099071	2	29
36	e31	0.001547987616099071	2	29
37	e24	0.001547987616099071	2	29
38	e121	0.001547987616099071	2	29
39	e101	0.001547987616099071	2	29
40	d37	0.001547987616099071	2	29
41	d36	0.001547987616099071	2	29
42	d35	0.001547987616099071	2	29
43	d33	0.001547987616099071	2	29
44	d30	0.001547987616099071	2	29
45	d28	0.001547987616099071	2	29
46	d26	0.001547987616099071	2	29
47	d21	0.001547987616099071	2	29
48	d20	0.001547987616099071	2	29
49	d19	0.001547987616099071	2	29
50	d17	0.001547987616099071	2	29
51	d15	0.001547987616099071	2	29
52	d12	0.001547987616099071	2	29
53	d10	0.001547987616099071	2	29
54	c93	0.001547987616099071	2	29
55	c92	0.001547987616099071	2	29
56	c89	0.001547987616099071	2	29
57	c86	0.001547987616099071	2	29
58	c85	0.001547987616099071	2	29
59	c83	0.001547987616099071	2	29
60	c80	0.001547987616099071	2	29
61	c7	0.001547987616099071	2	29
62	c68	0.001547987616099071	2	29
63	c65	0.001547987616099071	2	29
64	c64	0.001547987616099071	2	29
65	c62	0.001547987616099071	2	29
66	c6	0.001547987616099071	2	29
67	c57	0.001547987616099071	2	29
68	c55	0.001547987616099071	2	29
69	c54	0.001547987616099071	2	29
70	c52	0.001547987616099071	2	29
71	c46	0.001547987616099071	2	29
72	c32	0.001547987616099071	2	29
73	c31	0.001547987616099071	2	29
74	c2	0.001547987616099071	2	29
75	c126	0.001547987616099071	2	29
76	c125	0.001547987616099071	2	29
77	c124	0.001547987616099071	2	29
78	c123	0.001547987616099071	2	29
79	c116	0.001547987616099071	2	29
80	c106	0.001547987616099071	2	29
81	b78	0.001547987616099071	2	29
82	b77	0.001547987616099071	2	29
83	b71	0.001547987616099071	2	29
84	b69	0.001547987616099071	2	29
85	b5	0.001547987616099071	2	29
86	b49	0.001547987616099071	2	29
87	b45	0.001547987616099071	2	29
88	b41	0.001547987616099071	2	29
89	b35	0.001547987616099071	2	29
90	b28	0.001547987616099071	2	29
91	b22	0.001547987616099071	2	29
92	b20	0.001547987616099071	2	29
93	b18	0.001547987616099071	2	29
94	t	0.000773993808049536	1	94
95	f38	0.000773993808049536	1	94
96	e77	0.000773993808049536	1	94
97	e69	0.000773993808049536	1	94
98	e68	0.000773993808049536	1	94
99	e63	0.000773993808049536	1	94
100	e60	0.000773993808049536	1	94

Rows: 1-100 | Columns: 4

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.

In [8]:

CountVectorizer("cabin_voc").fit("titanic", ["cabin"]).transform(
                )["token"].str_slice(1, 1).groupby(
                columns = ["token"], expr = ["SUM(cnt)"]).head(30)

Out[8]:

	Abc token Varchar(4)	123 SUM Integer
1	[null]	948
2	a	20
3	b	92
4	c	113
5	d	47
6	e	43
7	f	19
8	g	9
9	t	1

Rows: 1-9 | Columns: 2

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.

In [9]:

titanic.drop(["body", "home.dest", "embarked", "ticket"])

Out[9]: