Wine Quality¶

This example uses the Wine Quality dataset to predict the quality of white wine. You can download the Jupyter Notebook of the study here.

fixed acidity
volatile acidity
citric acid
residual sugar
total sulfur dioxide
free sulfur dioxide
density
pH
sulphates
alcohol
quality (score between 0 and 10)

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 VerticaPy:

In [10]:

import verticapy as vp
vp.__version__

Out[10]:

'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")

Let's create a Virtual DataFrame of the dataset.

In [3]:

from verticapy.datasets import load_winequality
winequality = load_winequality()
winequality.head(5)

Out[3]:

	123 fixed_acidity Numeric(6,3)	123 volatile_acidity Numeric(7,4)	123 citric_acid Numeric(6,3)	123 residual_sugar Numeric(7,3)	123 chlorides Float	123 free_sulfur_dioxide Numeric(7,2)	123 total_sulfur_dioxide Numeric(7,2)	123 density Float	123 pH Numeric(6,3)	123 sulphates Numeric(6,3)	123 alcohol Float	123 quality Int	123 good Int	Abc color Varchar(20)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white

Rows: 1-5 | Columns: 14

Data Exploration and Preparation¶

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

In [4]:

winequality.describe()

Out[4]:

	count	mean	std	min	approx_25%	approx_50%	approx_75%	max
"fixed_acidity"	6497	7.21530706479914	1.29643375779982	3.8	6.4	7.0	7.7	15.9
"volatile_acidity"	6497	0.339665999692165	0.164636474084679	0.08	0.23	0.29	0.4	1.58
"citric_acid"	6497	0.318633215330153	0.145317864897591	0.0	0.25	0.31	0.39	1.66
"residual_sugar"	6497	5.44323533938742	4.75780374314741	0.6	1.8	3.0	8.1	65.8
"chlorides"	6497	0.0560338617823611	0.0350336013724591	0.009	0.038	0.047	0.065	0.611
"free_sulfur_dioxide"	6497	30.5253193781746	17.7493997720025	1.0	17.0	29.0	41.0	289.0
"total_sulfur_dioxide"	6497	115.744574418963	56.5218545226304	6.0	77.0	118.0	156.0	440.0
"density"	6497	0.994696633831	0.00299867300371915	0.98711	0.99234	0.99489	0.99699	1.03898
"pH"	6497	3.21850084654456	0.160787202103987	2.72	3.11	3.21	3.32	4.01
"sulphates"	6497	0.531268277666614	0.14880587361449	0.22	0.43	0.51	0.6	2.0
"alcohol"	6497	10.4918008311529	1.192711748871	8.0	9.5	10.3	11.3	14.9
"quality"	6497	5.81837771279051	0.873255271531123	3.0	5.0	6.0	6.0	9.0
"good"	6497	0.19655225488687	0.397421408895367	0.0	0.0	0.0	0.0	1.0

Rows: 1-13 | Columns: 9

The quality of a wine is based on the equilibrium between certain components:

For red wines: tannin/smoothness/acidity
For white wines: smoothness/acidity

Based on this, we don't have the data to create a good model for red wines (the tannins weren't extracted). We do, however, have enough data to make a good model for white wines, so let's filter out red wines from our study.

In [5]:

winequality.filter(winequality["color"] == 'white').drop(["good", "color"])

1599 elements were filtered

Out[5]:

	123 fixed_acidity Numeric(6,3)	123 volatile_acidity Numeric(7,4)	123 citric_acid Numeric(6,3)	123 residual_sugar Numeric(7,3)	123 chlorides Float	123 free_sulfur_dioxide Numeric(7,2)	123 total_sulfur_dioxide Numeric(7,2)	123 density Float	123 pH Numeric(6,3)	123 sulphates Numeric(6,3)	123 alcohol Float	123 quality Int
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8
6	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6
7	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7
8	4.5	0.19	0.21	0.95	0.033	89.0	159.0	0.99332	3.34	0.42	8.0	5
9	4.6	0.445	0.0	1.4	0.053	11.0	178.0	0.99426	3.79	0.55	10.2	5
10	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6
11	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5
12	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7
13	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5
14	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6
15	4.8	0.13	0.32	1.2	0.042	40.0	98.0	0.9898	3.42	0.64	11.8	7
16	4.8	0.17	0.28	2.9	0.03	22.0	111.0	0.9902	3.38	0.34	11.3	7
17	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7
18	4.8	0.225	0.38	1.2	0.074	47.0	130.0	0.99132	3.31	0.4	10.3	6
19	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7
20	4.8	0.29	0.23	1.1	0.044	38.0	180.0	0.98924	3.28	0.34	11.9	6
21	4.8	0.33	0.0	6.5	0.028	34.0	163.0	0.9937	3.35	0.61	9.9	5
22	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6
23	4.8	0.65	0.12	1.1	0.013	4.0	10.0	0.99246	3.32	0.36	13.5	4
24	4.9	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6
25	4.9	0.33	0.31	1.2	0.016	39.0	150.0	0.98713	3.33	0.59	14.0	8
26	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5
27	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5
28	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5
29	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5
30	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6
31	5.0	0.17	0.56	1.5	0.026	24.0	115.0	0.9906	3.48	0.39	10.8	7
32	5.0	0.2	0.4	1.9	0.015	20.0	98.0	0.9897	3.37	0.55	12.05	6
33	5.0	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6
34	5.0	0.24	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5
35	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6
36	5.0	0.24	0.34	1.1	0.034	49.0	158.0	0.98774	3.32	0.32	13.1	7
37	5.0	0.255	0.22	2.7	0.043	46.0	153.0	0.99238	3.75	0.76	11.3	6
38	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7
39	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7
40	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6
41	5.0	0.29	0.54	5.7	0.035	54.0	155.0	0.98976	3.27	0.34	12.9	8
42	5.0	0.3	0.33	3.7	0.03	54.0	173.0	0.9887	3.36	0.3	13.0	7
43	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6
44	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6
45	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6
46	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6
47	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6
48	5.0	0.33	0.23	11.8	0.03	23.0	158.0	0.99322	3.41	0.64	11.8	6
49	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6
50	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6
51	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6
52	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7
53	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8
54	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5
55	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6
56	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6
57	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6
58	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5
59	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5
60	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7
61	5.1	0.26	0.33	1.1	0.027	46.0	113.0	0.98946	3.35	0.43	11.4	7
62	5.1	0.26	0.34	6.4	0.034	26.0	99.0	0.99449	3.23	0.41	9.2	6
63	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6
64	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6
65	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6
66	5.1	0.305	0.13	1.75	0.036	17.0	73.0	0.99	3.4	0.51	12.3333333333333	5
67	5.1	0.31	0.3	0.9	0.037	28.0	152.0	0.992	3.54	0.56	10.1	6
68	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7
69	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7
70	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7
71	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7
72	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6
73	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6
74	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6
75	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6
76	5.1	0.42	0.01	1.5	0.017	25.0	102.0	0.9894	3.38	0.36	12.3	7
77	5.1	0.52	0.06	2.7	0.052	30.0	79.0	0.9932	3.32	0.43	9.3	5
78	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8
79	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8
80	5.2	0.16	0.34	0.8	0.029	26.0	77.0	0.99155	3.25	0.51	10.1	6
81	5.2	0.17	0.27	0.7	0.03	11.0	68.0	0.99218	3.3	0.41	9.8	5
82	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6
83	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7
84	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7
85	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5
86	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6
87	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8
88	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8
89	5.2	0.25	0.23	1.4	0.047	20.0	77.0	0.99001	3.32	0.62	11.4	5
90	5.2	0.28	0.29	1.1	0.028	18.0	69.0	0.99168	3.24	0.54	10.0	6
91	5.2	0.285	0.29	5.15	0.035	64.0	138.0	0.9895	3.19	0.34	12.4	8
92	5.2	0.3	0.34	1.5	0.038	18.0	96.0	0.98942	3.56	0.48	13.0	8
93	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7
94	5.2	0.31	0.36	5.1	0.031	46.0	145.0	0.9897	3.14	0.31	12.4	7
95	5.2	0.335	0.2	1.7	0.033	17.0	74.0	0.99002	3.34	0.48	12.3	6
96	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6
97	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6
98	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6
99	5.2	0.37	0.33	1.2	0.028	13.0	81.0	0.9902	3.37	0.38	11.7	6
100	5.2	0.38	0.26	7.7	0.053	20.0	103.0	0.9925	3.27	0.45	12.2	6

Rows: 1-100 of 4898 | Columns: 12

Let's draw the correlation matrix of the dataset.

In [6]:

%matplotlib inline
winequality.corr(method = "spearman")

Out[6]:

	"fixed_acidity"	"volatile_acidity"	"citric_acid"	"residual_sugar"	"chlorides"	"free_sulfur_dioxide"	"total_sulfur_dioxide"	"density"	"pH"	"sulphates"	"alcohol"	"quality"
"fixed_acidity"	1.0	-0.0429534411554621	0.294351955705597	0.101644430364492	0.0900110347529423	-0.0277926483315694	0.109388718710484	0.265711053951591	-0.415781573031458	-0.0141936686269647	-0.103450490979059	-0.0796745702348406
"volatile_acidity"	-0.0429534411554621	1.0	-0.145406908379083	0.107237277630878	-0.00339782066183659	-0.0833819088988573	0.114748541540603	0.00854780369850073	-0.0449435976968945	-0.0174366686847451	0.0353025244854393	-0.158558078043926
"citric_acid"	0.294351955705597	-0.145406908379083	1.0	0.0248910326181368	0.0362089188706347	0.0900564485820748	0.0960064563460948	0.0946381002313568	-0.147249625241941	0.0813914907919214	-0.0351334061758074	0.00381493685138116
"residual_sugar"	0.101644430364492	0.107237277630878	0.0248910326181368	1.0	0.224659958793694	0.345230015537777	0.430103454386088	0.779068758176437	-0.180627450864797	-0.00657339265069306	-0.441559643430315	-0.0923288244199536
"chlorides"	0.0900110347529423	-0.00339782066183659	0.0362089188706347	0.224659958793694	1.0	0.165469886245389	0.373355118347669	0.505577326845011	-0.0559896819779941	0.0887708062447246	-0.569837905062804	-0.318382425676257
"free_sulfur_dioxide"	-0.0277926483315694	-0.0833819088988573	0.0900564485820748	0.345230015537777	0.165469886245389	1.0	0.618959642455467	0.329172049904512	-0.00827826694115271	0.0510877316189511	-0.273420456750706	0.00379066722904633
"total_sulfur_dioxide"	0.109388718710484	0.114748541540603	0.0960064563460948	0.430103454386088	0.373355118347669	0.618959642455467	1.0	0.563857044809788	-0.0130697862621815	0.155569043482336	-0.477228700782309	-0.204178237570967
"density"	0.265711053951591	0.00854780369850073	0.0946381002313568	0.779068758176437	0.505577326845011	0.329172049904512	0.563857044809788	1.0	-0.11066033283187	0.0921206844814246	-0.821563801759593	-0.357150131425647
"pH"	-0.415781573031458	-0.0449435976968945	-0.147249625241941	-0.180627450864797	-0.0559896819779941	-0.00827826694115271	-0.0130697862621815	-0.11066033283187	1.0	0.140408175157992	0.148864208230435	0.113164400131828
"sulphates"	-0.0141936686269647	-0.0174366686847451	0.0813914907919214	-0.00657339265069306	0.0887708062447246	0.0510877316189511	0.155569043482336	0.0921206844814246	0.140408175157992	1.0	-0.0413556773991299	0.0303598456826122
"alcohol"	-0.103450490979059	0.0353025244854393	-0.0351334061758074	-0.441559643430315	-0.569837905062804	-0.273420456750706	-0.477228700782309	-0.821563801759593	0.148864208230435	-0.0413556773991299	1.0	0.444900539842434
"quality"	-0.0796745702348406	-0.158558078043926	0.00381493685138116	-0.0923288244199536	-0.318382425676257	0.00379066722904633	-0.204178237570967	-0.357150131425647	0.113164400131828	0.0303598456826122	0.444900539842434	1.0

Rows: 1-12 | Columns: 13

We can see a strong correlation between the density and the alcohol degree (the alcohol degree describes the density of pure ethanol in the wine). We can drop the 'density' column since it doesn't influence the quality of the white wine (instead, its presence will just bias the data).

In [7]:

winequality.drop(["density"])

Out[7]:

	123 fixed_acidity Numeric(6,3)	123 volatile_acidity Numeric(7,4)	123 citric_acid Numeric(6,3)	123 residual_sugar Numeric(7,3)	123 chlorides Float	123 free_sulfur_dioxide Numeric(7,2)	123 total_sulfur_dioxide Numeric(7,2)	123 pH Numeric(6,3)	123 sulphates Numeric(6,3)	123 alcohol Float	123 quality Int
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	3.75	0.44	12.4	6
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	3.57	0.36	12.8	8
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	3.65	0.89	12.0	7
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	3.42	0.44	8.0	3
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	3.46	0.36	12.8	8
6	4.4	0.46	0.1	2.8	0.024	31.0	111.0	3.48	0.34	13.1	6
7	4.4	0.54	0.09	5.1	0.038	52.0	97.0	3.41	0.4	12.2	7
8	4.5	0.19	0.21	0.95	0.033	89.0	159.0	3.34	0.42	8.0	5
9	4.6	0.445	0.0	1.4	0.053	11.0	178.0	3.79	0.55	10.2	5
10	4.7	0.145	0.29	1.0	0.042	35.0	90.0	3.76	0.49	11.3	6
11	4.7	0.335	0.14	1.3	0.036	69.0	168.0	3.47	0.46	10.5	5
12	4.7	0.455	0.18	1.9	0.036	33.0	106.0	3.21	0.83	14.0	7
13	4.7	0.67	0.09	1.0	0.02	5.0	9.0	3.3	0.34	13.6	5
14	4.7	0.785	0.0	3.4	0.036	23.0	134.0	3.53	0.92	13.8	6
15	4.8	0.13	0.32	1.2	0.042	40.0	98.0	3.42	0.64	11.8	7
16	4.8	0.17	0.28	2.9	0.03	22.0	111.0	3.38	0.34	11.3	7
17	4.8	0.21	0.21	10.2	0.037	17.0	112.0	3.66	0.48	12.2	7
18	4.8	0.225	0.38	1.2	0.074	47.0	130.0	3.31	0.4	10.3	6
19	4.8	0.26	0.23	10.6	0.034	23.0	111.0	3.46	0.28	11.5	7
20	4.8	0.29	0.23	1.1	0.044	38.0	180.0	3.28	0.34	11.9	6
21	4.8	0.33	0.0	6.5	0.028	34.0	163.0	3.35	0.61	9.9	5
22	4.8	0.34	0.0	6.5	0.028	33.0	163.0	3.36	0.61	9.9	6
23	4.8	0.65	0.12	1.1	0.013	4.0	10.0	3.32	0.36	13.5	4
24	4.9	0.235	0.27	11.75	0.03	34.0	118.0	3.07	0.5	9.4	6
25	4.9	0.33	0.31	1.2	0.016	39.0	150.0	3.33	0.59	14.0	8
26	4.9	0.335	0.14	1.3	0.036	69.0	168.0	3.47	0.46	10.4666666666667	5
27	4.9	0.335	0.14	1.3	0.036	69.0	168.0	3.47	0.46	10.4666666666667	5
28	4.9	0.345	0.34	1.0	0.068	32.0	143.0	3.24	0.4	10.1	5
29	4.9	0.345	0.34	1.0	0.068	32.0	143.0	3.24	0.4	10.1	5
30	4.9	0.47	0.17	1.9	0.035	60.0	148.0	3.27	0.35	11.5	6
31	5.0	0.17	0.56	1.5	0.026	24.0	115.0	3.48	0.39	10.8	7
32	5.0	0.2	0.4	1.9	0.015	20.0	98.0	3.37	0.55	12.05	6
33	5.0	0.235	0.27	11.75	0.03	34.0	118.0	3.07	0.5	9.4	6
34	5.0	0.24	0.19	5.0	0.043	17.0	101.0	3.67	0.57	10.0	5
35	5.0	0.24	0.21	2.2	0.039	31.0	100.0	3.69	0.62	11.7	6
36	5.0	0.24	0.34	1.1	0.034	49.0	158.0	3.32	0.32	13.1	7
37	5.0	0.255	0.22	2.7	0.043	46.0	153.0	3.75	0.76	11.3	6
38	5.0	0.27	0.32	4.5	0.032	58.0	178.0	3.45	0.31	12.6	7
39	5.0	0.27	0.32	4.5	0.032	58.0	178.0	3.45	0.31	12.6	7
40	5.0	0.27	0.4	1.2	0.076	42.0	124.0	3.32	0.47	10.1	6
41	5.0	0.29	0.54	5.7	0.035	54.0	155.0	3.27	0.34	12.9	8
42	5.0	0.3	0.33	3.7	0.03	54.0	173.0	3.36	0.3	13.0	7
43	5.0	0.31	0.0	6.4	0.046	43.0	166.0	3.3	0.63	9.9	6
44	5.0	0.33	0.16	1.5	0.049	10.0	97.0	3.48	0.44	10.7	6
45	5.0	0.33	0.16	1.5	0.049	10.0	97.0	3.48	0.44	10.7	6
46	5.0	0.33	0.16	1.5	0.049	10.0	97.0	3.48	0.44	10.7	6
47	5.0	0.33	0.18	4.6	0.032	40.0	124.0	3.18	0.4	11.0	6
48	5.0	0.33	0.23	11.8	0.03	23.0	158.0	3.41	0.64	11.8	6
49	5.0	0.35	0.25	7.8	0.031	24.0	116.0	3.39	0.4	11.3	6
50	5.0	0.35	0.25	7.8	0.031	24.0	116.0	3.39	0.4	11.3	6
51	5.0	0.44	0.04	18.6	0.039	38.0	128.0	3.37	0.57	10.2	6
52	5.0	0.455	0.18	1.9	0.036	33.0	106.0	3.21	0.83	14.0	7
53	5.0	0.55	0.14	8.3	0.032	35.0	164.0	3.53	0.51	12.5	8
54	5.0	0.61	0.12	1.3	0.009	65.0	100.0	3.26	0.37	13.5	5
55	5.1	0.11	0.32	1.6	0.028	12.0	90.0	3.57	0.52	12.2	6
56	5.1	0.14	0.25	0.7	0.039	15.0	89.0	3.22	0.43	9.2	6
57	5.1	0.165	0.22	5.7	0.047	42.0	146.0	3.18	0.55	9.9	6
58	5.1	0.21	0.28	1.4	0.047	48.0	148.0	3.5	0.49	10.4	5
59	5.1	0.23	0.18	1.0	0.053	13.0	99.0	3.22	0.39	11.5	5
60	5.1	0.25	0.36	1.3	0.035	40.0	78.0	3.23	0.64	12.1	7
61	5.1	0.26	0.33	1.1	0.027	46.0	113.0	3.35	0.43	11.4	7
62	5.1	0.26	0.34	6.4	0.034	26.0	99.0	3.23	0.41	9.2	6
63	5.1	0.29	0.28	8.3	0.026	27.0	107.0	3.36	0.37	11.0	6
64	5.1	0.29	0.28	8.3	0.026	27.0	107.0	3.36	0.37	11.0	6
65	5.1	0.3	0.3	2.3	0.048	40.0	150.0	3.29	0.46	12.2	6
66	5.1	0.305	0.13	1.75	0.036	17.0	73.0	3.4	0.51	12.3333333333333	5
67	5.1	0.31	0.3	0.9	0.037	28.0	152.0	3.54	0.56	10.1	6
68	5.1	0.33	0.22	1.6	0.027	18.0	89.0	3.51	0.38	12.5	7
69	5.1	0.33	0.22	1.6	0.027	18.0	89.0	3.51	0.38	12.5	7
70	5.1	0.33	0.22	1.6	0.027	18.0	89.0	3.51	0.38	12.5	7
71	5.1	0.33	0.27	6.7	0.022	44.0	129.0	3.36	0.39	11.0	7
72	5.1	0.35	0.26	6.8	0.034	36.0	120.0	3.38	0.4	11.5	6
73	5.1	0.35	0.26	6.8	0.034	36.0	120.0	3.38	0.4	11.5	6
74	5.1	0.35	0.26	6.8	0.034	36.0	120.0	3.38	0.4	11.5	6
75	5.1	0.39	0.21	1.7	0.027	15.0	72.0	3.5	0.45	12.5	6
76	5.1	0.42	0.01	1.5	0.017	25.0	102.0	3.38	0.36	12.3	7
77	5.1	0.52	0.06	2.7	0.052	30.0	79.0	3.32	0.43	9.3	5
78	5.2	0.155	0.33	1.6	0.028	13.0	59.0	3.3	0.84	11.9	8
79	5.2	0.155	0.33	1.6	0.028	13.0	59.0	3.3	0.84	11.9	8
80	5.2	0.16	0.34	0.8	0.029	26.0	77.0	3.25	0.51	10.1	6
81	5.2	0.17	0.27	0.7	0.03	11.0	68.0	3.3	0.41	9.8	5
82	5.2	0.185	0.22	1.0	0.03	47.0	123.0	3.55	0.44	10.15	6
83	5.2	0.2	0.27	3.2	0.047	16.0	93.0	3.44	0.53	10.1	7
84	5.2	0.21	0.31	1.7	0.048	17.0	61.0	3.24	0.37	12.0	7
85	5.2	0.22	0.46	6.2	0.066	41.0	187.0	3.19	0.42	9.73333333333333	5
86	5.2	0.24	0.15	7.1	0.043	32.0	134.0	3.24	0.48	9.9	6
87	5.2	0.24	0.45	3.8	0.027	21.0	128.0	3.55	0.49	11.2	8
88	5.2	0.24	0.45	3.8	0.027	21.0	128.0	3.55	0.49	11.2	8
89	5.2	0.25	0.23	1.4	0.047	20.0	77.0	3.32	0.62	11.4	5
90	5.2	0.28	0.29	1.1	0.028	18.0	69.0	3.24	0.54	10.0	6
91	5.2	0.285	0.29	5.15	0.035	64.0	138.0	3.19	0.34	12.4	8
92	5.2	0.3	0.34	1.5	0.038	18.0	96.0	3.56	0.48	13.0	8
93	5.2	0.31	0.2	2.4	0.027	27.0	117.0	3.56	0.45	13.0	7
94	5.2	0.31	0.36	5.1	0.031	46.0	145.0	3.14	0.31	12.4	7
95	5.2	0.335	0.2	1.7	0.033	17.0	74.0	3.34	0.48	12.3	6
96	5.2	0.34	0.37	6.2	0.031	42.0	133.0	3.25	0.41	12.5	6
97	5.2	0.36	0.02	1.6	0.031	24.0	104.0	3.44	0.35	12.2	6
98	5.2	0.365	0.08	13.5	0.041	37.0	142.0	3.46	0.39	9.9	6
99	5.2	0.37	0.33	1.2	0.028	13.0	81.0	3.37	0.38	11.7	6
100	5.2	0.38	0.26	7.7	0.053	20.0	103.0	3.27	0.45	12.2	6

Rows: 1-100 of 4898 | Columns: 11

We're working with the scores given by wine tasters, so it's likely that two closely competing wines will have a similar score. Knowing this, a k-nearest neighbors (KNN) model would be best.

KNN is sensitive to unnormalized data so we'll have to normalize our data.

In [8]:

winequality.normalize(["free_sulfur_dioxide", 
                       "residual_sugar", 
                       "pH", 
                       "sulphates", 
                       "volatile_acidity", 
                       "fixed_acidity",
                       "citric_acid",
                       "chlorides",
                       "total_sulfur_dioxide",
                       "alcohol"],
                       method = "robust_zscore")

Out[8]:

	123 fixed_acidity Float	123 volatile_acidity Float	123 citric_acid Float	123 residual_sugar Float	123 chlorides Float	123 free_sulfur_dioxide Float	123 total_sulfur_dioxide Float	123 pH Float	123 sulphates Float	123 alcohol Float	123 quality Int
1	-4.046944556859571	0.562075632897163	-3.372453797382976	1.1054154113644201	-0.674490759476594	-0.858442784788394	-0.465166041018341	3.844597329016592	-0.289067468347112	1.34898151895319	6
2	-3.912046404964252	-0.393452943028014	0.89932101263546	-0.18735854429905424	-1.25262569617082	-0.306586708852998	-0.372132832814673	2.630513961958721	-1.059914050606078	1.61877782274383	8
3	-3.507351949278295	-1.011736139214893	0.44966050631773	-0.6370190506167844	-1.34898151895319	3.617723164465374	0.627974155374761	3.170106569539997	4.046944556859571	1.07918521516255	7
4	-3.507351949278295	-0.505868069607446	-1.011736139214893	-0.018735854429905423	-0.192711645564741	1.839520253117987	0.534940947171093	1.618777822743828	-0.289067468347112	-1.61877782274383	3
5	-3.237555645487657	0.674490759476595	0.786905886056028	-0.16862268986914883	-1.25262569617082	-0.183952025311799	-0.16280811435642	1.888574126534466	-1.059914050606078	1.61877782274383	8
6	-3.237555645487657	2.248302531588651	-2.473132784747516	-0.4496605063177302	-1.83076063286504	-0.183952025311799	-0.534940947171093	2.023472278429786	-1.25262569617082	1.82112505058681	6
7	-3.237555645487657	3.147623544224111	-2.585547911326948	-0.018735854429905423	-0.481779113911853	1.103712151870792	-0.860557175883932	1.551328746796169	-0.674490759476595	1.21408336705787	7
8	-3.102657493592338	-0.786905886056028	-1.236566392373758	-0.7962738132709806	-0.963558227823706	3.372453797382976	0.581457551272927	1.079185215162552	-0.481779113911854	-1.61877782274383	5
9	-2.967759341697019	2.079679841719502	-3.597284050541841	-0.7119624683364062	0.963558227823706	-1.41029886072379	1.023365290240351	4.114393632807231	0.770846582258966	-0.13489815189532	5
10	-2.8328611898017	-1.292773955663474	-0.337245379738298	-0.7869058860560278	-0.09635582278237	0.0613173417706	-1.023365290240351	3.912046404964252	0.192711645564741	0.607041683528936	6
11	-2.8328611898017	0.843113449345744	-2.023472278429786	-0.7306983227663115	-0.674490759476594	2.146106961970985	0.790782269731181	1.956023202482126	-0.096355822782371	0.0674490759476593	5
12	-2.8328611898017	2.192094968298934	-1.573811772112055	-0.618283196186879	-0.674490759476594	-0.0613173417706	-0.651232457425678	0.202347227842979	3.468809620165347	2.42816673411574	7
13	-2.8328611898017	4.609020189756734	-2.585547911326948	-0.7869058860560278	-2.21618392399452	-1.778202911347387	-2.907287756364634	0.809388911371914	-1.25262569617082	2.1583704303251	5
14	-2.8328611898017	5.901794145420208	-3.597284050541841	-0.33724537973829766	-0.674490759476594	-0.674490759476595	0.0	2.360717658168083	4.336012025206683	2.29326858222042	6
15	-2.697963037906381	-1.461396645532623	0.0	-0.749434177196217	-0.09635582278237	0.367904050623597	-0.837298873833015	1.618777822743828	1.638048987300303	0.944287063267234	7
16	-2.697963037906381	-1.011736139214893	-0.44966050631773	-0.43092465188782475	-1.25262569617082	-0.735808101247195	-0.534940947171093	1.34898151895319	-1.25262569617082	0.607041683528936	7
17	-2.697963037906381	-0.562075632897163	-1.236566392373758	0.9367927214952713	-0.578134936694223	-1.042394810100193	-0.511682645120176	3.237555645487657	0.096355822782371	1.21408336705787	7
18	-2.697963037906381	-0.393452943028014	0.674490759476595	-0.749434177196217	2.98703050625349	0.797125443017794	-0.093033208203668	0.876837987319574	-0.674490759476595	-0.0674490759476593	6
19	-2.697963037906381	0.0	-1.011736139214893	1.011736139214893	-0.867202405041335	-0.674490759476595	-0.534940947171093	1.888574126534466	-1.830760632865044	0.741939835424254	7
20	-2.697963037906381	0.337245379738298	-1.011736139214893	-0.7681700316261224	0.0963558227823707	0.245269367082398	1.069881894342185	0.674490759476595	-1.25262569617082	1.01173613921489	6
21	-2.697963037906381	0.786905886056028	-3.597284050541841	0.24356610758877054	-1.44533734173556	0.0	0.674490759476595	1.146634291110212	1.34898151895319	-0.337245379738298	5
22	-2.697963037906381	0.89932101263546	-3.597284050541841	0.24356610758877054	-1.44533734173556	-0.0613173417706	0.674490759476595	1.214083367057871	1.34898151895319	-0.337245379738298	6
23	-2.697963037906381	4.384189936597869	-2.248302531588651	-0.7681700316261224	-2.89067468347112	-1.839520253117987	-2.884029454313717	0.944287063267233	-1.059914050606078	2.09092135437744	4
24	-2.563064886011062	-0.281037816448581	-0.562075632897163	1.2271984651588053	-1.25262569617082	0.0	-0.372132832814673	-0.741939835424255	0.289067468347112	-0.674490759476595	6
25	-2.563064886011062	0.786905886056028	-0.112415126579433	-0.749434177196217	-2.60160721512401	0.306586708852998	0.372132832814673	1.011736139214893	1.156269873388449	2.42816673411574	8
26	-2.563064886011062	0.843113449345744	-2.023472278429786	-0.7306983227663115	-0.674490759476594	2.146106961970985	0.790782269731181	1.956023202482126	-0.096355822782371	0.0449660506317956	5
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Rows: 1-100 | Columns: 11

Machine Learning¶

Let's create our KNN model.

In [9]:

from verticapy.learn.neighbors import KNeighborsRegressor
from verticapy.learn.model_selection import cross_validate

predictors = winequality.get_columns(exclude_columns = ["quality"])
model = KNeighborsRegressor(name = "winequality_KNN", n_neighbors = 50)
cross_validate(model, winequality, predictors, "quality")

Out[9]:

	explained_variance	max_error	median_absolute_error	mean_absolute_error	mean_squared_error	root_mean_squared_error	r2	r2_adj	aic	bic	time
1-fold	0.34320264334275	2.76	0.46	0.562748930971289	0.510453023824068	0.7144599525684193	0.343056235772346	0.33901599121990045	-1078.649099958337	-1019.404735145716	0.12743496894836426
2-fold	0.313518521159198	3.38	0.46	0.568897058823529	0.530461274509804	0.7283277246609551	0.312869876969154	0.30863094962164717	-1012.5386180336403	-953.3284041058387	0.13380694389343262
3-fold	0.34557954055378	3.16	0.46	0.563229357798165	0.513766850152905	0.71677531357665	0.345370939235581	0.34133997211264744	-1066.7239828734503	-1007.4932656943286	0.1439499855041504
avg	0.334100235018576	3.1	0.46	0.564958449197661	0.5182270494955923	0.7198543302686748	0.33376568399236034	0.32966230431806504	-1052.6372336218092	-993.4088016486278	0.13506396611531576
std	0.017863863499532662	0.3143246729100343	0.0	0.0034193839829318434	0.010723924538535073	0.007428932194128158	0.018133271254629893	0.018250715933145367	35.234590013761625	35.21788574735633	0.00832895681398358

Rows: 1-5 | Columns: 12

Our model is pretty good. Our predicted scores have a median absolute error of less than 0.5. If we want to improve this model, we'll probably need more relevant features.

Conclusion¶

We've solved our problem in a Pandas-like way, all without ever loading data into memory!

VerticaPy

Python API for Vertica Data Science at Scale

About the Author

Badr Ouali
Head of Data Science

Badr Ouali works as a Lead Data Scientist for Vertica worldwide. He can embrace data projects end to end through a clear understanding of the “big picture” as well as attention to details, resulting in achieving great business outcomes – a distinctive differentiator in his role. Badr enjoys sharing knowledge and insights related to data analytics with colleagues & peers and has a sweet spot for Python. He loves helping customers finding the best value from their data and empower them to solve their use-cases.