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< EDA Iris | Contents | EDA Wine Quality Prediction binary >

EDA: Wine Cultivars¶

Wine dataset - chemical analysis to determine the origin of wines.

In [1]:

%reload_ext autoreload
%autoreload 2

In [2]:

from sklearn.datasets import load_wine
from sklearn.decomposition import FastICA
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

from helpers.base_imports import *

Create new EDA¶

In [3]:

eda = EDA(name="winec")
eda

Loading 'edas.csv'
Creating experiment: 'winec'

Out[3]:

EDA: winec
Columns: Index(['description', 'n features', 'n samples', 'f/n ratio', 'noise', 'stats',
       'class balance', 'outliers', 'skewness', 'correlations',
       'DR potential'],
      dtype='object')
Datasets: Index([], dtype='object', name='dataset')

Get raw dataset from remote source¶

In [4]:

# fetch dataset
data = load_wine(as_frame=True)
data.frame.head(5)

Out[4]:

	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline	target
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0	0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0	0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0	0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0	0

In [5]:

list(data.target_names)

Out[5]:

[np.str_('class_0'), np.str_('class_1'), np.str_('class_2')]

In [6]:

disp_df(data.frame)

	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline	target
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.640000	1.040	3.92	1065.0	0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.380000	1.050	3.40	1050.0	0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.680000	1.030	3.17	1185.0	0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.800000	0.860	3.45	1480.0	0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.320000	1.040	2.93	735.0	0
5	14.20	1.76	2.45	15.2	112.0	3.27	3.39	0.34	1.97	6.750000	1.050	2.85	1450.0	0
6	14.39	1.87	2.45	14.6	96.0	2.50	2.52	0.30	1.98	5.250000	1.020	3.58	1290.0	0
7	14.06	2.15	2.61	17.6	121.0	2.60	2.51	0.31	1.25	5.050000	1.060	3.58	1295.0	0
8	14.83	1.64	2.17	14.0	97.0	2.80	2.98	0.29	1.98	5.200000	1.080	2.85	1045.0	0
9	13.86	1.35	2.27	16.0	98.0	2.98	3.15	0.22	1.85	7.220000	1.010	3.55	1045.0	0
10	14.10	2.16	2.30	18.0	105.0	2.95	3.32	0.22	2.38	5.750000	1.250	3.17	1510.0	0
11	14.12	1.48	2.32	16.8	95.0	2.20	2.43	0.26	1.57	5.000000	1.170	2.82	1280.0	0
12	13.75	1.73	2.41	16.0	89.0	2.60	2.76	0.29	1.81	5.600000	1.150	2.90	1320.0	0
13	14.75	1.73	2.39	11.4	91.0	3.10	3.69	0.43	2.81	5.400000	1.250	2.73	1150.0	0
14	14.38	1.87	2.38	12.0	102.0	3.30	3.64	0.29	2.96	7.500000	1.200	3.00	1547.0	0
15	13.63	1.81	2.70	17.2	112.0	2.85	2.91	0.30	1.46	7.300000	1.280	2.88	1310.0	0
16	14.30	1.92	2.72	20.0	120.0	2.80	3.14	0.33	1.97	6.200000	1.070	2.65	1280.0	0
17	13.83	1.57	2.62	20.0	115.0	2.95	3.40	0.40	1.72	6.600000	1.130	2.57	1130.0	0
18	14.19	1.59	2.48	16.5	108.0	3.30	3.93	0.32	1.86	8.700000	1.230	2.82	1680.0	0
19	13.64	3.10	2.56	15.2	116.0	2.70	3.03	0.17	1.66	5.100000	0.960	3.36	845.0	0
20	14.06	1.63	2.28	16.0	126.0	3.00	3.17	0.24	2.10	5.650000	1.090	3.71	780.0	0
21	12.93	3.80	2.65	18.6	102.0	2.41	2.41	0.25	1.98	4.500000	1.030	3.52	770.0	0
22	13.71	1.86	2.36	16.6	101.0	2.61	2.88	0.27	1.69	3.800000	1.110	4.00	1035.0	0
23	12.85	1.60	2.52	17.8	95.0	2.48	2.37	0.26	1.46	3.930000	1.090	3.63	1015.0	0
24	13.50	1.81	2.61	20.0	96.0	2.53	2.61	0.28	1.66	3.520000	1.120	3.82	845.0	0
25	13.05	2.05	3.22	25.0	124.0	2.63	2.68	0.47	1.92	3.580000	1.130	3.20	830.0	0
26	13.39	1.77	2.62	16.1	93.0	2.85	2.94	0.34	1.45	4.800000	0.920	3.22	1195.0	0
27	13.30	1.72	2.14	17.0	94.0	2.40	2.19	0.27	1.35	3.950000	1.020	2.77	1285.0	0
28	13.87	1.90	2.80	19.4	107.0	2.95	2.97	0.37	1.76	4.500000	1.250	3.40	915.0	0
29	14.02	1.68	2.21	16.0	96.0	2.65	2.33	0.26	1.98	4.700000	1.040	3.59	1035.0	0
30	13.73	1.50	2.70	22.5	101.0	3.00	3.25	0.29	2.38	5.700000	1.190	2.71	1285.0	0
31	13.58	1.66	2.36	19.1	106.0	2.86	3.19	0.22	1.95	6.900000	1.090	2.88	1515.0	0
32	13.68	1.83	2.36	17.2	104.0	2.42	2.69	0.42	1.97	3.840000	1.230	2.87	990.0	0
33	13.76	1.53	2.70	19.5	132.0	2.95	2.74	0.50	1.35	5.400000	1.250	3.00	1235.0	0
34	13.51	1.80	2.65	19.0	110.0	2.35	2.53	0.29	1.54	4.200000	1.100	2.87	1095.0	0
35	13.48	1.81	2.41	20.5	100.0	2.70	2.98	0.26	1.86	5.100000	1.040	3.47	920.0	0
36	13.28	1.64	2.84	15.5	110.0	2.60	2.68	0.34	1.36	4.600000	1.090	2.78	880.0	0
37	13.05	1.65	2.55	18.0	98.0	2.45	2.43	0.29	1.44	4.250000	1.120	2.51	1105.0	0
38	13.07	1.50	2.10	15.5	98.0	2.40	2.64	0.28	1.37	3.700000	1.180	2.69	1020.0	0
39	14.22	3.99	2.51	13.2	128.0	3.00	3.04	0.20	2.08	5.100000	0.890	3.53	760.0	0
40	13.56	1.71	2.31	16.2	117.0	3.15	3.29	0.34	2.34	6.130000	0.950	3.38	795.0	0
41	13.41	3.84	2.12	18.8	90.0	2.45	2.68	0.27	1.48	4.280000	0.910	3.00	1035.0	0
42	13.88	1.89	2.59	15.0	101.0	3.25	3.56	0.17	1.70	5.430000	0.880	3.56	1095.0	0
43	13.24	3.98	2.29	17.5	103.0	2.64	2.63	0.32	1.66	4.360000	0.820	3.00	680.0	0
44	13.05	1.77	2.10	17.0	107.0	3.00	3.00	0.28	2.03	5.040000	0.880	3.35	885.0	0
45	14.21	4.04	2.44	18.9	111.0	2.85	2.65	0.30	1.25	5.240000	0.870	3.33	1080.0	0
46	14.38	3.59	2.28	16.0	102.0	3.25	3.17	0.27	2.19	4.900000	1.040	3.44	1065.0	0
47	13.90	1.68	2.12	16.0	101.0	3.10	3.39	0.21	2.14	6.100000	0.910	3.33	985.0	0
48	14.10	2.02	2.40	18.8	103.0	2.75	2.92	0.32	2.38	6.200000	1.070	2.75	1060.0	0
49	13.94	1.73	2.27	17.4	108.0	2.88	3.54	0.32	2.08	8.900000	1.120	3.10	1260.0	0
50	13.05	1.73	2.04	12.4	92.0	2.72	3.27	0.17	2.91	7.200000	1.120	2.91	1150.0	0
51	13.83	1.65	2.60	17.2	94.0	2.45	2.99	0.22	2.29	5.600000	1.240	3.37	1265.0	0
52	13.82	1.75	2.42	14.0	111.0	3.88	3.74	0.32	1.87	7.050000	1.010	3.26	1190.0	0
53	13.77	1.90	2.68	17.1	115.0	3.00	2.79	0.39	1.68	6.300000	1.130	2.93	1375.0	0
54	13.74	1.67	2.25	16.4	118.0	2.60	2.90	0.21	1.62	5.850000	0.920	3.20	1060.0	0
55	13.56	1.73	2.46	20.5	116.0	2.96	2.78	0.20	2.45	6.250000	0.980	3.03	1120.0	0
56	14.22	1.70	2.30	16.3	118.0	3.20	3.00	0.26	2.03	6.380000	0.940	3.31	970.0	0
57	13.29	1.97	2.68	16.8	102.0	3.00	3.23	0.31	1.66	6.000000	1.070	2.84	1270.0	0
58	13.72	1.43	2.50	16.7	108.0	3.40	3.67	0.19	2.04	6.800000	0.890	2.87	1285.0	0
59	12.37	0.94	1.36	10.6	88.0	1.98	0.57	0.28	0.42	1.950000	1.050	1.82	520.0	1
60	12.33	1.10	2.28	16.0	101.0	2.05	1.09	0.63	0.41	3.270000	1.250	1.67	680.0	1
61	12.64	1.36	2.02	16.8	100.0	2.02	1.41	0.53	0.62	5.750000	0.980	1.59	450.0	1
62	13.67	1.25	1.92	18.0	94.0	2.10	1.79	0.32	0.73	3.800000	1.230	2.46	630.0	1
63	12.37	1.13	2.16	19.0	87.0	3.50	3.10	0.19	1.87	4.450000	1.220	2.87	420.0	1
64	12.17	1.45	2.53	19.0	104.0	1.89	1.75	0.45	1.03	2.950000	1.450	2.23	355.0	1
65	12.37	1.21	2.56	18.1	98.0	2.42	2.65	0.37	2.08	4.600000	1.190	2.30	678.0	1
66	13.11	1.01	1.70	15.0	78.0	2.98	3.18	0.26	2.28	5.300000	1.120	3.18	502.0	1
67	12.37	1.17	1.92	19.6	78.0	2.11	2.00	0.27	1.04	4.680000	1.120	3.48	510.0	1
68	13.34	0.94	2.36	17.0	110.0	2.53	1.30	0.55	0.42	3.170000	1.020	1.93	750.0	1
69	12.21	1.19	1.75	16.8	151.0	1.85	1.28	0.14	2.50	2.850000	1.280	3.07	718.0	1
70	12.29	1.61	2.21	20.4	103.0	1.10	1.02	0.37	1.46	3.050000	0.906	1.82	870.0	1
71	13.86	1.51	2.67	25.0	86.0	2.95	2.86	0.21	1.87	3.380000	1.360	3.16	410.0	1
72	13.49	1.66	2.24	24.0	87.0	1.88	1.84	0.27	1.03	3.740000	0.980	2.78	472.0	1
73	12.99	1.67	2.60	30.0	139.0	3.30	2.89	0.21	1.96	3.350000	1.310	3.50	985.0	1
74	11.96	1.09	2.30	21.0	101.0	3.38	2.14	0.13	1.65	3.210000	0.990	3.13	886.0	1
75	11.66	1.88	1.92	16.0	97.0	1.61	1.57	0.34	1.15	3.800000	1.230	2.14	428.0	1
76	13.03	0.90	1.71	16.0	86.0	1.95	2.03	0.24	1.46	4.600000	1.190	2.48	392.0	1
77	11.84	2.89	2.23	18.0	112.0	1.72	1.32	0.43	0.95	2.650000	0.960	2.52	500.0	1
78	12.33	0.99	1.95	14.8	136.0	1.90	1.85	0.35	2.76	3.400000	1.060	2.31	750.0	1
79	12.70	3.87	2.40	23.0	101.0	2.83	2.55	0.43	1.95	2.570000	1.190	3.13	463.0	1
80	12.00	0.92	2.00	19.0	86.0	2.42	2.26	0.30	1.43	2.500000	1.380	3.12	278.0	1
81	12.72	1.81	2.20	18.8	86.0	2.20	2.53	0.26	1.77	3.900000	1.160	3.14	714.0	1
82	12.08	1.13	2.51	24.0	78.0	2.00	1.58	0.40	1.40	2.200000	1.310	2.72	630.0	1
83	13.05	3.86	2.32	22.5	85.0	1.65	1.59	0.61	1.62	4.800000	0.840	2.01	515.0	1
84	11.84	0.89	2.58	18.0	94.0	2.20	2.21	0.22	2.35	3.050000	0.790	3.08	520.0	1
85	12.67	0.98	2.24	18.0	99.0	2.20	1.94	0.30	1.46	2.620000	1.230	3.16	450.0	1
86	12.16	1.61	2.31	22.8	90.0	1.78	1.69	0.43	1.56	2.450000	1.330	2.26	495.0	1
87	11.65	1.67	2.62	26.0	88.0	1.92	1.61	0.40	1.34	2.600000	1.360	3.21	562.0	1
88	11.64	2.06	2.46	21.6	84.0	1.95	1.69	0.48	1.35	2.800000	1.000	2.75	680.0	1
89	12.08	1.33	2.30	23.6	70.0	2.20	1.59	0.42	1.38	1.740000	1.070	3.21	625.0	1
90	12.08	1.83	2.32	18.5	81.0	1.60	1.50	0.52	1.64	2.400000	1.080	2.27	480.0	1
91	12.00	1.51	2.42	22.0	86.0	1.45	1.25	0.50	1.63	3.600000	1.050	2.65	450.0	1
92	12.69	1.53	2.26	20.7	80.0	1.38	1.46	0.58	1.62	3.050000	0.960	2.06	495.0	1
93	12.29	2.83	2.22	18.0	88.0	2.45	2.25	0.25	1.99	2.150000	1.150	3.30	290.0	1
94	11.62	1.99	2.28	18.0	98.0	3.02	2.26	0.17	1.35	3.250000	1.160	2.96	345.0	1
95	12.47	1.52	2.20	19.0	162.0	2.50	2.27	0.32	3.28	2.600000	1.160	2.63	937.0	1
96	11.81	2.12	2.74	21.5	134.0	1.60	0.99	0.14	1.56	2.500000	0.950	2.26	625.0	1
97	12.29	1.41	1.98	16.0	85.0	2.55	2.50	0.29	1.77	2.900000	1.230	2.74	428.0	1
98	12.37	1.07	2.10	18.5	88.0	3.52	3.75	0.24	1.95	4.500000	1.040	2.77	660.0	1
99	12.29	3.17	2.21	18.0	88.0	2.85	2.99	0.45	2.81	2.300000	1.420	2.83	406.0	1
100	12.08	2.08	1.70	17.5	97.0	2.23	2.17	0.26	1.40	3.300000	1.270	2.96	710.0	1
101	12.60	1.34	1.90	18.5	88.0	1.45	1.36	0.29	1.35	2.450000	1.040	2.77	562.0	1
102	12.34	2.45	2.46	21.0	98.0	2.56	2.11	0.34	1.31	2.800000	0.800	3.38	438.0	1
103	11.82	1.72	1.88	19.5	86.0	2.50	1.64	0.37	1.42	2.060000	0.940	2.44	415.0	1
104	12.51	1.73	1.98	20.5	85.0	2.20	1.92	0.32	1.48	2.940000	1.040	3.57	672.0	1
105	12.42	2.55	2.27	22.0	90.0	1.68	1.84	0.66	1.42	2.700000	0.860	3.30	315.0	1
106	12.25	1.73	2.12	19.0	80.0	1.65	2.03	0.37	1.63	3.400000	1.000	3.17	510.0	1
107	12.72	1.75	2.28	22.5	84.0	1.38	1.76	0.48	1.63	3.300000	0.880	2.42	488.0	1
108	12.22	1.29	1.94	19.0	92.0	2.36	2.04	0.39	2.08	2.700000	0.860	3.02	312.0	1
109	11.61	1.35	2.70	20.0	94.0	2.74	2.92	0.29	2.49	2.650000	0.960	3.26	680.0	1
110	11.46	3.74	1.82	19.5	107.0	3.18	2.58	0.24	3.58	2.900000	0.750	2.81	562.0	1
111	12.52	2.43	2.17	21.0	88.0	2.55	2.27	0.26	1.22	2.000000	0.900	2.78	325.0	1
112	11.76	2.68	2.92	20.0	103.0	1.75	2.03	0.60	1.05	3.800000	1.230	2.50	607.0	1
113	11.41	0.74	2.50	21.0	88.0	2.48	2.01	0.42	1.44	3.080000	1.100	2.31	434.0	1
114	12.08	1.39	2.50	22.5	84.0	2.56	2.29	0.43	1.04	2.900000	0.930	3.19	385.0	1
115	11.03	1.51	2.20	21.5	85.0	2.46	2.17	0.52	2.01	1.900000	1.710	2.87	407.0	1
116	11.82	1.47	1.99	20.8	86.0	1.98	1.60	0.30	1.53	1.950000	0.950	3.33	495.0	1
117	12.42	1.61	2.19	22.5	108.0	2.00	2.09	0.34	1.61	2.060000	1.060	2.96	345.0	1
118	12.77	3.43	1.98	16.0	80.0	1.63	1.25	0.43	0.83	3.400000	0.700	2.12	372.0	1
119	12.00	3.43	2.00	19.0	87.0	2.00	1.64	0.37	1.87	1.280000	0.930	3.05	564.0	1
120	11.45	2.40	2.42	20.0	96.0	2.90	2.79	0.32	1.83	3.250000	0.800	3.39	625.0	1
121	11.56	2.05	3.23	28.5	119.0	3.18	5.08	0.47	1.87	6.000000	0.930	3.69	465.0	1
122	12.42	4.43	2.73	26.5	102.0	2.20	2.13	0.43	1.71	2.080000	0.920	3.12	365.0	1
123	13.05	5.80	2.13	21.5	86.0	2.62	2.65	0.30	2.01	2.600000	0.730	3.10	380.0	1
124	11.87	4.31	2.39	21.0	82.0	2.86	3.03	0.21	2.91	2.800000	0.750	3.64	380.0	1
125	12.07	2.16	2.17	21.0	85.0	2.60	2.65	0.37	1.35	2.760000	0.860	3.28	378.0	1
126	12.43	1.53	2.29	21.5	86.0	2.74	3.15	0.39	1.77	3.940000	0.690	2.84	352.0	1
127	11.79	2.13	2.78	28.5	92.0	2.13	2.24	0.58	1.76	3.000000	0.970	2.44	466.0	1
128	12.37	1.63	2.30	24.5	88.0	2.22	2.45	0.40	1.90	2.120000	0.890	2.78	342.0	1
129	12.04	4.30	2.38	22.0	80.0	2.10	1.75	0.42	1.35	2.600000	0.790	2.57	580.0	1
130	12.86	1.35	2.32	18.0	122.0	1.51	1.25	0.21	0.94	4.100000	0.760	1.29	630.0	2
131	12.88	2.99	2.40	20.0	104.0	1.30	1.22	0.24	0.83	5.400000	0.740	1.42	530.0	2
132	12.81	2.31	2.40	24.0	98.0	1.15	1.09	0.27	0.83	5.700000	0.660	1.36	560.0	2
133	12.70	3.55	2.36	21.5	106.0	1.70	1.20	0.17	0.84	5.000000	0.780	1.29	600.0	2
134	12.51	1.24	2.25	17.5	85.0	2.00	0.58	0.60	1.25	5.450000	0.750	1.51	650.0	2
135	12.60	2.46	2.20	18.5	94.0	1.62	0.66	0.63	0.94	7.100000	0.730	1.58	695.0	2
136	12.25	4.72	2.54	21.0	89.0	1.38	0.47	0.53	0.80	3.850000	0.750	1.27	720.0	2
137	12.53	5.51	2.64	25.0	96.0	1.79	0.60	0.63	1.10	5.000000	0.820	1.69	515.0	2
138	13.49	3.59	2.19	19.5	88.0	1.62	0.48	0.58	0.88	5.700000	0.810	1.82	580.0	2
139	12.84	2.96	2.61	24.0	101.0	2.32	0.60	0.53	0.81	4.920000	0.890	2.15	590.0	2
140	12.93	2.81	2.70	21.0	96.0	1.54	0.50	0.53	0.75	4.600000	0.770	2.31	600.0	2
141	13.36	2.56	2.35	20.0	89.0	1.40	0.50	0.37	0.64	5.600000	0.700	2.47	780.0	2
142	13.52	3.17	2.72	23.5	97.0	1.55	0.52	0.50	0.55	4.350000	0.890	2.06	520.0	2
143	13.62	4.95	2.35	20.0	92.0	2.00	0.80	0.47	1.02	4.400000	0.910	2.05	550.0	2
144	12.25	3.88	2.20	18.5	112.0	1.38	0.78	0.29	1.14	8.210000	0.650	2.00	855.0	2
145	13.16	3.57	2.15	21.0	102.0	1.50	0.55	0.43	1.30	4.000000	0.600	1.68	830.0	2
146	13.88	5.04	2.23	20.0	80.0	0.98	0.34	0.40	0.68	4.900000	0.580	1.33	415.0	2
147	12.87	4.61	2.48	21.5	86.0	1.70	0.65	0.47	0.86	7.650000	0.540	1.86	625.0	2
148	13.32	3.24	2.38	21.5	92.0	1.93	0.76	0.45	1.25	8.420000	0.550	1.62	650.0	2
149	13.08	3.90	2.36	21.5	113.0	1.41	1.39	0.34	1.14	9.400000	0.570	1.33	550.0	2
150	13.50	3.12	2.62	24.0	123.0	1.40	1.57	0.22	1.25	8.600000	0.590	1.30	500.0	2
151	12.79	2.67	2.48	22.0	112.0	1.48	1.36	0.24	1.26	10.800000	0.480	1.47	480.0	2
152	13.11	1.90	2.75	25.5	116.0	2.20	1.28	0.26	1.56	7.100000	0.610	1.33	425.0	2
153	13.23	3.30	2.28	18.5	98.0	1.80	0.83	0.61	1.87	10.520000	0.560	1.51	675.0	2
154	12.58	1.29	2.10	20.0	103.0	1.48	0.58	0.53	1.40	7.600000	0.580	1.55	640.0	2
155	13.17	5.19	2.32	22.0	93.0	1.74	0.63	0.61	1.55	7.900000	0.600	1.48	725.0	2
156	13.84	4.12	2.38	19.5	89.0	1.80	0.83	0.48	1.56	9.010000	0.570	1.64	480.0	2
157	12.45	3.03	2.64	27.0	97.0	1.90	0.58	0.63	1.14	7.500000	0.670	1.73	880.0	2
158	14.34	1.68	2.70	25.0	98.0	2.80	1.31	0.53	2.70	13.000000	0.570	1.96	660.0	2
159	13.48	1.67	2.64	22.5	89.0	2.60	1.10	0.52	2.29	11.750000	0.570	1.78	620.0	2
160	12.36	3.83	2.38	21.0	88.0	2.30	0.92	0.50	1.04	7.650000	0.560	1.58	520.0	2
161	13.69	3.26	2.54	20.0	107.0	1.83	0.56	0.50	0.80	5.880000	0.960	1.82	680.0	2
162	12.85	3.27	2.58	22.0	106.0	1.65	0.60	0.60	0.96	5.580000	0.870	2.11	570.0	2
163	12.96	3.45	2.35	18.5	106.0	1.39	0.70	0.40	0.94	5.280000	0.680	1.75	675.0	2
164	13.78	2.76	2.30	22.0	90.0	1.35	0.68	0.41	1.03	9.580000	0.700	1.68	615.0	2
165	13.73	4.36	2.26	22.5	88.0	1.28	0.47	0.52	1.15	6.620000	0.780	1.75	520.0	2
166	13.45	3.70	2.60	23.0	111.0	1.70	0.92	0.43	1.46	10.680000	0.850	1.56	695.0	2
167	12.82	3.37	2.30	19.5	88.0	1.48	0.66	0.40	0.97	10.260000	0.720	1.75	685.0	2
168	13.58	2.58	2.69	24.5	105.0	1.55	0.84	0.39	1.54	8.660000	0.740	1.80	750.0	2
169	13.40	4.60	2.86	25.0	112.0	1.98	0.96	0.27	1.11	8.500000	0.670	1.92	630.0	2
170	12.20	3.03	2.32	19.0	96.0	1.25	0.49	0.40	0.73	5.500000	0.660	1.83	510.0	2
171	12.77	2.39	2.28	19.5	86.0	1.39	0.51	0.48	0.64	9.899999	0.570	1.63	470.0	2
172	14.16	2.51	2.48	20.0	91.0	1.68	0.70	0.44	1.24	9.700000	0.620	1.71	660.0	2
173	13.71	5.65	2.45	20.5	95.0	1.68	0.61	0.52	1.06	7.700000	0.640	1.74	740.0	2
174	13.40	3.91	2.48	23.0	102.0	1.80	0.75	0.43	1.41	7.300000	0.700	1.56	750.0	2
175	13.27	4.28	2.26	20.0	120.0	1.59	0.69	0.43	1.35	10.200000	0.590	1.56	835.0	2
176	13.17	2.59	2.37	20.0	120.0	1.65	0.68	0.53	1.46	9.300000	0.600	1.62	840.0	2
177	14.13	4.10	2.74	24.5	96.0	2.05	0.76	0.56	1.35	9.200000	0.610	1.60	560.0	2

In [7]:

X = data.frame.drop(columns="target")
y = data.frame.target
X.shape, y.shape

Out[7]:

((178, 13), (178,))

In [9]:

eda.update_param(
    "description", "Classify wines into 3 classes based on chemical analysis"
)
eda.update_param("n features", X.shape[1])
eda.update_param("n samples", X.shape[0])
eda.update_param("f/n ratio", len(data.target_names) / X.shape[0])

In [10]:

eda.summary_df

Out[10]:

	description	n features	n samples	f/n ratio	noise	stats	class balance	outliers	skewness	correlations	DR potential
dataset
winec	Classify wines into 3 classes based on chemica...	13	178	0.016854	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Noise¶

In [11]:

# check for missing values
X.isna().sum().sum()

Out[11]:

np.int64(0)

In [12]:

eda.update_param("noise", "None, no missing vals")

Stats¶

In [13]:

skewness = X.skew()
summary_stats = X.describe().T
summary_stats["skewness"] = skewness
disp_df(summary_stats[["min", "max", "mean", "std", "skewness", "25%", "50%", "75%"]])

	min	max	mean	std	skewness	25%	50%	75%
alcohol	11.03	14.83	13.000618	0.811827	-0.051482	12.3625	13.050	13.6775
malic_acid	0.74	5.80	2.336348	1.117146	1.039651	1.6025	1.865	3.0825
ash	1.36	3.23	2.366517	0.274344	-0.176699	2.2100	2.360	2.5575
alcalinity_of_ash	10.60	30.00	19.494944	3.339564	0.213047	17.2000	19.500	21.5000
magnesium	70.00	162.00	99.741573	14.282484	1.098191	88.0000	98.000	107.0000
total_phenols	0.98	3.88	2.295112	0.625851	0.086639	1.7425	2.355	2.8000
flavanoids	0.34	5.08	2.029270	0.998859	0.025344	1.2050	2.135	2.8750
nonflavanoid_phenols	0.13	0.66	0.361854	0.124453	0.450151	0.2700	0.340	0.4375
proanthocyanins	0.41	3.58	1.590899	0.572359	0.517137	1.2500	1.555	1.9500
color_intensity	1.28	13.00	5.058090	2.318286	0.868585	3.2200	4.690	6.2000
hue	0.48	1.71	0.957449	0.228572	0.021091	0.7825	0.965	1.1200
od280/od315_of_diluted_wines	1.27	4.00	2.611685	0.709990	-0.307285	1.9375	2.780	3.1700
proline	278.00	1680.00	746.893258	314.907474	0.767822	500.5000	673.500	985.0000

In [14]:

fig, ax = plot_feature_statistics(X, X.columns, line=False)
fig.savefig(f"{FIGS_DIR}/{eda.name}_feature-statistics.png")

In [15]:

eda.update_param("skewness", "some skewness")
eda.update_param("stats", "fine")
eda.update_param("outliers", "some outliers in ~7 features")

In [16]:

# class distribution of whole dataset
ax = sns.countplot(x=data.target_names[y])
plt.title(f"Target Class Distribution ({eda.name})")
plt.xlabel("Class")
plt.ylabel("Count")

# Annotate each bar with the count
for p in ax.patches:
    height = p.get_height()
    ax.annotate(
        f"{height}",
        (p.get_x() + p.get_width() / 2.0, height),
        ha="center",
        va="center",
        xytext=(0, 5),
        textcoords="offset points",
    )

plt.savefig(f"{FIGS_DIR}/{eda.name}_target-class-distribution.png")
plt.show()

In [17]:

eda.update_param("class balance", "~Imbalanced (59, 71, 48)")

Feature Correlations¶

In [18]:

df = data.frame.copy()
df["target"] = data.target_names[y]
# df.head(5)
sns.pairplot(data=df, hue="target", palette="bright")
plt.savefig(f"{FIGS_DIR}/{eda.name}_pairplot.png")

In [19]:

# Create a heatmap of the correlation matrix
plt.figure(figsize=(12, 8))
sns.heatmap(X.corr(), annot=True, cmap="coolwarm", fmt=".2f")
plt.title(f"Correlation Matrix ({eda.name})")

plt.savefig(f"{FIGS_DIR}/{eda.name}_correlation-matrix.png")
plt.show()

In [20]:

eda.update_param("correlations", "a couple medium-strong correlations, but not much")

Dimensionality Reduction Potential¶

In [21]:

# PCA - number of components to explain 95% variance
pca_pipe = Pipeline(
    [
        ("scaler", StandardScaler()),
        ("pca", PCA()),
    ]
)
pca_pipe.fit(X)

Out[21]:

Pipeline(steps=[('scaler', StandardScaler()), ('pca', PCA())])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

In [22]:

explained_variance_ratio = pca_pipe.named_steps["pca"].explained_variance_ratio_
cumulative_explained_variance = np.cumsum(explained_variance_ratio)

plt.figure(figsize=(8, 6))
plt.plot(cumulative_explained_variance, marker="o", linestyle="--")
plt.xlabel("Number of Principal Components")
plt.ylabel("Cumulative Explained Variance")
plt.title(f"PCA - Cumulative Explained Variance ({eda.name})")
plt.axhline(y=0.95, color="r", linestyle="--")  # Threshold for 95% explained variance
plt.show()

# Number of components to explain 95% variance
num_components_95 = np.argmax(cumulative_explained_variance >= 0.95) + 1
print(f"Number of components to explain 95% of the variance: {num_components_95}")

Number of components to explain 95% of the variance: 10

In [23]:

# ICA - number of independent components
ica_pipe = Pipeline(
    [
        ("scaler", StandardScaler()),
        ("ica", FastICA()),
    ]
)
components = ica_pipe.fit_transform(X)

# Number of independent components
num_independent_components = components.shape[1]
print(f"Number of independent components found: {num_independent_components}")

Number of independent components found: 13

/Users/yarik/vc_projects/ML/ml-prep/.venv/lib/python3.12/site-packages/sklearn/decomposition/_fastica.py:128: ConvergenceWarning: FastICA did not converge. Consider increasing tolerance or the maximum number of iterations.
  warnings.warn(

In [24]:

eda.update_param(
    "DR potential",
    "PCA: 95% variance explained with 10 components\nICA: 13 independent components",
)

Save EDA results¶

In [25]:

eda.save()

Loading 'edas.csv'
Saving winec to results/edas.csv

---

Create and save a shuffled 80/20 train/test split¶

< EDA Iris | Contents | EDA Wine Quality Prediction binary >