In [43]:
%%HTML
<img src="home/nati/Pictures/otto_competition.JPG",width=10,height=10>
The goals of this tutorial notebook are to: a) introduce you to the process and approach for performing Exploratory Data Analysis (EDA) b) get you train various classifiers and explore their results c) use these trained models to predict the target variable (in this example dataset it is the type of a product)
lets begin with importing some common libraries we discussed about in the previous part.
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.model_selection import train_test_split
my_color_map = ['green','aqua','pink','blue','red','black','yellow','teal','orange','grey']
now lets load our data set for this tutorial: the Otto dataset
In [47]:
tr_data = pd.read_csv('../input/train.csv')
te_data = pd.read_csv('../input/test.csv')
print('train shape is: {} \r\n\ test shape is: {}'.format(tr_data.shape, te_data.shape))
train shape is: (61878, 95)
\ test shape is: (144368, 94)
CPU times: user 1.42 s, sys: 80 ms, total: 1.5 s
Wall time: 1.5 s
pandas has lots of great features that can help us get insights to the data with very little effort lets begin with exploring some statistics of the numerical features:
In [4]:
tr_data.describe()
Out[4]:
id
feat_1
feat_2
feat_3
feat_4
feat_5
feat_6
feat_7
feat_8
feat_9
...
feat_84
feat_85
feat_86
feat_87
feat_88
feat_89
feat_90
feat_91
feat_92
feat_93
count
61878.000000
61878.00000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
...
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
61878.000000
mean
30939.500000
0.38668
0.263066
0.901467
0.779081
0.071043
0.025696
0.193704
0.662433
1.011296
...
0.070752
0.532306
1.128576
0.393549
0.874915
0.457772
0.812421
0.264941
0.380119
0.126135
std
17862.784315
1.52533
1.252073
2.934818
2.788005
0.438902
0.215333
1.030102
2.255770
3.474822
...
1.151460
1.900438
2.681554
1.575455
2.115466
1.527385
4.597804
2.045646
0.982385
1.201720
min
1.000000
0.00000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
25%
15470.250000
0.00000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
50%
30939.500000
0.00000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
...
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
75%
46408.750000
0.00000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
1.000000
0.000000
...
0.000000
0.000000
1.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
0.000000
max
61878.000000
61.00000
51.000000
64.000000
70.000000
19.000000
10.000000
38.000000
76.000000
43.000000
...
76.000000
55.000000
65.000000
67.000000
30.000000
61.000000
130.000000
52.000000
19.000000
87.000000
8 rows × 94 columns
this format is somewhat problematic since:
1) when we scroll aside we notice that not all columns are presented so we cannot explore them
2) the data is very wide and we're not using the screen very efficiently
we can solve the first problem by setting some of pandas display parameters as for the screen usage - we can transpose the resulting dataframe
In [5]:
#set number of rows and columns to see
pd.options.display.max_rows = 200
pd.options.display.max_columns = 50
#use transposed view of the features
tr_data.describe().T
Out[5]:
count
mean
std
min
25%
50%
75%
max
id
61878.0
30939.500000
17862.784315
1.0
15470.25
30939.5
46408.75
61878.0
feat_1
61878.0
0.386680
1.525330
0.0
0.00
0.0
0.00
61.0
feat_2
61878.0
0.263066
1.252073
0.0
0.00
0.0
0.00
51.0
feat_3
61878.0
0.901467
2.934818
0.0
0.00
0.0
0.00
64.0
feat_4
61878.0
0.779081
2.788005
0.0
0.00
0.0
0.00
70.0
feat_5
61878.0
0.071043
0.438902
0.0
0.00
0.0
0.00
19.0
feat_6
61878.0
0.025696
0.215333
0.0
0.00
0.0
0.00
10.0
feat_7
61878.0
0.193704
1.030102
0.0
0.00
0.0
0.00
38.0
feat_8
61878.0
0.662433
2.255770
0.0
0.00
0.0
1.00
76.0
feat_9
61878.0
1.011296
3.474822
0.0
0.00
0.0
0.00
43.0
feat_10
61878.0
0.263906
1.083340
0.0
0.00
0.0
0.00
30.0
feat_11
61878.0
1.252869
3.042333
0.0
0.00
0.0
1.00
38.0
feat_12
61878.0
0.140874
0.567089
0.0
0.00
0.0
0.00
30.0
feat_13
61878.0
0.480979
2.014697
0.0
0.00
0.0
0.00
72.0
feat_14
61878.0
1.696693
3.163212
0.0
0.00
0.0
2.00
33.0
feat_15
61878.0
1.284398
3.862236
0.0
0.00
0.0
1.00
46.0
feat_16
61878.0
1.413459
2.226163
0.0
0.00
0.0
2.00
37.0
feat_17
61878.0
0.366108
1.477436
0.0
0.00
0.0
0.00
43.0
feat_18
61878.0
0.575423
1.335985
0.0
0.00
0.0
1.00
32.0
feat_19
61878.0
0.551699
4.636145
0.0
0.00
0.0
0.00
121.0
feat_20
61878.0
0.471525
1.438727
0.0
0.00
0.0
0.00
27.0
feat_21
61878.0
0.204014
0.696050
0.0
0.00
0.0
0.00
14.0
feat_22
61878.0
0.729969
1.446220
0.0
0.00
0.0
1.00
22.0
feat_23
61878.0
0.142522
0.782979
0.0
0.00
0.0
0.00
64.0
feat_24
61878.0
2.643880
4.629015
0.0
0.00
1.0
3.00
263.0
feat_25
61878.0
1.534520
2.332994
0.0
0.00
1.0
2.00
30.0
feat_26
61878.0
0.563108
1.710305
0.0
0.00
0.0
0.00
33.0
feat_27
61878.0
0.696613
2.873222
0.0
0.00
0.0
0.00
123.0
feat_28
61878.0
0.238970
0.828112
0.0
0.00
0.0
0.00
22.0
feat_29
61878.0
0.275768
1.901294
0.0
0.00
0.0
0.00
69.0
feat_30
61878.0
0.150312
1.640880
0.0
0.00
0.0
0.00
87.0
feat_31
61878.0
0.148680
0.897354
0.0
0.00
0.0
0.00
59.0
feat_32
61878.0
1.043796
2.416849
0.0
0.00
0.0
1.00
149.0
feat_33
61878.0
0.696516
1.310202
0.0
0.00
0.0
1.00
24.0
feat_34
61878.0
0.946411
3.368622
0.0
0.00
0.0
1.00
84.0
feat_35
61878.0
0.666263
3.197965
0.0
0.00
0.0
0.00
105.0
feat_36
61878.0
0.709089
2.555119
0.0
0.00
0.0
1.00
84.0
feat_37
61878.0
0.263632
0.756934
0.0
0.00
0.0
0.00
22.0
feat_38
61878.0
0.582129
1.602579
0.0
0.00
0.0
1.00
39.0
feat_39
61878.0
0.485585
3.298315
0.0
0.00
0.0
0.00
78.0
feat_40
61878.0
1.653059
3.299798
0.0
0.00
0.0
2.00
41.0
feat_41
61878.0
0.303468
1.085672
0.0
0.00
0.0
0.00
36.0
feat_42
61878.0
0.698019
1.961189
0.0
0.00
0.0
1.00
41.0
feat_43
61878.0
0.451146
1.706013
0.0
0.00
0.0
0.00
42.0
feat_44
61878.0
0.560829
1.346090
0.0
0.00
0.0
1.00
34.0
feat_45
61878.0
0.238130
2.587131
0.0
0.00
0.0
0.00
80.0
feat_46
61878.0
0.641375
2.348359
0.0
0.00
0.0
0.00
41.0
feat_47
61878.0
0.249669
1.446203
0.0
0.00
0.0
0.00
47.0
feat_48
61878.0
1.584893
2.577071
0.0
0.00
1.0
2.00
49.0
feat_49
61878.0
0.348314
1.369380
0.0
0.00
0.0
0.00
81.0
feat_50
61878.0
0.324283
1.720470
0.0
0.00
0.0
0.00
73.0
feat_51
61878.0
0.053298
0.513820
0.0
0.00
0.0
0.00
44.0
feat_52
61878.0
0.213485
1.044788
0.0
0.00
0.0
0.00
48.0
feat_53
61878.0
0.442063
2.006485
0.0
0.00
0.0
0.00
53.0
feat_54
61878.0
2.072465
4.113319
0.0
0.00
0.0
2.00
63.0
feat_55
61878.0
0.323120
0.998743
0.0
0.00
0.0
0.00
27.0
feat_56
61878.0
0.303775
1.925806
0.0
0.00
0.0
0.00
62.0
feat_57
61878.0
0.309108
1.082148
0.0
0.00
0.0
0.00
30.0
feat_58
61878.0
0.697970
3.983722
0.0
0.00
0.0
0.00
117.0
feat_59
61878.0
0.388603
2.577693
0.0
0.00
0.0
0.00
97.0
feat_60
61878.0
1.029930
3.028469
0.0
0.00
0.0
0.00
40.0
feat_61
61878.0
0.239746
1.017553
0.0
0.00
0.0
0.00
38.0
feat_62
61878.0
1.187563
2.666742
0.0
0.00
0.0
1.00
56.0
feat_63
61878.0
0.168590
0.946158
0.0
0.00
0.0
0.00
51.0
feat_64
61878.0
1.256796
3.402080
0.0
0.00
0.0
1.00
73.0
feat_65
61878.0
0.222228
0.783052
0.0
0.00
0.0
0.00
38.0
feat_66
61878.0
0.571706
1.361874
0.0
0.00
0.0
1.00
36.0
feat_67
61878.0
2.897653
4.974322
0.0
0.00
1.0
4.00
104.0
feat_68
61878.0
0.392902
1.761054
0.0
0.00
0.0
0.00
109.0
feat_69
61878.0
0.811128
4.111091
0.0
0.00
0.0
0.00
76.0
feat_70
61878.0
0.892789
1.941368
0.0
0.00
0.0
1.00
46.0
feat_71
61878.0
0.319290
1.162443
0.0
0.00
0.0
0.00
31.0
feat_72
61878.0
0.858722
2.411646
0.0
0.00
0.0
1.00
30.0
feat_73
61878.0
0.591050
5.783233
0.0
0.00
0.0
0.00
352.0
feat_74
61878.0
0.579851
3.757822
0.0
0.00
0.0
0.00
231.0
feat_75
61878.0
0.726817
3.200095
0.0
0.00
0.0
0.00
80.0
feat_76
61878.0
0.748457
2.920038
0.0
0.00
0.0
0.00
102.0
feat_77
61878.0
0.124196
0.906621
0.0
0.00
0.0
0.00
29.0
feat_78
61878.0
0.366415
2.778317
0.0
0.00
0.0
0.00
80.0
feat_79
61878.0
0.300446
1.285569
0.0
0.00
0.0
0.00
25.0
feat_80
61878.0
0.698067
2.245671
0.0
0.00
0.0
0.00
54.0
feat_81
61878.0
0.078461
0.461244
0.0
0.00
0.0
0.00
26.0
feat_82
61878.0
0.187983
0.836269
0.0
0.00
0.0
0.00
24.0
feat_83
61878.0
0.496719
2.434921
0.0
0.00
0.0
0.00
79.0
feat_84
61878.0
0.070752
1.151460
0.0
0.00
0.0
0.00
76.0
feat_85
61878.0
0.532306
1.900438
0.0
0.00
0.0
0.00
55.0
feat_86
61878.0
1.128576
2.681554
0.0
0.00
0.0
1.00
65.0
feat_87
61878.0
0.393549
1.575455
0.0
0.00
0.0
0.00
67.0
feat_88
61878.0
0.874915
2.115466
0.0
0.00
0.0
1.00
30.0
feat_89
61878.0
0.457772
1.527385
0.0
0.00
0.0
0.00
61.0
feat_90
61878.0
0.812421
4.597804
0.0
0.00
0.0
0.00
130.0
feat_91
61878.0
0.264941
2.045646
0.0
0.00
0.0
0.00
52.0
feat_92
61878.0
0.380119
0.982385
0.0
0.00
0.0
0.00
19.0
feat_93
61878.0
0.126135
1.201720
0.0
0.00
0.0
0.00
87.0
another great feature of the pandas package is the simplisity of exploring the values distribution of the target variable & for each of the feature
In [6]:
print('the value counts of the target are:')
print(tr_data.iloc[:,-1].value_counts())
print(tr_data.iloc[:,-1].value_counts().plot(kind = 'bar'))
the value counts of the target are:
Class_2 16122
Class_6 14135
Class_8 8464
Class_3 8004
Class_9 4955
Class_7 2839
Class_5 2739
Class_4 2691
Class_1 1929
Name: target, dtype: int64
Axes(0.125,0.125;0.775x0.755)
In [7]:
for i,feat in enumerate(tr_data.columns[1:-1]): #we start from the second feature as the first one is the item id
print('the value counts of feature {} are:'.format(feat))
print(tr_data[feat].value_counts())
the value counts of feature feat_1 are:
0 51483
1 5906
2 1829
3 981
4 521
5 471
6 207
7 192
8 71
9 55
22 32
11 24
10 15
13 10
15 8
26 6
19 5
24 5
12 5
28 4
14 4
21 4
23 4
17 4
48 3
16 3
27 3
25 3
39 2
31 2
42 2
43 2
30 2
47 2
56 1
20 1
37 1
34 1
29 1
61 1
32 1
40 1
Name: feat_1, dtype: int64
the value counts of feature feat_2 are:
0 55018
1 4012
2 1215
3 549
4 310
5 170
6 155
7 84
10 60
8 53
9 51
12 41
11 38
14 30
13 17
15 15
16 10
18 8
21 7
17 7
20 5
19 5
25 2
23 2
24 2
26 1
27 1
35 1
36 1
39 1
37 1
22 1
38 1
51 1
41 1
30 1
31 1
Name: feat_2, dtype: int64
the value counts of feature feat_3 are:
0 49295
1 5346
2 1674
3 909
4 643
5 529
6 486
8 381
7 377
9 355
10 332
11 274
12 229
13 198
14 160
15 122
16 118
17 87
18 83
19 51
20 48
22 30
21 25
23 24
24 22
25 12
28 11
26 9
27 8
31 6
30 6
44 3
38 3
36 3
29 3
32 2
37 2
34 2
59 1
64 1
61 1
41 1
35 1
40 1
50 1
49 1
42 1
52 1
Name: feat_3, dtype: int64
the value counts of feature feat_4 are:
0 48448
1 5947
2 2297
3 1258
4 949
5 629
6 493
7 353
8 283
9 199
10 183
11 146
12 111
14 72
13 68
16 53
15 45
18 39
20 35
22 30
17 30
19 18
24 17
30 16
32 15
26 14
21 12
23 12
34 11
25 10
27 10
28 8
33 6
36 6
38 4
40 4
46 4
44 4
42 4
29 3
50 3
56 3
35 3
55 2
31 2
48 2
58 2
57 2
54 2
67 2
41 1
60 1
52 1
68 1
51 1
37 1
70 1
47 1
63 1
Name: feat_4, dtype: int64
the value counts of feature feat_5 are:
0 58907
1 2357
2 342
3 110
4 56
7 23
5 21
6 19
10 14
8 13
9 6
11 5
12 3
19 1
13 1
Name: feat_5, dtype: int64
the value counts of feature feat_6 are:
0 60710
1 883
2 201
3 57
5 14
4 10
10 1
8 1
6 1
Name: feat_6, dtype: int64
the value counts of feature feat_7 are:
0 56443
1 3346
2 905
3 417
4 231
5 146
6 87
7 61
8 57
9 35
10 32
11 22
13 16
12 13
14 12
15 12
18 9
20 6
17 5
19 5
16 4
38 3
21 3
31 2
30 1
22 1
26 1
27 1
29 1
32 1
Name: feat_7, dtype: int64
the value counts of feature feat_8 are:
0 45312
1 9332
2 3485
3 1239
4 749
5 379
6 310
7 188
8 152
9 134
10 107
11 60
12 52
13 52
14 51
15 41
16 32
18 29
17 25
19 22
21 11
20 8
32 8
22 6
39 6
26 5
25 5
40 5
42 5
23 5
36 4
29 4
33 4
41 4
24 4
27 4
38 4
43 4
28 3
30 3
35 3
37 3
75 2
34 2
31 2
45 2
56 2
54 2
48 1
46 1
59 1
52 1
76 1
51 1
57 1
Name: feat_8, dtype: int64
the value counts of feature feat_9 are:
0 49836
1 5476
2 1887
3 668
4 399
14 373
13 357
12 355
11 290
15 236
10 225
16 203
5 183
9 172
17 164
18 146
6 120
19 117
8 104
7 100
20 92
21 78
22 55
23 51
24 43
25 34
27 29
26 28
28 14
29 12
30 8
32 5
34 5
31 5
33 2
43 2
35 1
37 1
38 1
41 1
Name: feat_9, dtype: int64
the value counts of feature feat_10 are:
0 54195
1 4531
2 1526
3 612
4 314
5 193
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8 62
9 55
10 37
11 32
12 24
13 22
14 17
16 8
15 7
22 6
17 5
19 3
26 2
18 2
20 2
24 2
30 2
25 1
Name: feat_10, dtype: int64
the value counts of feature feat_11 are:
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1 6703
2 1959
7 1155
8 1134
9 972
6 823
3 765
10 705
5 532
4 486
11 451
12 283
13 181
14 141
16 134
15 110
17 92
18 75
19 61
20 31
21 21
22 9
23 4
24 4
25 2
38 1
26 1
Name: feat_11, dtype: int64
the value counts of feature feat_12 are:
0 55342
1 5279
2 879
3 221
4 76
5 23
6 16
7 12
8 8
10 5
9 4
28 3
17 2
19 2
11 1
12 1
13 1
21 1
25 1
30 1
Name: feat_12, dtype: int64
the value counts of feature feat_13 are:
0 50430
1 6816
2 2163
3 706
4 360
5 326
10 258
11 210
6 128
12 84
7 70
13 54
30 36
8 36
9 34
19 31
14 19
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17 11
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18 9
25 7
21 7
22 7
23 4
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40 3
33 3
31 3
26 3
37 2
47 2
60 2
50 2
68 1
29 1
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35 1
36 1
44 1
55 1
41 1
51 1
38 1
39 1
49 1
48 1
72 1
45 1
Name: feat_13, dtype: int64
the value counts of feature feat_14 are:
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1 9694
2 4693
3 2783
4 2165
5 1634
6 1363
7 1077
8 832
9 648
10 527
11 443
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15 168
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17 104
18 97
19 59
20 33
21 31
22 18
23 14
24 12
25 10
26 7
30 5
28 3
33 1
27 1
32 1
Name: feat_14, dtype: int64
the value counts of feature feat_15 are:
0 43770
1 8056
2 3380
3 1916
4 878
5 420
6 332
7 249
18 193
19 191
14 190
21 188
20 186
8 186
17 180
15 174
16 164
22 152
23 152
9 128
13 127
24 127
12 109
10 99
11 95
25 89
26 47
28 36
27 29
29 16
31 7
30 4
32 3
34 2
46 1
33 1
36 1
Name: feat_15, dtype: int64
the value counts of feature feat_16 are:
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1 10999
2 6652
3 4201
4 2775
5 1797
6 1225
7 808
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10 285
11 175
12 113
13 85
14 38
15 23
16 11
17 10
18 7
19 5
21 4
20 2
23 2
26 1
37 1
22 1
25 1
27 1
Name: feat_16, dtype: int64
the value counts of feature feat_17 are:
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1 6086
2 1881
3 762
4 451
5 249
6 160
7 103
8 99
9 77
10 53
11 47
12 24
14 16
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16 5
33 4
29 4
23 3
21 3
25 3
35 3
20 3
24 3
37 2
34 2
28 2
41 2
32 1
36 1
43 1
Name: feat_17, dtype: int64
the value counts of feature feat_18 are:
0 44037
1 10021
2 3876
3 1766
4 935
5 442
6 307
7 166
8 99
10 80
9 45
12 21
11 21
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20 4
21 3
19 2
27 2
29 1
17 1
22 1
23 1
24 1
25 1
32 1
30 1
Name: feat_18, dtype: int64
the value counts of feature feat_19 are:
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1 3375
2 841
3 331
4 187
5 126
6 75
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9 31
24 23
11 22
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18 9
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121 1
116 1
77 1
110 1
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83 1
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102 1
91 1
47 1
79 1
105 1
Name: feat_19, dtype: int64
the value counts of feature feat_20 are:
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1 7218
2 2480
3 1092
4 615
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6 262
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11 58
12 46
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17 16
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18 6
19 6
20 3
22 3
24 3
23 2
26 1
21 1
27 1
Name: feat_20, dtype: int64
the value counts of feature feat_21 are:
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1 4384
2 1740
3 647
4 287
5 131
6 84
7 26
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9 6
13 3
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14 1
11 1
Name: feat_21, dtype: int64
the value counts of feature feat_22 are:
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1 10202
2 5125
3 2584
4 1427
5 661
6 375
7 225
8 150
9 69
13 49
10 48
11 30
12 25
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18 7
20 1
17 1
22 1
Name: feat_22, dtype: int64
the value counts of feature feat_23 are:
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1 2556
2 901
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4 214
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6 68
7 39
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15 5
11 4
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21 1
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64 1
54 1
Name: feat_23, dtype: int64
the value counts of feature feat_24 are:
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1 11905
2 8694
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4 3660
5 2242
6 1747
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59 1
263 1
80 1
137 1
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109 1
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158 1
64 1
48 1
63 1
Name: feat_24, dtype: int64
the value counts of feature feat_25 are:
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1 14799
2 7088
3 4075
4 2865
5 1846
6 1119
7 789
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28 1
22 1
26 1
27 1
Name: feat_25, dtype: int64
the value counts of feature feat_26 are:
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1 5871
2 2553
3 1367
4 891
5 554
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20 8
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30 2
28 1
33 1
27 1
Name: feat_26, dtype: int64
the value counts of feature feat_27 are:
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1 2883
2 1721
3 932
4 650
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38 3
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58 2
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108 1
123 1
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45 1
53 1
52 1
Name: feat_27, dtype: int64
the value counts of feature feat_28 are:
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1 4555
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3 752
4 409
5 187
6 119
7 52
8 36
9 24
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14 4
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20 1
15 1
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22 1
Name: feat_28, dtype: int64
the value counts of feature feat_29 are:
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1 5169
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35 1
62 1
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68 1
69 1
38 1
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54 1
21 1
43 1
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63 1
Name: feat_29, dtype: int64
the value counts of feature feat_30 are:
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80 4
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87 1
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31 1
21 1
Name: feat_30, dtype: int64
the value counts of feature feat_31 are:
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1 2880
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40 1
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32 1
30 1
Name: feat_31, dtype: int64
the value counts of feature feat_32 are:
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1 7521
2 4123
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7 1318
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30 1
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65 1
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149 1
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71 1
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31 1
Name: feat_32, dtype: int64
the value counts of feature feat_33 are:
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1 12020
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13 7
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23 1
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24 1
Name: feat_33, dtype: int64
the value counts of feature feat_34 are:
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1 9031
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42 5
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84 1
46 1
Name: feat_34, dtype: int64
the value counts of feature feat_35 are:
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78 1
Name: feat_35, dtype: int64
the value counts of feature feat_36 are:
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1 8041
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4 715
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38 1
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61 1
Name: feat_36, dtype: int64
the value counts of feature feat_37 are:
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1 7068
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3 723
4 330
5 129
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22 2
20 1
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Name: feat_37, dtype: int64
the value counts of feature feat_38 are:
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1 8876
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30 3
38 2
23 2
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36 1
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Name: feat_38, dtype: int64
the value counts of feature feat_39 are:
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1 4804
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65 3
74 2
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70 2
42 2
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55 2
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72 1
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64 1
Name: feat_39, dtype: int64
the value counts of feature feat_40 are:
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1 11485
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32 7
31 5
30 5
34 4
33 3
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39 1
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38 1
41 1
Name: feat_40, dtype: int64
the value counts of feature feat_41 are:
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1 7907
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26 3
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29 1
36 1
28 1
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Name: feat_41, dtype: int64
the value counts of feature feat_42 are:
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26 6
29 6
34 4
28 4
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37 2
35 2
41 1
30 1
36 1
33 1
32 1
31 1
Name: feat_42, dtype: int64
the value counts of feature feat_43 are:
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1 5780
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26 1
42 1
28 1
Name: feat_43, dtype: int64
the value counts of feature feat_44 are:
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1 8919
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12 32
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23 2
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34 1
22 1
29 1
Name: feat_44, dtype: int64
the value counts of feature feat_45 are:
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1 2628
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76 1
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Name: feat_45, dtype: int64
the value counts of feature feat_46 are:
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1 4286
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32 4
34 3
29 3
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28 2
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41 1
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36 1
31 1
Name: feat_46, dtype: int64
the value counts of feature feat_47 are:
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1 2820
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28 4
37 3
36 3
29 3
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32 2
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30 1
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47 1
Name: feat_47, dtype: int64
the value counts of feature feat_48 are:
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37 1
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49 1
Name: feat_48, dtype: int64
the value counts of feature feat_49 are:
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1 6343
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35 1
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72 1
30 1
Name: feat_49, dtype: int64
the value counts of feature feat_50 are:
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1 4974
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Name: feat_50, dtype: int64
the value counts of feature feat_51 are:
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1 1088
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11 6
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17 3
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24 1
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Name: feat_51, dtype: int64
the value counts of feature feat_52 are:
0 55740
1 3527
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3 558
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32 1
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48 1
27 1
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Name: feat_52, dtype: int64
the value counts of feature feat_53 are:
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1 8346
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21 31
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Name: feat_53, dtype: int64
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Name: feat_54, dtype: int64
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Name: feat_55, dtype: int64
the value counts of feature feat_56 are:
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Name: feat_56, dtype: int64
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Name: feat_57, dtype: int64
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Name: feat_58, dtype: int64
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Name: feat_59, dtype: int64
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Name: feat_60, dtype: int64
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Name: feat_61, dtype: int64
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Name: feat_62, dtype: int64
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Name: feat_63, dtype: int64
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Name: feat_64, dtype: int64
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Name: feat_65, dtype: int64
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Name: feat_66, dtype: int64
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Name: feat_67, dtype: int64
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Name: feat_68, dtype: int64
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Name: feat_69, dtype: int64
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Name: feat_70, dtype: int64
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Name: feat_71, dtype: int64
the value counts of feature feat_72 are:
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Name: feat_72, dtype: int64
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Name: feat_73, dtype: int64
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Name: feat_74, dtype: int64
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Name: feat_75, dtype: int64
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Name: feat_76, dtype: int64
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Name: feat_77, dtype: int64
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Name: feat_78, dtype: int64
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Name: feat_79, dtype: int64
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Name: feat_80, dtype: int64
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Name: feat_81, dtype: int64
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Name: feat_82, dtype: int64
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Name: feat_83, dtype: int64
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Name: feat_84, dtype: int64
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Name: feat_85, dtype: int64
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Name: feat_86, dtype: int64
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Name: feat_87, dtype: int64
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Name: feat_88, dtype: int64
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Name: feat_89, dtype: int64
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Name: feat_90, dtype: int64
the value counts of feature feat_91 are:
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6 51
39 41
8 36
7 31
14 28
11 19
21 19
19 19
10 18
12 18
9 18
18 18
22 17
17 17
16 15
20 14
13 12
15 11
23 11
26 11
24 8
27 8
28 8
32 7
35 6
37 6
31 6
29 6
34 5
38 5
33 5
36 5
25 4
41 4
30 3
40 1
52 1
47 1
51 1
48 1
46 1
45 1
42 1
44 1
Name: feat_91, dtype: int64
the value counts of feature feat_92 are:
0 48286
1 8603
2 2877
3 1025
4 461
5 240
6 124
7 92
9 60
8 53
10 16
11 12
12 10
13 7
14 4
15 3
18 2
17 2
19 1
Name: feat_92, dtype: int64
the value counts of feature feat_93 are:
0 58132
1 2606
2 639
3 215
4 90
5 34
6 27
7 15
8 14
9 13
10 10
20 7
12 7
15 6
18 6
11 5
37 4
41 4
13 4
17 4
16 3
19 3
36 3
14 3
39 2
30 2
27 2
26 2
38 2
54 1
87 1
40 1
24 1
25 1
21 1
83 1
50 1
35 1
28 1
34 1
60 1
62 1
63 1
Name: feat_93, dtype: int64
In [9]:
def value_counts_plots(dat,rows = 4, cols = 4):
_,ax = plt.subplots(rows,cols,sharey='row',sharex='col',figsize = (cols*5,rows*5))
for i,feat in enumerate(dat.columns[:(rows*cols)]):
dat[feat].value_counts().iloc[:20].plot(kind = 'bar',ax=ax[int(i/cols), int(i%cols)],title='value_counts {}'.format(feat))
value_counts_plots(tr_data.iloc[:,1:17],4,4)
In [10]:
cor_mat = tr_data.iloc[:,1:-1].corr()
In [11]:
cor_mat
Out[11]:
feat_1
feat_2
feat_3
feat_4
feat_5
feat_6
feat_7
feat_8
feat_9
feat_10
feat_11
feat_12
feat_13
feat_14
feat_15
feat_16
feat_17
feat_18
feat_19
feat_20
feat_21
feat_22
feat_23
feat_24
feat_25
...
feat_69
feat_70
feat_71
feat_72
feat_73
feat_74
feat_75
feat_76
feat_77
feat_78
feat_79
feat_80
feat_81
feat_82
feat_83
feat_84
feat_85
feat_86
feat_87
feat_88
feat_89
feat_90
feat_91
feat_92
feat_93
feat_1
1.000000
0.031332
-0.027807
-2.752941e-02
0.042973
0.043603
0.298952
0.056321
-0.032285
0.097776
-0.042928
0.056934
0.139254
0.063517
-0.045738
0.027086
0.053004
0.084856
2.302499e-03
0.070511
-0.027026
0.063283
0.048686
0.067255
0.187237
...
0.007544
0.165442
0.013712
-0.029983
0.140815
0.051365
0.011596
0.153808
0.123752
0.279202
0.228912
-0.013303
0.032427
-0.026085
0.059165
0.049634
-0.008739
0.107947
0.089374
0.020830
0.096851
0.010310
0.037264
0.054777
0.081783
feat_2
0.031332
1.000000
0.082573
1.349870e-01
0.020926
0.041343
0.222386
0.019815
-0.025630
0.051925
0.118534
0.090153
0.157467
-0.070057
-0.048798
0.108046
0.074902
0.242716
1.766549e-01
0.449160
0.014113
0.215106
0.162065
0.253684
-0.096366
...
0.307406
0.112968
-0.002336
-0.023267
0.039192
0.070724
0.093689
0.259360
0.014911
0.094256
0.033668
0.155768
0.052101
0.119109
0.371691
0.009845
-0.006764
-0.039090
0.047451
-0.047035
0.105527
0.515022
0.026383
-0.008219
0.054593
feat_3
-0.027807
0.082573
1.000000
5.835232e-01
0.010880
0.004288
0.001294
-0.053462
-0.063551
0.036944
0.596243
0.050037
0.013870
-0.111105
-0.065285
0.221426
-0.023093
0.115655
-1.222845e-02
-0.011069
0.354925
0.251082
-0.002427
-0.031596
-0.157459
...
-0.032748
-0.018774
-0.053020
-0.045339
-0.013972
0.041559
-0.044724
-0.028670
-0.001584
-0.021979
-0.020566
0.442036
0.013089
0.438458
-0.019914
0.011159
-0.048626
-0.096093
-0.009838
-0.082336
0.174781
-0.015068
-0.012417
0.066921
0.006814
feat_4
-0.027529
0.134987
0.583523
1.000000e+00
0.017290
0.014059
0.014490
-0.046184
-0.046250
0.059514
0.389409
0.057434
0.028973
-0.099215
-0.051222
0.211078
-0.007554
0.214895
-3.519107e-07
0.044657
0.232923
0.247738
0.030622
0.003728
-0.134231
...
-0.014461
0.020798
-0.042413
-0.029796
-0.011285
0.049097
-0.031454
-0.013792
0.015318
-0.014499
-0.010835
0.405772
0.028284
0.436541
-0.001052
0.005684
-0.033153
-0.071029
0.005055
-0.067484
0.183715
0.009454
-0.010312
0.087631
0.015746
feat_5
0.042973
0.020926
0.010880
1.729026e-02
1.000000
0.145355
0.075047
0.035861
-0.024708
0.091324
0.004882
0.036668
0.059081
-0.037607
-0.007000
0.062877
0.062197
0.052186
-8.555966e-03
0.046200
0.003288
0.075161
0.017281
0.075222
-0.003610
...
-0.003294
0.118510
0.056428
0.005177
0.001609
0.017265
0.015279
0.035570
0.030462
0.070709
0.055115
0.026223
0.129333
0.057400
0.008006
0.467329
0.034062
0.013879
0.013999
-0.019201
0.119951
0.004842
0.012012
0.065331
0.002038
feat_6
0.043603
0.041343
0.004288
1.405895e-02
0.145355
1.000000
0.088014
0.012867
-0.009373
0.041940
0.014504
0.028588
0.036293
-0.027350
-0.018328
0.021934
0.015488
0.048710
3.849262e-02
0.057813
0.008046
0.038939
0.043651
0.082124
-0.023319
...
0.074836
0.052401
0.011901
-0.011090
0.025023
0.043160
0.006951
0.073867
0.006501
0.061250
0.009942
0.017648
0.044136
0.014907
0.035145
0.177777
0.004290
0.010455
0.015256
-0.015437
0.035042
0.054034
0.012465
0.015479
0.008521
feat_7
0.298952
0.222386
0.001294
1.448981e-02
0.075047
0.088014
1.000000
0.038121
-0.027146
0.194258
0.012418
0.056230
0.199142
-0.044671
-0.035721
0.043957
0.127245
0.098972
5.807104e-02
0.364972
-0.022908
0.162620
0.186462
0.244813
-0.048820
...
0.131430
0.237907
0.115813
-0.014921
0.022819
0.053059
0.039865
0.375114
0.005769
0.567084
0.066753
0.028860
0.144308
0.022059
0.282069
0.062634
0.037874
-0.009169
0.089574
-0.033646
0.063511
0.129578
0.068506
-0.032261
0.034912
feat_8
0.056321
0.019815
-0.053462
-4.618407e-02
0.035861
0.012867
0.038121
1.000000
-0.039281
-0.000023
-0.065923
0.091424
0.095365
-0.061799
-0.056960
-0.004659
0.173912
0.087777
1.938742e-02
0.062595
-0.041095
0.029032
0.012774
0.161848
-0.036939
...
0.046258
0.023089
0.081664
-0.029868
0.028999
-0.000431
0.031466
0.081682
0.027486
0.079623
0.083714
-0.038382
0.035102
-0.034409
0.033479
0.005064
-0.003416
-0.029395
0.059929
-0.050931
0.007974
0.026807
0.095990
0.013608
0.005131
feat_9
-0.032285
-0.025630
-0.063551
-4.624977e-02
-0.024708
-0.009373
-0.027146
-0.039281
1.000000
-0.024323
-0.075820
-0.021885
-0.040164
-0.110188
0.009858
-0.082664
-0.028709
-0.043642
-1.671680e-04
-0.023397
-0.028409
-0.062348
0.006940
0.073618
-0.025279
...
-0.029335
-0.056205
0.043286
-0.058147
0.022679
0.007594
-0.027313
-0.027424
-0.020185
-0.015922
-0.036116
-0.046721
-0.005847
-0.039806
-0.032875
-0.013569
-0.031462
-0.019144
-0.016925
0.001160
-0.019147
-0.020698
-0.014742
-0.069707
-0.006038
feat_10
0.097776
0.051925
0.036944
5.951396e-02
0.091324
0.041940
0.194258
-0.000023
-0.024323
1.000000
0.006010
0.048969
0.086682
-0.029598
-0.021700
0.063997
0.092959
0.071635
9.015307e-03
0.176373
-0.005134
0.141405
0.096666
0.081684
0.009792
...
0.077354
0.322857
0.104834
0.004225
0.000240
0.008912
0.003828
0.106752
0.019069
0.091760
0.113659
0.019042
0.135928
0.029741
0.052025
0.017939
0.086758
0.159447
0.077421
0.054635
0.061498
0.049908
0.024025
-0.006869
0.041316
feat_11
-0.042928
0.118534
0.596243
3.894092e-01
0.004882
0.014504
0.012418
-0.065923
-0.075820
0.006010
1.000000
0.053391
0.005500
-0.151635
-0.100255
0.134924
-0.031807
0.079620
3.367902e-02
-0.005641
0.324080
0.154861
0.026500
0.064213
-0.206207
...
0.010697
-0.050540
-0.071717
-0.086496
-0.006893
0.186561
-0.034513
0.021357
-0.008064
-0.009866
-0.024491
0.561843
0.004795
0.420361
-0.000190
0.017724
-0.074293
-0.123339
-0.032969
-0.114491
0.137374
0.045074
-0.029511
0.013179
0.003326
feat_12
0.056934
0.090153
0.050037
5.743356e-02
0.036668
0.028588
0.056230
0.091424
-0.021885
0.048969
0.053391
1.000000
0.115288
-0.021695
0.028407
0.113804
0.031434
0.100066
3.474800e-02
0.071224
0.048215
0.119511
0.070561
0.085558
-0.012281
...
0.057074
0.071630
0.028629
0.031888
0.181833
0.087012
0.052536
0.113921
0.021418
0.026525
0.036666
0.064162
0.052954
0.054571
0.036529
0.009807
0.019283
-0.007214
0.016089
-0.024324
0.082220
0.062721
0.063965
0.063922
0.012722
feat_13
0.139254
0.157467
0.013870
2.897317e-02
0.059081
0.036293
0.199142
0.095365
-0.040164
0.086682
0.005500
0.115288
1.000000
-0.021555
-0.032725
0.087992
0.319352
0.177439
4.295357e-02
0.194816
-0.005092
0.209427
0.056463
0.192249
0.006019
...
0.086611
0.145294
0.052337
-0.008543
0.030927
0.021072
0.022496
0.189751
0.178263
0.224472
0.233604
0.010431
0.065423
0.010074
0.115747
0.023221
0.002594
0.004850
0.093870
-0.036259
0.062990
0.107722
0.044338
0.071953
0.038989
feat_14
0.063517
-0.070057
-0.111105
-9.921490e-02
-0.037607
-0.027350
-0.044671
-0.061799
-0.110188
-0.029598
-0.151635
-0.021695
-0.021555
1.000000
-0.039565
0.018640
-0.040148
-0.076035
-4.274776e-02
-0.060775
-0.005645
-0.063087
-0.046284
-0.118920
0.557929
...
-0.058352
0.054226
-0.076935
-0.022358
-0.040175
-0.033498
-0.079099
-0.058841
0.007714
-0.041530
-0.045473
-0.104507
-0.037389
-0.084089
-0.070643
-0.027058
-0.021455
0.145787
-0.020229
0.323089
-0.038881
-0.060240
-0.038444
-0.040133
-0.018127
feat_15
-0.045738
-0.048798
-0.065285
-5.122155e-02
-0.007000
-0.018328
-0.035721
-0.056960
0.009858
-0.021700
-0.100255
0.028407
-0.032725
-0.039565
1.000000
0.344157
-0.045991
-0.044733
-3.202887e-02
-0.034214
0.086686
0.056194
-0.029100
-0.062806
0.008228
...
-0.052924
0.118795
-0.033807
0.764664
-0.019194
-0.013632
-0.048894
-0.061486
-0.016673
-0.027200
-0.039033
-0.057948
-0.019557
-0.050702
-0.052090
-0.009311
0.246847
-0.002529
-0.023191
0.010840
0.029547
-0.046616
-0.034402
-0.018206
-0.020369
feat_16
0.027086
0.108046
0.221426
2.110780e-01
0.062877
0.021934
0.043957
-0.004659
-0.082664
0.063997
0.134924
0.113804
0.087992
0.018640
0.344157
1.000000
0.014567
0.244849
-1.011014e-03
0.088638
0.199338
0.433154
0.037233
-0.016491
0.039487
...
-0.021699
0.255740
0.001594
0.394998
-0.026285
0.034125
-0.059511
-0.015473
0.129884
-0.003965
0.045703
0.205393
0.076330
0.168008
0.017558
0.035211
0.110850
0.003610
0.077770
-0.007257
0.248364
0.016863
0.048494
0.210499
0.031467
feat_17
0.053004
0.074902
-0.023093
-7.553867e-03
0.062197
0.015488
0.127245
0.173912
-0.028709
0.092959
-0.031807
0.031434
0.319352
-0.040148
-0.045991
0.014567
1.000000
0.154259
3.731379e-02
0.261954
-0.040070
0.157098
0.133157
0.257106
-0.034996
...
0.100656
0.108789
0.150286
-0.023103
0.002826
0.001770
0.025506
0.134841
0.099665
0.069539
0.114670
-0.015193
0.123831
-0.012734
0.113987
0.000334
0.015559
0.049102
0.214221
-0.034139
0.035390
0.045218
0.088508
-0.006538
0.056695
feat_18
0.084856
0.242716
0.115655
2.148952e-01
0.052186
0.048710
0.098972
0.087777
-0.043642
0.071635
0.079620
0.100066
0.177439
-0.076035
-0.044733
0.244849
0.154259
1.000000
3.652979e-02
0.204010
0.043272
0.364125
0.098466
0.134752
-0.063139
...
0.071926
0.169238
0.074981
-0.002190
-0.008668
0.038278
-0.011790
0.059279
0.102684
0.042958
0.080390
0.182025
0.135914
0.156176
0.085116
0.028752
-0.001555
-0.029295
0.126886
-0.035981
0.247462
0.094336
0.037275
0.126640
0.058100
feat_19
0.002302
0.176655
-0.012228
-3.519107e-07
-0.008556
0.038493
0.058071
0.019387
-0.000167
0.009015
0.033679
0.034748
0.042954
-0.042748
-0.032029
-0.001011
0.037314
0.036530
1.000000e+00
0.105428
-0.024122
0.019088
0.100028
0.290475
-0.065112
...
0.397621
0.031171
-0.020871
-0.022584
0.056837
0.022614
0.036357
0.179548
-0.007951
0.006204
0.012499
-0.002391
0.000812
-0.006879
0.056721
-0.002847
-0.008292
-0.014560
0.000412
-0.018485
0.011116
0.450925
0.004085
-0.027662
0.014243
feat_20
0.070511
0.449160
-0.011069
4.465657e-02
0.046200
0.057813
0.364972
0.062595
-0.023397
0.176373
-0.005641
0.071224
0.194816
-0.060775
-0.034214
0.088638
0.261954
0.204010
1.054279e-01
1.000000
-0.037885
0.244537
0.225089
0.344270
-0.070439
...
0.348327
0.316436
0.098633
0.012246
0.029960
0.033726
0.053898
0.359596
0.017077
0.196400
0.074068
0.025698
0.144703
0.023469
0.364803
0.001723
0.084570
0.016850
0.220475
0.004081
0.111231
0.370282
0.079181
-0.018715
0.110054
feat_21
-0.027026
0.014113
0.354925
2.329227e-01
0.003288
0.008046
-0.022908
-0.041095
-0.028409
-0.005134
0.324080
0.048215
-0.005092
-0.005645
0.086686
0.199338
-0.040070
0.043272
-2.412224e-02
-0.037885
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feat_23
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feat_24
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feat_26
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feat_27
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feat_28
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feat_29
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feat_30
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feat_31
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feat_33
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feat_34
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feat_35
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feat_36
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feat_37
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feat_38
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feat_39
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feat_40
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feat_41
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feat_42
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feat_43
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feat_44
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feat_46
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feat_48
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feat_49
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feat_50
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feat_51
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feat_52
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feat_53
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feat_54
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feat_55
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feat_56
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feat_57
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feat_58
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feat_59
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feat_60
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feat_61
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feat_62
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feat_63
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feat_64
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feat_65
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feat_66
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feat_67
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0.160418
0.291884
-0.004988
0.043339
0.031099
0.012623
0.009379
-0.001795
0.243942
-0.006013
-0.003931
-0.019803
0.014696
-0.031679
0.027764
1.000000
0.014812
-0.035311
0.039864
feat_91
0.037264
0.026383
-0.012417
-1.031241e-02
0.012012
0.012465
0.068506
0.095990
-0.014742
0.024025
-0.029511
0.063965
0.044338
-0.038444
-0.034402
0.048494
0.088508
0.037275
4.085393e-03
0.079181
-0.019779
0.104921
0.010438
0.090833
-0.045759
...
0.041206
0.030560
0.050622
-0.008936
0.007193
0.016941
-0.002625
0.175163
0.026376
0.068450
0.021616
-0.017815
0.017243
-0.014641
0.095801
-0.003444
-0.023091
-0.024005
0.028850
-0.033653
0.015917
0.014812
1.000000
0.104226
-0.000045
feat_92
0.054777
-0.008219
0.066921
8.763105e-02
0.065331
0.015479
-0.032261
0.013608
-0.069707
-0.006869
0.013179
0.063922
0.071953
-0.040133
-0.018206
0.210499
-0.006538
0.126640
-2.766153e-02
-0.018715
0.058008
0.200593
-0.031837
-0.024375
0.030135
...
-0.037961
0.007310
0.000368
0.005300
-0.024017
0.004497
-0.037710
-0.050887
0.076551
-0.028596
0.162033
0.063401
0.018565
0.049661
-0.018325
0.048431
-0.043484
-0.049393
0.001424
-0.070120
0.129622
-0.035311
0.104226
1.000000
-0.003653
feat_93
0.081783
0.054593
0.006814
1.574563e-02
0.002038
0.008521
0.034912
0.005131
-0.006038
0.041316
0.003326
0.012722
0.038989
-0.018127
-0.020369
0.031467
0.056695
0.058100
1.424267e-02
0.110054
-0.007677
0.113276
0.084945
0.089200
-0.015708
...
0.032052
0.093488
0.002001
-0.008233
-0.000163
0.021967
0.006208
0.029426
0.001715
0.016047
0.029082
0.012651
0.019378
0.005497
0.054188
0.003723
0.023390
0.029035
0.499990
-0.008631
0.030650
0.039864
-0.000045
-0.003653
1.000000
93 rows × 93 columns
In [12]:
sns.heatmap(cor_mat)
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fec10356c50>
In [13]:
#to have a better look we need to increase the plot area
f, ax = plt.subplots(figsize=(20,17))
sns.heatmap(cor_mat,vmax=0.8,square=True)
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fec0d85b780>
we would also like to explore the correlation with the target variable but it has a character type so let's convert it into a numerical feature
In [14]:
tr_data['parsed_target'] = [int(n.split('_')[1]) for n in tr_data.target]
tr_data.drop('target',axis=1,inplace=True)
f, ax = plt.subplots(figsize=(20,17))
cor_mat = tr_data.iloc[:,1:].corr()
sns.heatmap(cor_mat,vmax=0.8,square=True)
Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fec0ff11fd0>
In [15]:
f, ax = plt.subplots(figsize = (20,5))
cor_mat.iloc[:-1,-1].plot(kind = 'bar')
Out[15]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fec0f441ba8>
we can notice some features with weak positive corelation with the target and some others with moderate negative correlation
In [16]:
def target_bar_plots(dat,cols = 4, rows = 4):
_,ax = plt.subplots(rows,cols,sharey='row',sharex='col',figsize = (cols*5,rows*5))
for i,feat in enumerate(dat.columns[:(rows*cols)]):
try:
dat.pivot_table(index=['parsed_target'],values=dat.columns[i],aggfunc=np.count_nonzero).plot(
kind = 'bar',color=my_color_map ,ax=ax[int(i/cols), int(i%cols)],title =
'non_zero values by category for {}'.format(feat))
except:
pass
target_bar_plots(tr_data,4,24)
while examining these plots we can already make some assumptions
as to which categories will be easier to predict and which will be the harder ones - can you guess?
now lets look at the test set features and check if they resemble the train features
In [17]:
tr_data['source'] = 'train'
te_data['source'] = 'test'
all_data = pd.concat([tr_data,te_data],axis=0)
tr_data.drop('source',axis=1,inplace=True)
te_data.drop('source',axis=1,inplace=True)
In [18]:
[x for x in all_data.columns[1:7]]
Out[18]:
['feat_10', 'feat_11', 'feat_12', 'feat_13', 'feat_14', 'feat_15']
In [19]:
molten = pd.melt(all_data, id_vars = 'source',value_vars = ['feat_'+str(x) for x in range(13,15)])
plt.subplots(figsize = (20,8))
sns.violinplot(data=molten, x= 'variable',y='value',hue='source',split = True,palette=my_color_map)
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fec04711080>
In [20]:
from sklearn.model_selection import train_test_split
X_train, X_val, y_train, y_val = train_test_split(tr_data.iloc[:,1:-1],tr_data.parsed_target,test_size = 0.2,random_state =12345)
In [22]:
%%time
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
knn = KNeighborsClassifier(n_jobs=4,n_neighbors=4)
knn.fit(X_train,y_train)
knn4_pred = knn.predict(X_val)
print(confusion_matrix(y_pred=knn4_pred,y_true=y_val))
sns.heatmap(xticklabels=range(1,10),yticklabels=range(1,10),data = confusion_matrix(y_pred=knn4_pred,y_true=y_val),cmap='Greens')
[[ 247 20 3 2 3 20 9 34 72]
[ 5 2723 413 47 6 4 15 5 4]
[ 1 817 663 41 1 1 21 4 1]
[ 3 225 126 169 8 11 13 1 1]
[ 1 11 3 0 520 0 0 0 0]
[ 34 29 7 6 2 2652 32 28 20]
[ 31 80 55 10 4 37 300 18 3]
[ 51 21 8 0 2 60 24 1496 33]
[ 71 27 6 2 4 32 2 36 879]]
Out[22]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fec156f76a0>
as we can see our assamption was indeed correct - categories 6,8 and 2 are those easiest to predict
In [23]:
from sklearn.metrics import classification_report
print('classification report results:\r\n' + classification_report(y_pred=knn4_pred,y_true=y_val))
classification report results:
precision recall f1-score support
1 0.56 0.60 0.58 410
2 0.69 0.85 0.76 3222
3 0.52 0.43 0.47 1550
4 0.61 0.30 0.41 557
5 0.95 0.97 0.96 535
6 0.94 0.94 0.94 2810
7 0.72 0.56 0.63 538
8 0.92 0.88 0.90 1695
9 0.87 0.83 0.85 1059
avg / total 0.78 0.78 0.77 12376
In [48]:
%%time
#this will give higher importance to successfully classifying the 4th class items
class_weights = {1:8,2:1,3:2,4:5,5:5,6:1,7:5,8:2,9:3}
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier(class_weight=class_weights,max_depth=100,max_features=92,min_samples_split=2,random_state=12345)
dtc.fit(X_train,y_train)
tree_pred = dtc.predict(X_val)
print(confusion_matrix(y_pred=tree_pred,y_true=y_val))
sns.heatmap(confusion_matrix(y_pred=tree_pred,y_true=y_val),cmap='Greens',xticklabels=range(1,10),yticklabels=range(1,10))
print('classification report results:\r\n' + classification_report(y_pred=tree_pred,y_true=y_val))
[[ 168 25 9 2 2 46 19 64 75]
[ 13 2213 665 179 9 25 59 28 31]
[ 8 589 741 117 3 12 51 19 10]
[ 4 159 101 242 3 19 18 8 3]
[ 0 9 1 3 516 1 2 2 1]
[ 34 39 17 25 2 2480 58 89 66]
[ 20 50 55 30 1 64 274 32 12]
[ 71 30 20 2 9 118 40 1329 76]
[ 90 45 21 14 1 61 18 71 738]]
classification report results:
precision recall f1-score support
1 0.41 0.41 0.41 410
2 0.70 0.69 0.69 3222
3 0.45 0.48 0.47 1550
4 0.39 0.43 0.41 557
5 0.95 0.96 0.95 535
6 0.88 0.88 0.88 2810
7 0.51 0.51 0.51 538
8 0.81 0.78 0.80 1695
9 0.73 0.70 0.71 1059
avg / total 0.71 0.70 0.70 12376
CPU times: user 2.22 s, sys: 100 ms, total: 2.32 s
Wall time: 2.21 s
lets see if support vector machines will do any better
In [24]:
from sklearn.svm import SVC
svc = SVC(kernel='linear',C=0.1,max_iter=100,random_state=12345)
svc.fit(X_train,y_train)
svc_pred = svc.predict(X_val)
print(confusion_matrix(y_pred=svc_pred,y_true=y_val))
sns.heatmap(confusion_matrix(y_pred=svc_pred,y_true=y_val),cmap='Greens',xticklabels=range(1,10),
yticklabels=range(1,10))
print('classification report results:\r\n' + classification_report(y_pred=svc_pred,y_true=y_val))
/opt/conda/lib/python3.6/site-packages/sklearn/svm/base.py:218: ConvergenceWarning: Solver terminated early (max_iter=100). Consider pre-processing your data with StandardScaler or MinMaxScaler.
% self.max_iter, ConvergenceWarning)
[[ 41 2 0 2 2 21 11 46 285]
[ 8 704 235 1045 451 125 176 30 448]
[ 3 390 178 454 161 85 132 20 127]
[ 1 112 28 225 50 37 34 0 70]
[ 0 5 0 2 524 1 0 0 3]
[ 36 16 7 187 1 1522 131 21 889]
[ 57 30 29 27 5 66 162 12 150]
[ 151 98 14 2 2 112 92 558 666]
[ 88 0 3 4 3 32 6 17 906]]
classification report results:
precision recall f1-score support
1 0.11 0.10 0.10 410
2 0.52 0.22 0.31 3222
3 0.36 0.11 0.17 1550
4 0.12 0.40 0.18 557
5 0.44 0.98 0.60 535
6 0.76 0.54 0.63 2810
7 0.22 0.30 0.25 538
8 0.79 0.33 0.47 1695
9 0.26 0.86 0.39 1059
avg / total 0.52 0.39 0.39 12376
In [26]:
from sklearn.preprocessing import MinMaxScaler
svc = SVC(kernel='linear',C=0.1,max_iter=10000,random_state=12345)
mms = MinMaxScaler()
mms.fit(X_train)
X_train_scaled = mms.transform(X_train)
X_val_scaled = mms.transform(X_val)
svc.fit(X_train_scaled,y_train)
svc_pred = svc.predict(X_val_scaled)
print(confusion_matrix(y_pred=svc_pred,y_true=y_val))
sns.heatmap(confusion_matrix(y_pred=svc_pred,y_true=y_val),cmap='Greens',xticklabels=range(1,10),
yticklabels=range(1,10))
print('classification report results:\r\n' + classification_report(y_pred=svc_pred,y_true=y_val))
[[ 15 32 0 0 0 27 1 242 93]
[ 0 3086 51 0 38 8 4 30 5]
[ 0 1381 117 0 19 3 7 19 4]
[ 0 485 30 10 6 17 6 3 0]
[ 0 44 0 0 487 0 0 4 0]
[ 1 70 1 0 0 2581 17 117 23]
[ 0 179 13 0 1 36 152 154 3]
[ 2 44 0 0 3 43 2 1579 22]
[ 3 79 0 0 0 41 0 157 779]]
classification report results:
precision recall f1-score support
1 0.71 0.04 0.07 410
2 0.57 0.96 0.72 3222
3 0.55 0.08 0.13 1550
4 1.00 0.02 0.04 557
5 0.88 0.91 0.89 535
6 0.94 0.92 0.93 2810
7 0.80 0.28 0.42 538
8 0.69 0.93 0.79 1695
9 0.84 0.74 0.78 1059
avg / total 0.74 0.71 0.65 12376
we can see that we get less accurate results on the whole,
but we achieved better results for the class we selected to be more important
lets try ensamble learning - we'll start with random forest
In [49]:
%%time
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_jobs=4,n_estimators=100)
rfc.fit(X_train,y_train)
rfc_pred = rfc.predict(X_val)
print(confusion_matrix(y_pred=rfc_pred,y_true=y_val))
sns.heatmap(confusion_matrix(y_pred=rfc_pred,y_true=y_val),cmap='Greens',xticklabels=range(1,10),yticklabels=range(1,10))
print('classification report results:\r\n' + classification_report(y_pred=rfc_pred,y_true=y_val))
[[ 160 14 1 0 0 35 10 93 97]
[ 0 2846 334 10 5 6 9 5 7]
[ 0 727 778 17 0 4 14 8 2]
[ 1 213 74 234 3 19 10 3 0]
[ 0 9 1 0 523 1 0 0 1]
[ 3 26 2 3 1 2701 26 33 15]
[ 6 72 44 5 4 44 316 40 7]
[ 10 11 4 0 2 50 8 1588 22]
[ 24 26 2 0 2 32 3 37 933]]
classification report results:
precision recall f1-score support
1 0.78 0.39 0.52 410
2 0.72 0.88 0.79 3222
3 0.63 0.50 0.56 1550
4 0.87 0.42 0.57 557
5 0.97 0.98 0.97 535
6 0.93 0.96 0.95 2810
7 0.80 0.59 0.68 538
8 0.88 0.94 0.91 1695
9 0.86 0.88 0.87 1059
avg / total 0.81 0.81 0.80 12376
CPU times: user 20.4 s, sys: 316 ms, total: 20.7 s
Wall time: 6.16 s
yes!
rf model got highest score so far with no special effort just applying fit - predict
let's check if gradient boosting can further improve on that
In [ ]:
%%time
from sklearn.ensemble import GradientBoostingClassifier
gbc = GradientBoostingClassifier(n_estimators=100,max_depth=6)
gbc.fit(X_train,y_train)
gbc_pred = gbc.predict(X_val)
print(confusion_matrix(y_pred=gbc_pred,y_true=y_val))
sns.heatmap(confusion_matrix(y_pred=gbc_pred,y_true=y_val),cmap='Greens',xticklabels=range(1,10),yticklabels=range(1,10))
print('classification report results:\r\n' + classification_report(y_pred=gbc_pred,y_true=y_val))
In [60]:
%%time
import xgboost as xgb
dtrain = xgb.DMatrix(data=X_train,label=y_train-1) #xgb classes starts from zero
dval = xgb.DMatrix(data=X_val,label=y_val-1) #xgb classes starts from zero
watchlist = [(dval,'eval'), (dtrain,'train')]
xgb_params = {
'eta': 0.05,
'max_depth': 7,
'subsample': 0.9,
'colsample_bytree': 0.9,
'colsample_bylevel': 0.7,
'alpha':0.1,
#'objective': 'binary:logistic',
'objective': 'multi:softmax',
#'eval_metric': 'auc',
'eval_metric': 'mlogloss',
'watchlist':watchlist,
'print_every_n':5,
'min_child_weight':2,
'num_class' : 9
}
bst = xgb.train(params=xgb_params,dtrain=dtrain,num_boost_round=400)
xgb_pred = bst.predict(dval)
print(confusion_matrix(y_pred=xgb_pred,y_true=y_val))
sns.heatmap(confusion_matrix(y_pred=xgb_pred+1,y_true=y_val),cmap='Greens',xticklabels=range(1,10),yticklabels=range(1,10))
print('classification report results:\r\n' + classification_report(y_pred=xgb_pred+1,y_true=y_val))
[[ 0 0 0 0 0 0 0 0 0 0]
[ 208 12 3 0 1 30 11 58 87 0]
[ 3 2779 373 25 8 4 16 9 5 0]
[ 0 674 811 19 0 2 33 7 4 0]
[ 1 164 80 274 5 16 13 3 1 0]
[ 0 7 5 0 523 0 0 0 0 0]
[ 11 20 3 6 0 2689 29 31 21 0]
[ 8 63 37 6 0 31 375 17 1 0]
[ 23 9 6 0 2 44 15 1576 20 0]
[ 36 22 1 1 0 27 6 32 934 0]]
classification report results:
precision recall f1-score support
1 0.72 0.51 0.59 410
2 0.74 0.86 0.80 3222
3 0.61 0.52 0.57 1550
4 0.83 0.49 0.62 557
5 0.97 0.98 0.97 535
6 0.95 0.96 0.95 2810
7 0.75 0.70 0.72 538
8 0.91 0.93 0.92 1695
9 0.87 0.88 0.88 1059
avg / total 0.82 0.82 0.82 12376
CPU times: user 23min 53s, sys: 6.14 s, total: 23min 59s
Wall time: 6min 15s
wow! we got an average F1 score of 82% this looks great!
lets predict the results on the test set using the gradient boosting model and create a submission to the kaggle platform
In [61]:
xgb_params = {
'eta': 0.05,
'max_depth': 7,
'subsample': 0.9,
'colsample_bytree': 0.9,
'colsample_bylevel': 0.7,
'alpha':0.1,
#'objective': 'binary:logistic',
'objective': 'multi:softprob',
#'eval_metric': 'auc',
'eval_metric': 'mlogloss',
'watchlist':watchlist,
'print_every_n':5,
'min_child_weight':2,
'num_class' : 9
}
bst = xgb.train(params=xgb_params,dtrain=dtrain,num_boost_round=400)
In [62]:
test_pred = bst.predict(xgb.DMatrix(te_data.iloc[:,1:]))
In [63]:
Out[63]:
array([[ 3.32776515e-04, 1.74447030e-01, 1.35695562e-01, ...,
7.60735199e-03, 7.34019326e-04, 1.29689695e-04],
[ 3.07031139e-03, 1.89991444e-02, 4.14931448e-03, ...,
7.95385800e-03, 3.11259240e-01, 3.43891024e-03],
[ 2.37065815e-05, 1.59545380e-05, 2.23141742e-05, ...,
6.22077860e-05, 4.30120475e-04, 4.70685372e-05],
...,
[ 9.02672589e-04, 6.33337677e-01, 2.00326040e-01, ...,
7.59304920e-03, 9.46243177e-04, 2.25924829e-04],
[ 1.85424651e-04, 2.50830024e-01, 1.83459725e-02, ...,
5.72313787e-04, 7.65306395e-05, 1.91406361e-05],
[ 2.67061463e-04, 5.12850702e-01, 3.88109177e-01, ...,
6.27091229e-02, 4.30107088e-04, 2.92594137e-04]], dtype=float32)
In [64]:
subm = pd.DataFrame(test_pred)
subm.columns = ['class_'+ str(x) for x in range(1,10)]
subm.index = te_data.id
subm.to_csv('../subm/xgboost_classification_submission.csv')
In [65]:
#lets make sure our prediction fits the desired format:
print(subm.head())
print('submission shape: {}'.format(subm.shape))
print('')
print("great! we're good to go on and submit our results")
class_1 class_2 class_3 class_4 class_5 class_6 class_7 \
id
1 0.000333 0.174447 0.135696 0.680417 5.881051e-05 0.000578 0.007607
2 0.003070 0.018999 0.004149 0.001198 7.166839e-04 0.649215 0.007954
3 0.000024 0.000016 0.000022 0.000020 9.076740e-07 0.999378 0.000062
4 0.000733 0.642544 0.336534 0.016710 7.275416e-05 0.000394 0.000547
5 0.140267 0.000839 0.001343 0.000334 6.410003e-04 0.011988 0.004103
class_8 class_9
id
1 0.000734 0.000130
2 0.311259 0.003439
3 0.000430 0.000047
4 0.000463 0.002003
5 0.230982 0.609502
submission shape: (144368, 9)
great! we're good to go on and submit our results
Content source: QuantScientist/Deep-Learning-Boot-Camp
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