March Madness Predictions
./elo_rankings.csv
./tourney_input.csv
./init.ipynb
./march-madness-cis3200.ipynb
./deepnote_exports/2022-04-25T23:15:39+00:00_gf0k.csv
./mens-march-mania-2022/MDataFiles_Stage1/MNCAATourneyCompactResults.csv
./mens-march-mania-2022/MDataFiles_Stage1/MNCAATourneyDetailedResults.csv
./mens-march-mania-2022/MDataFiles_Stage1/MTeams.csv
./mens-march-mania-2022/MDataFiles_Stage1/MConferenceTourneyGames.csv
./mens-march-mania-2022/MDataFiles_Stage1/MSeasons.csv
./mens-march-mania-2022/MDataFiles_Stage1/MSecondaryTourneyCompactResults.csv
./mens-march-mania-2022/MDataFiles_Stage1/MNCAATourneySlots.csv
./mens-march-mania-2022/MDataFiles_Stage1/MRegularSeasonDetailedResults.csv
./mens-march-mania-2022/MDataFiles_Stage1/MTeamSpellings.csv
./mens-march-mania-2022/MDataFiles_Stage1/MGameCities.csv
./mens-march-mania-2022/MDataFiles_Stage1/MTeamCoaches.csv
./mens-march-mania-2022/MDataFiles_Stage1/MTeamConferences.csv
./mens-march-mania-2022/MDataFiles_Stage1/MNCAATourneySeedRoundSlots.csv
./mens-march-mania-2022/MDataFiles_Stage1/MSecondaryTourneyTeams.csv
./mens-march-mania-2022/MDataFiles_Stage1/MMasseyOrdinals.csv
./mens-march-mania-2022/MDataFiles_Stage1/Conferences.csv
./mens-march-mania-2022/MDataFiles_Stage1/MNCAATourneySeeds.csv
./mens-march-mania-2022/MDataFiles_Stage1/MSampleSubmissionStage1.csv
./mens-march-mania-2022/MDataFiles_Stage1/MRegularSeasonCompactResults.csv
./processed_datasets/conf_rankings.csv
./processed_datasets/season_team_seed_conf_w_SOS.csv
./processed_datasets/season_data.csv
./processed_datasets/season_team_seed.csv
./processed_datasets/win_loss_tourn.csv
./.deepnote/id-resolver.json
0
2003
1104
1
2003
1272
2
2003
1266
3
2003
1296
4
2003
1400
5
2003
1458
6
2003
1161
7
2003
1186
8
2003
1194
9
2003
1458
count
6892
6892
mean
0.4941018245
69.46113996
std
0.1882088609
5.835748286
min
0
49.24
25%
0.3571428571
65.5
50%
0.5
69.42857143
75%
0.6333333333
73.34375
max
1
95.55172414
2350
2021
Z13
2351
2021
Z14
2352
2021
Z15
2353
2021
Z16
2360
2021
1417
2361
2021
1211
count
2230
2230
mean
3.186290295e-18
-1.019612894e-16
std
0.1444629319
7.292186406
min
-0.6333333333
-22.89285714
25%
-0.09249231951
-4.845643939
50%
0
0
75%
0.09249231951
4.845643939
max
0.6333333333
22.89285714
count
132
132
mean
0
0
std
0.1705852401
8.068857252
min
-0.4
-19.26923077
25%
-0.1219093407
-5.05825416
50%
0
0
75%
0.1219093407
5.05825416
max
0.4
19.26923077
Random Forest Classifier
(2230, 22) (132, 22) (2230,) (132,)
Elo Rankings
0
Baylor
28-2
1
Gonzaga
31-1
2
Illinois
24-7
3
Houston
27-4
4
Michigan
23-5
5
Alabama
26-7
6
Arkansas
25-7
7
USC
25-8
8
Iowa
22-9
9
Loyola-Chicago
24-5
0
2003
1421
1
2003
1112
2
2003
1113
3
2003
1141
4
2003
1143
5
2003
1163
6
2003
1181
7
2003
1211
8
2003
1228
9
2003
1242
count
1729
1729
mean
0.008468599544
0.4479496155
std
0.1414099118
7.294525883
min
-0.6333333333
-22.89285714
25%
-0.08453837597
-4.438095238
50%
0.006628787879
0.4885057471
75%
0.09714795009
5.474798387
max
0.5151515152
22.89285714
count
105
105
mean
0.008596023081
0.4536846177
std
0.1749546822
8.371753014
min
-0.4
-19.26923077
25%
-0.119047619
-4.866883117
50%
0.02188552189
1.12
75%
0.130952381
5.434782609
max
0.4
19.26923077
Neural Network
Epoch 1/50
357/357 [==============================] - 1s 1ms/step - loss: 0.7102 - accuracy: 0.6183
Epoch 2/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5515 - accuracy: 0.7108
Epoch 3/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5418 - accuracy: 0.7136
Epoch 4/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5358 - accuracy: 0.7113
Epoch 5/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5316 - accuracy: 0.7180
Epoch 6/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5290 - accuracy: 0.7214
Epoch 7/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5255 - accuracy: 0.7136
Epoch 8/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5226 - accuracy: 0.7180
Epoch 9/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5229 - accuracy: 0.7209
Epoch 10/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5183 - accuracy: 0.7281
Epoch 11/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5166 - accuracy: 0.7220
Epoch 12/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5153 - accuracy: 0.7248
Epoch 13/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5151 - accuracy: 0.7237
Epoch 14/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5151 - accuracy: 0.7242
Epoch 15/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5127 - accuracy: 0.7197
Epoch 16/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5108 - accuracy: 0.7321
Epoch 17/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5102 - accuracy: 0.7281
Epoch 18/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5088 - accuracy: 0.7315
Epoch 19/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5075 - accuracy: 0.7304
Epoch 20/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5050 - accuracy: 0.7349
Epoch 21/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5049 - accuracy: 0.7349
Epoch 22/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5039 - accuracy: 0.7304
Epoch 23/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5038 - accuracy: 0.7349
Epoch 24/50
357/357 [==============================] - 1s 2ms/step - loss: 0.5030 - accuracy: 0.7343
Epoch 25/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5008 - accuracy: 0.7349
Epoch 26/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4999 - accuracy: 0.7416
Epoch 27/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5014 - accuracy: 0.7337
Epoch 28/50
357/357 [==============================] - 1s 1ms/step - loss: 0.5018 - accuracy: 0.7382
Epoch 29/50
357/357 [==============================] - 0s 1ms/step - loss: 0.5000 - accuracy: 0.7354
Epoch 30/50
357/357 [==============================] - 1s 1ms/step - loss: 0.4992 - accuracy: 0.7489
Epoch 31/50
357/357 [==============================] - 1s 2ms/step - loss: 0.4980 - accuracy: 0.7405
Epoch 32/50
357/357 [==============================] - 1s 2ms/step - loss: 0.4991 - accuracy: 0.7399
Epoch 33/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4974 - accuracy: 0.7377
Epoch 34/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4975 - accuracy: 0.7494
Epoch 35/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4989 - accuracy: 0.7377
Epoch 36/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4978 - accuracy: 0.7377
Epoch 37/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4965 - accuracy: 0.7427
Epoch 38/50
357/357 [==============================] - 1s 2ms/step - loss: 0.4962 - accuracy: 0.7410
Epoch 39/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4960 - accuracy: 0.7489
Epoch 40/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4967 - accuracy: 0.7438
Epoch 41/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4947 - accuracy: 0.7382
Epoch 42/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4966 - accuracy: 0.7399
Epoch 43/50
357/357 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.7438
Epoch 44/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4943 - accuracy: 0.7466
Epoch 45/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4931 - accuracy: 0.7444
Epoch 46/50
357/357 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.7438
Epoch 47/50
357/357 [==============================] - 1s 2ms/step - loss: 0.4938 - accuracy: 0.7483
Epoch 48/50
357/357 [==============================] - 1s 2ms/step - loss: 0.4943 - accuracy: 0.7461
Epoch 49/50
357/357 [==============================] - 0s 1ms/step - loss: 0.4930 - accuracy: 0.7500
Epoch 50/50
357/357 [==============================] - 1s 1ms/step - loss: 0.4946 - accuracy: 0.7382
14/14 [==============================] - 0s 1ms/step - loss: 0.6377 - accuracy: 0.6704
Accuracy: 67.04
Custom Algorithm
/root/venv/lib/python3.7/site-packages/xgboost/sklearn.py:797: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.
UserWarning,
0
2012
-0.9874929592
1
2012
-0.7091802566
2
2012
0.1257578513
3
2012
-0.9874929592
4
2012
0.4040705539
5
2012
0.4040705539
6
2012
0.9606959591
7
2012
0.9606959591
8
2012
-1.265805662
9
2012
0.4040705539
Further Experiments
0
2016
1195
99
2016
1462
98
2016
1458
97
2016
1458
96
2016
1458
95
2016
1437
94
2016
1437
93
2016
1437
92
2016
1437
90
2016
1437
89
2016
1433
88
2016
1433
87
2016
1401
86
2016
1401
85
2016
1401
100
2016
1462
84
2016
1393
82
2016
1393
81
2016
1393
80
2016
1393
79
2016
1386
78
2016
1386
77
2016
1372
76
2016
1372
75
2016
1332
74
2016
1332
73
2016
1332
72
2016
1332
71
2016
1328
70
2016
1328
69
2016
1328
83
2016
1393
68
2016
1328
406
2016
1435
408
2016
1409
405
2016
1192
437
2016
1451
436
2016
1338
435
2016
1421
434
2016
1333
433
2016
1453
432
2016
1173
431
2016
1153
430
2016
1452
429
2016
1167
428
2016
1400
427
2016
1277
426
2016
1355
425
2016
1396
407
2016
1380
424
2016
1143
422
2016
1112
421
2016
1214
420
2016
1201
419
2016
1425
418
2016
1138
417
2016
1392
416
2016
1122
415
2016
1233
414
2016
1151
413
2016
1371
412
2016
1423
411
2016
1160
410
2016
1403
409
2016
1345
423
2016
1124
67
2016
1328
91
2016
1437
65
2016
1323
30
2016
1242
29
2016
1242
28
2016
1242
27
2016
1242
26
2016
1235
25
2016
1235
24
2016
1235
23
2016
1231
22
2016
1231
21
2016
1231
20
2016
1211
19
2016
1211
18
2016
1211
17
2016
1181
31
2016
1246
16
2016
1181
14
2016
1163
13
2016
1163
12
2016
1139
11
2016
1139
10
2016
1114
66
2016
1323
8
2016
1276
7
2016
1276
6
2016
1221
5
2016
1221
4
2016
1455
3
2016
1455
2
2016
1455
1
2016
1195
15
2016
1181
Final Test
5/5 [==============================] - 0s 2ms/step - loss: 0.7236 - accuracy: 0.6288
Accuracy: 62.88