bankruptcy-data-exp

所属分类:金融证券系统
开发工具:Jupyter Notebook
文件大小:78767KB
下载次数:9
上传日期:2022-01-08 20:39:19
上 传 者sh-1993
说明:  破产数据exp,机器学习模型预测公司状况的探索、分析和应用
(bankruptcy-data-exp,Exploration, analysis and application of machine learning models to predict companies status)

文件列表:
LICENSE (1065, 2022-01-09)
api (0, 2022-01-09)
api\api (0, 2022-01-09)
api\api\__init__.py (0, 2022-01-09)
api\api\asgi.py (383, 2022-01-09)
api\api\settings.py (3219, 2022-01-09)
api\api\urls.py (340, 2022-01-09)
api\api\wsgi.py (383, 2022-01-09)
api\core (0, 2022-01-09)
api\core\__init__.py (0, 2022-01-09)
api\core\admin.py (63, 2022-01-09)
api\core\apps.py (83, 2022-01-09)
api\core\data_preprocessing.py (3397, 2022-01-09)
api\core\migrations (0, 2022-01-09)
api\core\migrations\__init__.py (0, 2022-01-09)
api\core\models.py (57, 2022-01-09)
api\core\tests.py (60, 2022-01-09)
api\core\urls.py (214, 2022-01-09)
api\core\views.py (7079, 2022-01-09)
api\db.sqlite3 (131072, 2022-01-09)
api\manage.py (659, 2022-01-09)
api\media (0, 2022-01-09)
api\media\test_file.csv (448951, 2022-01-09)
api\static (0, 2022-01-09)
api\static\best_model (0, 2022-01-09)
api\static\best_model\gbm.sav (2714309, 2022-01-09)
api\static\best_model\lda.sav (3487, 2022-01-09)
api\static\best_model\lr.sav (1249, 2022-01-09)
api\static\best_model\mlp.sav (171919, 2022-01-09)
api\static\best_model\rf.sav (15028265, 2022-01-09)
api\static\best_model\scaler.sav (1947, 2022-01-09)
api\static\best_model\svm.sav (2268560, 2022-01-09)
api\static\best_model\ulr.sav (995, 2022-01-09)
api\static\best_model\umlp.sav (99495, 2022-01-09)
api\static\best_model\uscaler.sav (1194, 2022-01-09)
api\static\core (0, 2022-01-09)
api\static\core\base.css (2581, 2022-01-09)
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# bankruptcy-data-exp **The dataset is provided by Sebastian Tomczak and collected from Emerging Markets Information Service ([EMIS](https://www.emis.com/)) : https://archive.ics.uci.edu/ml/datasets/Polish+companies+bankruptcy+data** ***STARTER BELLOW*** The dataset is about bankruptcy prediction of Polish companies. In theses datasets, we retrieve information about emerging markets around the word (or Poland, who knows ?). A dataset is composed of thousands of rows where each row corresponds to a company. The attribute about theses companies is given in data/description.txt file. Here, is a sample of what we can have in a dataset :
bankrupt
0 0.034279 0.42448 -0.075832 0.67532 -77.334 -0.01497 0.044048 1.3558 1.1287 0.57552 0.044048 0.1886 0.11021 0.044048 2069.8 0.17635 2.3558 0.044048 0.0***853 22.179 1.0305 0.077574 0.050469 -0.016044 0.57552 0.15333 1.2892 -0.090033 5.1839 0.61859 0.0***853 141.67 2.57*** 0.18275 0.077574 0.67974 0.60997 0.76***4 0.11421 0.04225 0.12876 0.11421 79.459 57.28 0.83056 0.4***61 25.035 0.046766 0.068854 0.37158 0.23356 0.38815 0.6833 0.90997 -11581.0 0.11406 0.059561 0.88594 0.33173 1***57 6.3722 125.51 2.908 0.80639
0                                         0.096308                                         0.50574                                         0.48163                                         1.9523                                         229.04                                         0                                         0.096308                                         0.97731                                         3.7***1                                         0.49426                                         0.15378                                         0.19043                                         0.42351                                         0.096308                                         114.76                                         3.1806                                         1.9773                                         0.096308                                         0.025357                                         6.514                                         0.60105                                         0                                         0.025357                                         0.32281                                         0.45095                                         3.1806                                         0                                         38.13                                         3.0624                                         0.026525                                         0.059***5                                         85.534                                         4.2673                                         4.2673                                         0.0045052                                         3.7***1                                         ?                                         0.49426                                         0.0011862                                         0.80652                                         0.011148                                         0                                         55.688                                         49.174                                         1.4208                                         1.8183                                         11.4***                                         -1.5122                                         -0.3***15                                         1.9523                                         0.50574                                         0.23434                                         39.13                                         39.13                                         556.01                                         0.43179                            &nb ... ...
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