The dataset is provided by Sebastian Tomczak and collected from Emerging Markets Information Service (EMIS) : 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 :
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| 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 | 0.064853 | 22.179 | 1.0305 | 0.077574 | 0.050469 | -0.016044 | 0.57552 | 0.15333 | 1.2892 | -0.090033 | 5.1839 | 0.61859 | 0.064853 | 141.67 | 2.5764 | 0.18275 | 0.077574 | 0.67974 | 0.60997 | 0.76644 | 0.11421 | 0.04225 | 0.12876 | 0.11421 | 79.459 | 57.28 | 0.83056 | 0.49861 | 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 | 16.457 | 6.3722 | 125.51 | 2.908 | 0.80639 |
| 0 | 0.096308 | 0.50574 | 0.48163 | 1.9523 | 229.04 | 0 | 0.096308 | 0.97731 | 3.7981 | 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 | 0.059985 | 85.534 | 4.2673 | 4.2673 | 0.0045052 | 3.7981 | ? | 0.49426 | 0.0011862 | 0.80652 | 0.011148 | 0 | 55.688 | 49.174 | 1.4208 | 1.8183 | 11.464 | -1.5122 | -0.39815 | 1.9523 | 0.50574 | 0.23434 | 39.13 | 39.13 | 556.01 | 0.43179 | 0.19485 | 0.58486 | 0 | 56.033 | 7.4227 | 48.601 | 7.5101 | 300.69 |
| 0 | -0.20902 | 1.2022 | -0.2562 | 0.053378 | -108.75 | -0.38107 | -0.20902 | -0.16822 | 0.82685 | -0.20224 | -0.14916 | -0.77232 | -0.098138 | -0.20902 | -5407.8 | -0.067495 | 0.83178 | -0.20902 | -0.25279 | 0 | ? | -0.14916 | -0.25279 | -0.20902 | -0.59009 | -0.067495 | -2.4917 | -0.25995 | 2.8692 | 1.454 | -0.1804 | 101.21 | 3.6062 | 0.81183 | -0.14916 | 0.82685 | 0.015507 | 0.72936 | -0.1804 | 9.9866e-006 | -1.883 | -0.1804 | 6.376 | 6.376 | ? | 0.053378 | 0 | -0.27704 | -0.33505 | 0.012016 | 0.27064 | 0.2773 | -0.20521 | 0.74005 | -189.58 | -0.1804 | 1.0335 | 1.2528 | -4.6064 | ? | 57.246 | 119.47 | 3.0551 | 0.83897 |
| 0 | 0.20097 | 0.19291 | 0.23709 | 2.229 | 93.472 | 0 | 0.20097 | 4.1836 | 2.8936 | 0.80709 | 0.20097 | 1.0418 | 0.41244 | 0.20097 | 59.001 | 6.1864 | 5.1836 | 0.20097 | 0.069453 | 9.6467 | 0.85696 | 0 | 0.069453 | 0.51819 | 0.76878 | 6.1864 | ? | 0.41595 | 3.7389 | 0.040859 | 0.15536 | 43.706 | 8.3512 | 8.3512 | 0.0066855 | 2.8936 | ? | 0.80709 | 0.0023105 | 0.38714 | 0.0064793 | 0 | 44.82 | 35.174 | 2.6279 | 1.8326 | 17.326 | -0.99247 | -0.34299 | 2.229 | 0.19291 | 0.11974 | 1.416 | 1.416 | 1299.7 | 0.44323 | 0.24901 | 0.55688 | 0 | 37.837 | 10.377 | 24.334 | 14.999 | 5.0765 |
| 0 | -0.11132 | 0.64559 | 0.0041018 | 1.0071 | -38.084 | 0 | -0.11132 | 0.54897 | 2.5568 | 0.35441 | -0.026645 | -0.19222 | -0.022736 | -0.11132 | -4053.6 | -0.090045 | 1.549 | -0.11132 | -0.043539 | 37.421 | 1.0872 | -0.09603 | -0.043539 | -0.1175 | -0.085977 | -0.090045 | -1.1341 | 0.0098424 | 3.3675 | 0.25123 | -0.033709 | 81.987 | 4.4519 | 3.9938 | -0.021489 | 2.5568 | 7.0439 | 0.4 | -0.0084045 | 0.021281 | -0.50233 | -0.037558 | 81.502 | 44.081 | -0.42467 | 0.55446 | 37.109 | -0.14922 | -0.058361 | 0.90344 | 0.57915 | 0.22462 | 0.85041 | 0.9598 | 9.56 | -0.0084045 | -0.31411 | 1.042 | 0.12863 | 9.7539 | 8.2802 | 82.676 | 4.4148 | 6.1352 |
| 1 | -0.40937 | 0.58325 | 0.20188 | 1.3461 | -0.7769 | 0.0 | -0.40937 | 0.71453 | 9.8193 | 0.41675 | -0.25112 | -0.70189 | -0.024009 | -0.40937 | -903.02 | -0.4042 | 1.7145 | -0.40937 | -0.041691 | 2.7925 | ? | -0.37487 | -0.041691 | -0.40937 | -0.20825 | -0.4042 | -2.3689 | 0.94005 | 1.9031 | 0.016142 | -0.041691 | 20.883 | 17.478 | 17.478 | -0.37487 | 9.943 | ? | 0.41675 | -0.038178 | 0.98264 | -0.096605 | -0.038178 | 7.8804 | 5.0879 | -5.4493 | 1.1114 | 2.6898 | -0.5485 | -0.05586 | 1.3461 | 0.58325 | 0.057214 | 1.9406 | 1.9406 | 16.15 | -0.03819 | -0.9823 | 1.0253 | 0.0 | 130.71 | 71.739 | 21.681 | 16.835 | 45.724 |
| 1 | -0.19899 | 0.42164 | 0.57836 | 2.3717 | 50.094 | -0.20152 | -0.19899 | 1.3717 | 3.9931 | 0.57836 | -0.19797 | -0.47193 | -0.041069 | -0.19899 | -938.46 | -0.38893 | 2.3717 | -0.19899 | -0.049833 | 0 | ? | -0.19831 | -0.049833 | -0.19899 | -0.38529 | -0.38893 | -195.5 | ? | 1.772 | -0.054405 | -0.049833 | 36.719 | 9.9407 | 9.9407 | -0.19831 | 3.9932 | ? | 0.57836 | -0.049663 | 1.5152 | -0.086059 | -0.049663 | 33.009 | 33.009 | ? | 1.5152 | 0 | -0.23331 | -0.058428 | 2.3717 | 0.42164 | 0.1006 | ? | ? | 34.21 | -0.049621 | -0.34405 | 1.0496 | 0.0 | ? | 11.058 | 38.541 | 9.4703 | ? |
| 1 | 0.14806 | 0.83471 | -0.050636 | 0.8059 | -43.448 | -0.34617 | 0.16452 | 0.19804 | 0.82432 | 0.16531 | 0.22872 | 0.63064 | 0.26792 | 0.16452 | 1379.5 | 0.26459 | 1.198 | 0.16452 | 0.19958 | 25.982 | ? | 0.2287 | 0.17961 | 0.16452 | -0.19827 | 0.24487 | 3.5621 | -0.064112 | 3.5149 | 0.95298 | 0.20139 | 94.239 | 3.8957 | 1.2175 | -0.18627 | 1.2451 | 0.28541 | 0.69632 | -0.22597 | 0.21346 | 0.097614 | 0.27744 | 68.433 | 42.451 | 2.5232 | 0.43839 | 21.074 | 0.17236 | 0.2091 | 0.25187 | 0.26087 | 0.25669 | 0.2093 | 0.88164 | -165.73 | -0.22572 | 0.89565 | 0.81625 | 3.2123 | 14.048 | 8.5981 | 115.51 | 3.1599 | 1.0437 |
| 1 | -0.092469 | 0.8223 | -0.18051 | 0.75948 | -200.23 | 0.0 | -0.096245 | 0.2161 | 1.1797 | 0.1777 | -0.059312 | -0.12824 | -0.046759 | -0.096245 | -5441.4 | -0.067079 | 1.2161 | -0.096245 | -0.081588 | 126.65 | 0.98898 | -0.07112 | -0.078387 | 0.91179 | 0.088247 | -0.062487 | -1.9257 | -0.41978 | 4.4644 | 0.68549 | -0.058165 | 258.64 | 1.4744 | 1.3456 | -0.079529 | 1.1797 | 5.0715 | 0.20938 | -0.067417 | 0.021861 | -0.91265 | -0.060289 | 171.28 | 44.637 | -0.22591 | 0.21409 | 135.02 | -0.11221 | -0.095118 | 0.69316 | 0.7505 | 0.67826 | 0.41323 | 0.48691 | -5259 | 0.10219 | -0.52038 | 0.9693 | 0.17829 | 2.882 | 8.177 | 232.21 | 1.5718 | 2.7433 |
| 1 | -0.006009 | 0.87154 | -0.30285 | 0.65251 | -20.725 | -2.1532 | -0.006009 | 0.14746 | 5.5023 | 0.12852 | 0.12882 | -0.0068951 | 0.018794 | -0.006009 | 3076.2 | 0.11865 | 1.1474 | -0.006009 | -0.0010922 | 0.0078939 | ? | 0.12882 | -0.0010922 | -0.006009 | -2.1592 | 0.11865 | 0.95543 | -0.70217 | 2.2255 | 0.10544 | -0.0010381 | 57.891 | 6.305 | 6.305 | 0.007199 | 5.6238 | ? | 0.12852 | 0.0013084 | 0.34244 | 0.12194 | 0.023411 | 17.927 | 17.919 | -50.5 | 0.34257 | 0.0079043 | 0.019397 | 0.0035252 | 0.65251 | 0.87154 | 0.15861 | 0.29797 | 0.29797 | -50.9 | 0.0013192 | -0.046759 | 0.97709 | 0.0 | 46239 | 20.369 | 57.815 | 6.3133 | 12.757 |
We are in a case of supervised learning, a classification with labelled data which indicates whether the company bankrupted or not.
The goal of this project is to explore, analyse and make data visualisation before applying machine learning algorithms in order to predict and classify, with the best accuracy, the situation of a company.
In total in this dataset, we have more than 43 000 companies' status inequitably distributed on 5 years. The columns represent the 64 variables we will use to predict bankruptcy. Among these variables, we can see there is underlying variables which appear and impact a lot of variables, like total assets, total liabilities, net profit and much more that we can use directly in order to have fewer variables to include in our model without loss of information. We can also see there is multiple missing values symbolised by a "?" in many fields.
Start to clone the project :
git clone https://github.com/a-brice/bankruptcy-data-exp.git
cd bankruptcy-data-expTo launch the API, you must install some dependencies first. (Window) From a shell from the root directory, enter the following :
python -m venv env
.\env\Scripts\activate
pip install -r requirement.txtWhen all installations are completed (a bit long), you can now run the API :
cd api
python manage.py runserverAnd after that, go to http://127.0.0.1:8000 and explore !