Data Science Project in R

Machine Learning Project 2019

Project description

The task in this project is to build a model to predict bankruptcy of polish companies.

The specific challenge is to deal with a very large number of financial attributes.

What you need to do is analyze the data to try to reduce the number of attributes and then try to build the most accurate model and find the most important predictors of bankruptcy of public companies (at least in Poland).

Project submission

Submit a report that sums up all the work done and of course all the relevant (preferably as a Bitbucket repository)

The project cab be done in teams up to 4 students

Data Set Information:

The dataset is about bankruptcy of Polish companies.

The data was collected from Emerging Markets Information Service (EMIS), which is a database containing information on emerging markets around the world.

The data contains financial rates from 1st year of the forecasting period and corresponding class label that indicates bankruptcy status after 5 years. The data contains 7027 instances (financial statements), 271 represents bankrupted companies, 6756 firms that did not bankrupt in the forecasting period.

Attribute Information:

X1 net profit / total assets

X2 total liabilities / total assets

X3 working capital / total assets

X4 current assets / short-term liabilities

X5 [(cash + short-term securities + receivables - short-term liabilities) / (operating expenses - depreciation)] * 365

X6 retained earnings / total assets

X7 EBIT / total assets

X8 book value of equity / total liabilities

X9 sales / total assets

X10 equity / total assets

X11 (gross profit + extraordinary items + financial expenses) / total assets

X12 gross profit / short-term liabilities

X13 (gross profit + depreciation) / sales

X14 (gross profit + interest) / total assets

X15 (total liabilities * 365) / (gross profit + depreciation)

X16 (gross profit + depreciation) / total liabilities

X17 total assets / total liabilities

X18 gross profit / total assets

X19 gross profit / sales

X20 (inventory * 365) / sales

X21 sales (n) / sales (n-1)

X22 profit on operating activities / total assets

X23 net profit / sales

X24 gross profit (in 3 years) / total assets

X25 (equity - share capital) / total assets

X26 (net profit + depreciation) / total liabilities

X27 profit on operating activities / financial expenses

X28 working capital / fixed assets

X29 logarithm of total assets

X30 (total liabilities - cash) / sales

X31 (gross profit + interest) / sales

X32 (current liabilities * 365) / cost of products sold

X33 operating expenses / short-term liabilities

X34 operating expenses / total liabilities

X35 profit on sales / total assets

X36 total sales / total assets

X37 (current assets - inventories) / long-term liabilities

X38 constant capital / total assets

X39 profit on sales / sales

X40 (current assets - inventory - receivables) / short-term liabilities

X41 total liabilities / ((profit on operating activities + depreciation) * (12/365))

X42 profit on operating activities / sales

X43 rotation receivables + inventory turnover in days

X44 (receivables * 365) / sales

X45 net profit / inventory

X46 (current assets - inventory) / short-term liabilities

X47 (inventory * 365) / cost of products sold

X48 EBITDA (profit on operating activities - depreciation) / total assets

X49 EBITDA (profit on operating activities - depreciation) / sales

X50 current assets / total liabilities

X51 short-term liabilities / total assets

X52 (short-term liabilities * 365) / cost of products sold)

X53 equity / fixed assets

X54 constant capital / fixed assets

X55 working capital

X56 (sales - cost of products sold) / sales

X57 (current assets - inventory - short-term liabilities) / (sales - gross profit - depreciation)

X58 total costs /total sales

X59 long-term liabilities / equity

X60 sales / inventory

X61 sales / receivables

X62 (short-term liabilities *365) / sales

X63 sales / short-term liabilities

X64 sales / fixed assets

My Budget is Fixed to 100$

Beceriler: Machine Learning (ML)

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İşveren Hakkında:
( 1 değerlendirme ) Boca raton, United States

Proje NO: #21347846

Bu iş için 21 freelancer ortalamada $130 teklif veriyor


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$200 USD in 7 gün içinde
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$250 USD in 7 gün içinde
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$100 USD in 5 gün içinde
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$100 USD in 3 gün içinde
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$140 USD in 7 gün içinde
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$140 USD in 7 gün içinde
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$140 USD in 7 gün içinde
(3 Değerlendirme)

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$100 USD in 4 gün içinde
(4 Değerlendirme)

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$100 USD in 5 gün içinde
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$140 USD in 7 gün içinde
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$100 USD in 4 gün içinde
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$100 USD in 7 gün içinde
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