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$