/Machine_Learning_Web_Project_To_Predict_Bankruptcy

It is a machine learning web app to predict bankruptcy of a company based on certain input values.

Primary LanguageJupyter Notebook

Project name:-

Bank ruptcy prediction using machine learning web app

Period:-

April 2020-May 2020

Business objective:-

This project is to predict company which took bank loan will go bankrupt or not .based on certain features like total liabilities / total assets, current assets / short-term liabilities, equity / total assets…..like this there are 64 features based on that we have to predict bankruptcy. Dataset information:- The dataset is about bankruptcy prediction of Polish companies. The data was collected from Emerging Markets Information Service (EMIS, [Web Link]), which is a database containing information on emerging markets around the world.

Algorithms:-

XGBoosting algorithm

Steps:-

EDA, insights of data, feature engineering, feature selection, analysis of different algo.

Deployment:-

on flask as integrating platform on pycharm as tool for integration

Video Demo link for explanation :-

https://www.linkedin.com/posts/saket-nandan-758117106_machinelearning-datascience-dataanalytics-activity-6719835624259518464-3h34

Data Description :-

The dataset is about bankruptcy prediction of Polish companies. The data was collected from Emerging Markets Information Service (EMIS, [Web Link]), which is a database containing information on emerging markets around the world. The bankrupt companies were analysed in the period 2000-2012, while the still operating companies were evaluated from 2007 to 2013.

1st Year: 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.

2nd Year: the data contains financial rates from 2nd year of the forecasting period and corresponding class label that indicates bankruptcy status after 4 years. The data contains 10173 instances (financial statements), 400 represents bankrupted companies, 9773 firms that did not bankrupt in the forecasting period.

3rd Year: the data contains financial rates from 3rd year of the forecasting period and corresponding class label that indicates bankruptcy status after 3 years. The data contains 10503 instances (financial statements), 495 represents bankrupted companies, 10008 firms that did not bankrupt in the forecasting period.

4th Year: the data contains financial rates from 4th year of the forecasting period and corresponding class label that indicates bankruptcy status after 2 years. The data contains 9792 instances (financial statements), 515 represents bankrupted companies, 9277 firms that did not bankrupt in the forecasting period.

5thYear: the data contains financial rates from 5th year of the forecasting period and corresponding class label that indicates bankruptcy status after 1 year. The data contains 5910 instances (financial statements), 410 represents bankrupted companies, 5500 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

Class - 0 did not get bankrupt/ 1 - got bankrupt