Project-Bankruptcy-Prevention

The project is to create a classification model that predicts the chances of a business facing bankruptcy based on the key feature like Industrial Risk, Management Risk, Financial Flexibility, Credibility, Competitiveness, Operating Risk.

Presentation:

View Presentation

Business Objective:

• This is a classification project, since the variable to predict is binary (bankruptcy or non-bankruptcy).
• The goal here is to model the probability that a business goes bankrupt from different features.

Details :

The data file contains 7 features about 250 companies including the following variables:

• industrial_risk: 0=low risk, 0.5=medium risk, 1=high risk.
• management_risk: 0=low risk, 0.5=medium risk, 1=high risk.
• financial flexibility: 0=low flexibility, 0.5=medium flexibility, 1=high flexibility.
• credibility: 0=low credibility, 0.5=medium credibility, 1=high credibility.
• competitiveness: 0=low competitiveness, 0.5=medium competitiveness, 1=high competitiveness.
• operating_risk: 0=low risk, 0.5=medium risk, 1=high risk.
• class: bankruptcy, non-bankruptcy (target variable).

Acceptance Criterion is We need to deploy the end results using Flask /Streamlit.etc.

Algorithm Used:

1. Logistic Regression
2. DecisionTreeClassifier
3. KNeighborsClassifier
4. SupportVectorClassifier
5. NaiveBayes
6. RandomForest

Solution:

Using your choice of classifiers, use python to produce several models to predict whether or not it is bankrupt, assessing model performance on a validation partition.

Solution: Used Random Forest Method for a conclusion

Final Output:

Thank You for Visiting....