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https://github.com/abinashsahoo007/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.
https://github.com/abinashsahoo007/project-bankruptcy-prevention
data-analysis data-mining data-visualization deployments eda machine-learning pickle python statistics streamlit
Last synced: 8 days ago
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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.
- Host: GitHub
- URL: https://github.com/abinashsahoo007/project-bankruptcy-prevention
- Owner: abinashsahoo007
- Created: 2024-06-29T12:51:13.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-07-03T11:45:07.000Z (5 months ago)
- Last Synced: 2024-07-04T07:30:22.554Z (5 months ago)
- Topics: data-analysis, data-mining, data-visualization, deployments, eda, machine-learning, pickle, python, statistics, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 1.36 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://docs.google.com/presentation/d/1_KWVI3N4jZLliNp6raZn05W5cZouKzAd/edit#slide=id.p1)
## 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.
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Solution: Used **Random Forest Method** for a conclusion
## Final Output:
## Thank You for Visiting....