A README file is included in the supplementary materials to guide users on how to set up the environment, run the code, and interpret the results.
markdown
Copy code
# Enhanced risk management Model (HFRP model)

## Description
This repository contains the implementation of the Hybrid Financial Risk Predictor (HFRP) model to improve financial risk prediction. The model uses CNNs and LSTM networks.

## Requirements
- Python 3.8
- Jupyter Notebook
- Libraries: pandas, scikit-learn, openpyxl

## Setup
1. Clone the repository
2. Install the required libraries
3. Run the Jupyter Notebook `Umar_Financial_Paper.ipynb`

## Data
The dataset `Financial_Dataset_with_Diverse_Disclosures.csv` contains financial reports of different companies with features such as Company Name, Report Date, Revenue, Net Income, Earnings Per Share (EPS),Total Assets, Total Liabilities, Operating Income, Cash Flow from Operations, Textual Disclosures.

## Running the Code
1. Open the Jupyter Notebook `Umar_Financial_Paper.ipynb`
2. Follow the steps in the notebook to preprocess the data, train the models, and evaluate their performance.

## Results
The results include Risk Score, Credit Risk, Market Risk, Operational Risk, Liquidity Risk, Risk Reduction Percentage. The notebook also contains visualizations of the evaluation metrics.