Corportate-Risk-Analysis-EDA

📘 Overview

This project, Corporate Risk Analysis, focuses on understanding how financial structure, liquidity, and profitability contribute to a company’s overall risk level. Using Python’s analytical capabilities, the dataset was cleaned, processed, and analyzed to detect patterns that reveal why certain industries or companies become financially vulnerable. The project aims to bring a clear, data-backed picture of corporate stability and highlight the early signs of distress.

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🎯 Objective

To explore company-level financial and macroeconomic data and identify the critical financial indicators that determine whether a company falls under a high-risk or low-risk category. The end goal is to make data-driven conclusions about financial health and provide practical improvement insights.

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⚙️ Technologies Used

• Programming Language: Python
• Libraries: Pandas, NumPy, Matplotlib
• Environment: Jupyter Notebook
• Data Format: CSV (5000 records, 20+ columns)

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📈 Flow of Project

1. Load: Imported and explored the dataset using Pandas.
2. Cleaned: Removed duplicates, fixed datatypes, and added new calculated columns such as Debt Ratio, Profit Margin, and Leverage Score.
3. Insights: Conducted EDA across five major areas — risk overview, leverage, profitability, liquidity, and sectoral analysis.
4. Charts: Built clear and structured visualizations in Matplotlib to display patterns across risk levels, industries, and financial metrics.

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🔍 Key Findings

1. High Debt Exposure: Finance and Healthcare sectors showed the highest leverage, making them more prone to risk.
2. Liquidity Imbalance: Firms with current ratios close to or below 1.0 were more likely to experience short-term financial stress.
3. Profitability Link: Companies with consistent ROA above 10% and profit margins over 15% were almost always classified as low-risk.

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💡 Suggestions

1. Maintain Healthy Leverage: Keep debt-equity ratio under control to reduce default probability.
2. Enhance Cash Flow Management: Maintain sufficient working capital and improve liquidity planning.
3. Focus on Efficiency: Strengthen operational performance and margin control to reduce overall financial exposure.

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📂 Project Structure

Corporate-Risk-Analysis/
│
├── charts/                # Matplotlib visual outputs
├── dataset/               # Raw and cleaned datasets
├── cleaning.ipynb         # Data cleaning and preprocessing notebook
├── analysis.ipynb         # Exploratory analysis and visualization notebook
└── README.md              # Project overview file

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👤 Author

Analyzed by: Ashirbad Routray
Date: November 3, 2025