https://github.com/thee-unruly/esg-risk-analysis

Problem: Predicting ESG Risk Scores for Companies
https://github.com/thee-unruly/esg-risk-analysis

analytics esg predictive-modeling statistics

Last synced: about 1 month ago
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Problem: Predicting ESG Risk Scores for Companies

• Host: GitHub
• URL: https://github.com/thee-unruly/esg-risk-analysis
• Owner: Thee-Unruly
• Created: 2024-08-12T12:58:26.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-12T13:10:03.000Z (4 months ago)
• Last Synced: 2024-10-28T14:22:23.126Z (about 2 months ago)
• Topics: analytics, esg, predictive-modeling, statistics
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/pritish509/s-and-p-500-esg-risk-ratings
• Size: 1.15 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ESG-Risk-Analysis

**Problem**: **Predicting ESG Risk Scores for Companies**

**Problem Overview**

*Environmental, Social, and Governance (ESG)* risk scores are critical for investors who are increasingly looking to invest in companies with sustainable and ethical practices. 

However, obtaining accurate ESG risk scores can be challenging due to the subjective nature of ESG criteria and the vast amount of unstructured data available (e.g., news articles, reports, social media).

**Proposed ML Solution**

Using machine learning, we can predict *ESG risk scores* by analyzing various data sources and identifying relevant patterns and indicators. 

The model can continuously learn from new data, making the ESG risk scores more accurate and up-to-date.