This repository contains code and data for a machine learning model that predicts ESG (Environmental, Social, and Governance) scores based on sustainability reports and company data. It's a valuable resource for researchers, investors, and sustainability professionals interested in ESG score prediction using machine learning techniques.
*Word Embeddings: Methods like Word2Vec or GloVe can convert words into dense vectors that capture semantic relationships.
Models: Feed these embeddings into neural networks such as Feedforward Neural Networks or more advanced architectures like LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Units).
Advantage**: These models can capture the sequential nature of text and understand context better.
*Word Embeddings: Methods like Word2Vec or GloVe can convert words into dense vectors that capture semantic relationships. Models: Feed these embeddings into neural networks such as Feedforward Neural Networks or more advanced architectures like LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Units). Advantage**: These models can capture the sequential nature of text and understand context better.