Next steps and resources

Clean the data – The data might include a significant amount of irrelevant information that is unrelated to ESG factors and financial data. It is important to remove this irrelevant data and retain only the information needed to perform the required analytics. You can use tools, such as like yfinance, to help clean the data.

Extract and transform the data – Extract the relevant features or variables from the raw data and transform them into a format that is suitable for analysis. You can transform the data into tabular format for better readability and clarity. You can use a library, such as pandas, to refine the data. You can also use feature engineering, data normalization, and derived metrics to transform the data.

Perform analytics – You can perform various analytical tasks. This might include generating descriptive statistics, creating data visualizations, and conducting exploratory data analysis to gain insights into the ESG performance of the companies.

Apply machine learning – You can use the cleaned and transformed data to train machine learning models. These models can help you identify companies that are currently demonstrating financial sustainability and project their future sustainability performance.