Build ESG Scoring Model
Create a custom ESG scoring methodology on top of the Kaggle ESG & Financial Performance dataset — weighted pillars, sector-adjusted benchmarks, and percentile rankings.
Build ESG Scoring Model
Python / R • Step 2
Create a custom ESG scoring methodology on top of the Kaggle ESG & Financial Performance dataset — weighted pillars, sector-adjusted benchmarks, and percentile rankings.
Learning Objectives
- →Normalize ESG pillar scores across companies
- →Build a weighted composite score
- →Produce sector-adjusted (industry-relative) ratings
Dataset
View on Kaggle1,000 companies × 11 years — ESG pillar scores + financials (Revenue, MarketCap, GrowthRate, CarbonEmissions).
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/company_esg_financial_dataset.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named company_esg_financial_dataset.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "company_esg_financial_dataset.csv").
Dataset Columns
CompanyIDCompanyNameIndustryRegionYearESG_EnvironmentalESG_SocialESG_GovernanceESG_Overallcustom_esgesg_percentileesg_z_scoreesg_ratingCode Language
- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Shape: Columns (subset):
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