Carbon Credit Price ML
Predict carbon credit price from project features. Train a Random Forest regressor and inspect feature importance to understand what drives carbon credit prices.
4
Carbon Credit Price ML
python • Step 4
Predict carbon credit price from project features. Train a Random Forest regressor and inspect feature importance to understand what drives carbon credit prices.
pandassklearnregressionmlclimate
Learning Objectives
- →Prepare features for a regression model
- →Train/test split and evaluate with MAE/RMSE
- →Interpret feature importance for carbon credit pricing
- →Understand the limits of small ML models in carbon markets
Dataset
View on data.worlddata.world filename
Lab path (browser)
Carbon offset project metadata, methodologies, and issuances.
*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/carbon_credit_prices.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? data.world ships offset_projects.csv. Point read_csv at that path and everything else works unchanged.
Dataset Columns
project_idissuance_tco2evintagemethodology_encodedprice_usd- 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
Python
Loading editor...
*bleep* Output will appear here after running your code. Figures from matplotlib/seaborn render inline.
Expected output:
MAE: $X.XX Importances:
0/3
Export to GitHub
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