co2-emissions-ml 1.0.1

End-to-end ML pipeline for vehicle CO2 emissions prediction

CO₂ Emissions Prediction from Vehicle Features

Authors: Shashvat Jain
Affiliation: Integrated M.Tech. in Mathematics & Computing, IIT Dhanbad
GitHub: https://github.com/Shashvat-Jain/CO2-predictions-using-Automotive-Features

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co2_emissions_ml

CO₂ Emissions Prediction from Vehicle Features
End-to-end Python package for analyzing and predicting on-road vehicle CO₂ emissions (g/km) via machine learning.

Features

• Preprocessing & Feature Engineering: scaling, one-hot encoding, target transformation
• Baseline Models: linear, polynomial, ridge/lasso, random forest, XGBoost, LightGBM, CatBoost
• Stacked Ensemble: LightGBM + XGBoost + CatBoost → MLP meta-learner → Ridge residual correction
• Bayesian Hyperparameter Tuning: Optuna pruners, early stopping
• Diagnostics & Explainability: parity plots, residual analysis, learning curves, permutation importance, SHAP

Key result:

Test set: (R^2 = 0.9830), MAE ≈ 3.08 g/km, RMSE ≈ 8.64 g/km

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📦 Repository Structure

.
├── README.md
├── LICENSE
├── CITATION.cff
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── DATA_DICTIONARY.md
├── .gitignore
├── environment.yml
├── requirements.txt
├── setup.py
├── Dockerfile
│
├── data/
│ └── New Dataset.csv
│
├── notebooks/
│ └── co2-emissions-predict.ipynb
│
├── src/
│ ├──models
│ └──co2_emissions_ml
│    ├── __init__.py
│    ├── preprocessing.py
│    ├── models.py
│    ├── evaluation.py
│    └── pipeline.py
│
├── tests/
│ └── test_pipeline.py
│
├── scripts/
│ └── train_and_save.py
│
├── Figures/
│ ├── parity_plot.png
│ ├── residual_hist.png
│ ├── qq_plot.png
│ ├── residuals_vs_pred.png
│ ├── mae_decile.png
│ ├── learning_curve.png
│ ├── perm_importance.png
│ ├── shap_summary.png
│ ├── shap_dependence.png
│ └── pipeline_diagram.png
│
├── Slides/
│ └── End Evaluation.pdf
│
└── Reports/
├── Split Report
└── Final Report with plag report.pdf

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⚙️ Installation

# From PyPI
pip install co2_emissions_ml

# Or install latest from GitHub
pip install git+https://github.com/Shashvat-Jain/CO2-predictions-using-Automotive-Features.git

Quickstart

1. Predict via CLI

run_co2 \
  --data path/to/your_new_data.csv \
  --model path/to/pretrained_bundle.pkl \
  --output path/to/predictions.csv

• --data (required): input CSV with vehicle features

• --model (optional): path to serialized bundle.pkl (default: models/bundle.pkl)

• --output (optional): CSV path for predictions

• --target (optional): dependent variable name in input CSV

2. Programmatic API

import pandas as pd
import joblib
from co2_emissions_ml.models import predict_bundle

# Load pre-trained bundle
bundle = joblib.load("models/bundle.pkl")

# Prepare new data
df_new = pd.read_csv("your_new_data.csv")
X_new  = df_new.copy()

# Predict
df_new["predicted_CO2"] = predict_bundle(bundle, X_new)
df_new.to_csv("predictions.csv", index=False)

🚀 Usage of GitHub Repository

1. Prepare data Place New Dataset.csv under data/.

2. Run notebook Open and execute notebooks/co2_emissions_predict.ipynb to reproduce EDA, model training, and evaluation.

3. Diagnostics & plots Generated in figures/:

   • Parity plot
   • Residual histogram & Q-Q plot
   • Learning curve
   • Permutation & SHAP importance charts

Note: The notebook co2_emissions_predict.ipynb contains the complete code for the thesis whereas the src folder only contains the code for the new pipeline presented in this research.

📊 Results Snapshot

Figure: Figure:

📚 References

• Smith A., Jones B., Lee C. (2020). Random Forest–Based Prediction of Vehicle CO₂ Emissions. Int. J. Automotive Technol.

• Gupta R., Ramesh S. (2021). XGBoost Regression for Estimating Vehicle Emissions. IEEE Trans. Intelligent Vehicles.

• Tansini A., Pavlović I., Fontaras G. (2022). Forecasting CO₂ Emissions Using Ensemble, ML & DL. PeerJ.

• Zhao P., Zhang X., Li Y. (2023). Global Fuel- and Vehicle-Type-Specific CO₂ Emissions. Earth Syst. Sci. Data.

• Government of Canada (2024). Fuel Consumption Ratings. Open Gov. Portal.

• U.S. EPA (2022). 2022 EPA Automotive Trends Report. EPA-420-S-22-001.

• (See full bibliography in reports/.)

📄 License

This project is licensed under the MIT License. See LICENSE for details.