Tutorial

This tutorial walks you through installing the package, running the CLI tool, and using the Python API for custom workflows.

Contents:

Installation
CLI Usage
Python API
Loading Your Own Model

—

Installation

First, install from PyPI:

pip install co2_emissions_ml

Or install the bleeding‐edge version from GitHub:

pip install git+https://github.com/Shashvat-Jain/CO2-predictions-using-Automotive-Features.git

—

CLI Usage

Once installed, the run_co2 console script is available.

Predict on a new dataset without CO₂ targets:

run_co2 --data path/to/new_data.csv \
        --model path/to/bundle.pkl \
        --output path/to/predictions.csv

Train and evaluate on data with CO₂ targets:

run_co2 --data path/to/labeled_data.csv \
        --target "CO2 Emissions (g/km)" \
        --output path/to/results.csv

—

Python API

You can also call everything programmatically:

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

# 1) Train & evaluate
bundle, metrics = run_pipeline(
    data_path="data/labeled_data.csv",
    target_col="CO2 Emissions (g/km)"
)
print(metrics)

# 2) Inference only
df_new = pd.read_csv("data/new_data.csv")
predictions = predict_bundle(bundle, df_new)
df_new["predicted_co2"] = predictions
df_new.to_csv("data/predicted.csv", index=False)

—

Loading Your Own Model

If you have a serialized bundle saved as bundle.pkl:

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

bundle = joblib.load("models/bundle.pkl")
df = pd.read_csv("path/to/new_vehicles.csv")
df["predicted_co2"] = predict_bundle(bundle, df)
df.to_csv("predictions.csv", index=False)