# 073. Fuel economy prediction

**Question:** Can technical specifications estimate historical city-cycle fuel economy?

## Result

The largest computed metric is 6.101 for Median baseline; the smallest is 1.885 for Extra trees. Metric: mae (MPG MAE).

Car names and row identifiers are excluded. Horsepower imputation is fitted only on training observations.

![Fuel economy prediction](outputs/chart.png)

| model | mae | rmse | r2 | bias |
| --- | --- | --- | --- | --- |
| Median baseline | 6.101 | 7.275 | -0.002077 | -0.3312 |
| Ridge | 2.496 | 3.492 | 0.7692 | 0.2085 |
| Extra trees | 1.885 | 2.805 | 0.851 | 0.007504 |

The chart shows 3 of 3 result rows; the table previews the first 3 in the analysis-defined order. [Download the full result table](outputs/results.csv). Numerical values are computed from the source; missing results stay unavailable.

## Method

Fixed grouped-input regression with median and linear baselines.

The study uses shared source preparation and reusable statistical routines. Its specific transformations are in [analysis.py](analysis.py), and common model/evaluation code is in [portfolio/methods.py](../../portfolio/methods.py). The [notebook](analysis.ipynb) executes the study and displays the saved results.

## Evaluation

Fixed 80/20 split of unique input groups (seed 42); exact input duplicates stay together. Training observations: 318; test observations: 80. Fixed configurations specified before scoring; no tuning on the holdout.

Target: `mpg`. Features: cylinders, displacement, horsepower, weight, acceleration, model_year, origin. Model results and row membership are recorded in [evaluation.json](outputs/evaluation.json).

## Decision and limitations

Validate on new vehicle designs and test conditions before making engineering predictions.

Historical city-cycle vehicle observations are not current fleet performance. Vehicle mix and model year are confounded. A '?' horsepower value means missing, not zero. 

Related studies may share observations or holdouts. These are focused analytical studies, not independent replications or deployed business systems. Any model refinements informed by these results need new untouched evaluation data. No commercial impact is inferred from an association or backtest.

## Reproduce

From the repository root, after installing `requirements.txt`:

```powershell
python projects/073-fuel-economy-prediction/analysis.py
```

Source data are downloaded automatically if absent. Original archives are retained unchanged and checked by SHA-256. The cleaned cache normalizes column names; field-specific changes are visible in [data preparation](../../portfolio/data.py). Runtime evidence is in [receipt.json](outputs/receipt.json).

## Source

[Auto MPG](https://archive.ics.uci.edu/dataset/9/auto+mpg), Quinlan (1993). [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Source data are transformed and aggregated in this study. [Source provenance](../../data/provenance/auto.json) and [prepared-data audit](../../data/provenance/auto_prepared.json) record the downloaded files, field coverage and hashes.