# Numerical data: Conclusion

A machine learning (ML) model's health is determined by its data. Feed your
model healthy data and it will thrive; feed your model junk and its
predictions will be worthless.

Best practices for working with numerical data:

- Remember that your ML model interacts with the data in the [**feature vector**](https://developers.google.com/machine-learning/glossary#feature_vector), not the data in the [**dataset**](https://developers.google.com/machine-learning/glossary#dataset).
- [**Normalize**](https://developers.google.com/machine-learning/glossary#normalization) most numerical [**features**](https://developers.google.com/machine-learning/glossary#feature).
- If your first normalization strategy doesn't succeed, consider a different way to normalize your data.
- [**Binning**](https://developers.google.com/machine-learning/glossary#binning), also referred to as [**bucketing**](https://developers.google.com/machine-learning/glossary#bucketing), is sometimes better than normalizing.
- Considering what your data *should* look like, write verification tests to validate those expectations. For example:
  - The absolute value of latitude should never exceed 90. You can write a test to check if a latitude value greater than 90 appears in your data.
  - If your data is restricted to the state of Florida, you can write tests to check that the latitudes fall between 24 through 31, inclusive.
- Visualize your data with scatter plots and histograms. Look for anomalies.
- Gather statistics not only on the entire dataset but also on smaller subsets of the dataset. That's because aggregate statistics sometimes obscure problems in smaller sections of a dataset.
- Document all your data transformations.

Data is your most valuable resource, so treat it with care.

## Additional Information

- The *Rules of Machine Learning* guide contains a valuable [Feature Engineering](https://developers.google.com/machine-learning/rules-of-ml/#ml_phase_ii_feature_engineering) section.

## What's next

Congratulations on finishing this module!

We encourage you to explore the various [MLCC modules](https://developers.google.com/machine-learning/crash-course)
at your own pace and interest. If you'd like to follow a recommended order,
we suggest that you move to the following module next:
**[Representing categorical data](https://developers.google.com/machine-learning/crash-course/categorical-data)**.

*** ** * ** ***

> [!IMPORTANT]
> **Key terms:**
>
> - [Binning](https://developers.google.com/machine-learning/glossary#binning)
> - [Bucketing](https://developers.google.com/machine-learning/glossary#bucketing)
> - [Dataset](https://developers.google.com/machine-learning/glossary#dataset)
> - [Feature](https://developers.google.com/machine-learning/glossary#feature)
> - [Feature vector](https://developers.google.com/machine-learning/glossary#feature_vector)
> - [Normalization](https://developers.google.com/machine-learning/glossary#normalization)

[Help Center](https://support.google.com/machinelearningeducation)