# Evaluation

As a final step to your synthetic data project, you can evaluate and visualize the synthetic data against the real data.

```python
from sdv.evaluation.single_table import run_diagnostic, evaluate_quality
from sdv.evaluation.single_table import get_column_plot

# 1. perform basic validity checks
diagnostic = run_diagnostic(real_data, synthetic_data, metadata)

# 2. measure the statistical similarity
quality_report = evaluate_quality(real_data, synthetic_data, metadata)

# 3. plot the data
fig = get_column_plot(
    real_data=real_data,
    synthetic_data=synthetic_data,
    metadata=metadata,
    column_name='amenities_fee'
)
    
fig.show()
```

Explore the functionality in more detail below.

<table data-view="cards"><thead><tr><th></th><th></th></tr></thead><tbody><tr><td><a href="evaluation/diagnostic"><strong>Diagnostic</strong></a></td><td>Perform basic checks to ensure the synthetic data is valid.</td></tr><tr><td><a href="evaluation/data-quality"><strong>Data Quality</strong></a></td><td>Compare the real and synthetic data's statistical similarity.</td></tr><tr><td><a href="evaluation/visualization"><strong>Visualization</strong></a></td><td>Visualize the real and synthetic data side-by-side</td></tr></tbody></table>

## Need more evaluation options?

{% hint style="success" %}
**See the** [**SDMetrics library**](https://docs.sdv.dev/sdmetrics/)**.**

This library includes many more metrics (some experimental) that you can apply based on your goals. All you need is your real data, synthetic data and metadata to get started.
{% endhint %}


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