> ## Documentation Index
> Fetch the complete documentation index at: https://docs.synthefy.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Examples

> Worked examples using Synthefy Nori (Tabular).

End-to-end examples on real datasets. Each one runs locally against the
open-source package. See [Installation](/setup/installation) if you haven't
installed it yet.

## Quickstart

Load a dataset, split it, and get predictions. The model figures out the pattern
from your training rows, with no configuration needed.

```python theme={null}
import pandas as pd
from sklearn.datasets import fetch_california_housing
from synthefy_nori import NoriRegressor

X, y = fetch_california_housing(return_X_y=True, as_frame=True)
df = X.copy()
df["price"] = y

target_col = "price"
feature_cols = [c for c in df.columns if c != target_col]

train = df.sample(frac=0.8, random_state=42)
test  = df.drop(train.index)

model = NoriRegressor(model="nori-30m").fit(
    train[feature_cols].values,
    train[target_col].values,
)
test["predicted_price"] = model.predict(test[feature_cols].values)
print(test[["price", "predicted_price"]].head())
```

```
    price  predicted_price
9   2.611         2.692868
11  2.418         2.749135
13  1.913         1.884583
16  1.525         2.121786
24  1.326         1.822688
```

## Next steps

<CardGroup cols={2}>
  <Card title="Embeddings" icon="vector-square" href="/nori/embeddings">
    Pull Nori's own target-aware row vectors out and use them downstream.
  </Card>

  <Card title="Explainability" icon="magnifying-glass-chart" href="/nori/explainability">
    SHAP values, feature interactions, and partial dependence.
  </Card>
</CardGroup>