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# Categorical Encoding

## Target Encoding

* **High-cardinality features**: A feature with a large number of categories can be troublesome to encode: a one-hot encoding would generate too many features and alternatives, like a label encoding, might not be appropriate for that feature. A target encoding derives numbers for the categories using the feature's most important property: its relationship with the target.
* **Domain-motivated features**: From prior experience, you might suspect that a categorical feature should be important even if it scored poorly with a feature metric. A target encoding can help reveal a feature's true informativeness.

```python
from sklearn.preprocessing import TargetEncoder
```

## Label Encoding

Can be used for features with 2 categories.

## One Hot Encoder

Good when there are more than 2 categories but will result in a sparse matrix when there are a large number of categories

## Feature Binning

When there are a lot of features like above 20, we can try binning them into 2 or 3 categories. for example: different car models can be binned into sports, sedans, and luxury depending on the problem statement.

## Time and Date Transformation

```python
df['hr_sin'] = np.sin(df.hr*(2.*np.pi/24))
df['hr_cos'] = np.cos(df.hr*(2.*np.pi/24))
df['mnth_sin'] = np.sin((df.mnth-1)*(2.*np.pi/12))
df['mnth_cos'] = np.cos((df.mnth-1)*(2.*np.pi/12))
```