feat: Add StringListBinarizer to encode multi-label strings and lists

[ankitlade12]

Description

This PR introduces the StringListBinarizer to the feature_engine.encoding module.

When dealing with modern datasets (e.g., e-commerce, web logs, or NLP metadata), it's extremely common to have columns containing multiple categories per row. This data usually arrives in one of two ways:

1. Comma-delimited strings: "action, comedy, thriller"
2. Python lists evaluated from JSON: ["action", "comedy", "thriller"]

Currently in scikit-learn, users are forced to write messy custom pandas .apply functions or wrestle with MultiLabelBinarizer (which returns raw numpy arrays, strips feature names, and requires iterable-of-iterables).

The StringListBinarizer acts as a native Feature-engine transformer that smoothly splits string lists by a given separator and applies one-hot encoding across all the tags identified in the dataset. It operates directly on pandas DataFrames and returns beautifully named Boolean columns (e.g., genres_action, genres_comedy).

Changes:

• Added StringListBinarizer class in feature_engine/encoding/string_list_binarizer.py.
• Exported StringListBinarizer in feature_engine/encoding/init.py.
• Included rigorous tests for delimited string formats, python list formats, unseen categories fallback, and parameter validation.
• Added full API documentation in docs/api_doc/encoding/StringListBinarizer.rst.
• Added User Guide explanations and examples in docs/user_guide/encoding/StringListBinarizer.rst.

Examples:

import pandas as pd
from feature_engine.encoding import StringListBinarizer

df = pd.DataFrame({
    "user_id": [1, 2, 3],
    "genres": ["action, comedy", "comedy", "action, thriller"]
})

encoder = StringListBinarizer(
    variables=["genres"],
    separator=", " 
)

encoder.fit(df)
df_encoded = encoder.transform(df)

# Output:
#    user_id  genres_action  genres_comedy  genres_thriller
# 0        1              1              1                0
# 1        2              0              1                0
# 2        3              1              0                1

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 98.30%. Comparing base (f72a2b7) to head (eae5f5e).

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #916      +/-   ##
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+ Coverage   98.27%   98.30%   +0.02%     
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  Files         116      117       +1     
  Lines        4978     5063      +85     
  Branches      795      814      +19     
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+ Hits         4892     4977      +85     
  Misses         55       55              
  Partials       31       31              

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