Resolves an large amount of warnings of the type DeprecationWarning: `np.float` is a deprecated alias for the builtin `float` that were present in scikit-learn when using NumPy > 1.20.

scikit-learn>=1.0 is also ABI stable and follows semantic versioning, making maintaining a range of versions easier.

Resolves the following warning:

tests/test_osmnx.py::test_features
  /home/runner/work/osmnx/osmnx/osmnx/features.py:684: SettingWithCopyWarning: 
  A value is trying to be set on a copy of a slice from a DataFrame.
  Try using .loc[row_indexer,col_indexer] = value instead
  
  See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
    gdf.loc[:, "geometry"] = gdf["geometry"].make_valid()

Resolved a VisibleDeprecationWarning by specifying dtype=object when creating a NumPy array from potentially ragged nested sequences in the _verify_edge_attribute function. This ensures that the array can handle irregular shapes without raising deprecation warnings, maintaining compatibility with future NumPy versions.


tests/test_osmnx.py::test_routing
  /home/runner/work/osmnx/osmnx/osmnx/routing.py:473: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
    values_float = (np.array(tuple(G.edges(data=attr)))[:, 2]).astype(float)