My code:
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
car_data = pd.read_csv('car_data.csv')
# Create X
X = car_data.drop('Buy Rate', axis=1)
# Create Y
y = car_data['Buy Rate']
clf = RandomForestClassifier()
clf.get_params()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
clf.fit(X_train, y_train)After the line with clf.fit, this error pops up:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/tmp/ipykernel_51905/2395142735.py in ?()
----> 1 clf.fit(X_train, y_train)
~/Desktop/ml-course/env/lib/python3.10/site-packages/sklearn/base.py in ?(estimator, *args, **kwargs)
1147 skip_parameter_validation=(
1148 prefer_skip_nested_validation or global_skip_validation
1149 )
1150 ):
-> 1151 return fit_method(estimator, *args, **kwargs)
~/Desktop/ml-course/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py in ?(self, X, y, sample_weight)
344 """
345 # Validate or convert input data
346 if issparse(y):
347 raise ValueError("sparse multilabel-indicator for y is not supported.")
--> 348 X, y = self._validate_data(
349 X, y, multi_output=True, accept_sparse="csc", dtype=DTYPE
350 )
351 if sample_weight is not None:
~/Desktop/ml-course/env/lib/python3.10/site-packages/sklearn/base.py in ?(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)
617 if "estimator" not in check_y_params:
618 check_y_params = {**default_check_params, **check_y_params}
619 y = check_array(y, input_name="y", **check_y_params)
620 else:
--> 621 X, y = check_X_y(X, y, **check_params)
622 out = X, y
623
624 if not no_val_X and check_params.get("ensure_2d", True):
~/Desktop/ml-course/env/lib/python3.10/site-packages/sklearn/utils/validation.py in ?(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
1143 raise ValueError(
1144 f"{estimator_name} requires y to be passed, but the target y is None"
1145 )
1146
-> 1147 X = check_array(
1148 X,
1149 accept_sparse=accept_sparse,
1150 accept_large_sparse=accept_large_sparse,
~/Desktop/ml-course/env/lib/python3.10/site-packages/sklearn/utils/validation.py in ?(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
914 )
915 array = xp.astype(array, dtype, copy=False)
916 else:
917 array = _asarray_with_order(array, order=order, dtype=dtype, xp=xp)
--> 918 except ComplexWarning as complex_warning:
919 raise ValueError(
920 "Complex data not supported\n{}\n".format(array)
921 ) from complex_warning
~/Desktop/ml-course/env/lib/python3.10/site-packages/sklearn/utils/_array_api.py in ?(array, dtype, order, copy, xp)
376 # Use NumPy API to support order
377 if copy is True:
378 array = numpy.array(array, order=order, dtype=dtype)
379 else:
--> 380 array = numpy.asarray(array, order=order, dtype=dtype)
381
382 # At this point array is a NumPy ndarray. We convert it to an array
383 # container that is consistent with the input's namespace.
~/Desktop/ml-course/env/lib/python3.10/site-packages/pandas/core/generic.py in ?(self, dtype)
2082 def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
2083 values = self._values
-> 2084 arr = np.asarray(values, dtype=dtype)
2085 if (
2086 astype_is_view(values.dtype, arr.dtype)
2087 and using_copy_on_write()
ValueError: could not convert string to float: 'Hyundai'