How can I use a dataframe of multi-value in each cell as an input to machine learning for classification

Question

I build a data frame with multivalued in each cell as picture below and I want to use logistic regression for classification>>>> my code is :

fds1 = pd.DataFrame(featuresdata)
    fds1.fillna('', inplace=True)
    from sklearn.model_selection import train_test_split, cross_val_score
    X_train, X_test, y_train, y_test = train_test_split(fds1, y, test_size=0.30, random_state=100)
    from sklearn.linear_model import LogisticRegression
    classifier = LogisticRegression()
    classifier.fit(X_train, y_train)
    score = classifier.score(X_test, y_test)
    print("Accuracy for logistic regression:", score)

but there was an error with this code:

File "C:\Users\hp\PycharmProjects\pythonProject\FE2.py", line 317, in CLS2butclick
    classifier.fit(X_train, y_train)
  File "C:\Users\hp\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\linear_model\_logistic.py", line 1138, in fit
    X, y = self._validate_data(
  File "C:\Users\hp\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\base.py", line 596, in _validate_data
    X, y = check_X_y(X, y, **check_params)
  File "C:\Users\hp\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\utils\validation.py", line 1074, in check_X_y
    X = check_array(
  File "C:\Users\hp\PycharmProjects\pythonProject\venv\lib\site-packages\sklearn\utils\validation.py", line 856, in check_array
    array = np.asarray(array, order=order, dtype=dtype)
  File "C:\Users\hp\PycharmProjects\pythonProject\venv\lib\site-packages\pandas\core\generic.py", line 2064, in __array__
    return np.asarray(self._values, dtype=dtype)
ValueError: setting an array element with a sequence.

How to fix that?

Answer 1

You need to do a label encoding before the training and convert string values to make them understandable for machine.

Refer to https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html