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

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?

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

Error `` `MultiLabelBinarizer``` when importing strings from a csv to a fit () function to train a model with scikit-learn

import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv('coords.csv',sep=';') #Cargo el archivo csv
x = df.iloc[1:,1:] #features values
y = df.iloc[1:,0] #target value
y = y.apply(lambda y: y.encode())
print(x)
print(y)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1234)
print(x_train)
print(y_train)
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression, RidgeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
pipelines = {
'lr':make_pipeline(StandardScaler(), LogisticRegression()),
'rc':make_pipeline(StandardScaler(), RidgeClassifier()),
'rf':make_pipeline(StandardScaler(), RandomForestClassifier()),
'gb':make_pipeline(StandardScaler(), GradientBoostingClassifier()),
}
fit_models = {}
for algo, pipeline in pipelines.items():
model = pipeline.fit(x_train, y_train)
fit_models[algo] = model
print(fit_models)
print(fit_models['lr'].predict(x_test))
print(fit_models['rc'].predict(x_test))
print(fit_models['rf'].predict(x_test))
print(fit_models['gb'].predict(x_test))
I was having a problem when trying to load strings from a csv file, because it tells me:
Traceback (most recent call last):
File "3_Train_Custom_Model_Using_Scikit_Learn.py", line 99, in <module>
model = pipeline.fit(x_train, y_train)
File "C:\Users\PC0\Anaconda3\lib\site-packages\sklearn\utils\optimize.py", line 243, in _check_optimize_result
).format(solver, result.status, result.message.decode("latin1"))
AttributeError: 'str' object has no attribute 'decode'
And when I add y = y.apply (lambda y: y.encode ()) because I thought I needed to transform strings to bytes, I get this:
Traceback (most recent call last):
File "3_Train_Custom_Model_Using_Scikit_Learn.py", line 99, in <module>
model = pipeline.fit(x_train, y_train)
File "C:\Users\PC0\Anaconda3\lib\site-packages\sklearn\pipeline.py", line 335, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "C:\Users\PC0\Anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py", line 1345, in fit
check_classification_targets(y)
File "C:\Users\PC0\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py", line 169, in check_classification_targets
y_type = type_of_target(y)
File "C:\Users\PC0\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py", line 263, in type_of_target
raise ValueError('You appear to be using a legacy multi-label data'
ValueError: You appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead - the MultiLabelBinarizer transformer can convert to this format.
How do I so that the data framed in red from the csv that you see in the following Excel screenshot, which would be the targets, are saved in the variable y, and those that are framed in blue that It would be the features (x1, y1, z1, v1, x2, y2, z2, v2, ..., x501, y501, z501, v501) that must be saved in the variable x.

Try this:
df = pd.read_csv('testing.csv',sep=';',header=1)
x = df.iloc[:,1:] #features values
y = df.iloc[:,0] #target value
#y = y.apply(lambda y: y.encode())
print(x)
print(y)
...

Incompatible dimension for X and Y matrices

I was wondering what i have wrong here i get the error
Traceback (most recent call last):
File "main.py", line 37, in <module>
y_pred = knn.predict(X_test)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/neighbors/classification.py", line149, in predict
neigh_dist, neigh_ind = self.kneighbors(X)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/neighbors/base.py", line 434, in kneighbors
**kwds))
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/metrics/pairwise.py", line 1448, in pairwise_distances_chunked
n_jobs=n_jobs, **kwds)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/metrics/pairwise.py", line 1588, in pairwise_distances
return _parallel_pairwise(X, Y, func, n_jobs, **kwds)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/metrics/pairwise.py", line 1206, in _parallel_pairwise
return func(X, Y, **kwds)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/metrics/pairwise.py", line 232, ineuclidean_distances
X, Y = check_pairwise_arrays(X, Y)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/metrics/pairwise.py", line 125, incheck_pairwise_arrays
X.shape[1], Y.shape[1]))
ValueError: Incompatible dimension for X and Y matrices: X.shape[1] == 38 while Y.shape[1] == 43
I'm new to ai and cant find anything on the internet that really solves this problem, any comment appreciated. This is my code
from sklearn.preprocessing import OneHotEncoder
from sklearn import metrics
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
fileName = "breast-cancer-fixed.csv";
df = pd.read_csv(fileName)
X = df[df.columns[:-1]]
y = df[df.columns[-1]]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)
X_train = OneHotEncoder().fit_transform(X_train)
X_test = OneHotEncoder().fit_transform(X_test)
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
print("kNN model accuracy:", metrics.accuracy_score(y_test, y_pred))
My csv is massive and i cant upload it here so i put a small snippet in
age,menopause,tumor-size,inv-nodes,node-caps,deg-malig,breast,breast-quad,irradiat,Class
40-49,premeno,15-19,0-2,yes,3,right,left_up,no,recurrence-events
50-59,ge40,15-19,0-2,no,1,right,central,no,no-recurrence-events
50-59,ge40,35-39,0-2,no,2,left,left_low,no,recurrence-events
40-49,premeno,35-39,0-2,yes,3,right,left_low,yes,no-recurrence-events
40-49,premeno,30-34,3-5,yes,2,left,right_up,no,recurrence-events
50-59,premeno,25-29,3-5,no,2,right,left_up,yes,no-recurrence-events
50-59,ge40,40-44,0-2,no,3,left,left_up,no,no-recurrence-events
40-49,premeno,10-14,0-2,no,2,left,left_up,no,no-recurrence-events
40-49,premeno,0-4,0-2,no,2,right,right_low,no,no-recurrence-events
40-49,ge40,40-44,15-17,yes,2,right,left_up,yes,no-recurrence-events
50-59,premeno,25-29,0-2,no,2,left,left_low,no,no-recurrence-events
60-69,ge40,15-19,0-2,no,2,right,left_up,no,no-recurrence-events
Also if i get rid of the last two line of code ( the prediction code ) it runs fine with no errors

trying adding this line anywhere above the transforms
enc = OneHotEncoder(handle_unknown='ignore')
then change the transform lines to the following
enc = enc.fit(X_train)
X_train = enc.transform(X_train)
X_test = enc.transform(X_test)

I get this error
```Traceback (most recent call last):
File "main.py", line 25, in <module>
X_test = OneHotEncoder().transform(X_test)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py", line 726, in transform
check_is_fitted(self, 'categories_')
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 914, in check_is_fitted
raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.exceptions.NotFittedError: This OneHotEncoder instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.```

GradientBoostingClassifier implementation

I want to implement Gradient Boosting Classifier to my Titanic ML solution based on sklearn library.
I use VS Code on Ubuntu 18.04.
I've tried:
# Splitting the Training Data
from sklearn.model_selection import train_test_split
predictors = train.drop(['Survived', 'PassengerId'], axis=1)
target = train["Survived"]
x_train, x_val, y_train, y_val = train_test_split(predictors,
target, test_size = 0.22, random_state = 0)
# Gradient Boosting Classifier
from sklearn.ensemble import GradientBoostingClassifier
gbk = GradientBoostingClassifier()
gbk.fit(x_train, y_train)
..which returns:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py", line 1395, in fit
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'], dtype=DTYPE)
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 756, in check_X_y
estimator=estimator)
File "/home/sj/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py", line 527, in check_array
array = np.asarray(array, dtype=dtype, order=order)
File "/home/sj/anaconda3/lib/python3.7/site-packages/numpy/core/numeric.py", line 501, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: 'Baby'
Help would be appreciated. I'm quite new to DS.

I think you may a non numerical values in your train data. Your classifier can take numerical inputs. That's why it tries to convert a string, here 'Baby', to a float. As this operation is not supported, it fails.
Maybe look again at your data.

MLP classification fitting

I'm new to Machine Learning and I'm working on a python application that classifies poker hands using a dataset which I will post snippets. It does not seem to work well. And I am getting the following error:
Traceback (most recent call last):
File "C:\Users\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-62-0d21cd839ce4>", line 1, in <module>
mlp.fit(X_test, y_train.values.reshape(len(y_train), 1))
File "C:\Users\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 618, in fit
return self._fit(X, y, incremental=False)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 330, in _fit
X, y = self._validate_input(X, y, incremental)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 902, in _validate_input
multi_output=True)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 531, in check_X_y
check_consistent_length(X, y)
File "C:\Users\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 181, in check_consistent_length
" samples: %r" % [int(l) for l in lengths])
ValueError: Found input variables with inconsistent numbers of samples: [6253, 18757]
here is the code I am trying to produce:
import pandas as pnd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report, confusion_matrix
training_data = pnd.read_csv("train.csv")
training_data['id'] = range(1, len(training_data) + 1) # For 1-base index
training_datafile = training_data
target = training_datafile['hand']
data = training_datafile.drop(['id', 'hand'], axis=1)
X = data
y = target
X_train, X_test, y_train, y_test = train_test_split(X, y)
X_train.shape
y_train.shape
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
mlp = MLPClassifier(hidden_layer_sizes=(100, 100, 100))
mlp.fit(X_test, y_train.values.reshape(len(y_train), 1))
predictions = mlp.predict(X_test)
len(mlp.coefs_)
len(mlp.coefs_[0])
len(mlp.intercepts_[0])
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))
The shape of X_train.shape is (18757, 10) and the shape of y_train.shape is (18757,)
I have tried using following previous post
y_train.values.reshape(len(y_train), 1)
But I still get the same error. Some guidance would be of much help since I am not sure of what the shape has wrong.
Data snippet:

You are fiting X_test instead of X_train.
mlp.fit(X_train, y_train.values.reshape(len(y_train), 1))