i generated a model with random data like so:

rca = ['High', 'Medium', 'Low']
cr = ['Positive', 'Negative']
ms = ['Married', 'Single', 'Not Specified']
nod = ['No  dependent', 'Less than two', 'More than two']
yoe = np.random.randint(0, 8, size=30)
mi = ['Very Low', 'Low', 'Moderate', 'High', 'Very High']
me = ['Very Low', 'Low', 'Moderate', 'High', 'Very High']
df = pd.DataFrame()
a = []
b = []
c = []
d = []
e = []
f = []

 

for i in range(30):
  a.append(random.choice(rca))
  b.append(random.choice(cr))
  c.append(random.choice(ms))
  d.append(random.choice(nod))
  e.append(random.choice(mi))
  f.append(random.choice(me))

 

df['Credit Rating'] = b
df['Requested credit amount'] = a
df['Marital status'] = c
df['Number of Dependents'] = d
df['Years of education after high school'] = yoe.tolist()
df['Monthly income'] = e
df['Monthly expense'] = f

 

with this data I am unable to fit logistic regression. Below is the error , please help in resolving

1gr = LogisticRegression() pipe2=make_pipeline (scaler,lgr) pipe2.fit((x_train,y_train)) ValueError Input In [31], in () --> 1 pipe2.fit((x_train,y_train)) File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\pipeline.py: 390, in Pipeline.fit (self, x, y, **fit_params) 364 """Fit the model. 365 366 Fit all the transformers one after the other and transform the (...) 387 388 Pipeline with fitted steps. 389 fit_params_steps = self._check_fit_params (**fit_params) --> 390 Xt= self._fit(x, y, **fit_params_steps) 391 with _print_elapsed_time("Pipeline", self._log_message(len(self.steps) - 1)): 392 if self._final_estimator != "passthrough": || || || Traceback (most recent call last) File C:\ProgramData\Anaconda3\lib\site-packages\sklearn\pipeline.py:348, in Pipeline._fit(self, X, y, **fit_params_steps 346 cloned_transformer = clone (transformer) 347 # Fit or load from cache the current transformer 351 352 --> 348 X, fitted_transformer = fit_transform_one_cached ( 349 cloned_transformer, 350 X, y, None, 353 message_clsname="Pipeline", 354 355 356 ) message=self._log_message(step_idx), **fit_params_steps [name],