Predicting accuracy with one Python logistic regression column

from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()

import numpy as np

import pandas as pd

print(cancer.data.shape, cancer.target.shape)

import matplotlib.pyplot as plt

import numpy as np

plt.boxplot(cancer.data)

plt.xlabel('feature')

plt.ylabel('value')

plt.show()

cancer.feature_names[[3,13,23]]

x = cancel.data (=>) I put cancel.data[3] and proceeded with the following code, but it doesn't work.)

y = cancer.target

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, stratify = y, test_size=0.2, random_state=42)


#Implementing logistic regression
class LogisticNeuron:

  def __init__(self):
    self.w = None
    self.b = None

  def forpass(self, x):
    z = np.sum(x*self.w) + self.b #Calculate the equation of the straight line
    return z

  def backprop(self, x, err):
    w_grad = x*err #calculate gradients for weights
    b_grad = 1*err #Calculate the gradient for the intercept
    return w_grad, b_grad

    #Implementing a training method
  def fit(self, x, y, epochs = 100):
    self.w = np.ones(x.shape[1]) #weight initialization
    Self b = 0 # early intercept.
    For I in range (epochs) : repeated as many times as # epochs
      For x y I, _ I in zip (x, y) : # on all samples again.
        z = self.Forpass (_ x I) # positive evaluation.
        a= self.Activation (z) # Apply the activation function.
        Err = - (I - _ y a) #.
        w_grad, b_grad = self.Backprop the calculation in reverse (x I, err) #.
        Update - = weighted grad of # _ w w self.
        Self grad of # _ b - = b section updates.

  def activation(self,z):
    a=1/(1+np.exp(-z))
    return a

  # Every third to to predict.
  def predict(self, x):
    z=[self.forpass(x_i) for x_i in x] #Calculate Forward
    a = self.Activation(np.array(z)) #Apply activation function
    return a>0.5

#Training logistic regression models
neuron = LogisticNeuron()
neuron.fit(x_train, y_train)

#Evaluate the accuracy of the model using the test set if the predictions are correct
np.mean(neuron.predict(x_test) == y_test)

x = cancer.data[3]

x_train, x_test, y_train, y_test = train_test_split(x, y, stratify = y, test_size=0.2, random_state=42)

ValueError                                Traceback (most recent call last)
<ipython-input-51-49b444fce24c> in <module>()
----> 1 x_train, x_test, y_train, y_test = train_test_split(x, y, stratify = y, test_size=0.2, random_state=42)

2 frames
/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
    210     if len(uniques) > 1:
    211         raise ValueError("Found input variables with inconsistent numbers of"
--> 212                          " samples: %r" % [int(l) for l in lengths])
    213 
    214 

ValueError: Found input variables with inconsistent numbers of samples: [30, 569]