The number of classes has to be greater than one; got 1 class in SVM

Question

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.linspace(-5.0, 5.0, 100)
y = np.sqrt(10**2 - x**2)
y=np.hstack([y,-y])
x=np.hstack([x,-x])

x1 = np.linspace(-5.0, 5.0, 100)
y1 = np.sqrt(5**2 - x1**2)
y1=np.hstack([y1,-y1])
x1=np.hstack([x1,-x1])


plt.scatter(y,x)
plt.scatter(y1,x1)
# print(plt.show())

import pandas as pd
df1 =pd.DataFrame(np.vstack([y,x]).T,columns=['X1','X2'])
df1['Y']=0

df2 =pd.DataFrame(np.vstack([y1,x1]).T,columns=['X1','X2'])
df2['Y']=1


df1.merge(df2)

# We need to find components for the Polynomical Kernel
#X1,X2,X1_square,X2_square,X1*X2
df1['X1_Square']= df1['X1']**2
df1['X2_Square']= df1['X2']**2
df1['X1*X2'] = (df1['X1'] *df1['X2'])

# print(df1.head())

### Independent and Dependent features
X = df1[['X1','X2','X1_Square','X2_Square','X1*X2']]
y = df1['Y']

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                                    test_size = 0.25, 
                                                    random_state = 0)


from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
classifier = SVC(kernel="linear")
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
accuracy_score(y_test, y_pred)

ValueError: The number of classes has to be greater than one; got 1 class

I Don't know How to resolve this one error.May be there is error in merge of two data frames or I to append df1 and df2 but i tried it that doesn't work.

Answer 1

The error is because y has only one value of 0 because your code logic is at line y = df1['Y'].

You can replace line df1.merge(df2) code like this:

df1 = pd.concat([df1,df2])