[Python] Use of classifiers on a simple dataset. Error using score

I 'm new to ML and I am trying to use classifiers on a simple dataset


labels = [1, 0, 1, 0, 1, 0]
data = pd.DataFrame({
'Delta': delta_energy,
'Theta': theta_energy,
'Alpha': alpha_energy,
'Beta': beta_energy,
'Gamma': gamma_energy,
'Label': labels
})


but I've encountered an erorr - AttributeError: 'NoneType' object has no attribute 'split' after trying to run this code

clf_index = 5
feature_indexes = [0,3]
names = [
"Nearest Neighbors",
"Linear SVM",
"RBF SVM",
"Gaussian Process",
"Decision Tree",
"Random Forest",
"Neural Net",
"AdaBoost",
"Naive Bayes",
"QDA",
]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025, random_state=42),
SVC(gamma=2, C=1, random_state=42),
GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42),
DecisionTreeClassifier(max_depth=5, random_state=42),
RandomForestClassifier(
max_depth=5, n_estimators=10, max_features=1, random_state=42
),
MLPClassifier(max_iter=1000, random_state=42),
AdaBoostClassifier(algorithm="SAMME", random_state=42),
GaussianNB(),
QuadraticDiscriminantAnalysis(),
]
table_data = (data.values[:,feature_indexes], data[data.columns[clf_index]])
datasets = [table_data]
figure = plt.figure(figsize=(27, 3))
i = 1
for ds_cnt, ds in enumerate(datasets):
X, y = ds
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42
)

x_range = abs(X[:, 0].max()) - abs(X[:, 0].min())
y_range = abs(X[:, 1].max()) - abs(X[:, 1].min())

x_min, x_max = X[:, 0].min() - 0.05*x_range, X[:, 0].max() + 0.05*x_range
y_min, y_max = X[:, 1].min() - 0.05*y_range, X[:, 1].max() + 0.05*y_range

# just plot the dataset first, red and blue = 0000FF color. Red = 0. Blue = 1 status
cm = plt.cm.RdBu
cm_bright = ListedColormap(["#FF0000", "#0000FF"])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data")
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
# Plot the testing points
ax.scatter(
X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xlabel(data.columns[feature_indexes[0]])
ax.set_ylabel(data.columns[feature_indexes[1]])
i += 1
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf = make_pipeline(StandardScaler(), clf)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)
DecisionBoundaryDisplay.from_estimator(
clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
)
# Plot the training points
ax.scatter(
X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
)
# Plot the testing points
ax.scatter(
X_test[:, 0],
X_test[:, 1],
c=y_test,
cmap=cm_bright,
edgecolors="grey",
alpha=0.4,
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
if ds_cnt == 0:
ax.set_title(name)
ax.text(
x_max - x_range*0.1,
y_min + y_range*0.1,
("%.2f" % score).lstrip("0"),
size=25,
horizontalalignment="right",
)
i += 1

plt.tight_layout()
plt.show()


in this specific place: clf = make_pipeline(StandardScaler(), clf) clf.fit(X_train, y_train) How can i fix it? i ve tried to change dataset but it still doesn't work

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