Perfect scores in multiclassclassification?

I am working on a multiclass classification problem with 3 (1, 2, 3) classes being perfectly distributed. (70 instances of each class resulting in (210, 8) dataframe).

Now my data has all the 3 classes distributed in order i.e first 70 instances are class1, next 70 instances are class 2 and last 70 instances are class 3. I know that this kind of distribution will lead to good score on train set but poor score on test set as the test set has classes that the model has not seen. So I used stratify parameter in train_test_split. Below is my code:-

# SPLITTING 
train_x, test_x, train_y, test_y = train_test_split(data2, y, test_size = 0.2, random_state = 
69, stratify = y)

cross_val_model = cross_val_score(pipe, train_x, train_y, cv = 5,
                              n_jobs = -1, scoring = 'f1_macro')
s_score = cross_val_model.mean()


def objective(trial):

    model__n_neighbors = trial.suggest_int('model__n_neighbors', 1, 20)
    model__metric = trial.suggest_categorical('model__metric', ['euclidean', 'manhattan', 
    'minkowski'])
    model__weights = trial.suggest_categorical('model__weights', ['uniform', 'distance'])

    params = {'model__n_neighbors' : model__n_neighbors, 
          'model__metric' : model__metric, 
          'model__weights' : model__weights}

    pipe.set_params(**params)

    return np.mean( cross_val_score(pipe, train_x, train_y, cv = 5, 
                                    n_jobs = -1, scoring = 'f1_macro'))

knn_study = optuna.create_study(direction = 'maximize')
knn_study.optimize(objective, n_trials = 10)

knn_study.best_params
optuna_gave_score = knn_study.best_value    

pipe.set_params(**knn_study.best_params)
pipe.fit(train_x, train_y)
pred = pipe.predict(test_x)
c_matrix = confusion_matrix(test_y, pred)
c_report = classification_report(test_y, pred)

Now the problem is that I am getting perfect scores on everything. The f1 macro score from performing cv is 0.898. Below are my confusion matrix and classification report:-

14  0   0 
0   14  0 
0   0   14

Classification Report:-

              precision    recall  f1-score   support

       1       1.00      1.00      1.00        14
       2       1.00      1.00      1.00        14
       3       1.00      1.00      1.00        14

accuracy                            1.00        42
macro avg       1.00      1.00      1.00        42
weighted avg    1.00      1.00      1.00        42

Am I overfitting or what?

Finally got the answer. The dataset I was using was the issue. The dataset was tailor made for knn algorithm and that was why I was getting perfect scores as I was using the same algorithm.

I got came to this conclusion after I performed a clustering exercise on this dataset and the K-Means algorithm perfectly predicted the clusters.