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I'm trying to create an SVM classifier with Sci-Kit Learn with a dataset consisting of 69 cols, from which the last column is the output. Each collumn has a float consisting of an euclidian distance, whereas the output is an integer. Following is the written code:

import numpy as np
import os
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from numpy import float64

os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'

training=np.genfromtxt("Data/ck-train.csv", delimiter=",", skip_header=1)
model_svc = SVC(kernel='linear', probability=True, tol=1e-3)
score_list=
full_size=len(training)
tr_size=int(round(full_size*0.8,0))
ts_size=int(round(full_size*0.2))
print("Training Sample Size: %s samples " %tr_size)
print("Testing Sample Size: %s samples " %ts_size)
print("Total: ", full_size)

training_input=np.zeros((full_size,68))
training_output=np.zeros((full_size))

def create_sets():

 np.random.shuffle(training)

 for i in range(0,full_size):
 training_input[i]=training[i][:-1]
 training_output[i]=training[i][-1]

 for i in range(0,full_size):
 normTraining=np.linalg.norm(training[i][:-1])
 training_input[i]=training[i][:-1]/normTraining

 X_train, X_test, y_train, y_test = train_test_split(training_input, training_output, test_size = 0.20)

 return X_train, X_test, y_train, y_test

### Training ###

def training_svm():
 for i in range(0,10):
 print("Starting training: round ", (i+1))
 tr_in, ts_in, tr_out, ts_out = create_sets()
 model_svc.fit(tr_in, tr_out)
 print("Getting score: round ", (i+1))
 score = model_svc.score(ts_in, ts_out)
 print("Score: ", score)
 score_list.append(score)

 print("Mean score: ", np.mean(score_list))

training_svm()

I don't know why, but when i try to train the model, i get this error:

File "sklearnsvmlibsvm.pyx", line 54, in sklearn.svm.libsvm.fit
ValueError: Buffer dtype mismatch, expected 'float64_t' but got 'float'

Can someone explain what i'm missing? Thank you in advance for your help.