import numpy as np
import math
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error, log_loss
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from tqdm import tqdm_notebook 
import seaborn as sns
import time
from IPython.display import HTML
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import OneHotEncoder
import torch

df = pd.read_csv('/content/drive/My Drive/Dataset/heart rate/heart_failure_clinical_records_dataset.csv')
df.head(5)

data = df.drop(['DEATH_EVENT'], axis=1).to_numpy()
labels = df['DEATH_EVENT'].to_numpy()

X_train, X_test, Y_train, Y_test = train_test_split(data, labels, stratify=labels, random_state=0)
print(X_train.shape, X_test.shape, labels.shape)