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from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold

from sklearn.preprocessing import StandardScaler

import pandas as pd

df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv')
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv')

print(df_train.isnull().sum())
df_train = df_train.drop('id', axis=1)
df_test = df_test.drop('id', axis=1)
df_train['prognosis'] = pd.Categorical(df_train['prognosis'])
X_train = df_train.drop('prognosis', axis=1)
y_train = df_train['prognosis']
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(df_test)
X_train, X_val, y_train, y_val = train_test_split(X_train_scaled, y_train, test_size=0.2, random_state=42)