Hello, when i'm training my model with 80% data and testing with 20% data the accuracy is 49% and without split it's 99%

Hello, when i’m training my model with 80% data and testing with 20% data the accuracy is 49%. And when i’m training my data without splitting it’s giving around 99%. I’m confused. Please help me with this

The below code is with split which got 49% accuracy

data = pd.read_csv(r"dataset.csv")

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

objList = data.select_dtypes(include = "object").columns

for feat in objList:
    data[feat] = le.fit_transform(data[feat].astype(str))

X = data.iloc[:, data.columns != 'Outcome'].values
y = data.iloc[:, data.columns == 'Outcome'].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1)

from sklearn.ensemble import RandomForestClassifier
randomforest = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
randomforest.fit(X_train, y_train)

y_pred = randomforest.predict(X_test)

from sklearn.metrics import confusion_matrix
from sklearn import metrics
print(metrics.accuracy_score(y_test, y_pred))
cm = confusion_matrix(y_test, y_pred)
print(cm)

The below code is without split which got 99% accuracy

data = pd.read_csv(r'dataset.csv')

le = LabelEncoder()

objList = data.select_dtypes(include = "object").columns

for feat in objList:
    data[feat] = le.fit_transform(data[feat].astype(str))

X = data.iloc[:, data.columns != 'Outcome'].values
y = data.iloc[:, data.columns == 'Outcome'].values

file = open(r'finalized_model.pkl', 'rb')

data = pickle.load(file)

y_pred = data.predict(X)

from sklearn.metrics import confusion_matrix
from sklearn import metrics
print(metrics.accuracy_score(y, y_pred))
cm = confusion_matrix(y, y_pred)
print(cm)

Total data is the same used for 2 codes.