I am doing the Kaggle competition “Titanic - Machine Learning from Disaster”, my code:
"# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
!pip install tensorflow
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
import numpy as np
import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, InputLayer, Dropout
from tensorflow.keras.utils import to_categorical
train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
test_data = pd.read_csv("/kaggle/input/titanic/test.csv")
train_data.head(100)
test_data.head(5)
# Preprocessing
age_imputer = SimpleImputer(strategy='median')
train_data['Age'] = age_imputer.fit_transform(train_data[['Age']])
test_data['Age'] = age_imputer.transform(test_data[['Age']])
fare_imputer = SimpleImputer(strategy='median')
train_data['Fare'] = fare_imputer.fit_transform(train_data[['Fare']])
test_data['Fare'] = fare_imputer.transform(test_data[['Fare']])

# Encoding categorical variables
train_data = pd.get_dummies(train_data, columns=["Sex", "Embarked"])
test_data = pd.get_dummies(test_data, columns=["Sex", "Embarked"])

# Selecting features and target
features = ["Pclass", "Age", "SibSp", "Parch", "Fare", "Sex_female", "Sex_male", "Embarked_C", "Embarked_Q", "Embarked_S","PassengerId"]
X = train_data[features]
y = train_data["Survived"]
X_test = test_data[features]

# Neural Network requires scaled input features.
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_test_scaled = scaler.transform(X_test)
# Splitting the data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
model = Sequential([
    Dense(128, activation='relu', input_shape=(X.shape[1],)),
    Dropout(0.5),  # Add dropout to reduce overfitting
    Dense(128, activation='relu'),
    Dropout(0.5),  # Add dropout to reduce overfitting
    Dense(128, activation='relu'),
    Dropout(0.3),  # Add dropout to reduce overfitting
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Training with validation data
model.fit(X_train, y_train, epochs=50, batch_size=16, validation_data=(X_val, y_val))
# Predicting on the test set
predictions = model.predict(X_test_scaled)
predictions = (predictions > 0.5).astype(int).reshape(X_test_scaled.shape[0])

output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('submission_nn_val.csv', index=False)
print("Your submission was successfully saved using neural network with a validation set!")
"can I save my model , then the competition will used my saved model directly instead of training it again ?