我是机器学习的初学者，我正试图通过卡格尔的巨大问题来学习。我已经完成了我的代码，并获得了0.78的准确度分数，但现在我需要生成一个CSV文件，其标题行包含418个条目，但idk如何处理它。

这是我应该制作的一个例子：

PassengerId,Survived
 892,0
 893,1
 894,0
 Etc.

数据来自我的test\u预测

这是我的代码：

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

"""Assigning the train & test datasets' adresses to variables"""
train_path = "C:\\Users\\Omar\\Downloads\\Titanic Data\\train.csv"
test_path = "C:\\Users\\Omar\\Downloads\\Titanic Data\\test.csv"

"""Using pandas' read_csv() function to read the datasets
and then assigning them to their own variables"""
train_data = pd.read_csv(train_path)
test_data = pd.read_csv(test_path)

"""Using pandas' factorize() function to represent genders (male/female)
with binary values (0/1)"""
train_data['Sex'] = pd.factorize(train_data.Sex)[0]
test_data['Sex'] = pd.factorize(test_data.Sex)[0]

"""Replacing missing values in the training and test dataset with 0"""
train_data.fillna(0.0, inplace = True)
test_data.fillna(0.0, inplace = True)

"""Selecting features for training"""
columns_of_interest = ['Pclass', 'Sex', 'Age']

"""Dropping missing/NaN values from the training dataset"""
filtered_titanic_data = train_data.dropna(axis=0)

"""Using the predictory features in the data to handle the x axis"""
x = filtered_titanic_data[columns_of_interest]

"""The survival (what we're trying to find) is the y axis"""
y = filtered_titanic_data.Survived

"""Splitting the train data with test"""
train_x, val_x, train_y, val_y = train_test_split(x, y, random_state=0)

"""Assigning the DecisionClassifier model to a variable"""
titanic_model = DecisionTreeClassifier()

"""Fitting the x and y values with the model"""
titanic_model.fit(train_x, train_y)

"""Predicting the x-axis"""
val_predictions = titanic_model.predict(val_x)

"""Assigning the feature columns from the test to a variable"""
test_x = test_data[columns_of_interest]

"""Predicting the test by feeding its x axis into the model"""
test_predictions = titanic_model.predict(test_x)

"""Printing the prediction"""
print(val_predictions)

"""Checking for the accuracy"""
print(accuracy_score(val_y, val_predictions))

"""Printing the test prediction"""
print(test_predictions)