Solving the Cosmic Mystery of Spaceship Titanic with Data Science

# 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

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

# Load the Spaceship Titanic training dataset from a CSV file and display the first 5 rows.
train_data = pd.read_csv("/kaggle/input/spaceship-titanic/train.csv")
train_data.head()

# Load the Spaceship Titanic test dataset from a CSV file and display the first 5 rows.
test_data = pd.read_csv('/kaggle/input/spaceship-titanic/test.csv')
test_data.head()

# Importing necessary libraries for building a machine learning model
from sklearn.ensemble import RandomForestClassifier # RandomForestClassifier for classification tasks
from sklearn.pipeline import Pipeline # Pipeline to chain together preprocessing and model training

# Define the model
model = RandomForestClassifier(n_estimators=100, random_state=0)
# Creating a RandomForestClassifier model with 100 trees (n_estimators) and a fixed random state for reproducibility

# Creating the preprocessing and training pipeline
pipeline = Pipeline(steps=[('preprocessor', preprocessor), # First step: preprocessing the data using the earlier defined preprocessor
                           ('model', model)]) # Second step: training the RandomForestClassifier model

# Splitting the data into features and target
X_train = train_data[features] # Extracting the features for training from the train_data
y_train = train_data[target] # Extracting the target variable (what we want to predict) from the train_data

# Training the model
pipeline.fit(X_train, y_train)
# Fitting the pipeline to the training data: this includes preprocessing the data and then training the model

# Creating the submission file
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Transported': test_predictions})
# Creating a DataFrame 'output' with two columns: 'PassengerId' and 'Transported'.
# 'PassengerId' is taken from the test dataset and 'Transported' contains the predictions from the model.

output.to_csv('submission_spaceship_titanic.csv', index=False)
# Saving the DataFrame to a CSV file named 'submission_spaceship_titanic.csv'.
# The 'index=False' parameter is used to indicate that the index of the DataFrame should not be written to the file.

print("Your submission was successfully saved!")
# Printing a message to the console to confirm that the submission file has been successfully created and saved.