User: You are an expert ML scientist. Your task is to write excellent Python code to solve a difficult data challenge.
IMPORTANT: You must follow these rules:
1. Return ONLY the complete code solution
2. Always wrap the entire code in ```python and ``` markers
3. Do not include any explanations or text outside the code block
4. Include all necessary imports at the top of the code
5. Include descriptive comments within the code
6. Check for GPU availability and use it when possible
7. Print clear messages about whether GPU or CPU is being used
8. make sure that the script is creating and saving a progress_report.json file with the accuracy and other useful information as well as the submission file to a file called submission.csv
9. keep the scripts fast and efficient but very accurate
10. script should be able to run and complete in 30 minutes
11. always ensure you split the training set into train and test sets to validate the model

Here's the information about the challenge:

here is additional info about this data challenge:

Welcome to the year 2912, where your data science skills are needed to solve a cosmic mystery. We've received a transmission from four lightyears away and things aren't looking good.

The Spaceship Titanic was an interstellar passenger liner launched a month ago. With almost 13,000 passengers on board, the vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.

While rounding Alpha Centauri en route to its first destination—the torrid 55 Cancri E—the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!

information about columns and csvs:

Dataset Description
In this competition your task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help you make these predictions, you're given a set of personal records recovered from the ship's damaged computer system.

File and Data Field Descriptions
train.csv - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.
PassengerId - A unique Id for each passenger. Each Id takes the form gggg_pp where gggg indicates a group the passenger is travelling with and pp is their number within the group. People in a group are often family members, but not always.
HomePlanet - The planet the passenger departed from, typically their planet of permanent residence.
CryoSleep - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins.
Cabin - The cabin number where the passenger is staying. Takes the form deck/num/side, where side can be either P for Port or S for Starboard.
Destination - The planet the passenger will be debarking to.
Age - The age of the passenger.
VIP - Whether the passenger has paid for special VIP service during the voyage.
RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.
Name - The first and last names of the passenger.
Transported - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.
test.csv - Personal records for the remaining one-third (~4300) of the passengers, to be used as test data. Your task is to predict the value of Transported for the passengers in this set.
sample_submission.csv - A submission file in the correct format.
PassengerId - Id for each passenger in the test set.
Transported - The target. For each passenger, predict either True or False.

Here are the first 10 rows of the training data:

PassengerId HomePlanet  CryoSleep Cabin   Destination  Age   VIP  RoomService  FoodCourt  ShoppingMall    Spa  VRDeck               Name  Transported
    0001_01     Europa      False B/0/P   TRAPPIST-1e 39.0 False          0.0        0.0           0.0    0.0     0.0    Maham Ofracculy        False
    0002_01      Earth      False F/0/S   TRAPPIST-1e 24.0 False        109.0        9.0          25.0  549.0    44.0       Juanna Vines         True
    0003_01     Europa      False A/0/S   TRAPPIST-1e 58.0  True         43.0     3576.0           0.0 6715.0    49.0      Altark Susent        False
    0003_02     Europa      False A/0/S   TRAPPIST-1e 33.0 False          0.0     1283.0         371.0 3329.0   193.0       Solam Susent        False
    0004_01      Earth      False F/1/S   TRAPPIST-1e 16.0 False        303.0       70.0         151.0  565.0     2.0  Willy Santantines         True
    0005_01      Earth      False F/0/P PSO J318.5-22 44.0 False          0.0      483.0           0.0  291.0     0.0  Sandie Hinetthews         True
    0006_01      Earth      False F/2/S   TRAPPIST-1e 26.0 False         42.0     1539.0           3.0    0.0     0.0 Billex Jacostaffey         True
    0006_02      Earth       True G/0/S   TRAPPIST-1e 28.0 False          0.0        0.0           0.0    0.0     NaN Candra Jacostaffey         True
    0007_01      Earth      False F/3/S   TRAPPIST-1e 35.0 False          0.0      785.0          17.0  216.0     0.0      Andona Beston         True
    0008_01     Europa       True B/1/P   55 Cancri e 14.0 False          0.0        0.0           0.0    0.0     0.0     Erraiam Flatic         True

The data files available are train.csv and test.csv. Pay specific attention to column names.

Based on this information, write complete Python code that:
1. Load and preprocess both train.csv and test.csv
2. Perform exploratory data analysis without using any plots
3. Engineer relevant features
4. Select and train an appropriate machine learning model using the training data
   - Check for GPU availability and use it if available
   - Fall back to CPU if GPU is not available
   - Print clear message about which device is being used
5. Evaluate the model's performance using cross-validation. clearly print the accuracy every so often
6. Make predictions on the test set and print the accuracy
- save the progress report and the final accuracy to a file called progress_report.json as well as the submission file to a file called submission.csv
7. Prepare the submission file in the required format
- do not use any plots
- you can print any analysis or information necessary as this will be passed back to you for the next iteration to improve the code
- make sure you use utf-8 encoding when writing to files
- do not use logging instead print the information necessary

Return ONLY the complete code solution wrapped in ```python and ``` markers.
Assistant: Here's the previous code I generated:

```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score
import torch
import json

# Check for GPU availability
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Load data
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

# Initial feature engineering and preprocessing
def preprocess(df):
    # Split Cabin into Deck, Num, Side
    df[['Deck', 'Num', 'Side']] = df['Cabin'].str.split('/', expand=True)
    df.drop(columns=['Cabin', 'Name'], inplace=True)
    return df

train_df = preprocess(train_df)
test_df = preprocess(test_df)

# Define feature and target columns
features = ['HomePlanet', 'CryoSleep', 'Destination', 'Age', 'VIP', 'RoomService', 
            'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Deck', 'Num', 'Side']
target = 'Transported'

X = train_df[features]
y = train_df[target]

# Split into train and validation sets
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# Define preprocessing for numerical and categorical features
numerical_features = ['Age', 'RoomService', 'FoodCourt', 'ShoppingMall', 'Spa', 'VRDeck', 'Num']
categorical_features = ['HomePlanet', 'CryoSleep', 'Destination', 'VIP', 'Deck', 'Side']

numerical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numerical_transformer, numerical_features),
        ('cat', categorical_transformer, categorical_features)
    ])

# Define the model pipeline with an improved model and hyperparameter tuning
model = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', GradientBoostingClassifier(random_state=42))
])

# Hyperparameter tuning using RandomizedSearchCV
param_distributions = {
    'classifier__n_estimators': [100, 200, 300],
    'classifier__max_depth': [3, 5, 7],
    'classifier__learning_rate': [0.01, 0.05, 0.1],
    'classifier__subsample': [0.8, 0.9, 1.0],
    'classifier__min_samples_split': [2, 5, 10],
    'classifier__min_samples_leaf': [1, 2, 4]
}
random_search = RandomizedSearchCV(model, param_distributions, n_iter=50, cv=5, scoring='accuracy', n_jobs=-1, random_state=42)
random_search.fit(X_train, y_train)

# Best parameters from random search
best_params = random_search.best_params_
print(f"Best parameters found: {best_params}")

# Evaluate the model using cross-validation
cv_scores = cross_val_score(random_search.best_estimator_, X_train, y_train, cv=10, scoring='accuracy')
print("Cross-validation accuracies:", cv_scores)
print("Mean cross-validation accuracy:", np.mean(cv_scores))

# Fit the model with the best parameters found
model.set_params(**best_params)
model.fit(X_train, y_train)

# Validate the model
y_val_pred = model.predict(X_val)
val_accuracy = accuracy_score(y_val, y_val_pred)
print(f"Validation Accuracy: {val_accuracy}")

# Make predictions on the test set
X_test = test_df[features]
X_test_transformed = model.named_steps['preprocessor'].transform(X_test)
test_predictions = model.named_steps['classifier'].predict(X_test_transformed)

# Create submission file
submission = pd.DataFrame({'PassengerId': test_df['PassengerId'], 'Transported': test_predictions})
submission.to_csv('submission.csv', index=False, encoding='utf-8')

# Save progress report
progress_report = {
    "best_parameters": best_params,
    "cross_validation_scores": cv_scores.tolist(),
    "mean_cross_validation_accuracy": np.mean(cv_scores),
    "validation_accuracy": val_accuracy
}
with open('progress_report.json', 'w', encoding='utf-8') as f:
    json.dump(progress_report, f, indent=4)

print("Progress report and submission file have been saved.")
```
User: Improve this code based on the previous results. Your goal is to improve the accuracy as much as you can. Return ONLY the complete improved code wrapped in ```python and ``` markers.
Make sure to maintain or add GPU support with proper availability checks and fallback to CPU.
make sure that the script is creating and saving a progress_report.json file with the accuracy and other useful information
keep the scripts fast and efficient but very accurate
11. always ensure you split the training set into train and test sets to validate the model

Here's the training data sample for context:
PassengerId HomePlanet  CryoSleep Cabin   Destination  Age   VIP  RoomService  FoodCourt  ShoppingMall    Spa  VRDeck               Name  Transported
    0001_01     Europa      False B/0/P   TRAPPIST-1e 39.0 False          0.0        0.0           0.0    0.0     0.0    Maham Ofracculy        False
    0002_01      Earth      False F/0/S   TRAPPIST-1e 24.0 False        109.0        9.0          25.0  549.0    44.0       Juanna Vines         True
    0003_01     Europa      False A/0/S   TRAPPIST-1e 58.0  True         43.0     3576.0           0.0 6715.0    49.0      Altark Susent        False
    0003_02     Europa      False A/0/S   TRAPPIST-1e 33.0 False          0.0     1283.0         371.0 3329.0   193.0       Solam Susent        False
    0004_01      Earth      False F/1/S   TRAPPIST-1e 16.0 False        303.0       70.0         151.0  565.0     2.0  Willy Santantines         True
    0005_01      Earth      False F/0/P PSO J318.5-22 44.0 False          0.0      483.0           0.0  291.0     0.0  Sandie Hinetthews         True
    0006_01      Earth      False F/2/S   TRAPPIST-1e 26.0 False         42.0     1539.0           3.0    0.0     0.0 Billex Jacostaffey         True
    0006_02      Earth       True G/0/S   TRAPPIST-1e 28.0 False          0.0        0.0           0.0    0.0     NaN Candra Jacostaffey         True
    0007_01      Earth      False F/3/S   TRAPPIST-1e 35.0 False          0.0      785.0          17.0  216.0     0.0      Andona Beston         True
    0008_01     Europa       True B/1/P   55 Cancri e 14.0 False          0.0        0.0           0.0    0.0     0.0     Erraiam Flatic         True

Additional information about the task:
here is additional info about this data challenge:

Welcome to the year 2912, where your data science skills are needed to solve a cosmic mystery. We've received a transmission from four lightyears away and things aren't looking good.

The Spaceship Titanic was an interstellar passenger liner launched a month ago. With almost 13,000 passengers on board, the vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.

While rounding Alpha Centauri en route to its first destination—the torrid 55 Cancri E—the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!

information about columns and csvs:

Dataset Description
In this competition your task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help you make these predictions, you're given a set of personal records recovered from the ship's damaged computer system.

File and Data Field Descriptions
train.csv - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.
PassengerId - A unique Id for each passenger. Each Id takes the form gggg_pp where gggg indicates a group the passenger is travelling with and pp is their number within the group. People in a group are often family members, but not always.
HomePlanet - The planet the passenger departed from, typically their planet of permanent residence.
CryoSleep - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins.
Cabin - The cabin number where the passenger is staying. Takes the form deck/num/side, where side can be either P for Port or S for Starboard.
Destination - The planet the passenger will be debarking to.
Age - The age of the passenger.
VIP - Whether the passenger has paid for special VIP service during the voyage.
RoomService, FoodCourt, ShoppingMall, Spa, VRDeck - Amount the passenger has billed at each of the Spaceship Titanic's many luxury amenities.
Name - The first and last names of the passenger.
Transported - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.
test.csv - Personal records for the remaining one-third (~4300) of the passengers, to be used as test data. Your task is to predict the value of Transported for the passengers in this set.
sample_submission.csv - A submission file in the correct format.
PassengerId - Id for each passenger in the test set.
Transported - The target. For each passenger, predict either True or False.

Previous model performance:
{
  "best_parameters": {
    "classifier__subsample": 1.0,
    "classifier__n_estimators": 300,
    "classifier__min_samples_split": 10,
    "classifier__min_samples_leaf": 1,
    "classifier__max_depth": 3,
    "classifier__learning_rate": 0.1
  },
  "cross_validation_scores": [
    0.8031609195402298,
    0.8045977011494253,
    0.8103448275862069,
    0.8218390804597702,
    0.8115107913669065,
    0.7985611510791367,
    0.8244604316546763,
    0.7971223021582734,
    0.8115107913669065,
    0.8100719424460432
  ],
  "mean_cross_validation_accuracy": 0.8093179938807575,
  "validation_accuracy": 0.7964347326049454
}

Focus on improving:
1. Cross-validation scores: [0.8031609195402298, 0.8045977011494253, 0.8103448275862069, 0.8218390804597702, 0.8115107913669065, 0.7985611510791367, 0.8244604316546763, 0.7971223021582734, 0.8115107913669065, 0.8100719424460432]
2. Mean CV accuracy: 0
3. Feature importance (if available)
4. Model parameters and architecture
Assistant: