# Condensed: How to use AutoGluon for Kaggle competitions

Summary: This tutorial demonstrates how to use AutoGluon for Kaggle competitions, focusing on automated machine learning workflows. It covers implementation techniques for data preparation, model training with TabularPredictor, and submission generation. Key functionalities include merging multiple datasets, configuring competition-specific metrics, optimizing model performance through presets and advanced parameters, and handling predictions for competition submissions. The tutorial helps with tasks like automated model training, probability-based predictions, and proper submission formatting for Kaggle competitions, while emphasizing best practices for competition-specific requirements and model optimization strategies.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on essential implementation details:

# Using AutoGluon for Kaggle Competitions

## Setup and Data Preparation
1. Install requirements:
```bash
pip install kaggle
```

2. Configure Kaggle API:
- Get API token from https://www.kaggle.com/account
- Place `kaggle.json` in `~/.kaggle/`

3. Download competition data:
```bash
kaggle competitions download -c [COMPETITION]
```

## Implementation Steps

### 1. Data Loading and Merging
```python
import pandas as pd
from autogluon.tabular import TabularPredictor

# Load data
train_identity = pd.read_csv('train_identity.csv')
train_transaction = pd.read_csv('train_transaction.csv')

# Merge if multiple files
train_data = pd.merge(train_transaction, train_identity, 
                     on='TransactionID', how='left')
```

### 2. Model Training
```python
predictor = TabularPredictor(
    label='isFraud',  # target variable
    eval_metric='roc_auc',  # competition metric
    path='AutoGluonModels/',
    verbosity=3
).fit(
    train_data,
    presets='best_quality',
    time_limit=3600  # adjust based on needs
)
```

### 3. Prediction and Submission
```python
# Prepare test data
test_data = pd.merge(test_transaction, test_identity, 
                     on='TransactionID', how='left')

# Get predictions
y_predproba = predictor.predict_proba(test_data, as_multiclass=False)

# Create submission
submission = pd.read_csv('sample_submission.csv')
submission['target_column'] = y_predproba
submission.to_csv('my_submission.csv', index=False)
```

### 4. Submit Results
```bash
kaggle competitions submit -c [COMPETITION] -f my_submission.csv -m "submission message"
```

## Key Best Practices

1. **Competition Metrics**:
- Always specify the competition's evaluation metric in `TabularPredictor`
- Verify correct probability class for `predict_proba` using `predictor.positive_class`

2. **Model Optimization**:
- Use `presets='best_quality'` for maximum accuracy
- Consider time-based validation for temporal data
- Focus on feature engineering over hyperparameter tuning

3. **Advanced Parameters**:
```python
predictor.fit(
    train_data,
    num_bag_folds=5,
    num_stack_levels=2,
    num_bag_sets=1
)
```

## Important Warnings
- Ensure consistent data merging strategy between train and test
- Verify prediction format matches competition requirements
- Check class labels match competition expectations for classification tasks
- Allow sufficient training time when using `best_quality` preset