Kaggle Competition Playbook

A systematic approach to Kaggle competitions, documenting reusable patterns, templates, and strategies for building competitive machine learning solutions.

Competition Methodology

1. Initial Exploration Phase

Before writing any code, understanding the data is crucial:

def initial_exploration(df):
    """Comprehensive data exploration checklist"""
    # Basic statistics
    print(f"Shape: {df.shape}")
    print(f"Memory: {df.memory_usage(deep=True).sum() / 1024**2:.2f} MB")
    
    # Data types
    print(df.dtypes.value_counts())
    
    # Missing values
    missing = df.isnull().sum()
    print(f"Columns with missing: {(missing > 0).sum()}")
    
    # Target distribution
    if 'target' in df.columns:
        print(df['target'].value_counts(normalize=True))
    
    return df.describe(include='all')

2. Feature Engineering Patterns

Common transformations that improve model performance:

Categorical Encoding:

def smart_encoding(train, test, cat_cols):
    """Target encoding with smoothing"""
    global_mean = train['target'].mean()
    
    for col in cat_cols:
        agg = train.groupby(col)['target'].agg(['mean', 'count'])
        smoothing = 20  # Regularization parameter
        
        # Smoothed target encoding
        smooth = (agg['count'] * agg['mean'] + smoothing * global_mean) / (agg['count'] + smoothing)
        
        train[f'{col}_encoded'] = train[col].map(smooth).fillna(global_mean)
        test[f'{col}_encoded'] = test[col].map(smooth).fillna(global_mean)
    
    return train, test

Feature Interaction:

def create_interactions(df, num_cols):
    """Create meaningful feature interactions"""
    for i, col1 in enumerate(num_cols):
        for col2 in num_cols[i+1:]:
            # Ratio features
            df[f'{col1}_{col2}_ratio'] = df[col1] / (df[col2] + 1)
            
            # Difference features
            df[f'{col1}_{col2}_diff'] = df[col1] - df[col2]
            
            # Product features
            df[f'{col1}_{col2}_product'] = df[col1] * df[col2]
    
    return df

3. Cross-Validation Strategy

Robust validation is essential:

def stratified_kfold_cv(X, y, n_splits=5, random_state=42):
    """Stratified K-Fold with shuffling"""
    from sklearn.model_selection import StratifiedKFold
    
    skf = StratifiedKFold(
        n_splits=n_splits,
        shuffle=True,
        random_state=random_state
    )
    
    for train_idx, val_idx in skf.split(X, y):
        X_train, X_val = X.iloc[train_idx], X.iloc[val_idx]
        y_train, y_val = y.iloc[train_idx], y.iloc[val_idx]
        
        yield X_train, X_val, y_train, y_val

4. Model Ensembling

Combining predictions for better performance:

class EnsembleBuilder:
    def __init__(self, models, weights=None):
        self.models = models
        self.weights = weights or [1/len(models)] * len(models)
    
    def fit(self, X, y):
        for model in self.models:
            model.fit(X, y)
        return self
    
    def predict(self, X):
        predictions = np.zeros((X.shape[0], len(self.models)))
        
        for i, model in enumerate(self.models):
            predictions[:, i] = model.predict(X)
        
        # Weighted average
        return np.average(predictions, axis=1, weights=self.weights)

Competition Templates

House Prices Prediction

# Template for tabular regression
class HousePricesPipeline:
    def __init__(self):
        self.numerical_features = []
        self.categorical_features = []
        self.imputer = None
        self.scaler = None
        self.model = None
    
    def preprocess(self, train, test):
        # Separate features
        X = train.drop('SalePrice', axis=1)
        y = np.log1p(train['SalePrice'])
        
        # Handle missing values
        # Feature engineering
        # Scaling
        
        return X, y, test
    
    def train(self, X, y):
        # Cross-validation
        # Hyperparameter tuning
        # Model fitting
        
        return self
    
    def predict(self, test):
        return np.expm1(self.model.predict(test))

Toxic Comment Classification

# Template for multi-label text classification
class ToxicCommentPipeline:
    def __init__(self):
        self.vectorizer = None
        self.models = {}
    
    def preprocess(self, texts):
        # Text cleaning
        # Tokenization
        # Vectorization
        
        return features
    
    def train(self, X, y):
        # Train separate classifiers for each label
        for label in y.columns:
            self.models[label] = LogisticRegression(C=4.0)
            self.models[label].fit(X, y[label])
        
        return self
    
    def predict(self, X):
        predictions = np.zeros((X.shape[0], len(self.models)))
        
        for i, (label, model) in enumerate(self.models.items()):
            predictions[:, i] = model.predict_proba(X)[:, 1]
        
        return predictions

Best Practices Checklist

Before Submitting

Cross-validation score is stable across folds
No data leakage (features not available at prediction time)
Appropriate metric optimization
Reasonable submission file format
Model is reproducible (fixed random seeds)

Code Organization

kaggle-playbook/
├── competitions/
│   ├── house-prices/
│   │   ├── data/
│   │   ├── notebooks/
│   │   │   ├── 01_eda.ipynb
│   │   │   ├── 02_feature_engineering.ipynb
│   │   │   ├── 03_modeling.ipynb
│   │   │   └── 04_ensemble.ipynb
│   │   └── README.md
│   └── toxic-comments/
├── templates/
│   ├── tabular_regression.py
│   ├── text_classification.py
│   └── image_classification.py
├── utils/
│   ├── preprocessing.py
│   ├── validation.py
│   └── ensemble.py
└── README.md

Competition Results

Competition	Score	Rank	Model
House Prices	0.118 RMSE	Top 10%	XGBoost + LightGBM Ensemble
Toxic Comments	0.983 AUC	Top 5%	Logistic Regression + TF-IDF
Titanic	0.835 Accuracy	Top 1%	Gradient Boosting

Learning Resources

Tech Stack: Python, Pandas, Scikit-learn, XGBoost, LightGBM, TensorFlow, Keras, Matplotlib, Seaborn Development Time: Ongoing Status: Active development with new competitions Last Updated: February 2026