A collection of reusable Python snippets for data science competitions (Kaggle, etc.).
Copy-paste these directly into notebook cells — they assume the standard Kaggle starter block with numpy, pandas, and os already imported.
snippets/
├── data_analysis/ # Explore and understand your data
│ ├── basic_eda.py - Shape, dtypes, head/tail, describe
│ ├── missing_values.py - Find, visualize, and handle missing data
│ ├── correlation_analysis.py - Correlation matrices and heatmaps
│ ├── distribution_plots.py - Histograms, KDE, box plots
│ ├── target_analysis.py - Analyze target variable distribution and balance
│ ├── outlier_detection.py - IQR and z-score based outlier detection
│ └── feature_relationships.py- Pairplots, scatter matrices, grouped stats
│
├── models/ # Ready-to-use model templates
│ ├── logistic_regression.py - Binary/multi-class logistic regression
│ ├── random_forest.py - Random Forest classifier and regressor
│ ├── xgboost_model.py - XGBoost with typical competition params
│ ├── lightgbm_model.py - LightGBM with typical competition params
│ ├── catboost_model.py - CatBoost (handles categoricals natively)
│ ├── knn.py - K-Nearest Neighbors
│ ├── svm.py - Support Vector Machines
│ ├── neural_net_tabular.py - Simple neural network for tabular data
│ └── cross_validation.py - K-Fold, Stratified K-Fold, GroupKFold
│
├── model_analysis/ # Evaluate and interpret model output
│ ├── classification_metrics.py - Accuracy, precision, recall, F1, AUC
│ ├── regression_metrics.py - MAE, MSE, RMSE, R²
│ ├── confusion_matrix.py - Confusion matrix visualization
│ ├── feature_importance.py - Tree-based and permutation importance
│ ├── learning_curves.py - Plot training vs validation curves
│ ├── prediction_analysis.py - Residuals, error distribution
│ └── shap_analysis.py - SHAP value explanations
│
└── utilities/ # Helpers and common operations
├── seed_everything.py - Reproducibility across all libraries
├── feature_engineering.py - Common feature transforms
├── encoding.py - Label, one-hot, target encoding
├── scaling.py - StandardScaler, MinMax, RobustScaler
├── memory_reduction.py - Reduce DataFrame memory usage
├── timer.py - Time your code blocks
└── submission.py - Generate Kaggle submission files
The docs/ folder contains step-by-step guides for common Kaggle competition types. Start with Getting Started if this is your first competition, then pick the guide that matches your task.
- Getting Started on Kaggle — environment setup, workflow overview, first submission
- Binary Classification — predict 0 or 1 (Titanic, fraud, churn)
- Multi-Class Classification — predict one of N classes
- Regression — predict a continuous value (prices, scores)
- Time Series — forecasting with temporal data
- NLP Classification — text-based competitions
- Feature Engineering — creating powerful features
- Model Selection & Tuning — choosing and optimizing models
- Ensembling — combining models for better scores
- Common Mistakes — pitfalls and how to avoid them
The examples/ folder has full end-to-end Jupyter notebooks that use mock data to walk through a complete competition workflow — from EDA to submission file. No external data files needed.
- Binary Classification — customer churn (LightGBM + Random Forest)
- Regression — house price prediction with log transform
- Time Series — 28-day store sales forecast with lag/rolling features
- Start a new Kaggle notebook (the standard imports are already in the first cell)
- Browse the folder that matches what you need
- Copy the snippet into a new cell
- Adjust variable names (
df,train,target, etc.) to match your data
