Feature engineering, K-Folds cross-validation, and ensemble methods. Discovered that simpler algorithms (KNN) can outperform complex ensembles when properly optimized.

Regression techniques with domain knowledge and SHAP for feature importance analysis. Discovered and exploited data leakage for near-perfect score.

Binary classification for financial risk assessment. Feature engineering combining credit metrics, debt-to-income ratios, and payment history.

Predicting song tempo from audio features. Combined signal processing with machine learning, leveraging music theory for feature engineering.

Ensemble methods predicting accident risk. Created temporal and weather interaction features. Containerized training pipeline using Docker.

Customer response prediction in 7 days. YAML configuration management for rapid prototyping and experimentation.

Time series regression with temporal features and advanced stacking techniques for improved predictions.

Multi-label classification with MAP@3 optimization and agricultural domain knowledge for crop recommendation.

First Kaggle experience. Feature engineering fundamentals and XGBoost for binary classification.

Lessons on overfitting and feature correlation. Improved model through careful feature selection.

Imbalanced data handling with SMOTE. Learned proper application within K-Folds cross-validation.

Limited data with advanced oversampling and Bayesian optimization for hyperparameter tuning.