## Tutorials for this Data/Competition
- [Base Model Development and Analysis Example](https://www.kaggle.com/code/mariofrei/ipm3211-base-analysis-and-model-example)
- [ML Model Tuning Example](https://www.kaggle.com/code/ponybiam/ipm3211-ml-tuning-and-feature-engineering)
- [Data Visualization Example](https://www.kaggle.com/code/mariofrei/ipm3211-visualization)

## General ML Tutorials (from edX Section 4)
- [Preprocessing pt. 1: data transformation](https://www.kaggle.com/ponybiam/preprocessing-pt-1-data-transformation)
- [Preprocessing pt. 2: encoding categorical variables](https://www.kaggle.com/ponybiam/preprocessing-pt-2-encoding-categorical-variable/)
- [Preprocessing pt. 3: handling missing values](https://www.kaggle.com/ponybiam/preprocessing-pt-3-handling-missing-values)
- [Feature engineering pt. 1: simple features](https://www.kaggle.com/code/ponybiam/feature-engineering-1-simple-features/notebook)
- [Feature engineering pt. 2: clustering & PCA](https://www.kaggle.com/ponybiam/feature-engineering-pt-2-clustering-pca)
- [Feature engineering pt. 3: target encoding](https://www.kaggle.com/ponybiam/feature-engineering-pt-3-target-encoding)

## Kaggle courses
- [Python](https://www.kaggle.com/learn/python)
- [Pandas](https://www.kaggle.com/learn/pandas)
- [Intermediate machine learning](https://www.kaggle.com/learn/intermediate-machine-learning)
- [Feature Engineering](https://www.kaggle.com/learn/feature-engineering)[[