## Tutorials for this Data/Competition
We have prepared some example notebooks for the competition data below. The purpose of these notebooks is twofold: Firstly, provide examples of how to wrangle with the data provided in this competition. Secondly, give some basic initial guidance and ideas for beginners.
- [Example Notebook 1 - Basic Visualizations](https://www.kaggle.com/code/mariofrei/01-example-basic-visualizations)
   This notebook visualizes the raw data provided for this competition. The visualizations give a high-level insight into the data and demonstrate how to access the data best. The notebook is beginner-friendly.
- [Example Notebook 2: Minimal Viable ML Example](https://www.kaggle.com/code/mariofrei/02-example-minimal-viable-ml-example)
   This notebook shows a minimal viable machine learning approach that uses the competition data to create a valid submission file. The notebook is beginner-friendly. The default score of the submission file is rather low, but suggestions for improvements are listed at each step of the process.
- [Example Notebook 3: Exploratory Data Analysis](https://www.kaggle.com/code/ponybiam/03-example-eda)
   This notebook goes beyond Example Notebook 1 and does a deeper, more detailed dive into the competition data.
- [Example Notebook 4: Data Processing & Feature Engineering](https://www.kaggle.com/code/ponybiam/04-example-data-processing-and-feature-engineer)
  This notebook goes into more detail about data processing and feature engineering using the competition data set.

## General ML Tutorials that might be helpful
Below, we have listed some more tutorial notebooks that could be helpful for beginners.
- [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)