# Week 9 - Missing Values

### This week we will...

* learn how to use dropout layers
* get to know different ways to handle missing values

### Learning Resources

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* [Example notebook](https://colab.research.google.com/github/opencampus-sh/einfuehrung-in-data-science-und-ml/blob/main/Fehlende%20Werte/missing_value_imputation.ipynb) for handling missing values
* Chapter 1 of [this](https://campus.datacamp.com/courses/dealing-with-missing-data-in-python/) course at datatcamp
* [Hugging Face course](https://huggingface.co/learn/nlp-course/chapter1/1) on the Transformers library

### Until next week you should...<br>

* [x] choose one (or several) methods to replace the missing values in your dataset.

* [x] (if you haven't already) divide the tasks well within your team: Who will work on data optimization, and who on model optimization?

* [x] using your best model, generate predictions for the Kaggle competition test dataset and upload them there.<br>

* [x] prepare your final presentation (see the guidelines for Week 10 [here](https://opencampus.gitbook.io/opencampus-machine-learning-program/einfuehrung-in-data-science-und-maschinelles-lernen/woche-9-projektpraesentationen)).