# 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
{% file src="/files/ilAR7TjVbBBwq3v9eU7h" %}
* [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...
* [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.
* [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)).
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/einfuehrung/woche-6-support-vector-maschinen.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.