# Choosing a Project

### Focus on Problems First, Then Data

Rather than starting with a dataset and trying to find something interesting to do with it, identify a meaningful problem that could benefit from machine learning. This approach tends to lead to more impactful and realistic projects.

### Look for Human Pain Points

Consider processes or tasks that are:

* Time-consuming for humans
* Tedious or repetitive
* Prone to human error

These often make excellent candidates for machine learning solutions.

### Project Sources

#### Bring Your Own Project

If you already have a problem in mind or access to interesting data:

* Present your idea in the course or course chat
* If you have any doubts about the project, formulate specific questions and ask in the course chat

#### Partner with Organizations

Local companies and academic departments often have real-world problems waiting for ML solutions:

* Reach out to businesses or research groups in your area
* Inquire about data-intensive problems they're facing
* We can help you define project parameters with your partner
* If data confidentiality is a concern, you can use the NDA template provided [here](#non-disclosure-agreement-nda)

#### Public Datasets

While convenient, using public datasets sometimes comes with limitations:

* Projects tend to focus more on model optimization than real-world implementation
* You miss valuable experience in data preparation and feature engineering
* The problem may be artificially clean compared to real-world scenarios

However, if you choose this route, consider adding complexity by:

* Combining multiple datasets
* Creating your own validation methodology
* Adding constraints that reflect real-world conditions

### Data Resources

If you're looking for public datasets, here are some valuable repositories:

* [**ChallengeData**](https://challengedata.ens.fr/challenges/challenges_search): Datasets from real-world challenges
* [**Hugging Face Datasets**](https://huggingface.co/datasets): Collections ready for NLP and other ML tasks
* [**UCI Machine Learning Repository**](https://archive.ics.uci.edu/): Classic, well-documented datasets
* [**Kaggle**](https://www.kaggle.com/datasets): Competitive datasets with community solutions
* [**Roboflow Universe**](https://universe.roboflow.com/): Computer vision datasets
* [**Papers with Code**](https://paperswithcode.com/datasets): Datasets linked to research papers

### Examples of Past Projects

To get inspiration for a project, you might also want to review the projects nominated for the past VDE Machine Learning Prize in 2025 presented in the document below.

{% file src="/files/0IBsXhR4xPIvITTJrxEn" %}

### Non-Disclosure Agreement (NDA)

If you need an NDA for data you are getting from an organization or partner, you can use the following:

{% file src="/files/-MOXBtzubKR3rCJmwf6R" %}
Non-Disclosure Agreement (NDA)
{% endfile %}


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