PREPARE Challenge: Proof of Principle Demonstration (Phase 3)

Advance algorithms and analytic approaches for early prediction of Alzheimer's disease and related dementias, with an emphasis on explainability of predictions. [Phase 3] #health

$222,500 in prizes
2 weeks left
24 joined

Community Code

This is where participants can share their work to help and inspire others. Posts can range from short snippets to longer guides covering data processing, modeling, bias mitigation, or something else!

At the end of the challenge, judges will select up to four posts for the "Community Code Bonus Prize." See the home page for details.

You will have the option to edit your post after submitting.

Tips

The best posts will be easy to follow. Some tips for creating a good post:

  • Include a short introduction summarizing what the post contains.
  • Break the post up into explanatory sections, such as "Intro", "Load the data", "Process the data", "Exploratory Data Analysis", and "Modeling". If you are working in a Jupyter notebook, use Markdown cells to add narrative and headers for different sections.
  • Document your code! Explain what different parts of your code do with comments, docstrings, or text blocks.
  • Do not print out full dataframes or other full data structures, unless they are very small. Long outputs make the narrative harder to follow. Instead, print out data samples and summary statistics.
    • For example, if you are working with a pandas DataFrame print out df.head() or df.sample(n=5) rather than df. Similarly, use df.describe() to see summary statistics.
  • Do not print out extremely long log outputs. Pro tip: you can suppress the output of a Jupyter notebook cell by adding %%capture to the top.

Examples

Below are some examples of useful topics for posts. However, this is not an exhaustive list!

  • Useful exploratory data analysis of data
  • Demonstration of good bias identification or mitigation. For some guidance on bias analysis techniques, see the annotated example from The Wellcome Trust authored by DrivenData.
  • How to effectively select features for a model
  • How to analyze the importance of different features in a trained model

Posts (26)

7mo 3w ago
#python #Modeling #Explainability #Visualization
8mo 1w ago
#python #Modeling #prepare_social-determinants
7mo ago
#python #Modeling #Explainability
8mo ago
#python #Preprocessing #Modeling #prepare_social-determinants
6mo 1w ago
#python #prepare_acoustic #Preprocessing #Dataset
6mo 1w ago
#python #prepare_acoustic #Preprocessing
6mo 1w ago
#python #Preprocessing #Modeling
6mo 1w ago
#python #prepare_acoustic #Preprocessing #LLMs
6mo 2w ago
#python #Preprocessing #Modeling #Visualization #Explainability #feature-selection
6mo 1w ago
#python #Modeling #Dataset #Tools #Explainability #feature-selection #prepare_acoustic
6mo 2w ago
#R #Preprocessing #Modeling #Visualization #Tools #feature-selection
6mo 1w ago
#python #Preprocessing #Visualization #Dataset
6mo 2w ago
#python #Preprocessing #Modeling #Explainability #feature-selection #prepare_social-determinants
6mo 2w ago
#python #Preprocessing #Modeling #feature-selection
7mo ago
#python #Preprocessing #Modeling
6mo 1w ago
#python #Preprocessing #Modeling #Visualization
6mo 1w ago
#python #Modeling #Dataset #Tools #Explainability #feature-selection #prepare_acoustic
6mo 1w ago
#python #Modeling #Dataset #Tools #Explainability #feature-selection #prepare_acoustic
6mo 1w ago
#python #Modeling #Dataset #Tools #Explainability #feature-selection #prepare_acoustic
6mo 1w ago
#python #Modeling #Visualization #Dataset #Tools #Explainability #feature-selection #prepare_acoustic
6mo 1w ago
#python #Tools #Preprocessing #Dataset #prepare_social-determinants
6mo 1w ago
#python #Modeling #Visualization #Dataset #Tools #Explainability #feature-selection #prepare_social-determinants
6mo 1w ago
#python #Preprocessing #Modeling #Visualization #Dataset #feature-selection
6mo 1w ago
#python #prepare_acoustic #Preprocessing #Dataset
6mo 1w ago
#python #prepare_acoustic #Preprocessing #LLMs