adhit-r/fairmind

Basic ML: Data Visualization & Analytics Dashboard

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#65 opened on Aug 27, 2025

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 (10 comments) (0 reactions) (4 assignees)Python (12 forks)auto 404
ai/mlenhancementgood first issuehelp wantedui/ux

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 (9 stars)
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Description

📈 Basic ML: Data Visualization & Analytics Dashboard

Assigned to: @27804balaji

Objective

Create data visualization and analytics dashboard for FairMind's ML pipeline.

Tasks

1. Data Visualization

  • Implement data distribution plots
  • Create correlation analysis visualizations
  • Add feature importance visualizations
  • Implement model performance charts
  • Create bias analysis visualizations

2. Analytics Dashboard

  • Create interactive dashboard components
  • Add real-time data visualization
  • Implement filtering and sorting
  • Create export functionality
  • Add dashboard customization

3. ML Metrics Visualization

  • Implement model performance metrics
  • Create bias detection visualizations
  • Add fairness metrics charts
  • Implement explainability visualizations
  • Create comparison charts

4. Integration with FairMind

  • Integrate with existing dashboard
  • Add new visualization components
  • Create responsive design
  • Implement real-time updates

Technical Requirements

  • Python, Matplotlib, Seaborn, Plotly
  • Basic understanding of data visualization
  • Experience with dashboard development

Success Criteria

  • 10+ visualization components created
  • Interactive dashboard functional
  • Real-time updates working
  • Integration with FairMind complete

Learning Opportunities

  • Learn advanced data visualization
  • Understand dashboard development
  • Practice with real-time data
  • Gain experience with ML metrics

🏷️ Labels

good first issue, enhancement, ai/ml, ui/ux

Contributor guide

Research direction
Start by implementing a single visualization type, such as a distribution plot using Matplotlib. Then explore Plotly for interactive elements and integrate with the existing dashboard. Study the FairMind codebase to understand how to add components.
Tech stack
python
Domain
datamachine learningai
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60