AI Model Performance Dashboard

Overview

AI Model Performance Dashboard is a Streamlit-based analytics project for reviewing and comparing AI model evaluation metrics in an interactive browser interface.

This project is positioned as a recruiter-ready data visualization and machine learning reporting portfolio piece. It allows users to compare model accuracy, precision, recall, and F1 score through a clean dashboard experience with metric cards, charts, and summary tables.

Real-World Business Use Case

This project maps to practical analytics and machine learning workflows used by:

• Data Analysts
• Machine Learning Engineers
• AI Product Teams
• Technical Stakeholders Reviewing Model Quality
• Students Building AI And Data Portfolios

A team may need to answer questions such as:

• Which model performs best overall?
• How do precision and recall differ across models?
• Which metric should be prioritized for a given use case?
• How can model performance be presented clearly to decision-makers?

This dashboard is useful for model review, reporting, portfolio presentation, and lightweight experiment comparison.

Key Features

• Interactive Model Filter
• Metric Selection Sidebar
• Metric Summary Cards
• Cross-Model Comparison Chart
• Selected Model Detail Table
• Overall Performance Table
• Accuracy Snapshot Chart
• Key Observation Summary

Tech Stack

• Python
• Streamlit
• Pandas
• Matplotlib

Repository Contents

• Dashboard.py
• requirements.txt
• README.md
• .gitignore

How To Run The Dashboard

1. Create And Activate A Virtual Environment

python -m venv .venv
.\.venv\Scripts\Activate.ps1