Marketplace Intelligence Platform v1.0 - Visual Dashboard Pack

This package contains a complete Python-generated dashboard suite designed to showcase business intelligence, Power BI-style reporting, data analysis, customer intelligence, seller risk analysis, network science, decision intelligence, and AI-augmented analytics execution using the Olist marketplace dataset.

Dashboards

1. Platform Hero / Architecture
2. Executive Intelligence Center
3. Customer Intelligence Engine
4. Seller Intelligence & Risk Center
5. Product & Category Intelligence
6. Network Science & Marketplace Fragility
7. Decision Intelligence Engine

Main Outputs

• marketplace_intelligence_platform_gallery.html - interactive visual gallery
• Marketplace_Intelligence_Platform_v1_Dashboards.pdf - multi-page PDF dashboard deck
• visual_dashboards/*.png - individual 16:9 dashboard images
• data_exports/*.csv - recovered tables used to build the dashboard suite

Business/Portfolio Signal

This is not only a dashboard pack. It demonstrates an analytics platform moving from descriptive BI to prescriptive decision intelligence:

Raw Data -> Data Model -> BI Layer -> Customer Intelligence -> Seller Risk -> Network Science -> Decision Engine.

It also demonstrates an AI-native analytics methodology: business questions, marketplace datasets, KPI structures and dashboard requirements are transformed into programmatic analytical outputs using AI-assisted Python, validation logic, documentation, and iterative design review.

AI-Augmented Analytics Workflow

The project represents a practical alternative to purely manual BI development. Instead of building every KPI, chart and dashboard element step by step inside a graphical interface, the workflow uses AI as a technical execution layer to industrialize the production of analytical assets.

The method is:

1. Define the marketplace business problem: customer behavior, seller performance, delivery risk, category contribution, revenue segmentation and platform fragility.
2. Structure the KPI framework and decision questions before building visuals.
3. Use AI-assisted iteration to generate and refine Python scripts, chart logic, visual layouts, summary tables and analytical explanations.
4. Validate outputs against the dataset, review metric consistency and refine the dashboard pack until the result is executive-ready.
5. Package outputs as reusable dashboard images, PDF decks, HTML galleries and CSV exports.

This makes the project tool-agnostic: the same analytical logic can support Power BI-style reporting, Python dashboards, executive decks, automated reports or future BI implementations. The value is not only the dashboard output, but the ability to convert raw marketplace data into a repeatable, AI-augmented analytics production workflow.

Key Figures

• Revenue: $13.59M
• Orders: 99,441
• Customers: 96,096
• Sellers: 3,095
• Categories: 73
• Late Delivery Rate: 6.8%

Skills Demonstrated

• KPI Development
• Executive Reporting
• Power BI-style dashboard design
• Customer segmentation
• Seller risk scoring
• Product/category analysis
• Review and delivery risk modeling
• Network centrality and marketplace fragility analysis
• Intervention ROI and decision intelligence
• AI-assisted dashboard prototyping
• Programmatic BI automation
• Tool-agnostic analytics execution
• Human-in-the-loop data validation and review

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Repository Deployment

This repository includes a Windows deployment script:

UPLOAD_TO_GITHUB.bat

It initializes Git, creates the GitHub repository with GitHub CLI, pushes the project, and applies relevant repository topics.

Recommended repository name:

Marketplace-Intelligence-Platform

Recommended topics:

business-intelligence, analytics, marketplace-analytics, decision-intelligence, executive-dashboard, customer-analytics, seller-analytics, network-science, python, data-visualization, ai-augmented-analytics, analytics-engineering, programmatic-bi.