How to Read These Projects

Welcome to the project documentation hub. This wiki is designed not just to show what code was written, but why it works and how it drives business value.

Every project in this portfolio follows a consistent documentation standard, ensuring that both technical developers and non-technical stakeholders can navigate the logic easily.

The Standard Documentation Structure

Each project wiki is broken down into six core sections. Here is what you can expect to find in each:

1. Project Overview & Tech Stack

A high-level executive summary. This section answers:

• What problem does this application solve?
• What technologies (Frontend, ML, Backend) were used?
• Key takeaway: The “Elevator Pitch” of the project.

2. System Architecture

We move beyond simple file lists. This section maps the physical codebase, explaining the relationship between the User Interface (Frontend), the Analytical Engines (Backend), and the Data Layer.

3. Core Workflows (Visualized)

Code is hard to read; diagrams are easy. I use Mermaid.js flowcharts to visualize how data travels through the system.

• Logic Flows: How the system decides “Yes” or “No”.
• Data Pipelines: How raw CSVs become actionable insights.
• User Journeys: The steps a user takes from login to result.

4. Analytical Modules (Deep Dive)

This is the technical heart of the documentation. For every Machine Learning model or complex algorithm, I document:

• The Algorithm: (e.g., XGBoost, K-Means, VADER).
• The “Why”: Why this specific algorithm was chosen over others.
• Key Features: Custom logic built on top of the model (e.g., “Self-Healing State”, “Smart Labeling”).

5. The “Business Logic” Layer

A unique feature of these projects is the explicit separation of Math and Meaning.

Most projects stop at the prediction (e.g., “Churn Probability: 80%”). My documentation highlights the Business Logic Layer—the code that translates that number into a business action (e.g., “If Risk > 80%, Recommend Discount”).

6. Installation & Troubleshooting

Practical steps to get the project running locally, including environment setup and common “gotchas.”

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Visualizing Workflows

I believe that if you cannot diagram it, you do not understand it. You will see diagrams like the one below in every project to explain complex logic:

graph LR
    A[Raw Data Input] --> B{Validation Gate}
    B -->|Fail| C[Error Handler]
    B -->|Pass| D[ML Engine]
    D --> E[Prediction Score]
    E --> F{Business Logic Layer}
    F -->|Score > Threshold| G[Trigger High Priority Action]
    F -->|Score < Threshold| H[Log as Low Risk]

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Why This Format?

Standard README.md files often lack context. By expanding them into full Wikis, we achieve:

• Transparency: You can see exactly how the “Black Box” of AI makes decisions.
• Maintainability: Future developers (or my future self) can understand the intent of the code, not just the syntax.
• Collaboration: Business stakeholders can read the “Business Logic” sections to verify that the code matches their strategy.

Next Steps

Explore the Customer Intelligence Hub to see this structure in action.

Check out the Data Science Setup Guide to see how I configure my environments.