# AI Data Analyzer

## Overview

Hands-on data analysis demo where students paste a small CSV, inspect traditional statistical output, and compare it with an AI-style narrative focused on trends, anomalies, business insights, or forecasting.

## Learning Objectives

- Explain the main ai/ml decision that AI Data Analyzer is designed to support.
- Use Enable AI narrative analysis, Analysis focus to test how different assumptions change the scenario.
- Interpret Traditional analysis in plain language and connect them to an action or conclusion.
- State one limitation, risk, or governance consideration before using the result in a real decision.

## Run Modes

- Browser

## Expected Setup / Startup Time

- Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

## Demo Type

- Interactive browser demo

## Files in This Folder

- `app.js`
- `index.html`
- `README.md`
- `style.css`

## How To Run

- Browser: open `index.html`.

## How To Use The Demo

1. Choose the run mode that fits the class: Browser.
2. Review the default assumptions before changing anything.
3. Change one or two inputs, then use `Analyze Dataset`.
4. Read Traditional analysis first, then compare any supporting metrics, charts, or AI text.
5. Capture one insight, one limitation, and one action recommendation.

## Inputs

- `Enable AI narrative analysis` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
- `Analysis focus` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
- `CSV dataset` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.

## Buttons / Actions

- `Analyze Dataset` is the main action that computes, compares, or generates the next result from the current inputs.

## Outputs

- `Traditional analysis` is the factual baseline because it summarizes the dataset numerically before interpretation.
- `AI interpretation` is the narrative layer that helps students discuss meaning, but it should always be validated against the traditional output.

## What To Notice

- The strongest classroom comparison is between what the data objectively says and what the narrative claims it means
- Change the analysis focus and watch how the same data can produce different but not equally useful interpretations
- Ask students which conclusion is evidence-backed and which one is a hypothesis needing validation

## Related Demos or Course Context

- Course path: [AI/ML Workflows](../../courses/ai-ml-workflows.html)
- Related demo: [AB Testing Framework](../ABTestingFramework/about.html)
- Related demo: [AI Cost Benefit Analyzer](../AICostBenefitAnalyzer/about.html)
- Related demo: [AI Decision Tracker](../AIDecisionTracker/about.html)

## Attribution

Created by **Professor Vinaya Sathyanarayana** as part of [KateelLearningDemosToStudents](https://github.com/VinayaSharada/KateelLearningDemosToStudents).
Attribution email: `vinallcontact@gmail.com`