Data Visualization Pro

AI-powered data visualization with smart chart recommendations.

Features

6 Chart Types: Bar, Line, Pie, Scatter, Heatmap, Radar
AI Chart Recommendations: Analyzes your data and suggests the best chart type
CSV/JSON Import: Drop in your data file and visualize instantly
Interactive Dashboards: Combine multiple charts into a single view
Export: PNG, SVG, PDF — publication-ready output
Responsive: Works on desktop and mobile

Quick Start

1. Visualize a CSV file

Visualize this data: [paste CSV or provide file path]

The agent will:

Parse the data (CSV, JSON, or raw text)
Analyze column types (numeric, categorical, temporal)
Recommend the best chart type
Generate an interactive visualization

2. Create a specific chart

Create a bar chart comparing Q1-Q4 revenue for 2024 and 2025

3. Build a dashboard

Build a dashboard from sales-data.csv with:
- Revenue trend (line chart)
- Regional breakdown (pie chart)
- Product comparison (bar chart)

Chart Selection Guide

| Data Pattern | Recommended Chart | When to Use | |-------------|-------------------|-------------| | Trends over time | Line | Time-series, stock prices, growth | | Category comparison | Bar | Revenue by region, product sales | | Part-of-whole | Pie | Market share, budget allocation | | Correlation | Scatter | Height vs weight, price vs demand | | Multi-variable | Radar | Product comparison, skill assessment | | Density/matrix | Heatmap | Correlation matrix, geographic data |

AI Recommendation Engine

The AI analyzes your data to recommend the optimal visualization:

Column type detection: Numeric, categorical, temporal, boolean
Relationship analysis: Correlation strength, distribution shape
Data volume assessment: Row count determines complexity level
Pattern recognition: Trends, clusters, outliers, proportions

Sample Datasets Included

sample-data.csv — Mixed business metrics
sample-categories.csv — Category comparison data
sample-correlation.csv — Multi-variable correlation data
sample-proportions.csv — Part-of-whole data

Technical Stack

Frontend: React + TypeScript + Vite
Charts: Recharts (built on D3.js)
Styling: Tailwind CSS
Export: html2canvas + jsPDF
Build: 382KB production build

Web App

Try the live demo: https://courageous-bonbon-d1af15.netlify.app

Usage Tips

For large datasets (>10K rows), use aggregation before visualizing
AI recommendations work best with 3-20 columns
Export at 2x resolution for print-quality output
Use the dashboard view to tell a complete data story