data-visualization-pro

# 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: 1. Parse the data (CSV, JSON, or raw text) 2. Analyze column types (numeric, categorical, temporal) 3. Recommend the best chart type 4. 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: 1. **Column type detection**: Numeric, categorical, temporal, boolean 2. **Relationship analysis**: Correlation strength, distribution shape 3. **Data volume assessment**: Row count determines complexity level 4. **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