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Claude Financial Data Analyst
Overview :
Claude Financial Data Analyst is a Next.js application that combines Claude AI capabilities with interactive data visualization, specifically designed for analyzing financial data. This product offers intelligent data analysis through chat, supports multi-format file uploads, and generates various charts based on the analysis. It is applicable not only in finance but also adaptable to areas like environmental data, sports performance, and social media analysis.
Target Users :
The target audience includes financial analysts, data scientists, environmental researchers, sports coaches, social media managers, and other professionals who need to analyze and visualize large datasets to make informed decisions.
Use Cases
Financial analysts use this product to analyze quarterly financial reports and market trends.
Environmental scientists utilize it to visualize pollution levels across different regions.
Sports coaches use it to track athlete performance and analyze team statistics.
Features
Intelligent Data Analysis: Provides text-based data analysis using Claude AI capabilities.
Multi-format File Upload Support: Allows uploading of text/code files, PDF documents, and images.
Interactive Data Visualization: Generates line charts, bar charts, multiple bar charts, area charts, stacked area charts, and pie charts based on the analysis.
Versatile Application Across Fields: Suitable for data analysis and visualization in various domains including environmental data, sports performance, and social media.
Data Extraction and Analysis: Upload financial documents to extract key metrics, analyze trends and patterns.
Visualization Creation: Generate charts based on data, customize visualizations, and compare multiple metrics.
Interactive Analysis: Get detailed insights into data by asking questions and requesting specific visualizations.
How to Use
1. Clone the code repository to your local machine.
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2. Install the necessary dependencies.
3. Create a .env.local file containing your Anthropic API key.
4. Run the development server.
5. Open http://localhost:3000 in your browser to view the results.
6. Upload financial files or data.
7. Extract key metrics and analyze trends.
8. Generate and customize data visualizations as needed.
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