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@italomagno/atm-total

A local tool for analyzing and correcting errors in Brazilian air traffic CSV files, providing statistical insights without the need for a database. Continuously developed since 25/01/2024.

@italomagno/atm-total

This project, created on 25/01/2024, is a tool for analyzing and correcting errors in Brazilian air traffic CSV files. It leverages Node.js file system capabilities to process files locally on the user's machine through their browser. The tool does not use any database, making it lightweight and easy to use. It provides statistical insights and corrections for specific errors, with plans for future enhancements including artificial intelligence integration and linear modeling for future scale predictability. The project is continuously being improved and expanded.

  • Local analysis of air traffic CSV files
  • Node.js file system capabilities
  • No database required
  • Runs in the user's browser
  • Provides error correction and statistical insights
  • Future plans for AI integration and linear modeling

Features

Local Analysis of Air Traffic CSV Files

Analyze and correct errors in air traffic data without the need for an internet connection or a database. All processing is done locally on the user's machine.

Node.js File System Capabilities

Utilize the power of Node.js file system capabilities to read, process, and analyze CSV files efficiently and effectively.

No Database Required

By not relying on a database, this tool remains lightweight and simple to use, with all data processing done locally.

Runs in the User's Browser

Designed to run directly in the user's browser, providing a seamless and user-friendly experience without the need for complex installations or configurations.

Provides Error Correction and Statistical Insights

Automatically corrects specific errors in the data and provides users with valuable statistical insights, helping them understand and improve their data quality.

Future Plans for AI Integration and Linear Modeling

The project is set to expand with the integration of artificial intelligence for more advanced error detection and correction, as well as linear modeling to predict future trends and scales.