Dashboard 62

Overview

This product analysis is about a ranking system for graduates of an academy based on their competencies using neural networks. The team consists of a backend developer, a UI/UX designer, and a frontend developer who is also the team captain. The application is developed using ReactTS, Flask, and PostgresQL. The ranking and evaluation of candidates' professional suitability are done using the Giga Chat language model developed by Sber. The ranking criteria include matching the candidate's specialty and hobbies, matching the specialty and hobby with the assigned task, the importance of each discipline in the diploma, and the cumulative score of the candidate's skills. The implemented features include user registration/authentication, resume upload in a data archive format, manual candidate profile creation, reading and saving resume data to the database, visual representation of candidate data, the ability to add candidates for in-depth comparison, candidate grouping (automatic and manual), building distribution graphs of candidate group scores, and calculating statistical parameters. The existing pages include the homepage, registration/authentication page, resume upload page, general candidate comparison page, in-depth comparison page, group member view page, group statistics view page, all candidate groups view page, automatic group creation page, and manual group creation page.

Features

• Ranking Candidates: Candidates are ranked based on their specialty and hobbies, as well as their match with the assigned task and the importance of each discipline in their diploma.
• Resume Upload: Users can upload resumes in a data archive format, which can contain multiple formats of resumes and the candidate's photo.
• Manual Profile Creation: Candidates can manually create their own profiles.
• Data Visualization: The system provides visual representation of candidate data and allows for adding candidates for in-depth comparison.
• Grouping Candidates: Candidates can be automatically or manually grouped.
• Distribution Graphs: The system can generate distribution graphs showing the scores of candidate groups.
• Statistics Calculation: The system calculates statistical parameters related to candidate groups.

Summary

This product is a ranking system for graduates based on their competencies. It uses neural networks and various criteria to evaluate candidates' professional suitability. The system allows for resume upload, manual profile creation, and visual representation of candidate data. Candidates can be grouped automatically or manually, and the system provides distribution graphs and statistical calculations for candidate groups. The installation process involves setting up the required technologies and running the application on the frontend and backend. Overall, this product aims to provide an efficient and comprehensive ranking system for graduates.