Efficient Job Recommendation Framework Using Genetic Optimization Techniques

Employers nowadays are having conversations with educated individuals about challenges. Based on abilities and qualifications, the web system suggests several job categories and post openings in the authority. Individuals who fulfill the requirements for positions at various businesses or financial institutions across the world receive higher wages and eventually advance in their careers. Therefore, in real life, it is essential to understand a website-based job suggestion system. Sorting individuals who are recommended for positions and those who do not require a critical dataset containing both numerical and textual data. In this work, we used a combination of Kaggle datasets to use machine learning-based variants of the genetic algorithm for foreseeing the job recommendation system. An algorithm using genetics can determine which candidates are the best fit for a position suggestion based on 1,000 records total across 11 attributes in the dataset. The model was able to produce the most exact outcomes using the fitness function’s settings. Tasks including mutation, crossover, and natural selection methods formed the basis of the proposed system. considering that producing candidates with the best fitness value and the highest level of qualification for the role is our main goal.