Abstract:
"In the information technology sector, it's critical for workers to receive a fair wage that
accounts for their education and experience. When beginning a new job or negotiating a pay
raise, many people, however, find it difficult to determine the right salary range. People may
receive less money than they deserve as a result, which could demotivate them and make
them unhappy at work. To solve this issue, we provide a salary recommendation system that
can appropriately measure a person's value on the job market.
Our system uses a hybrid recommendation strategy that combines content-based and random
Forest Classifier techniques to produce salary recommendations for each user. To create a
comprehensive database of pay data, the system compiles information from a variety of
sources. The system will produce a compensation range that is indicative of the current
market conditions and the individual's skill based on the job title, experience, company size,
education of the individual.
In addition to offering clients a tailored wage proposal, the system offers advice on
bargaining tactics and ideas to assist people in securing a fair salary. The system aspires to
establish a more equal and fair job market for all information technology workers.
The models were used to evaluate with the help of machine learning models such as the
Accuracy, F1 score, Precision, AUC/ROC score.
In conclusion, the salary recommendation system will help the individual to be assisted in
making informed decisions about the remuneration negotiation and ultimately up to a certain
extent to close the wage gap in the information technology industry.
"
