Closed ranamanish674zu closed 3 days ago
Hi there! Thanks for opening this issue. We appreciate your contribution to this open-source project. We aim to respond or assign your issue as soon as possible.
Can you assign it to me? @sanjay-kv Full name: Manish Rana GitHub profile link: https://github.com/ranamanish674zu Email ID: manish.rana2021@vitbhopal.ac.inn Approach for this : python & machine learning What is your participant role?: GSSoC-2024 contributor
Can you add the label for GSSoC, i want to work on it Thanks.
Hello @ranamanish674zu! Your issue #293 has been closed. Thank you for your contribution!
Is there an existing issue for this?
Feature Description
Target Variable: JobSatisfaction or CareerSatisfaction. Predictors: Variables like Employment, YearsCodingProf, FormalEducation, CompanySize, DevType, etc. Objective: Predict levels of job satisfaction based on personal and professional attributes.
Use Case
Predicting job satisfaction using variables such as Employment, YearsCodingProf, FormalEducation, CompanySize, and DevType can help organizations better understand the factors contributing to employee happiness and retention. By identifying key predictors of job satisfaction, companies can tailor their policies, work environments, and professional development programs to enhance employee well-being. This predictive analysis can aid in reducing turnover rates, improving job performance, and fostering a positive workplace culture. Additionally, it can guide recruitment strategies by highlighting attributes that align with higher satisfaction levels. Ultimately, this insight helps organizations create more fulfilling and productive work experiences for their employees.
Benefits
Predicting job satisfaction enables organizations to enhance employee well-being, reduce turnover, improve performance, tailor recruitment strategies, and foster a positive workplace culture.
Priority
High
Record