| [1] | Linear regression, Lasso, random forest | Salary prediction for Data Science Job | Kaggle—Glassdoor | MAE: For random forest—11.22, for linear regression—18.86, for ridge regression—19.67 |
| [2] | SVM | Skill based job recommendation system | Job portals, company websites, scraping data from other online sources | Accuracy, precision, recall, and F1 score was calculated |
| [3] | Bidirectional, decoder-encoder, stacked, Conv LSTM | Trend analysis system to predict future job markets using historical data | Web scraping, manually collecting data, government sources | Accuracy: for bidirectional LSTM—95.71%, for decoder– encoder LSTM—91.56%, for stacked LSTM—87.24%, for Conv LSTM—83.7% |
| [4] | NB, KNN, NBST | Predictive analysis | Student employment in the employment market of Chongqing S colleges and universities in the past 3 years | Mean value [test time (ms)]: NB—18.607, KNN—22.224, NBST—49.026 |
| [5] | MNB, SVM, DT, KNN, RF | Job posting classification | Kaggle, titled by “[real or fake] fake job posting prediction” | For MNB 95.6%, for SVM 97.7%, for DT 97.4%, for KNN 97.8%, for 98.2%, for RF 98.2% |
| [6] | LR, SVM, KNN, DT, RF, AdaBoost(DT), GB, voting classifier soft & hard, XGBoost | Campus placement analyzer: Using supervised machine learning algorithms | Training and placement department of MIT which consists of all the students of Bachelor of Engineering (B.E) from three different colleges of their campus | Accuracy: Logistic Regression 58%, support vector machine 69%, KNN 63.22%, decision tree 69%, random forest 75.25%, AdaBoost(DT) 77%, gradient boosting 77%, voting classifier soft 69.11%, voting classifier hard 68.43%, XGBoost 78% |
| [7] | Voting classifier | Ensemble approach for classifying job positions | Glassdoor website | For voting classifier soft—100% |
| [8] | NB, SGD, LR, KNN, RF classifier | Detecting and preventing fake job offers | Kaggle—real/fake job posting prediction | For random forest classifier—97.48% |
| [9] | NLP, KNN | Resume-based job recommendation system using NLP and deep learning | Combined from multiple sources | Improving the efficiency and success rate of the hiring process |
