Machine Learning Model for Prediction and Analysis of Job Market

The evolving job market demands data-driven insights to understand hiring trends, salary expectations, and company dynamics.This project integrates comprehensive data analysis with predictive modeling to extract meaningful insights from job market data.Through rigorous data cleaning, visualization, and feature engineering, key trends in salaries, job roles, and company ratings were explored.To predict outcomes such as salary ranges and job classifications, five machine learning algorithms -Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, and Random Forest -were implemented and evaluated.Notably, all models achieved over 93% accuracy, with Random Forest achieving the highest accuracy of 98%.Performance metrics such as accuracy, precision, recall, confusion matrix, and ROC curve analysis were employed to ensure robust evaluation.This system addresses the limitations of existing platforms by offering improved prediction capabilities, data-driven insights, and enhanced model evaluation.The insights gained from this project can guide job seekers in career planning, assist recruiters in setting competitive salary benchmarks, and support businesses in understanding market trends.Future enhancements may include expanding datasets, adopting deep learning techniques, and developing a user-friendly interface for real-time insights.