Job Market Analytics & Opportunity Prediction

The rapid growth of digital job platforms has created a vast amount of employment data, making it difficult for students and job seekers to understand market demand and career opportunities effectively. This project, Job Market Analytics and Opportunity Prediction, aims to analyze job market trends and predict job demand using Machine Learning techniques. The system accepts user inputs such as skills, location, industry, experience level, and expected salary to evaluate employment opportunities. The proposed system uses TF-IDF (Term Frequency–Inverse Document Frequency) for skill feature extraction and a Random Forest Classifier to predict job demand levels such as High, Medium, or Low. Based on prediction results, the application calculates an opportunity score and identifies skill gaps required to improve employability. The system is developed using Python, Flask framework, HTML, CSS, and Machine Learning libraries, providing an interactive web-based interface for real-time analysis. Additionally, the project includes a visualization dashboard that presents job trends, industry demand, and skill analytics through graphical representations. The system helps users understand current market requirements and guides them toward skill improvement and career planning.