Unlocking Potential: A Machine Learning Approach to Job Category Prediction

India's high unemployment rate (53.4%, ILO Department of Statistics, n.d.) despite a reasonable literacy rate (76.32%, Statista, 2024) suggests a mismatch between skills and job availability. This research addresses this using machine learning to identify suitable job categories for individuals. We explore various techniques like TF-IDF for word embedding, feature selection methods like cross-correlation with Rank-Sum, and different classifiers to predict job categories based on job descriptions and responsibilities. Data from Kaggle is used for training. The primary focus is to find the most effective approach for distinguishing job categories.