PERSONALITY PREDICTION SYSTEM BASED ON CV ANALYSIS USING MACHINE LEARNING

This project presents the development of a personality prediction system based on CV analysis using machine learning techniques. The system utilizes Natural Language Processing (NLP) and supervised learning algorithms to extract meaningful linguistic, semantic, and structural features from candidate CVs to infer personality traits. By applying data-driven methods, the system provides an objective and efficient approach to evaluating candidates' personalities, reducing human bias in recruitment. The study employs an iterative System Development Life Cycle (SDLC) model and integrates algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Neural Networks to evaluate model performance. Experimental results demonstrate the system’s effectiveness in accurately predicting the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism). The Random Forest model achieved superior performance with high accuracy in personality inference. The proposed system contributes to modern recruitment processes by enhancing fairness, speed, and data-driven decision-making. It can be adopted by HR departments to streamline hiring and identify candidates whose personalities align with organizational culture. Future research should focus on expanding datasets and improving model interpretability for broader industrial applications.