AI BASED PERSONAL ADVERTISING SYSTEM

Abstract

The digital advertising industry generates vast amounts of data daily, making manual campaign optimization inefficient and costly. This industry-oriented mini-project proposes an Artificial Intelligence (AI) based Advertising System designed to automate ad targeting, budget allocation, and predictive performance analysis. By leveraging Machine Learning (ML) algorithms such as Random Forest and Deep Neural Networks, the system analyzes consumer demographic data, browsing behavior, and historical engagement metrics to predict clickthrough rates (CTR) and optimize return on ad spend (ROAS). The proposed methodology includes a comprehensive data preprocessing pipeline, feature engineering, and real-time bidding simulation. Results indicate that the AI-driven approach significantly outperforms traditional rule-based advertising systems in both cost-efficiency and conversion rates. This project bridges the gap between theoretical machine learning models and practical, industry-standard digital marketing applications. Traditional advertising models often struggle with low engagement rates due to their generic, mass-targeting approach. This project presents an AI-Based Personal Advertising System designed to deliver highly customized marketing content tailored to individual users. By leveraging machine learning algorithms, predictive analytics, and data science techniques, the system continuously processes user data streams—including browsing behavior, engagement history, and preference indicators—to build dynamic profiles. Utilizing generative AI capabilities, the system can autonomously craft personalized ad creatives, adjusting imagery, video content, and copy to resonate with specific user tastes. Furthermore, the integration of demand forecasting models allows the system to determine the optimal timing and channel for ad delivery, maximizing conversion probabilities. Ultimately, this personalized framework significantly improves click-through rates, enhances user satisfaction by reducing irrelevant spam, and maximizes the return on investment (ROI) for advertisers, representing a critical shift from reactive mass marketing to proactive, individualized advertising.