INTELLIGENT WEBSITE GENERATION FRAMEWORK USING ARTIFICIAL INTELLIGENCE AND DATA MINING

Abstract

This study presents the design and development of an intelligent web-based system that integrates Artificial Intelligence (AI) and data mining techniques to analyze user behavior and improve web-based services. The proposed system identifies patterns and trends in user interactions by examining system usage, user inputs, and behavioral data. Machine learning approaches, including unsupervised learning, reinforcement learning, and neural networks, are employed to interpret user needs and adapt system responses accordingly. The system is capable of operating autonomously and continuously improving its performance over time with minimal human intervention.

Data preprocessing plays a vital role in enhancing model effectiveness and accuracy. Techniques such as data cleaning, normalization, feature selection, Artificial Neural Networks (ANNs), and K-Nearest Neighbors (KNN) are applied to prepare and optimize the dataset before analysis. Through data mining, meaningful patterns are extracted and stored, enabling the system to generate personalized web solutions and recommendations for future users.

Compared with traditional web development approaches, the proposed AI-driven system provides improved efficiency, higher automation, and the ability to process large-scale datasets. This enables faster analysis and more informed decision-making. The integration of advanced preprocessing and data mining methods ensures robust system performance and reliable results.

The findings highlight the significant potential of AI-driven data mining in modern web applications, particularly in predictive analytics, recommendation systems, and user-centered design. The proposed framework contributes to the development of intelligent, adaptive, and reliable digital systems that can enhance user experience and support future web technologies.