Data Analysis

Exploratory Data Analysis (EDA) is a fundamental step in data-driven research, enabling analysts to understand data structure, identify patterns, and detect anomalies. However, conventional EDA techniques are largely manual, time-intensive, and heavily dependent on domain expertise, often resulting in high cognitive load, subjective bias, and limited scalability when dealing with complex or high-dimensional datasets. To address these limitations, this paper presents Explainable AI-EDA, an intelligent and automated exploratory data analysis framework that integrates statistical analysis, machine learning, and explainable artificial intelligence into a unified system. The proposed framework performs automated data profiling, missing value analysis, skewness and kurtosis evaluation, and entropy-based dataset complexity assessment. Machine learning techniques such as K-Means clustering, Isolation Forest-based anomaly detection, and linear regression are employed to uncover hidden patterns, detect outliers, and analyze variable relationships. The system is implemented as an interactive web-based application that supports real-time visualization, natural language interaction through an AI research assistant, and automated generation of research-ready analytical reports. Experimental evaluation demonstrates that Explainable AI-EDA significantly reduces analyst cognitive load, improves analytical efficiency, and provides scalable and reproducible exploratory analysis.