Identifying Optimal Data Distributions for Enhanced Data Modeling in Machine Learning

Abstract

Understanding how data is distributed is crucial for building accurate models in machine learning and data science projects. In this paper, we explore practical methods to help identify the best-fitting distribution for real-world datasets. We cover visual techniques like histograms and Q-Q plots, as well as statistical tests such as Kolmogorov-Smirnov (KS) and Anderson-Darling (AD). We also look at model evaluation using criteria like Akaike information criterion (AIC) and Bayesian information criterion (BIC) to ensure a good fit. To illustrate these methods, we use the California Housing dataset, showing how wrong assumptions about data distribution can lead to poor model performance. By following the guidelines provided in this paper, data scientists can choose the right distribution, leading to more accurate models, better anomaly detection, and smarter decision-making across different fields.