Sparse Heterogeneous Data and Solution Strategies Using Advanced Machine Learning Techniques

This research examines the issues and solutions of machine learning with sparse, heterogeneous data. Heterogeneous data includes a variety of data kinds and architectures, such as structured, semi-structured, and unstructured data, which often results in sparse datasets where significant portions of the data are missing or zero. The research compares machine learning methods, including Ensemble Model, SVM, Naïve Bayes, Logistic Regression, and Random Forest, on datasets with and without sparse heterogeneous features. The research shows that the Ensemble Model performs better with sparse data, with an accuracy of 0.8912 and an F1 score of 0.8849. Models like SVM and Naïve Bayes exhibit very small gains, underscoring the limitations of dealing with sparse and diverse data. The results emphasize the need of using sophisticated approaches like feature learning, data imputation, regularization, managing missing values, and ensemble learning to successfully manage and analyse complicated data structures. This study sheds light on how to optimize machine learning models for improved predicted accuracy and resilience in the face of scarce, heterogeneous data.