Abstract: This research project focuses on the development of a comprehensive data processing framework tailored for advanced machine learning applications in the domains of classification, regression, and clustering. The primary objective of this endeavor is to empower users with the tools and methodologies necessary to effectively explore and exploit their datasets for predictive and exploratory data analysis.
The project leverages state-of-the-art machine learning algorithms and data processing techniques to facilitate the training, testing, and evaluation of machine learning models across diverse application scenarios. By offering a flexible and user-friendly environment, it enables researchers and data practitioners to harness the full potential of their data.
Key components of this project include data preprocessing, feature engineering, and model evaluation. Data preprocessing encompasses various techniques such as data cleaning, transformation, and normalization to ensure data quality and consistency. Feature engineering involves the creation of meaningful and informative features that enhance the performance of machine learning models. Model evaluation incorporates a variety of metrics and visualization tools to assess the effectiveness and robustness of the trained models.
Keywords:DatavisualizationLiterary,narrativedata,Database,managementsystem,Neo4J,Graphdatabase,Webapplication,Streamlit,Pyvis,Relationshipsbetweenentities,Bidirectional Encoder (BERT)
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DOI:
10.17148/IJARCCE.2024.13438
