An agnostic approach to anomaly detection for data quality

Mosca, Eduardo (A.A. 2023/2024) An agnostic approach to anomaly detection for data quality. Tesi di Laurea in Artificial intelligence and machine learning, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 39. [Bachelor's Degree Thesis]

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Abstract/Index

What is anomaly detection? What is data quality? The isolation forest algorithm. Project blueprint and intended outcome. Preprocessing. Anomaly detection and the root cause analysis. Filtering rules through data quality assessment. The complete flow of the project.

Thesis Type: Bachelor's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18)
Chair: Artificial intelligence and machine learning
Thesis Supervisor: Italiano, Giuseppe Francesco
Academic Year: 2023/2024
Session: Summer
Deposited by: Alessandro Perfetti
Date Deposited: 16 Oct 2024 14:53
Last Modified: 16 Oct 2024 14:53
URI: https://tesi.luiss.it/id/eprint/40056

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