The Department of Petroleum Engineering at the College of Engineering – University of Baghdad organized a scientific lecture entitled “Data Preparation for Machine Learning” in the Energy Hall on Sunday, March 15, attended by a number of faculty members from the department.
The lecture was delivered by Professor Dr. Mohammed Saleh Al-Jawad, who discussed the importance of the data preparation stage as one of the fundamental pillars for the success of machine learning models and the accuracy of their outputs. He emphasized that data quality represents the decisive factor in the efficiency of predictive models and the reliability of their results.
The lecturer reviewed the key concepts related to data processing prior to model development, including data cleaning, detection of errors and inconsistencies, handling missing values, as well as standardizing data formats obtained from multiple sources.
He also addressed the challenges faced by researchers when integrating data from different databases and systems, which may result in duplication, variations in information representation, or diversity in data types. In this context, he presented several methods used in data transformation and processing within the framework of Data Integration.
The lecture featured a set of practical examples derived from real-world petroleum industry applications, aiming to simplify concepts and enhance participants’ understanding. It also highlighted the importance of detecting duplication and inconsistencies in data and methods for addressing them in order to improve overall data quality.
At the conclusion of the lecture, the speaker stressed that proper data preparation is a pivotal stage in artificial intelligence and data analytics projects, given its direct role in improving the efficiency and accuracy of results.
