Fraud Detection System
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Summary
Created a classification model to identify fraudulent transactions within a financial dataset, addressing class imbalance and optimizing for high detection rates.
IBANGA Ibanga Jackson
Entry-Level Data Scientist | Public Health
Abak Rd, Uyo, NG.Work
Awarri language AI
|Data Annotator
Remote
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Summary
Conducted precise data annotation and labeling for machine learning models and AI applications, ensuring high-quality datasets for advanced analytical systems.
Highlights
Performed accurate data annotation and labeling for machine learning and AI applications, contributing to the development of robust models.
Collaborated effectively with data scientists and engineers to refine labeling guidelines, ensuring alignment with project-specific requirements and enhancing data utility.
Executed rigorous quality checks and validation processes on annotated data, identifying and correcting inconsistencies to maintain high data integrity and usability.
Participated in continuous training sessions to stay updated on annotation techniques and project requirements, fostering continuous improvement in data quality.
About
Highly motivated and analytical entry-level Data Scientist with intensive training in machine learning, statistical modeling, and data visualization. Proven ability to apply data science techniques to solve complex problems, as demonstrated by projects in Titanic survival prediction, house pricing, and fraud detection. Eager to leverage a strong foundation in data analysis, Python, and Microsoft excel to drive data-driven insights and contribute to impactful projects in a dynamic organization.
Education
University of Calabar
Calabar, Nigeria
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Public Health
University of Uyo
Uyo, Akwa Ibom State, Nigeria
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BSc
Physiology
Languages
English
Ibibio
Certificates
Certificate of participation in Data Science Training
Issued By
Altschool Africa, IHS Academy, and Dahel Consultants and Techies
Skills
Programming Languages
Python.
Tools & Software
Microsoft Word, Microsoft PowerPoint.
Machine Learning
Titanic Survival Prediction, House Pricing Prediction, Fraud Detection, Regression, Scikit-learn, Model Evaluation, Data Preprocessing.
Data Analysis & Visualization
Microsoft Excel, Data Analysis, Data Visualization, Pandas, NumPy, Matplotlib, Seaborn.
Interests
Coding
Research and writing
Chess and Scrabble
References
Prof. Uduak Okon
Dean, Faculty of Basic Medical Sciences, University of Uyo, Uyo Nigeria. chairmo2013@gmail.com +2347034731970
Dr Antor Ndep
HOD, Department of Public Health, University of Calabar, Calabar Nigeria. drndep@gmail.com +2349060381917
Mr Joseph Ogar
Sectional Head, Starville School, Abuja. josephatebogar@gmail.com +2348054812473
Projects
House Pricing Prediction
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Summary
Built a regression model to accurately predict house prices based on various features from a comprehensive dataset, emphasizing exploratory data analysis and model optimization.
Titanic Survival Prediction
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Summary
Developed a machine learning model to predict passenger survival on the Titanic dataset, focusing on data preprocessing, feature engineering, and classification algorithms.