OLUWAYEMISI JAIYEOBA
Phase 1, Lokongoma, Lokoja, Kogi State
- #-ï https://www.linkedin.com/in/oluwayemisi-jaiyeoba-b1/
§ https://oluwayemisi1.github.io/Oluwayemisi.Github.io/
Education
Salem Uiversity
Sep. 2014 – July 2018
Lokoja, Kogi State
Bachelor of Science in Computer Science
Federal University Lokoja
Oct. 2021 – August 2024
Lokoja, Kogi State
Masters in Computer Science
Relevant Coursework
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Data Structures
Software Methodology
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Algorithms Analysis
Machine Learning
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Artificial Intelligence
Internet Technology
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Systems Programming
Database Management
Experience
Department of Computer Science
Research Assistant
• I assisted in designing and conducting data science research projects focused on skin diseases.
• Collected, processed, and analyzed large datasets using Python.
• Developed and implemented machine learning models to identify patterns and trends in data.
• Prepared reports, presentations, and research papers for publication and conferences.
• Maintained accurate documentation of all research activities, data workflows, and findings.
Feb 2022 – July 2024
Lokoja, Kogi State
Upwork Freelancer
Novermber 2021 – Present
Data Analyst and Data Scientist
Remote
• Designed and implemented an interactive graph rendering tool using Jupyter Notebook.
• Developed a machine learning model to predict milk quality based on various attributes. applying algorithms such as
KNN, K-Means Clustering, Random Forest,Classification and Regression Tree.Logistic Regression, Random Forest,
Decision Tree, and Support Vector Machine.
• Built a classification system to categorize music genres using the RMA dataset using Random Forest, Decision Tree and
Support Vector Machine.
• As a Research Assistant for an Upwork client, I manage literature reviews, data analysis, paper editing and formatting,
journal submissions, presentation slide creation, and research proposal preparation.
Projects
Skin Disease Classification using Machine learning | Python, Google Colab, Flask
August 2021
• Developed a skin Disease Classification system using Machine Learning techniques and Python Programming language.
• Applied various machine learning algorithms to build a robust classification model, including Naive Bayes, Support
Vector Machine, Random Forest, Decision Tree, Gradient Boosting and Stacking Method.
• Deployed the final model using Flask, creating a user-friendly web interface that allows users to input patient data and
receive disease predictions in real-time
• Achieved a high classification accuracy of 99.30%.
Cervical Cancer Prediction | Python, Google Colab
August 2024
• Developed a predictive model to assess the risk of cervical cancer using machine learning algorithms on a dataset of risk
factors and screening results.
• Applied machine learning algorithms including Random Forest (99.40% accuracy), SVM (98.80% accuracy), AdaBoost
(99.80% accuracy), and Catboost (99.41% accuracy).
Skin disease classification Using Deep learning | Python, Django, Google Colab
April 2024
• Developed a deep learning model for classifying skin diseases using images of black skin, specifically for scabies, atopic
dermatitis, and acne.
• Collected data from Google search and by capturing images of affected areas on individuals.
• Implemented the solution in Python with Django for deployment and utilized Google Colab for model training and
experimentation.
Technical Skills
Languages: Python, R, HTML/CSS
Developer Tools: Jupyter Notebook, Google Colab , Visual Studio, Excel, Power BI
Libraries and Frameworks: Scikit-learn, TensorFlow/Keras, PyTorch, Pandas, NumPy, Matplotlib/Seaborn, Data
Visualization
Certifications
Data Analytics Certification
August 2022
Jahlics Technology Internship
Research Methodology Workshop Certification
February 2023
Federal University, Lokoja
IBM Data Analyst Professional Certificate
Coursera Online Course
February 2023
