Williams Olayinka

Prepared and preprocessed datasets, performed feature engineering, and trained and evaluated machine learning models to select the best approach based on accuracy and efficiency. Built a Flask application for real-time predictions and implemented protocols to track model performance after deployment.

Collaborated with sales and account management to create pricing proposals and tailored client quotes, while monitoring UK energy market trends and policy changes. Translated technical pricing models into actionable insights for non-technical stakeholders and supported customer satisfaction through accurate, timely pricing outputs.

Designed, developed, tested, and deployed five machine learning models using TensorFlow and Keras on Google Cloud infrastructure for production environments. Performed feature engineering across diverse data sources and fine-tuned and deployed large language models for NLP tasks with reported improvements in accuracy and performance.

Validated state-specific datasets containing polling unit information and used geocoding to fill missing latitude and longitude values. Performed spatial analysis to identify neighboring polling units, computed outlier scores to detect anomalies, and produced a report highlighting the top outliers and their closest units.

Conducted exploratory data analysis on the SampleSuperstore dataset to identify business insights, including loading, cleaning, and preprocessing the data for integrity. Built interactive dashboards and prepared a screen-recorded presentation covering the analysis process, visualizations, and recommendations to improve profitability.

Grade: 4.13/5.00 (Cumulative GPA)

Earned a Bachelor of Science in Electronics and Computer Engineering at Lagos State University, graduating in March 2024. Achieved a cumulative GPA of 4.13/5.00 and completed relevant coursework in statistics and probability and Python and computation.