Hi, I'm Olabanji Olaniyan

I built an AI-powered web application that makes it easy to digest long-form text by generating concise summaries and highlighting key entities like people, organisations, and locations. The app combines two powerful NLP models — facebook’s bart-large-cnn to create clear, informative summaries, and dslim’s bert-base-NER to identify important named entities.

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Built a machine learning pipeline to predict benzene concentrations using Gaussian Process Regression (GPR) and other models. After cleaning skewed data caused by -200 null encodings, I extracted temporal features and compared four models. GPR achieved the best performance (RMSE: 2.33, R²: 0.883) and provided uncertainty estimates for more informed environmental decisions.

This project tackles the SemEval-2017 Task 4A challenge: sentiment classification of English-language tweets. The goal is to classify each tweet as positive, neutral, or negative using supervised machine learning models.

This project involves classifying English news headlines and short descriptions into one of four categories: World, Sports, Business, and Sci/Tech using the AG News dataset.

In this project, we aim to predict customer churn using various features such as payment methods, contract type, and whether a customer streams TV or movies. This task is crucial for companies to identify potential 'churners' and take actions to retain them.