Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in this domain is often ...

This class is a graduate-level introduction to Natural Language Processing (NLP), the study of computing systems that can process, understand, or communicate in human language. The course covers ...

This Kaggle project aims to build a machine learning model to predict which tweets are about real disasters and which ones are not. The dataset consists of 10,000 tweets that were hand classified. The ...

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...

This was done as part of my Level 3 internship project under Coding Samurai ’s Data Science Internship track. The main goal was to apply NLP skills, clean and preprocess raw text data, and build a ...

In statistics and machine learning, ordinal regression is a variant of regression models that normally gets utilized when the data has an ordinal variable. Ordinal variable means a type of variable ...

If you want to learn the math behind data science and machine learning, 3Blue1Brown is the channel for you. Created by Grant Sanderson, 3Blue1Brown uses animation to explain complex mathematical ...

This project will be showcasing the steps to build two different emotion detection NLP models (using RoBERTa and Logistic Regression, as the title suggests). We will also be looking at factors such as ...

Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques ...

Before evaluating a logistic regression model, it's important to understand its summary, which provides information about the coefficients, significance, and overall performance. The summary typically ...

Conventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language processing has made it feasible to derive meaning from unstructured ...