Sentiment Analysis Model

https://github.com/tylercooksrice/sentiment-analysis-model
Predicting Using TF-IDF and Bag of Words (CountVectorizer) with Logistic Regression This project performs a key analyses on Amazon Fine Foods Reviews: conducting sentiment analysis, using two different models; TF-IDf and Bag of Words(CountVectorizer). The dataset, spanning from 1999 to 2012, contains over 500,000 reviews with features like product ID, user ID, helpfulness score, and review text. For sentiment analysis, we used Logistic Regression with TF-IDF to classify reviews as positive or negative based on the review text, achieving an accuracy of 87%. Compared to Bag of Words (CountVectorizer), which achiewved an accuracy of 80%. bThe project demonstrates effective use of machine learning models like Random Forest and Logistic Regression for text-based tasks, with optimizations to handle large datasets and feature extraction and the postives and negatives of each respective model.

Journal App

https://github.com/cse110-sp24-group35/journal.git
The application offers a comprehensive task management system, featuring a pie chart for task completion, a one-click checklist for today's tasks, and a seven-day upcoming task view. A calendar provides a quick overview of tasks by due date, with hover and click options for more details. The Kanban board includes customizable columns (PLANNED, ONGOING, COMPLETED, ABANDONED), allowing users to drag and drop tasks and quickly assign statuses. A journal system organizes entries in a file-explorer layout, supporting markdown with live preview and an auto-save feature with a 0.5-second delay. Additionally, offline functionality is enabled through ServiceWorker caching, ensuring continued access without an internet connection, and the platform operates as a Progressive Web App (PWA).