This project aims to explore machine learning methods and their development processes by training on an IMDb ratings dataset to predict whether a user had a positive or negative sentiment towards a given movie. In this project we utilized kNN, Logistic Regression, Feedforward Neural Network, and Random Forest models to identify the most accurate fit.

A robust web crawler to scrape extensive textual data from diverse ICS webpages + a custom search engine that indexes and queries data in under 300 milliseconds. This application utilizes libraries such as bs4, pandas, and NLTK, as well as advanced page ranking algorithms to optimize search result relevance and efficiency. This project not only deepened my expertise in web scraping and algorithmic optimization but also highlighted the practical applications of data acquisition.