The New York City Taxi and Limousine Commission seeks to utilize data collected from the New York City area to predict taxi fare amounts with Machine Learning. The project consists of five notebooks covering various aspects of the data analysis process.
This project aims to predict user churn in the Waze app using machine learning techniques. It comprises five notebooks, each focusing on a different aspect of the data analysis process.
This project aims to utilize machine learning techniques to classify each video according to whether it presents a claim or an opinion. It comprises five notebooks, each focusing on a different aspect of the data analysis process.
A FastAPI-based API for a social media platform with features like user authentication, post creation, and voting on posts. It utilizes PostgreSQL for data storage, Alembic for database migrations, and includes unit tests with pytest.
This project is part of the ‘Machine Learning with Python - From Linear Models to Deep Learning’ course offered by MIT. It focuses on implementing several classification algorithms for sentiment analysis tasks.
🚧 Under Construction 🚧