This project involves the extraction, processing, and analysis of a large dataset of tweets to perform sentiment analysis and time series forecasting. The primary goal is to understand the sentiment trends over time and predict future sentiments using advanced machine learning techniques.

This project explores the application of Recurrent Neural Networks (RNNs) to predict daily bike-sharing trips. The primary goal is to develop and evaluate models that can accurately forecast the number of bike trips, which can be beneficial for bike-sharing companies to optimize their operations and resources 

This project aims to predict the Delivery Performance time of food using various factors, such as distance, weather conditions, type of order, type of vehicle.

To achieve this,  several machine learning models were employed, including random forest, decision tree, gradient boosting, linear regression, XGBoost, CatBoosting, and AdaBoost

This project aims to explore the relationship between population trends and crime rates in Ireland. It uses data from the Central Statistics Office of Ireland and the Garda Síochána. Key questions include changes in Ireland's population, the impact of population growth on recorded crimes, and whether population trends can predict future crime rates.

This project involves building a book recommendation system using machine learning techniques. The system takes a book title as input and returns a list of 10 similar books. The project leverages web scraping, data preprocessing, and machine learning algorithms to achieve this goal 

This data analytics project provides a comprehensive comparative analysis of the transport sectors in Dublin, Ireland and Washington D.C,.  The project leverages various data science techniques to explore and contrast the transportation infrastructure, usage patterns, and policy impacts in both Cities.

The analysis provides valuable insights into the strengths and weaknesses of the transport sectors, offering data-driven recommendations for policy makers and stakeholders to enhance transportation efficiency and sustainability.