The goal of this project is to leverage advanced visualization techniques in R to analyze house prices in King County, Washington. The dataset, obtained from Kaggle, comprises 21 variables and 21,613 observations, spanning the period from 02 May 2014 to 27 May 2015

In this project, we conducted a detailed spatial cluster analysis of respiratory diases in Glasgow for the years 2004 and 2005. Our focus was on identifying patterns, and comparing clustering results using diverse methodologies. Leveraging spatial autocorrelation, Bayesian modeling with CARBayes, bivariate mixture model clustering, and k-means clustering methods are used to provide insights into the spatial distribution of respiratory diseases. .

In this set of exercises, we delved into the practical application of regular expressions (RegEx) for text manipulation. The exercises covered a range of scenarios, including pattern matching in vectors and word corpora.

In this study, sentiment analysis was performed on a dataset of 4000 tweets using the AFINN lexicon in R. The resulting sentiment scores were categorized into negative, neutral, and positive sentiments. The distribution of sentiments was visualized through a circular pie chart, highlighting that approximately 32.38% of tweets expressed negative sentiment, 46.42% were neutral, and 21.20% conveyed positive sentiment. The analysis provides insights into the overall sentiment distribution of the Twitter dataset, offering a quick and visually informative summary of the sentiments expressed in the tweets.