his project aims to predict the clusters of English Premier League teams based on their performance statistics using various machine learning techniques, including k-means clustering, principal component analysis, and correspondence analysis. The analysis provides insights into the key factors that contribute to a team's success in the Premier League and identifies teams that are likely to be grouped together, qualify for the Champions League, and face relegation. The project demonstrates how to perform PCA and correspondence analysis in R and visualize the results using ggplot2 and factoextra packages. The analysis offers a framework for predicting future results and helps stakeholders, including team managers, fans, and analysts, to make data-driven decisions.

This R Markdown file demonstrates the use of two semi-supervised classification algorithms, Nearest Mean Classifier and Laplacian SVM, for predicting the class of unlabeled data points. The code includes data preprocessing, label augmentation, and model building with both supervised and semi-supervised algorithms. The accuracy and confusion matrix of each model is reported, along with visualizations of the classification results. This tutorial can serve as a useful guide for those interested in exploring semi-supervised learning techniques in R.

In this project, we explore how JetBlue's brand is perceived in comparison to its competitors using data from a brand perception survey. We analyze the data using PCA, CA, and dendrogram techniques to create visualizations that illustrate JetBlue's position in the market. These visualizations can help stakeholders understand JetBlue's brand perception and identify areas for improvement.

As the Formula One 2023 season draws near and fans gear up for the excitement, let’s take a deeper look into the world of F1. If you are new to Formula One, this visualization is for you. With the help of data sourced from Kaggle, we’ll be using R to explore some common and not-so-common questions, bringing our own unique spin to the analysis. So fasten your seatbelts and join us on this journey into the world of F1!