The project implements the K-Nearest Neighbor (KNN) algorithm on the Iris dataset in R programming language. The aim is to classify the species of Iris flowers based on their petal length and width. The code loads the necessary libraries, preprocesses the data, creates a train-test split, trains the KNN model, evaluates its performance, and visualizes the results using ggplot2. The project can be useful for learning how to implement machine learning algorithms on real-world datasets and visualizing the results.

This is a code in R for importing, cleaning, and transforming data on Gini indices per country. The data is read from a CSV file downloaded from a Kaggle dataset. My objective was to analyze the global Gini index and observe how it has changed from 2000 to 2020. I wanted to identify which countries are more unequal and which ones are more egalitarian. One of my main goals was to create a map to visualize the geographical distribution of countries and to identify patterns and outliers. I also wanted to make it possible to filter the data using a button by year, to easily observe changes over time. The purpose of thus project is for entertainment and only to satisfy the viewers curiosity about the world and equality.

This is a data analyst project focused on analyzing the top 100 songs on Spotify of all time. The project will be published on my Linkeldn, and the data is sourced from a Kaggle dataset. The project aims to explore the characteristics of the top 100 songs in Spotify, the trends and patterns that emerge from the data, and to provide insights into the music industry, and general viewers interest. My main goal is to determine the average duration of songs in the top 100. This information can help singers identify the most effective duration range for building a successful song, which they can then apply to their own work. Additionally, I aim to identify outliers, such as artists with multiple songs in the top 100, or songs with unusually long durations. This analysis can provide valuable insights to artists on how to stand out and succeed in the music industry.