Jekyll-based static site for The Programming Historian

The repository and website hosting the peer review process for new Programming Historian lessons

Learn R in simple and easy steps starting from basic to advanced concepts with examples. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

This repo contains the code for my postgraduate thesis dealing with Short-term Load Forecasting, predicting the electric load demand per hour in Greece, developed in R, RStudio, R-markdown and R-Shiny using daily load datasets provided by the Greek Independent Power Transmission Operator (I.P.T.O.). A presentation of the thesis' results can be f…

Option pricing and Delta hedging performance comparison between Black and Scholes vs Artificial Neural Network

R Package to create and manage ChatGPT Images

StrangeR things: Visualizing Soccer Data with R… on a Soccer Pitch? How to analyze, visualize and report soccer data and strategies on a soccer pitch with the "ggsoccer" package

Data Science | Machine Learning | Data Analysis

What makes R Shiny so shiny? A step-by-step introduction to interactive dashboards in R

🌌 SWIRL-course on spatial data in R 🌐

R Guide

Can a Long Short-Term Memory Model Produce Accurate Stock Price Predictions?: A Deep Learning Approach to Predicting Apple Inc. Stock Price.

🔢 Documents de Mineria de Dades (MD) Q1 - UPC FIB

What's up This project was mainly training my self on training ML models 🤖 and also to train on doing EDA 📜 to get the acceptance of the loan.

Testing the R programming language environment.

StrangeR things: Building 2D and 3D models… with LEGO bricks? in R. How to emulate and visualize LEGO bricks in 2D and 3D with the "brickr" package in R

Survey of a 20,000 boardgame dataset. Used supervised machine learning to cluster data into meaningful groups for visualization and potential game-recommendation prediction.

This project analyzes MLB standings data from 2019 to 2022 to predict team wins based on runs scored and runs allowed. It includes visualizations, a linear regression model, and checks for model assumptions to ensure accuracy and robustness.