Presentation on R, tidy data, and useful functions

In this repository, I've explored R programming, from basics to advanced concepts, for statistical models, data analysis, and data science. 📊🔍 Join me on this enriching journey! 🚀

Work done for University of Pittsburgh course "Principles of Data Science" (STAT 1261) with Dr. Junshu Bao in Fall semester of 2018.

Empirical Comparison of Regression Methods for Variability-Aware Performance Prediction. Эмпирическое сравние регрессионных методов для предсказания производительности конфигурируемых систем

Coursework for Visual Analytics @ RIT [ISTE 782]

Link to Report: https://adminliveunc-my.sharepoint.com/:w:/r/personal/tadennis_ad_unc_edu/Documents/Capstone%20Group/Final%20Report%20Draft.docx?d=wba9e7182a9b948898133e4f89def1d90&csf=1&web=1&e=fQGAfy

As part of a group project, I developed separate regression models using R to predict the daily number of batteries and robberies in Chicago using four different datasets. I tested interactive and second-order terms and used stepwise feature selection to find the best model with the given data. I tested several potential models using cross-valid…

GIS Programming with Python Scripts. Functions: Automating Geoprocessing Tasks, Scripting Workflows, Data Management, Map Automation, Spatial Analysis and Batch Processing

Performed linear regression and residuals analysis on college tuition fees and admission rate in R.

This repository contains reproducible research on an epidemiological model for understanding COVID-19 spreading rates, as part of the DTU Data Science course 22100: R for Bio Data Science

Presentation for dplyr/tidyr workshop, Apr 2018

Text Sentiment Analysis Using R

Project and data courtesy of Datacamp. Taking a look at survey of tools used in data science.

Implementation of k-means task by DBSCAN with focus on noise reduction

Grouping pupils according to the performance at two intermediate examinations

This data analysis project is meant to analyze raw data of Uber pickups in New York City in 2014, using the R and its visualization packages. Project provided by Dataflair.

Data Wrangling is challenging and needs a lot of effort to first understand what you are looking at and then decide how to obtain it, after that check if you have done it right! using dplyr package in R you can achieve a high performance in data manipulation and extract useful information from you data in the right way to further use it.

Sample data of Indian Domestic flights operated between march and june of 2019 was explored. Machine learning models that predicts the cost of the ticket was built.

This repository contains very messy data on the Billboard Top 100 from 2000. I am using this data set to improve at data cleaning / wrangling. The aim was to create a Cleveland Dot Plot which shows the progression of number 1 songs over time from that year.