Work done for University of Pittsburgh course "Principles of Data Science" (STAT 1261) with Dr. Junshu Bao in Fall semester of 2018.
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Updated
Jan 12, 2019 - R
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
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…
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
census data analysis of Robeson county in NC, analyzing household income, race distribution, etc
Data cleaning and visualisation functions in R
Grouping pupils according to the performance at two intermediate examinations
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.
The purpose of this project is to demonstrate my ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis.
Demonstration of data wrangling with R. Common data cleaning and preprocessing tasks will be explored by locating open data from the web, import it into R, apply tidy data principles on the data and manipulate the data appropriately. The 'wh_staff_dataset.csv' contains the complete White House staff salary data from 1997-2020.
Project for Data Visualization Techniques course at WUT
Raster, NDVI, Zonal Stats and Census Analysis of Sacramento County
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