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119 R data science projects found

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Using co-word network community detection and LDA topic model to extract topics on TED Talks.

This project applies two automatic clustering (co-word network analysis with community detection & NLP techniques with LDA topic modeling) on 3 data columns (related tags, video description and transcript) to extract topics from TED Talks, and discerns whether more controlled vocabularies and...
Amber Hong
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Sentiment analyses of virus names and world’s sentiments on Taiwan’s inclusion in WHO/WHA under the influence of COVID-19.

Tweets with non-official references of the virus name (pneumonia & Wuhan virus) show lower and more negative sentiment scores in either Chinese or English context. The difference of sentiment scores inferred from some frequent hashtags are larger under tweets written in Simplified Chinese than...
Amber Hong
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Analysis of IPMUS NHIS Survey Data for Covid-19

In the United States, there have been over 47 million cases of COVID since the pandemic started in early March of 2020. Based on socioeconomic factors, certain groups of people have been affected by the pandemic more than others. In order to take a closer look at the disparities between...
Jay Bendre
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Predict the next word

The Coursera Data Science Specialization Capstone project from Johns Hopkins University (JHU) allows students to create a usable public data product that can show their skills to potential employers. For this iteration of the class, JHU partnered with SwiftKey (http://swiftkey.com/en/) to apply...
Roshan Khatiwada
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Medical Insurance Price Prediction

Performing EDA on a dataset to get insights on features and answer questions. Using Machine Learning Techniques to create a model capable of predicting the price. Purpose: To automate and ease the process of calculating insurance charges for insurance companies / Give quick price prediction...
Denis Ivanilov
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College Basketball Prediction App

Designed a machine learning model which predicts NCAA basketball games with 77% accuracy. Includes exploratory data analysis, data visualization, feature engineering, model training, evaluation, and Shiny app to display findings.
Ryan Egan
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Auto-Viz-ML - Automated Data Visualizer and ML Model Trainer

An R Shiny Application that lets users get inference from the data by the auto-generated visualizations and control, train and evaluate different ML models on the selected data.
Indrashis Paul
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IDA Credits and Grants Data

Analyses the IDA Credits and Grants dataset to answer questions about the distribution and trend of IDA funding. uncovered insights about the top receivers of IDA loans and grants, the distribution of loan disbursement among different regions, and the top projects funded by IDA.
Victor Nyakako
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Road Traffic and Accidents Resulting in a Casuality

STATS19 is strongly associated with road crashes that happened throughout the UK. Data on accidents, vehicles, and casualties will be examined and visualised to determine the reason for these accidents and later come up with a conclusion using proposed ideas for how they can be reduced.
Mohit Baliyan
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Multivariate regression between vitamin D use and gut microbiome of young infants

This project aimed to build a generalized linear model to evaluate association between vitamin D use, fecal metabolites, and microbiota, adjusting for covariables. The project used a cohort dataset of 575 infants, including questionnaire, NMR spectrum and DNA seq data.
David Zhao
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Machine learning classifiers for childhood obesity trajectrories using infant fecal microbiota data

Knowing obesity risk earlier could help healthcare professionals manage children’s weight more effectively. A GLMM-based classifier was built to predict children obesity with an AUC of 0.83, specificity of 73% and sensitivity of 85%, based on 2500 infants data.
David Zhao
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COVID-19 Bangladesh EDA and Daily Cases Prediction

Created the COVID-19 dataset of Bangladesh by web scrapping from the Wikipedia website. This project aims to predict daily cases of Covid-19 patients using different statistical models.
Mohammad Sahadat Hossain
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Tidy Tuesday

A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools...
Mohammad Sahadat Hossain
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Hateful Memes Detection

Dataset is extracted from DataDriven competition. The main goal of this project is to find Facebook memes that are hateful or non-hateful.
Mohammad Sahadat Hossain
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HOW DOES A BIKE-SHARE NAVIGATE SPEEDY SUCCESS?

This analysis was carried out as part of the capstone project for the Google Data Analytics Certification course offered on Coursera. All analysis was done using RStudio (as an R Markdown Notebook). All codes are written in R or otherwise stated. The codes were executed using Kaggle notebook.
Godwin Abah
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HOW DOES A BIKE-SHARE NAVIGATE SPEEDY SUCCESS?

This analysis was carried out as part of the capstone project for the Google Data Analytics Certification course offered on Coursera. All analysis was done using RStudio (as an R Markdown Notebook). All codes are written in R or otherwise stated. The codes were executed using Kaggle notebook.
Godwin Abah
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How to forecast sales with advertising spend

Sales forecasting based on historical data is an important and critical process for any business.
Anita Owens
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Malignant Tumor Identification in Breast Tissue

Utilized machine learning techniques to aid in identifying malignant tumors.
Claire Harris
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Mining Spatial & Text Data for Adoptable Dogs Dataset

By integrating county-level location data with a dataset of adoptable dogs, I not only cleaned web-scraped data for adoptable dogs, but visualized their location at the nation-wide and state level for the US.
John Trygier
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Time Series Forecasting w/ Self-Optimizing ARIMA Models

Utilizing WDI (World Development Index) data, I go step-by-step through the creation of ARIMA models for time-series forecasting, and utilize machine learning to create an optimized model using the BIC & AIC. This optimally fits an ARIMA model to the data for GDP growth in France, the UK, and India.
John Trygier
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