Topic: corrplot Goto Github
Some thing interesting about corrplot
Some thing interesting about corrplot
corrplot,A toy analysis/visualizations of the coffee-quality-database
User: amwolff
Home Page: https://docs.google.com/document/d/1TgPKhc6RtSofniUXHgyrN8SOqJJoZHfuygVOkFZKZ7A/edit?usp=sharing
corrplot,I use R to analyze survey data to position popular consumer electronics brands using Principal Component Analysis, uncovering key perceptual attributes and providing insights for targeted marketing strategies.
User: apoorvadudani
corrplot,Maldi and ANI correlation plot visualizer shiny app
Organization: bikc
Home Page: https://bioit.shinyapps.io/ManiR/
corrplot,Using advanced machine learning algorithms to predict Home Warranty Renewal success in R.
User: drev917
corrplot,an R/shiny application for creation of corrplot interactively
User: jzhouzhouzhouzhouzhou
Home Page: http://150.109.59.144:3838/shinyCorrplot/
corrplot,The data2gRaph project is a web-based data visualization tool that can also be used off-line.
User: muhendis
Home Page: https://muhendis.shinyapps.io/CDG_enginbozaba/
corrplot,Analyzing Air quality Index of Indian cities through various visualization packages in R and building a dashboard using Shiny package
User: prathibha13
corrplot,Random Forest, Decision tree and Logistic Regression
User: rahilmehta-24
corrplot,Prediction of whether a person has a heart disease or not depending on the variables given in the dataset.
User: rosina365
corrplot,Analyse and predict factors causing Diabetes
User: shivangbajaj
corrplot,A visual exploratory tool on correlation matrix
User: taiyun
Home Page: https://github.com/taiyun/corrplot
corrplot,My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.
User: tboudart
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