Boris Efraty's repositories
powerbi-visuals-funnel-1
Find outliers in your data, using a funnel plot
BYOS
Codes for BYOS logic Apps
PowerBI-visuals
Documentation for creating visuals for Power BI
PowerBI-visuals-forcasting-tbats
Forcasting tbats
powerbi-visuals-forcastingarima-1
An R-powered custom visual implementing Autoregressive Integrated Moving Average (ARIMA) modeling for the forecasting. Time series forecasting is the use of a model to predict future values based on previously observed values.
PowerBI-visuals-forcasting-exp-1
R-powered custom visual. Based on exponential smoothing time series forecasting
PowerBI-visuals-assorules-1
R-powered custom visual. Implements assosiation rules mining
PowerBI-visuals-clustering-kmeans-1
R-powered custom visual. Implements k-means clustering
PowerBI-visuals-decision-tree-1
R powered custom visual based on rpart package
UHRSproj
try
knnVisual
knn
rDataTable
R custom visual for Microsoft Power BI based on DT / DataTables
PowerBI-visuals-outliers
initial working version
AreaChart
area chart with plotly HTML Custom Visual
PowerBI-visuals-dbscan-1
Density-based spatial clustering of applications with noise visualization
PowerBI-visuals-forecasting-tbats
TBATS visual (non interactive)
tryM
try
todelete_TutorialFunnelPlot
Initial
PowerBI-visuals-spline-1
R-powered custom visual implements spline smoothing
powerbi-visuals-timeseriesdecomposition-1
R-powered custom visual implementing the “Seasonal and Trend decomposition using Loess” algorithm, offering several types of plots. Time series decomposition is an essential analytics tool to understand the time series components and to improve forecasting.
powerbi-visuals-forcastingarima
An R-powered custom visual implementing Autoregressive Integrated Moving Average (ARIMA) modeling for the forecasting. Time series forecasting is the use of a model to predict future values based on previously observed values.
PowerBI-visuals-RHTMLfunnel
Funnel plot
newRHTML
newRHTML
tg
try
powerbi-visuals-timeseriesdecomposition
R-powered custom visual implementing the “Seasonal and Trend decomposition using Loess” algorithm, offering several types of plots. Time series decomposition is an essential analytics tool to understand the time series components and to improve forecasting.