https://github.com/ketchbrookanalytics/portland_pug
Data Visualization through the Predictive Modeling Lifecycle with Power BI & R
https://github.com/ketchbrookanalytics/portland_pug
Last synced: 5 months ago
JSON representation
Data Visualization through the Predictive Modeling Lifecycle with Power BI & R
- Host: GitHub
- URL: https://github.com/ketchbrookanalytics/portland_pug
- Owner: ketchbrookanalytics
- Created: 2021-11-15T22:47:30.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-11-17T22:22:58.000Z (over 3 years ago)
- Last Synced: 2024-08-13T07:11:14.673Z (8 months ago)
- Language: R
- Homepage:
- Size: 344 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- jimsghstars - ketchbrookanalytics/portland_pug - Data Visualization through the Predictive Modeling Lifecycle with Power BI & R (R)
README
# Data Visualization through the Predictive Modeling Lifecycle with Power BI & R
This repository contains materials associated with the presentation given by [Michael Thomas](https://www.linkedin.com/in/michaeljthomas2/) to the [Portland Power BI Users Group](https://www.meetup.com/Portland-Power-BI-User-Group/events/278259627/) on 2021-11-17.
## Installation
1. Clone this repository to your local machine
2. Open the **noisy.Rproj** file from the directory on your local machine where you cloned this repository. This should install the {renv} package if you do not already have it installed, but if you don’t see that happen in the console, run `install.packages("renv")`.
3. Run `renv::restore()` to install the package dependencies needed to run this app successfully
## Purpose
The presentation uses data from [data.ct.gov](https://data.ct.gov) on prison population counts in the State of Connecticut to build *time-series forecasting* models to forecast the future monthly prison population in the State.
## Structure (What's in Here?)
1. The [data/](data) folder contains the raw *.csv* data with the total prison population counts by month
2. The [forecast.R](forecast.R) script builds the ARIMA & RNN (*recurrent neural network*) models, and simulates the next 12 months of values for the forecasted population
3. The [interactive_viz_example.R](interactive_viz_example.R) script creates a **{plotly}** visualization (which is HTML under the hood)
4. The [CT Prison Population Forecast.pbix](CT%20Prison%20Population%20Forecast.pbix) file contains the Power BI report used in the presentation
***
# Questions?
Contact us at [info@ketchbrookanalytics.com](mailto:info@ketchbrookanalytics.com) to learn more about how we can help you achieve your data science goals!