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- Bayesian Statistics: Analysis of Health Data
Here is an example of how Bayesian statistics can be used to indicate the best predictor of BMI fluctuations within a regression model.
- Cumulative Binomial Probability with R and Shiny
The following is an illustration of how cumulative binomial probability can be calculated, and how a Shiny Web App can be used to make the analysis more intuitive.
- Deploying Python application using Docker and AWS
In this example, we will see how a simple Python script can be incorporated into a Docker image, and this image will then be pushed to ECR (Elastic Container Registry) in AWS.
- Multilevel Modelling of U.S. Home Loan Data
In this example, multilevel modelling techniques are used to analyse data from the Federal Home Loan Bank System to determine the main influencing factors on loan-to-value ratios (LTVs) across the United States.
- Predicting Hotel Cancellations with Support Vector Machines and ARIMA
Here is an example of how Support Vector Machines and the ARIMA model can be used in Python to determine whether a potential customer will cancel their hotel booking or not.
- Summarizing Economic Bulletin Documents with TF-IDF
A selection of economic bulletins in PDF format from 2018 to 2019 are analysed in order to gauge economic sentiment. The bulletins in question are sourced from the European Central Bank website. tf-idf is used to rank words in a particular order of importance, and then a word cloud is used as a visual to highlight key words in the text.
- Text Generation with LSTM: Economic Analysis
In this example, an LSTM model is trained using text from a sample ECB policy document, in order to generate “new” text data, with a view to revealing insights from such text that could be used for policy purposes.
- Visualizing New York City WiFi Access with K-Means Clustering
A k-means clustering algorithm is used to analyse geographical data for free public WiFi in New York City, and the clusters are mapped geographically using nycmaps.