Analyzing open-ended survey responses is a crucial yet challenging task for social scientists, non-profit organizations, and educational institutions, as they often face the trade-off between obtaining rich data and the burden of reading and coding textual responses. This demo introduces FeedbackMap, a web-based tool that uses natural language processing techniques to facilitate the analysis of open-ended survey responses. FeedbackMap lets researchers generate summaries at multiple levels, identify interesting response examples, and visualize the response space through embeddings. We discuss the importance of examining survey results from multiple perspectives and the potential biases introduced by summarization methods, emphasizing the need for critical evaluation of the representation and omission of respondent voices.

This walkthrough uses HDInsight Spark to do data exploration and train binary classification and regression models using cross-validation and hyperparameter optimization on a sample of the NYC taxi trip and fare 2013 dataset. It walks you through the steps of the Data Science Process, end-to-end, using an HDInsight Spark cluster for processing and Azure blobs to store the data and the models. The process explores and visualizes data brought in from an Azure Storage Blob and then prepares the data to build predictive models. Python has been used to code the solution and to show the relevant plots. These models are build using the Spark MLlib toolkit to do binary classification and regression modeling tasks.

This topic describes how to load machine learning (ML) models that have been built using Spark MLlib and stored in Azure Blob Storage (WASB), and how to score them with datasets that have also been stored in WASB. It shows how to pre-process the input data, transform features using the indexing and encoding functions in the MLlib toolkit, and how to create a labeled point data object that can be used as input for scoring with the ML models. The models used for scoring include Linear Regression, Logistic Regression, Random Forest Models, and Gradient Boosting Tree Models. You need an Azure account and an HDInsight Spark cluster to begin this walkthrough. See the Overview of Data Science using Spark on Azure HDInsight for these requirements, for a description of the NYC 2013 Taxi data used here, and for instructions on how execute code from a Jupyter notebook on the Spark cluster.