Tag: predicting

Feed: R-bloggers. Author: Dr. Shirin Glander. Since I migrated my blog from Github Pages to blogdown and Netlify, I wanted to start migrating (most of) my old posts too – and use that opportunity to update them and make sure the code still works. Here I am updating my very first machine learning post from 27 Nov 2016: Can we predict flu deaths with Machine Learning and R?. Changes are marked as bold comments. The main changes I made are: using the tidyverse more consistently throughout the analysis focusing on comparing multiple imputations from the mice package, rather than comparing ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. Time series prediction (forecasting) has experienced dramatic improvements in predictive accuracy as a result of the data science machine learning and deep learning evolution. As these ML/DL tools have evolved, businesses and financial institutions are now able to forecast better by applying these new technologies to solve old problems. In this article, we showcase the use of a special type of Deep Learning model called an LSTM (Long Short-Term Memory), which is useful for problems involving sequences with autocorrelation. We analyze a famous historical data set called “sunspots” (a sunspot is a solar phenomenon ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Guest Author. Guest blog written by Bendi Sowjanya, a microbiologist and technologist at B3DS Health issues affect all populations globally but the treatment and prevention of these issues varies widely depending on geographic location.  The effectiveness of health care is limited due to different socio-economic and environmental conditions as well as the ability to provide education for prevention. A key challenge in healthcare is the evolution of pathogens.  New pathogens and vector strains are emerging that are resistant to existing drugs and the risk is that these diseases can become pandemic.  Understanding the cause ... Read More

Feed: Microsoft Azure Blog. Author: Anu Kohli. Data science and machine learning can be applied to solve many common business scenarios, yet there are many barriers preventing organizations from adopting them. Collaboration between data scientists, data engineers, and business analysts and curating data, structured and unstructured, from disparate sources are two examples of such barriers - and we haven’t even gotten to the complexity involved when trying to do these things with large volumes of data.   Recommendation engines, churn analysis, and intrusion detection are common scenarios that many organizations are solving across multiple industries. They require machine learning, streaming analytics, ... Read More

Feed: Neo4j Graph Database Platform. Author: Bryce Merkl Sasaki. Graph algorithms are the powerhouse behind the analysis of real-world networks — from identifying fraud rings and optimizing the location of public services to evaluating the strength of a group and predicting the spread of disease or ideas. In this series on graph algorithms, we’ll discuss the value of graph algorithm and what they can do for you. This week, we’ll take a look at how powerful graph algorithms offer a practical approach to graph analytics and review an example of how to find the most influential categories in Wikipedia using ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. (This article was first published on business-science.io - Articles, and kindly contributed to R-bloggers) We recently had an awesome opportunity to work with a great client that asked Business Science to build an open source anomaly detection algorithm that suited their needs. The business goal was to accurately detect anomalies for various marketing data consisting of website actions and marketing feedback spanning thousands of time series across multiple customers and web sources. Enter anomalize: a tidy anomaly detection algorithm that’s time-based (built on top of tibbletime) and scalable from one to many time series!! ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. By Eric Schmidt, Developer Advocate, Google Cloud and Allen Jarvis, Technical Account Manager, Google Cloud As we shared in this morning’s blog post, the Final Four this weekend signifies more than just the culmination of March Madness 2018. It’s also our opportunity to share our months-long project building a workstream that can analyze more than 80 years of NCAA data. As Ohio University Athletic Director Jim Schaus recently told us, it’s one thing to look at data year after year, but it’s ... Read More

Feed: R-bloggers. Author: free range statistics - R. (This article was first published on free range statistics - R, and kindly contributed to R-bloggers) Caution – this blog post discusses homicide and suicide statistics and could be upsetting. If you feel disturbed or unhappy then here is a list of ways to get some emergency counselling help around the world. A grim research question In my last post I looked at fitting a Poisson distribution to a set of count data that had been truncated so that observations below a certain threshold were not available. My reason for doing that ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. By Sergei Sokolenko, Cloud Dataflow Product Manager In Part 1 of our blog series we previewed TensorFlow models that combined traditional natural language processing (NLP) techniques such as entity and topic extraction, and deep learning techniques such as embeddings to predict with 92% accuracy which Reddit community (subreddit) a news article would land for discussion, and estimated the popularity score and volume of comments such post would generate. Part 2 of the series went into detail of developing and tuning these ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. By Kaz Sato, Staff Developer Advocate, Google Cloud Take a look at the three ramen bowls below. Can you believe that a machine learning (ML) model can identify the exact shop each bowl is made at, out of 41 ramen shops, with 95% accuracy? Data scientist Kenji Doi built an AI-enabled ramen expert classifier that can discern the minute details that make one shop’s bowl of ramen different from the next one’s. Ramen bowls made at three different Ramen Jiro shops ... Read More