Written by Matt Dancho on October 20, 2021
I'm super excited to introduce the new Modeltime Backend for Spark. Let's use it to perform forecasting with tidymodels.
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Written by Matt Dancho on August 26, 2021
I'm super excited to introduce the experimental feature for performing iterative forecasting.
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Written by Matt Dancho on July 19, 2021
I'm super excited to introduce the new panel data forecasting functionality in modeltime. It's perfect for making many forecasts at once without for-loops.
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Written by Matt Dancho and Alberto González Almuiña on June 17, 2021
I'm super excited to introduce the new parallel processing functionality in modeltime. It's perfect for speeding up hyperparameter tuning of forecast models using parallel processing.
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Written by Matt Dancho and Alberto González Almuiña on April 8, 2021
I'm super excited to introduce the new autoregressive forecast functionality in modeltime that allows you to convert any tidymodels regression algorithm into an autoregressive forecasting algorithm.
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Written by Matt Dancho and Alberto González Almuiña on March 15, 2021
I'm super excited to introduce modeltime.h2o, an H2O AutoML backend for forecasting
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Written by Matt Dancho on October 13, 2020
I'm super excited to introduce modeltime.ensemble, a new time series forecasting package designed to extend modeltime with ensemble methods like stacking, weighting, and averaging.
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Written by Matt Dancho on September 9, 2020
Modeltime unlocks time series models and machine learning in one framework.
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Written by Matt Dancho on September 2, 2020
Anomaly detection is the process of identifying items or events in data sets that are different than the norm. Anomaly detection is an important part of time series analysis: (1) Detecting anomalies can signify special events, and (2) Cleaning anomalies can improve forecast error.
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Written by Matt Dancho on August 26, 2020
Seasonality is the presence of variations that occur at specific regular intervals, such as weekly, monthly, or quarterly. Seasonality can be caused by factors, such as weather or holiday, and consists of periodic and repetitive patterns in a time series.
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Written by Matt Dancho on August 19, 2020
A collection of tools for working with time series in R Time series data wrangling is an essential skill for any forecaster. timetk includes the essential data wrangling tools.
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Written by Matt Dancho on June 29, 2020
I'm so excited to introduce modeltime, a new time series forecasting package designed to integrate tidymodels machine learning packages into a streamlined workflow for tidyverse forecasting.
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Written by Matt Dancho on June 17, 2020
The 2nd part in our Time Series in 5-minutes article series. Learn how to visualize autocorrelations and cross correlation.
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Written by Matt Dancho on June 8, 2020
The 1st part in our Time Series in 5-minutes article series. Learn how to visualize time series with the time plot.
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Written by Matt Dancho on June 5, 2020
Timetk Version 2.0.0 has just been released. Here's what's new for time series data analysis.
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Written by Matt Dancho on May 15, 2020
Python can be run from R to leverage the strengths of both R and Python Data Science langauges. Learn how to set up Python's TensorFlow Library in 5 minutes.
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Written by Matt Dancho on April 20, 2020
Python can be run from R to leverage the strengths of both R and Python Data Science langauges. Learn how to set up Python's Scikit-Learn Library in 5 minutes.
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Written by Matt Dancho on January 21, 2020
Learn how to model product prices using the tune library for hyperparameter tuning and cross-validation.
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Written by Diego Usai on November 18, 2019
Learn how to perform a tidy approach to classification problem with the new parsnip R package for machine learning.
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Written by Matt Dancho on August 15, 2019
Wrangling Big Data is one of the best features of the R programming language - which boasts a Big Data Ecosystem that contains fast in-memory tools (e.g. data.table) and distributed computational tools (sparklyr). With the NEW dtplyr package, data scientists with dplyr experience gain the benefits of data.table backend. We saw a 3X speed boost for dplyr!
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Written by Matt Dancho on August 7, 2019
I'm pleased to announce the introduction of correlationfunnel version 0.1.0, which officially hit CRAN yesterday. The correlationfunnel package is something I've been using for a while to efficiently explore data, understand relationships, and get to business insights as fast as possible.
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Written by Blaine Bateman on December 4, 2018
This article demonstrates a real-world case study for business forecasting with regression models including artificial neural networks (ANNs) with Keras
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Written by Brad Boehmke on August 13, 2018
Model interpretability is critical to businesses. If you want to use high performance models (GLM, RF, GBM, Deep Learning, H2O, Keras, xgboost, etc), you need to learn how to explain them. With machine learning interpretability growing in importance, several R packages designed to provide this capability are gaining in popularity. We analyze the IML package in this article.
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Written by Matt Dancho on August 7, 2018
Real world data science - Learn how to compete in a Kaggle Competition using Machine Learning with R.
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Written by Brad Boehmke on July 23, 2018
Interpret machine learning algorithms with R to explain why one prediction is made over another.
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Written by Matt Dancho on July 16, 2018
I’m pleased to announce that we released brand new content for our flagship course, Data Science For Business (DS4B 201). Over the course of 10 weeks, the DS4B 201 course teaches students and end-to-end data science project solving Employee Churn with R, H2O, & LIME. The latest content is focused on transitioning from modeling Employee Churn with H2O and LIME to evaluating our binary classification model using Return-On-Investment (ROI), thus delivering business value. We do this through application of a special tool called the Expected Value Framework. Let’s learn about the new course content available now in DS4B 201, Chapter 7, which covers the Expected Value Framework for modeling churn with H2O!
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Written by Sigrid Keydana, Matt Dancho on July 1, 2018
KERAS LSTM deep learning time series analysis. Use the NASA sunspots data set to predict sunspots ten years into the future with an KERAS LSTM deep learning model.
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Written by Brad Boehmke on June 25, 2018
Predict employee churn with H2O machine learning and LIME. Use LIME (local Interpretable Model-agnostic Explanations) for model explanation in data science for business.
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Written by Matt Dancho on April 30, 2018
We are pleased to announce that our Data Science For Business (#DS4B) Course (HR 201) is OFFICIALLY OPEN! This course is for intermediate to advanced data scientists looking to apply H2O and LIME to a real-world binary classification problem in an organization: Employee Attrition. If you are interested applying data science for business in a real-world setting with advanced tools using a client-proven system that delivers ROI to the organization, then this is the course for you. For a limited time we are offering 15% off enrollment.
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Written by Matt Dancho on April 25, 2018
Last November, our data science team embarked on a journey to build the ultimate Data Science For Business (DS4B) learning platform. We saw a problem: A gap exists in organizations between the data science team and the business. To bridge this gap, we’ve created Business Science University, an online learning platform that teaches DS4B, using high-end machine learning algorithms, and organized in the fashion of an on-premise workshop but at a fraction of the price. I’m pleased to announce that, in 5 days, we will launch our first course, HR 201, as part of a 4-course Virtual Workshop. We crafted the Virtual Workshop after the data science program that we wished we had when we began data science (after we got through the basics of course!). Now, our data science process is being opened up to you. We guide you through our process for solving high impact business problems with data science!
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Written by Matt Dancho on April 18, 2018
Learn time series analysis with Keras LSTM deep learning. Learn to predict sunspots ten years into the future with an LSTM deep learning model.
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Written by Matt Dancho on April 8, 2018
Anomaly detection algorithm using Anomolize: an open-source tidy anomaly detection algorithm that’s time-based.
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Written by Matt Dancho on November 28, 2017
Predict customer churn using deep Learning Keras in R, with a 82% model accuracy.
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Written by Matt Dancho on October 28, 2017
Learn R in this time series using H2O machine learning demonstration.
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Written by Matt Dancho on October 26, 2017
Tonight at 7PM EST, we will be giving a LIVE #DataTalk on Using Machine Learning to Predict Employee Turnover. Employee turnover (attrition) is a major cost to an organization, and predicting turnover is at the forefront of needs of Human Resources (HR) in many organizations. Until now the mainstream approach has been to use logistic regression or survival curves to model employee attrition. However, with advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. We used two cutting edge techniques: the h2o
package’s new FREE automatic machine learning algorithm, h2o.automl()
, to develop a predictive model that is in the same ballpark as commercial products in terms of ML accuracy. Then we used the new lime
package that enables breakdown of complex, black-box machine learning models into variable importance plots. The talk will cover HR Analytics and how we used R, H2O, and LIME to predict employee turnover.
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Written by Matt Dancho on October 16, 2017
Predictive sales analytics to predict product backorders can increase sales and customer satisfaction. Using a Kaggle dataset, we use H2O AutoML predict backorders.
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Written by Matt Dancho on September 18, 2017
Predict employee turnover using the H2O machine learning and Lime.
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Written on October 1, 2016
This post is the third and final part in the customer segmentation analysis. The first post focused on K-Means Clustering to segment customers into distinct groups based on purchasing habits. The second post takes a different approach, using Pricipal Component Analysis (PCA) to visualize customer groups. The third and final post performs Network Visualization (Graph Drawing) using the igraph
and networkD3
libraries as a method to visualize the customer connections and relationship strengths.
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Written on September 4, 2016
This post is the second part in the customer segmentation analysis. The first post focused on k-means clustering in R
to segment customers into distinct groups based on purchasing habits. This post takes a different approach, using Pricipal Component Analysis (PCA) in R
as a tool to view customer groups. Because PCA attacks the problem from a different angle than k-means, we can get different insights. We’ll compare both the k-means results with the PCA visualization. Let’s see what happens when we apply PCA.
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Written on August 7, 2016
In this machine learning with R tutorial, use k means clustering to segment customers into distinct groups based on purchasing habits.
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