Zillow is a FREE TOOL with an API that allows Data Scientists to connect to a massive repository of Home Prices and Features. In this lab, we use Shiny, Crosstalk, and Zillow API to create an ML-powered Zillow Home Price Explanation tool.
Google Trends Email Automation with Shiny
Google Trends is a FREE tool to gain insights about Google Search Terms your organization cares about. What if you could streamline the process of analyzing keyword search trends and emailing a report in 3-4 seconds? Learn how to do just that.
Product Price Prediction: A Tidy Hyperparameter Tuning and Cross Validation Tutorial
Learn how to model product prices using the tune library for hyperparameter tuning and cross-validation.
Customer Churn Modeling using Machine Learning with parsnip
Learn how to perform a tidy approach to classification problem with the new parsnip R package for machine learning.
Introducing correlationfunnel v0.1.0 - Speed Up Exploratory Data Analysis by 100X
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.
Excel to R, Part 2 - Speed Up Exploratory Data Analysis 100X (R Code!)
Learn how going from Excel to R can speed up Exploratory Data Analysis, getting business insights 100X FASTER.
Customer Segmentation and RFM Analysis with K-Means Clustering - Part 2
Kmeans clusering is an unsupervised machine learning algorithm that can be used to segment customers into similar groups for marketing.
A/B Testing with Machine Learning - A Step-by-Step Tutorial
Targeted Marketing with Customer Segmentation and RFM Analysis - Part 1
Customer segmentation is the process of grouping customers by specific likeness. Creating customer segmentation enables a business to target specific groups of customers and personalize marketing for each group.
New Cheat Sheet - Customer Segmentation and Clustering Workflow
Student feedback led to a BRAND NEW CHEAT SHEET on customer segmentation and a major overhaul to our Week 6 Modeling Chapter in our Business Analysis with R Course.
Excel to R, Part 1 - The 10X Productivity Boost
The first article in a 3-part series on Excel to R, this article walks the reader through a Marketing Case Study exposing the 10X productivity boost from switching from Excel to R.
Marketing Analytics and Data Science
This article uses a Kaggle competition as an opportunity to show how data science can be used in digital marketing to answer a specific question, and take what is learned from the data and apply it to marketing strategies.
Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn
Customer Segmentation Part 3: Network Visualization
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.
Customer Segmentation Part 2: PCA for Segment Visualization
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.
Customer Segmentation Part 1: K Means Clustering
In this machine learning with R tutorial, use k means clustering to segment customers into distinct groups based on purchasing habits.
Marketing Strategy: Why MBAs Can Benefit from Learning Analytics
Just because you’re a business professional does not mean you can’t or you shouldn’t pursue furthering yourself in analytics. Businesses view strategic decision making as a competitive advantage. You should too! Learning the basics behind data science not only adds value to your organization, it increases your value and thus your demand too.