Regression in R

My latest publicly available R notebook created in IBM's Data Science Experience is here!  This notebook provides a tutorial on:

This notebook covers:
  • Fitting and interpreting linear models;
  • Evaluating model assumptions; and
  • Selecting among competing models.
I hope you enjoy this notebook.  Please feel free to share and let me know your thoughts.

Comments

Post a Comment

Popular posts from this blog

AI, ML, NN and DL: a visual explanation

Image
There appears to be a lot of confusion between the terms Artificial Intelligence (AI), Machine Learning (ML), Neural Networks (NN) and Deep Learning (DL).  Based on research from various popular blogs and articles, here is my attempt at a simple visual explanation:
Read more

Understanding And Interpreting Gain And Lift Charts

Image
Lift and Gain Charts are a useful way of visualizing how good a predictive model is. In SPSS, a typical gain chart appears as follows: In today's post, we will attempt to understand the logic behind generating a gain chart and then discuss how gain and lift charts are interpreted. To do this, we will use the example of a direct mailing company. Let us assume that based on experience, the company knows that the average response rate on its direct mail campaigns is 10%. Let us further make the following assumptions: * Cost per ad mailed = $1 * Return per response = $50 Additionally, let us assume that the company mails out ads in lots of 10,000. Based on these assumptions, if the company mails out 100,000 ads, a table summarizing the results it would obtain from this campaign is provided below: Now let us assume that the company uses SPSS Modeler to develop a predictive model using data from previous campaigns. "Response / No Response" is identified as the ...
Read more

Creating a time series forecast using IBM SPSS Modeler

Image
In today's post, we discuss how to create a time series forecast using IBM SPSS Modeler.  For the purposes of our exercise, we will use historical sales data at a SKU (stock keeping unit) level.  This data is provided in a MicroSoft Excel .xlsx file and must be in the following format: In the image above, AAAAA through EEEEE are SKU numbers with the relevant monthly sales data provided in the respective columns.  There is also a column that indicates the grand total of all SKUs sold in a month (AAAAA +...+ EEEEE + other SKUs not shown in the image above).  The last column in the image above reflects the months for which historical sales data are provided. As with any modeling exercise, we first insert a source node into the modeling canvas.  Since our data is in the MicroSoft Excel .xlsx file format, we insert an Excel source node as follows: On exporting to a Table node, we see the output display as follows: We then add a Filter node to select t...
Read more