How to Integrate Machine Learning into Microsoft Access Applications

In this episode, we're going to level up our Microsoft Access application by integrating Machine Learning into it, so that we can see classification of customers that will show predictions of a fictional bank scenario where customers become part of a "preferred program" or not. We'll integrate the work we did in our last video where we created an Artificial Neural Network (ANN) to do this task. By integrating powerful Python scripting with our application, we will harness the power of Machine Learning in our MS Access application. I have been asked many times over the years on how to implement weighting, scoring, and predictive systems in complex environments. This episode's ANN integration can be used in a wide range of contexts where MS Access is used, and though the actual ML model used is one of the simplest that can be used, it is still FAR more powerful than most other weighting and scoring systems that can be (easily) made with MS Access. And, when you consider that the model can be extended into Deep Learning territory to tackle greater complexity with more precision, you can see the power that can be added to any of your applications. Related Videos: How to Use a File Dialog in MS Access to Allow Users to Pick a File or Folder https://youtu.be/o8VMtHRW3w4 How to Write a Custom Text File Using MS Access VBA https://youtu.be/omGFRdBfYvc How to Integrate Machine Learning into Microsoft Access Applications You are watching this video now! How to Use Base64 in Microsoft Access https://youtu.be/OOrVDjITTpM How to Use Sleep to Pause VBA in MS Access https://youtu.be/rrcoA02JcwM How to Open Excel from Access Using VBA https://youtu.be/A5LvSI4dIuc How to Use vbScript to Open and Work with Data in Access accdb Files - Late Binding Example https://youtu.be/7n80z9WkTcc Some things to remember when watching this video: - The ANN we created in my last video then integrated here is a simple one. It used many defaults (ie. learning rate, batch size) - It could easily be tuned for higher accuracy. - The purpose of this video was to demonstrate a sample integration. Every integration and application will be different. - Many datasets easily lend themselves to classification, but many do not and it is hard to get accurate classification. - This technique can also be used for computer vision (photo classification) - This example is called Supervised Learning, where we provide the labels of each case for the computer to learn from. Join me on Patreon! https://www.patreon.com/mackenziedataengineering Demo of my BZ RDP Cloaker: https://www.patreon.com/posts/how-to-block-rdp-98471703?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link Want the code from this video? http://mackenziemackenzie.com/downloads Interested in transforming your career or finding your next gig? https://system.billzone.com/careers.html Want my team to do a project for you? Let's get to it! https://buy.billzone.com/smdeprojects.html Contact form you can find at http://www.mackenziemackenzie.com/ Follow me on social media: https://www.facebook.com/mackenziedataanalytics https://twitter.com/seamacke https://www.instagram.com/seamacke/ https://www.reddit.com/user/seamacke https://www.linkedin.com/in/psmackenzie/ Get Microsoft Office including Access: https://click.linksynergy.com/fs-bin/click?id=BdGJecFFM8A&offerid=1160033.10004289&type=3&subid=0 Got a YouTube Channel? I use TubeBuddy, it is awesome. Give it a try: https://www.tubebuddy.com/seanmackenziedataengineering #msaccess #datascience #python